SÍNDROME METABÓLICO, OBESIDAD Y    RIESGO CARDIOMETABOLICO         Dr. Denis O. Granados Doña  Curso de actualización Sínd...
SÍNDROME METABÓLICO            Hiperglucemia           Hiperinsulinemia   Dislipidemia                               Infla...
SINDROME METABÓLICO Y RIESGO DE            ECV A 10 AÑOS                                          2.5                2.25 ...
FACTORES ASOCIADOS AL SÍNDROME • Factores de riesgo metabólicos     – Dislipidemia aterogénica     – Hipertensión     – Hi...
PREDISPOSICIÓN A SD METABOLICO   GENERADO POR OBESIDAD• Edad   – Disminución de la masa y la capacidad oxidativa     muscu...
RAIZ DEL PROBLEMA• Sobrepeso/obesidad• Inactividad física• Factores genéticos  – Alteraciones en la homeostasis de la ener...
INTERRELACIÓN ENTRE LOS COMPONENTES DEL           SÍNDROME METABÓLICO                                    Síndrome Metabóli...
VALORACIÓN DEL RIESGO CARDIOMETABÓLICO                                GLOBAL   Síndrome                         LDL       ...
FACTORES DE TOXICIDAD METABÓLICA A    Toxicidad de NAD(P)H oxidasa por activación Angiotensina II → ERO (ROS)      Toxicid...
IMPACTO EN MORTALIDAD • Second National Health and Nutrition Examination   Survey (NHANES II) • 6225 personas seguimiento ...
OBESIDAD
PRESPECTIVA HISTÓRICA DE           OBESIDADNature 404, 635-643 (6 April 2000)
CIRCUNFERENCIA DE CINTURA Y      GRASA INTRABDOMINALBMJ 2001;322: 716-720
RELACIÓN ENTRE IMC O GRASA INTRABDOMINAL        Y SENSIBILIDAD A LA INSULINALarsen: Williams Textbook of Endocrinology, 10...
FACTORES QUE INFLUENCIAN EL      DESARROLLO DE OBESIDAD                                       Genes                      S...
ACTIVIDAD HABITUAL DE ACUERDO A       AÑO ESCOLAR Y RAZAN Engl J Med 2002; 347:709-715
PARTICIPACIÓN EN ACTIVIDAD         FÍSICA > 50 MIN/SEM                                          NHIS   BRFSS              ...
CAMBIOS EN EL TIEMPO EN LAINGESTIÓN CALORICA PER CÁPITAData from the National Center for Health Statistics
ELEMENTOS QUE RELACIONAN LA    OBESIDAD CENTRAL A OTROS   COMPONENTES DEL SÍNDROME• ↑ AGL∀ ↓ adiponectina• Resistencia a l...
ACTIVDAD DEL TEJIDO ADIPOSO                             Leptina           Adiponectina                      Angiotensinóge...
INFILTRACIÓN DE MACRÓFAGOS      EN TEJIDO ADIPOSOJ Am Soc Nephrol 2004, 15: 2775–2791
CORRELACIÓN ENTRE TEJIDO ADIPOSO VICERAL Y SUBCUTÁNEO Y LA EXPRESIÓN DE GENES CB1                  Y FAAHDiabetes 55:3053-...
PROBABILIDAD DE PERMANECER      SIN OBESIDADAnn Intern Med, Jun 2002; 136: 857 - 864
CAMBIOS EN EL PESO Y RR DE      COMORBILIDADESN Engl J Med 1999; 341:427-434
PROPORCIÓN DE DIABETES ATRIBUIBLE AL            SOBREPESO
INCIDENCIA ACUMULATIVA DE ICC DE            ACUERDO A IMCN Engl J Med 2002; 347:305-313
EDAD, IMC Y RIESGO DE MUERTEN Engl J Med 1999; 341:427-434
RR PARA HTA DE ACUERDO A CAMBIOS EN   EL PESO DESPUES DE LOS 18 AÑOSAnn Intern Med1998; 128: 81 - 88
LA RESISTENCIA A LA INSULINA INCREMENTA EL RIESGO  DE DAÑO A ORGANO BLANCO EN LA HIPERTENSIÓN N= 354 con HTA no tratada   ...
NUEVOS COMPONENETES
SÍNDROME METABÓLICO   ORIGINADO POR OBESIDADJ Am Soc Nephrol 2004, 15: 2775–2791
RESISTENCIA A LOS EFECTOS DE INSULINA SOBRE LOS GLUCOTRANSPORTADORES•   Mutaciones en transportadores de glucosa•   Altera...
RESISTENCIA A LA INSULINA    EFECTOS EN MÚSCULO ESQUELÉTICO    • ↑ AGL        – Alteración señalización          de recept...
RESISTENCIA A LA INSULINA              EFECTOS EN HÍGADO      Efecto de AGL                    Déficit de adiponectina   –...
HEPATOPATÍA METABÓLICA                NAFLD/NASH • Enfermedad hepática de orígen no alcohólica   (NAFLD)     – Esteatosis ...
ESTEATOSIS HEPÁTICA• Concentraciones de plasmáticas de  enzimas hepáticas normales 10-15% de  la población general.• Estet...
RI EN HIPERTENSOS CON ESTEATOSIS             HEPATICA                    3.5      p < 0.05                     3          ...
FIBROSIS HEPÁTICA VS SCORE                   ATP III                                 10.0%                                ...
CONCENTRACIONES DE PCR mg/dL SEGÚN      STATUS DE PRUEBAS HEPÁTICAS          Media geométrica ajustada PCR (mg/dL)        ...
MICROALBUMINURIA• Cinco al 10% en tolerancia normal a la  glucosa• Doce a 20% en sindrome metabólico.• Veinticinco a 40% e...
PREVALENCIA DE ALTERACIONES LA FUNCIÓNRENAL EN RELACIÓN A COMPONENTES DE SD             METABÓLICO                        ...
TFG SEGÚN SCORE ATP III                              TFG (mL/min/1.73 m2)         90         85         80         75     ...
MECANISMOS            FISIOPATOLÓGICOS• Glomerulopatía asociada a la obesidad.• Glomerulomegalia inicial (100% de los  cas...
ALTERACIONES RENALES                     • Remodelación                       glomerular,                       tubulointe...
MECANISMOS QUE IMPLICAN A LA RESISTENCIA A LAINSULINA E HIPERINSULINEMIA COMPENSADORA CON ERC                             ...
EFECTOS EN PANCREAS• Gluconeogénesis hepática estimula la  hipersecreción de insulina (hiperinsulinemia  normoglucemica)• ...
ACTIVIDAD COAGULANTE Y      SÍNDROME METABÓLICO     FACTOR VII (% ACTIVIDAD)                FACTOR X (% ACTIVIDAD)      SC...
PAI-1 EN DMT2                                35                                            *                              ...
PCR Y SINDROME METABÓLICO                                   1.8     Valores medios (SE) Log PCR                           ...
PCR Y COMPONENTES DEL SD                          METABÓLICO                   10                    9                    ...
INFLAMACIÓN MECANISMOCONTRIBUYENTE AL DESARROLLO DE DMN = 1047                          25                          20    ...
ALTERACIONES MIOCARDICAS                     • Disfunción diastólica                       asociada a exceso y            ...
REMODELACIÓN DE LA MICROVASCULATURA                     • Enfermedad                       microvascular (vasa            ...
SINDROME METABÓLICO E ICC • RI → lipotoxicidad     – Acumulación de AGL → apoptosis     – Producción de radicales libres •...
ICC NHANES III > 40 AÑOS  Definición ATP III   Definición IDFJ Epidemiol Community Health 2007;61:67–73.   No SM   SM
ALTERACIONES           ELECTROFISIOLÓGICAS • Fibrilación y flutter     – Asociados a obesidad     – El riesgo ↑ en hombres...
ENFERMEDAD CARDIOVASCULAR           SUBCLÍNICA • Ventriculo izquierdo:    – hipertrofia (EKG, Eco), disfunción sistólica (...
PREVALENCIA DE ATEROESCLEROSIS SUBCLÍNICA O DAÑO A ORGANO BLANCO                                                          ...
RIESGO DE INCIDENCIA DE EVENTOS   CV EN SÍNDROME METABÓLICO                                           Según número de     ...
EVENTOS CARDIOVASCULARES CON Y    SIN SINDROME METABÓLICO J Clin Endocrinol Metab 2005; 90:5698–5703
SÍNDROME METABÓLICO Y RIESGO            CARDIOVASCULAR A 10 AÑOS                Mortalidad       ECV fatal         ECV no ...
ASOCIACIÓN ENTRE COMPONENTES DEL SINDROME METABÓLICO Y RIESGO EC ARIC                                  Riesgo de EC (11 añ...
TRATAMIENTO DE LOS FR EN ESTILOS DE VIDA PARA PREVENCIÓN A  LARGO PLAZO DE ECVA O PREVENCIÓN/TRATAMIENTO DE DMT2  Blancos ...
TRATAMIENTO DE LOS FR METABOLICOS PARA PREVENCIÓN A LARGO PLAZO DE ECVA O PREVENCIÓN/TRATAMIENTO DE DMT2  Blancos de la te...
TRATAMIENTO DE LOS FR METABOLICOS PARA PREVENCIÓN ALARGO PLAZO DE ECVA O PREVENCIÓN/TRATAMIENTO DE DMT2Blancos de la terap...
BLANCOS DEL TRATAMIENTO •   Obesidad Central      – Cambios en los estilos de vida, rimonabant, exenatide, sibutramina,   ...
Intervenciones en obesidad
EFECTO DE DIFERENTESPROGRAMAS DE EJERCICION Engl J Med 2002; 347:1483-1492
MECANISMO DE ACCIÓN DE         SIBUTRAMINAN Engl J Med 2002; 346: 591-602
MECANISMO DE ACCIÓN DE           ORLISTATN Engl J Med 2002; 346: 591-602
BLOQUEO DEL RECEPTOR CB1     Sitio de acción            Mecanísmo                  Objetivos                       ↓ Inges...
RIMONABANT VS PLACEBON Engl J Med 2005;353:2121-34.
PORCENTAJE DE PACIENTES QUE ALCANZAN 5% DE      PERDIDA DE PESO CORPORAL EN UN AÑO CON             DIFERENTES TRATAMIENTOS...
ESTUDIOS CONTROLADOS CON PLACEBO                     Sibutramina                           Orlistat                       ...
TRATAMIENTO   CRITERIOS PARA CIRUGIA BARIÁTRICA• Peso corporal > 100% del peso  corporal ideal.• Presencia de comorbilidad...
CIRUGÍA BARIÁTRICA EN USA            1992-2003N Engl J Med 2004; 350:1075-1079
CAMBIOS EN EL PESO EN SUJETOS   CON CIRUGÍA BARIÁTRICA N Engl J Med 2004; 351:2683-2693
PROMEDIO DE CONSUMO DEKILOCALORÍAS /DÍA EN PERSONAS CON        CIRUGÍA BARIÁTRICAN Engl J Med 2004; 351:2683-2693
ACTIVIDAD FÍSICA DURANTE EL TIEMPO  LIBRE EN SUJETOS CON CIRUGÍA            BARIÁTRICAN Engl J Med 2004; 351:2683-2693
ACTIVIDAD FÍSICA EN EL TRABAJO EN  SUJETOS CON CIRUGÍA BARIÁTRICAN Engl J Med 2004; 351:2683-2693
CAMBIOS EN EL PESO Y SOBREVIVENCIAN Engl J Med 2007 ;357:741
INCIDENCIA DE DISLIPIDEMIA ENSUJETOS CON CIRUGÍA BARIÁTRICAN Engl J Med 2004; 351:2683-2693
INCIDENCIA DIABETES, HTA EHIPERURICEMIA EN SUJETOS CON CIRUGÍA             BARIÁTRICA  N Engl J Med 2004; 351:2683-2693
RECUPERACIÓN DE DISLIPIDEMIA ENSUJETOS CON CIRUGÍA BARIÁTRICA N Engl J Med 2004; 351:2683-2693
RECUPERACIÓN DE DIABETES, HTA EHIPERURICEMIA EN SUJETOS CON CIRUGÍA             BARIÁTRICA N Engl J Med 2004; 351:2683-2693
META DE HbA1C DESPUES DE 1 AÑO                                     RIO-DIABETES                              Meta IDF < 6....
CAMBIOS EN PARAMETROS DEL SINDROME METABÓLICO  Cambios en circunferencia de                                               ...
EFECTO DE LA PERDIDA DE PESO SOBRE IL-6,               FNT-α Y PCR EN MUJERES OBESAS                   Después de una diet...
PREVENCIÓN
DPP: AL MEJORAR LA SENSIBILIDAD A   LA INSULINA SE PREVIENE DMN = 3234         30                         Placebo         ...
PREVENCIÓN DEL SÍNDROME METABÓLICO DPP Metformina 850 mg BID o Cambios intensivos en estilos de vida para mantener 7% de p...
PREVENCIÓN DE SÍNDROME           METABÓLICOIncidencia y Prevalencia (3 años) de componentes de Síndrome Metabólico en     ...
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  • METABOLIC SYNDROME AND 10-YEAR CVD RISK A number of definitions for metabolic syndrome exist; however, their clinical value is not clear. Therefore, Dekker and associates compared the definitions proposed by the National Cholesterol Education Program (NCEP) Adult Treatment Panel III, World Health Organization (WHO), European Group for the Study of Insulin Resistance (EGIR), and American College of Endocrinology (ACE) with respect to the prevalence of metabolic syndrome and the association with 10-year risk for fatal and nonfatal cardiovascular disease (CVD).   ·          The study population comprised 615 men and 749 women aged 50 to 75 years who were without diabetes or a history of CVD at baseline in 1989 to 1990. Participants were from the Hoorn Study, a cohort study of diabetes and diabetes complications in the Netherlands.   ·          As shown in this slide, the NCEP definition was associated with an approximate 2-fold increase in age-adjusted risk for all end points in men and for nonfatal CVD in women. In men, the increase was greatest for fatal CVD.   ·          After adjustment for the Framingham coronary risk score (FCRS), the hazard ratio for fatal and nonfatal CVD declined to 1.64 in men and disappeared in women (for all metabolic syndrome definitions, the average risk was reduced to 1.4 in men, and there was no increased risk in women). This decline in risk may be because of the overlap in components of the FCRS and metabolic syndrome.   ·       In general, metabolic syndrome (based on varying definitions) was associated with an increase in cardiovascular risk, although the increase tended to be more pronounced in men (with the exception of the ACE definition). After adjustment for the FCRS, however, the increased risk largely lessened or disappeared in both men and women.   Dekker JM et al. Circulation . 2005;112:666-673.  
  • Predisposition to Obesity-Initiated Metabolic Syndrome A number of factors influence risk for development of obesity-initiated metabolic syndrome. The increasing risk with age (4,71) parallels the declining muscle mass and muscle oxidative capacity of aging (52). Hormonal changes with aging are also involved: the compensatory increase in T3-mediated thermogenesis induced by dietary fat in young rats is lost with aging (72). Lifestyle factors are similarly influential. In cohorts followed prospectively, regular physical activity and adherence to the Mediterranean diet significantly reduced risk (10). In the Framingham Offspring Cohort, diets with low glycemic index and high whole-grain attributes also decreased risk of developing metabolic syndrome (3). In addition to demographic and lifestyle modulators, recent interest has focused on two mechanisms of predisposition operative early in life: environmenta l“programming” by adverse events operative during early growth and development and genetic factors. Both can permanently modify postnatal organ functional capacity, permanently alter gene expression patterns and homeostatic setpoints, and also exhibit maternal transmission across generations (73,74); as a result, the distinction between classic genetic inheritance and environmentally programmed effects can no longer rely solely on maternal phenotype.
  • Pathogenesis of Obesity-Initiated Metabolic Syndrome The NCEP panel identifies the root causes of metabolic syndrome as overweight/obesity, physical inactivity, and genetic factors (2). Unraveling underlying mechanisms has been complicated by the unique multiorgan complexity of this trait cluster. Fundamentally, the metabolic syndrome reflects disordered energy homeostasis. Just as evolution prepared us well for surviving hypotension but poorly for combating hypertension, it has apparently equipped us for surviving the fast but not the feast. Pathogenesis of Obesity-Initiated Metabolic Syndrome The NCEP panel identifies the root causes of metabolic syndrome as overweight/obesity, physical inactivity, and genetic factors (2). Unraveling underlying mechanisms has been complicated by the unique multiorgan complexity of this trait cluster. Fundamentally, the metabolic syndrome reflects disordered energy homeostasis. Just as evolution prepared us well for surviving hypotension but poorly for combating hypertension, it has apparently equipped us for surviving the fast but not the feast. Unger (19,20) described metabolic syndrome as “a failure of the system of intracellular lipid homeostasis which prevents lipotoxicity in organs of overnourished individuals,” a system that normally acts “by confining the lipid overload to cells specifically designed to store large quantities of surplus calories, the white adipocytes.” Central to the breakdown of this system are ( 1 ) exogenous fuel overload, ( 2 ) ectopic accumulation of lipid in nonadipose cells (21), and ( 3 ) insulin resistance (3,16).
  • Pro-coagulation and hypofibrinolysis The prothrombotic state in the atherosclerotic process encompasses platelet hyperaggregability, hypercoagulability and hypofibrinolysis. In obesity and metabolic syndrome, fibrinogen, von Willebrand factor (vWF) and PAI-1 have been most extensively studied as markers of the haemostatic and fibrinolytic system and as possible predictors for the development of cardiovascular disease and type 2 diabetes64. In obese individuals, only PAI-1 levels were increased in those with metabolic syndrome29 (Fig. 2). PAI-1 is expressed in visceral adipose tissue. It is mainly expressed in stromal cells, including monocytes, smooth muscle cells and pre-adipocytes65. Visceral adipose tissue seems to have up to five times the number of PAI-1-producing stromal cells compared with subcutaneous adipose tissue. Plasma PAI-1 levels are more closely related to fat accumulation and PAI-1 expression in the liver than in adipose tissue66, suggesting that in insulin-resistant individuals the fatty liver is an important site of PAI-1 production. This confirms the central role of the liver in these processes. The fibrinolytic system could have a role in the regulation of adipose tissue development and insulin signalling in adipocytes67. Fibrinogen, vWF and PAI-1 are also considered to be markers of the acute-phase reaction of inflammation, and thrombosis has been closely linked to inflammation68. Fibrinogen clusters with inflammation variables rather than with markers of pro-coagulation or metabolic factors69. CRP increases PAI-1 expression and activity in human aortic endothelial cells70. Figure 2 shows how disturbances in haemostasis and fibrinolysis relate to metabolic syndrome and its different components
  • The future of global cardiovascular disease risk assessment Better global risk-assessment algorithms are needed to quantify diabetes and CVD risk resulting from the presence of classical risk factors and the presence of abdominal obesity or insulin resistance-related metabolic markers. The term &apos;cardiometabolic risk&apos; has been coined by organizations such as the American Diabetes Association90 and the American Heart Association91 to describe the overall risk of developing type 2 diabetes and CVD92, 93, and this idea may potentially reconcile both supporters and detractors of the metabolic syndrome concept. As illustrated in Fig. 2, cardiometabolic risk encompasses the global risk of CVD and type 2 diabetes associated with traditional risk factors while also taking into consideration the potential additional contribution of abdominal obesity and/or insulin resistance and of related metabolic markers (to be identified) to global CVD risk. Current evidence does not suggest that the presence of clinical criteria for metabolic syndrome adds to global CVD risk. Only additional prospective studies, which will consider the measurement of sophisticated metabolic markers and direct measurements of abdominal visceral and subcutaneous adiposity, have the potential to answer this important question. Once these results become available we should be better positioned to address the key questions of what constitutes a high-risk abdominal obesity phenotype in various regions of the world and what the main determinants of risk in different populations are. However, a distinction must clearly be made between metabolic syndrome as a concept and the criteria used in clinical practice to identify individuals with features of metabolic syndrome. Although insulin resistance is a key component of a constellation of metabolic abnormalities, which increase the risk of type 2 diabetes and CVD, the most prevalent form of insulin resistance is associated with abdominal obesity and with &apos;dysfunctional&apos; adipose tissue that cannot properly handle the energy surplus resulting from a sedentary lifestyle combined with excessive calorie consumption49, 54. Initial indicators of high-risk abdominal obesity are an increased waist circumference along with raised fasting plasma triacylglycerol concentrations85. Although metabolic syndrome increases relative CVD risk, its diagnosis does not necessarily mean that a patient is at very high risk of a cardiovascular event. To properly evaluate cardiovascular risk, physicians must first consider traditional CVD risk factors. Whether the presence of the clinical criteria for the metabolic syndrome increases the risk of CVD beyond that of traditional risk factors is not yet clear. Resolving this is crucial for the optimal assessment of global CVD risk. Cardiometabolic risk is the overall risk of CVD resulting from the presence of metabolic syndrome but also of traditional risk factors such as lipids (LDL and HDL), hypertension, diabetes, age, male gender, smoking and other unknown risk factors (including genetic factors that cannot be assessed in clinical practice most of the time). According to this model, metabolic syndrome does not replace the need to assess global CVD risk, but may eventually have to be considered in global risk assessment. Whether metabolic syndrome is an independent factor that adds significantly to the global CVD risk as assessed with traditional risk factors is uncertain and much debated in the literature. The controversy over its added value is highlighted by the question mark
  • The cardiometabolic syndrome (CMS) is characterized by an array of metabolic, hemodynamic, islet, hepatic, neurovascular, cardiovascular (CV), and renal abnormalities coupled with multiple metabolic risk factor toxicities, all associated with increased formation of reactive oxygen species (ROS) (Figure 1). The A-FLIGHT-U acronym summarizing these factors is set forth in Table I.1 The driving force behind CMS is obesity (specifically, visceral obesity). Insulin resistance (IR) and the compensatory endocrine β-cell—derived hyperinsulinemia, hyperproinsulinemia, and hyperamylinemia contribute to many of the components of the CMS and seem to be central and unifying factors Multiple metabolic toxicities (A-FLIGHT-U) play a role in the development of ROS and cardiac disease in the CMS (Table I).5 In addition to accelerated ischemic and hypertensive cardiomyopathy, there exists a specific metabolic cardiomyopathy with minimal epicardial disease that is characterized by early diastolic dysfunction progressing to systolic dysfunction and eventual congestive heart failure.6 8 Additional abnormalities of the heart associated with the CMS are reviewed by Peterson and colleagues in this issue of the Journal of the CardioMetabolic Syndrome ( JCMS ).
  • Epidemiology of overweight and obesity Obesity can be defined as a disease in which excess body fat has accumulated such that health may be adversely affected. Conservative estimates of the economic costs of obesity in developed countries are between 2 and 7% of the total health costs, which represents a significant expenditure of national health-care budgets11. It is highly beneficial to be able to estimate prevalence and secular trends in obesity in order to identify those at risk and assist policy makers and public-health planners. The major health consequences of obesity are predictable from an understanding of the pathophysiology of increasing body fat. Obese individuals with excess fat in intra-abdominal depots are at particular risk of negative health consequences, with certain ethnic populations carrying different levels of risk12. To make true comparisons of the burden of obesity between countries it is necessary to compare population-based data on measured height and weight that followed identical protocols for measurement and collection during the same time period. The range of BMI of a population varies significantly according to the stage of economic transition and associated industrialization of a country (such as a shift from dietary deficit to one of dietary excess). As the proportion of the population with a low BMI decreases there is an almost symmetrical increase in the population with a BMI above 25. This indicates the tendency for a population-wide shift as socio-economic conditions improve, with overweight replacing thinness. In the first stages of the transition, wealthier sections of society show an increase in the proportion of people with a high BMI, whereas thinness remains the main concern among the less wealthy. The distribution of BMI tends to change again in the later phases of transition with an increasing prevalence of high BMI among the poor. Importantly, changes in adult prevalence of obesity are reflected by a striking increase in childhood and adolescent weight in both industrialized and developing countries. The early onset of obesity leads to an increased likelihood of obesity in later life as well as an increased prevalence of obesity-related disorders13, 14. Obesity (defined as a BMI above 30) is a common condition in every continent (Fig. 2). The most comprehensive information in Europe derives from data collected between 1983 and 1986 for the MONICA study15. On average, 15% of men and 22% of women were obese, with overweight also being more common among women than men. More than half the adult population between 35 and 65 years of age in Europe were either overweight or obese. In England and Wales the most recent health survey has confirmed an increase in the prevalence of obesity in adults from 6% in men and 8% in women in 1980 to 17% of men and 20% of women in 199716. National surveys in the United States have shown a marked increase in prevalence of obesity over time. The striking increase in prevalence between 1980 and 1994 confirms that population-wide increases in overweight and obesity may occur over a short period of time. The most recent data from the United States, derived from the third National Health and Nutrition Examination Survey (1988–94), shows 20% of US men and 25% of US women are obese17. Detailed sub-analysis shows African-American women and other minority populations to be particularly susceptible. Obesity is also prevalent in Latin America and a particular problem in the Caribbean18. FIGURE 2. Historic, current and projected obesity prevalence rates (BMI ≥ 30 kg m-2) for the United States, England and Wales, Mauritius, Australia and Brazil from 1960 to 2025.
  • Estudios epidemiológicos y metabólicos realizados en los últimos 15 años han enfatizado la noción introducida a principio de los años 40 por Jean Vague que las complicaciones encontradas en los pacientes obesos se debían más la localización de la grasa que al sobrepeso en sí. En esa época se describieron los patrones de obesidad andoride o central y obesidad ginecoide o gluteofemoral. La obesidad androide fue determinada como factor de riesgo cardiovascular. Los estudios epidemiológicos ahn usado mediciones antropometrica como la relación cintura cadera para estimar la proporción de tejido adiposo abdominal. Tecnicas de alta precisión como la RMN o la TAC pueden distinguir la grasa visceral de la grasa subcutánea. Estas tecnixcas demostraron que correlacionan mejor con la circunferencia de cintura en determinar la cantidad de tejido adiposo visceral. Hay estudios que ahn demostrado que los cambios en la circunferencia de cinturas en la mujer se correlacionan mejor con los cambios en al adiposidad visceral que los cambios en la relaci´pon cintura cadera.
  • Insulin Resistance A substantial amount of data indicates that insulin resistance plays a major role in the development of glucose intolerance and diabetes. Insulin resistance is a consistent finding in patients with type 2 diabetes, and resistance is present years before the onset of diabetes. [ 59 ][ 60 ][ 61 ][ 62 ][ 63 ][ 64 ] Prospective studies show that insulin resistance predicts the onset of diabetes. [ 60 ][ 61 ] The term insulin resistance indicates the presence of an impaired biologic response to either exogenously administered or endogenously secreted insulin . Insulin resistance is manifested by decreased insulin-stimulated glucose transport and metabolism in adipocytes and skeletal muscle and by impaired suppression of hepatic glucose output. Insulin sensitivity is influenced by a number of factors including age, [ 65 ] weight, ethnicity, body fat (especially abdominal), physical activity, and medications. Insulin resistance is associated with the progression to IGT and type 2 diabetes, [ 66 ] although diabetes is rarely seen in insulin-resistant persons without some degree of beta cell dysfunction. [ 64 ] First-degree relatives of type 2 diabetics have insulin resistance even at a time when they are nonobese, implying a strong genetic component in the development of insulin resistance. [ 60 ][ 66 ][ 67 ] There is also a strong influence of environmental factors on the genetic predisposition to insulin resistance and therefore to diabetes. [ 68 ][ 69 The reason for the relationship to intra-abdominal fat with abnormal metabolism is not clearly defined, but a number of hypotheses, which are not mutually exclusive, have been proposed. First, abdominal fat is more lipolytically active than subcutaneous fat, perhaps because of its greater complement of adrenergic receptors. [ 85 ][ 86 ] In addition, the abdominal adipose store is resistant to the antilipolytic effects of insulin [ 87 ] including alterations in lipoprotein lipase activity, which leads to increased lipase activity and a greater flux of fatty acids into the circulation with the portal circulation receiving the greatest fatty acid load. The role of the liver in insulin resistance and hyperglycemia is discussed subsequently. Figure 29-4 Relationship between body mass index (A) or intra-abdominal fat (B) and insulin sensitivity. (A, From Fujimoto WY, Bergstrom RW, Boyko EJ, et al. Obesity Res 1995; Suppl 2:1795–1863; B, from Kahn SE, Prigeon RL, McCulloch DK, et al. Quantification of the relationship between insulin sensitivity and beta-cell function in human subjects: evidence for a hyperbolic function. Diabetes 1993; 42:1663–1672.)
  • Factors influencing obesity Obesity is not a single disorder but a heterogeneous group of conditions with multiple causes. Body weight is determined by an interaction between genetic, environmental and psychosocial factors acting through the physiological mediators of energy intake and expenditure. Although genetic differences are of undoubted importance, the marked rise in the prevalence of obesity is best explained by behavioural and environmental changes that have resulted from technological advances (Fig. 3). FIGURE 3. Factors influencing the development of obesity.
  • Background Physical activity decl in es dur in g adolescence, but the underly in g reasons rema in unknown. Methods We prospectively followed 1213 black girls and 1166 white girls enrolled in the National Heart, Lung, and Blood In stitute Growth and Health Study from the ages of 9 or 10 to the ages of 18 or 19 years. We used a validated questionnaire to measure leisure-time physical activity on the basis of metabolic equivalents (MET) for reported activities and their frequency in MET-times per week; a higher score in dicated greater activity. Results The respective median activity scores for black girls and white girls were 27.3 and 30.8 MET-times per week at base l in e and decl in ed to 0 and 11.0 by year 10 of the study (a 100 percent decl in e for black girls and a 64 percent decl in e for white girls, P&lt;0.001). By the age of 16 or 17 years, 56 percent of the black girls and 31 percent of the white girls reported no habitual leisure-time activity. Lower levels of parental education were associated with greater decl in e in activity for white girls at both younger ages (P&lt;0.001) and older ages (P=0.005); for black girls, this association was seen only at the older ages (P=0.04). Pregnancy was associated with decl in e in activity among black girls (P&lt;0.001) but not among white girls, whereas cigarette smok in g was associated with decl in e in activity among white girls (P&lt;0.001). A higher body-mass in dex was associated with greater decl in e in activity among girls of both races (P 0.05). Conclusions Substantial decl in es in physical activity occur dur in g adolescence in girls and are greater in black girls than in white girls. Some determ in ants of this decl in e, such as higher body-mass in dex, pregnancy, and smok in g, may be modifiable. The median activity score for the whole group decreased by 83 percent from year 1 (age, 9 or 10 years) to year 10 (age, 18 or 19 years) ( Figure 2 ). Dur in g the same period, the decrease in median HAQ scores for black girls was 100 percent, as compared with 64 percent for white girls (P&lt;0.001). For both races, the mean annual decl in e in scores was greater at older ages (years 5 to 8: 4.1 MET-times per week for black girls and 3.5 MET-times per week for white girls) than at younger ages (years 1 to 5: 3.5 MET-times per week for black girls and 2.7 MET-times per week for white girls) ( Figure 2 ). Even at year 1, activity levels were lower for black girls than for white girls (P=0.008). By year 8 (age, 16 or 17 years), 56 percent of black girls and 31 percent of white girls reported no habitual leisure-time activity (HAQ scores of zero). School enrollment had no effect on HAQ scores at year 8 for either white or black girls. Figure 2. Median Habitual Activity Questionnaire Scores Accord in g to Year of Study and Race. Scores are expressed in MET-times per week. Solid circles represent black girls, and open circles white girls. Values in parentheses are the 25th and 75th percentiles
  • Examination of population trends indicates that there may be a link between physical inactivity and increases in body weight. For example, there is a significant increase in the prevalence of obesity as adults move from the third decade of life (20–29 y of age) to the sixth decade of life (50–59 y of age) (1 ). Moreover, examination of physical activity data from the National Health Interview Survey (NHIS) and the Behavioral Risk Factor Surveillance System (BRFSS) demonstrates that the percentage of adults meeting the minimal public health recommendations for physical activity decreases across this same period of time (10 ). The data for men are presented in Figure 1 , and a similar pattern exists for women. Thus, as obesity increases from 20 to 60 y of age, there is a corresponding decrease in physical activity, and this may partially contribute to the increase in body weight. FIGURE 1 Participation in &gt;150 min/wk of physical activity across age groups in men based on the National Health Interview Survey (NHIS) and the Behavioral Risk Factor Surveillance Survey (BRFSS) [see US Department of Health and Human Services (10 )].
  • Adult overweight and obesity [body mass index (BMI) 25 kg/m2] are more prevalent than ever before. In 1999 61% of U.S. adults were either overweight or obese (1 ), compared to 47% in 1976–1980 (2 ). Weight gain during adulthood has also become increasingly commonplace over the past several decades (3 ), and it appears that adults are now becoming obese at an earlier age than in previous years (4 ). The specific underlying causes of obesity and weight gain are not well understood but are likely to be multivariable rather than singular, representing some combination of factors that result in increases in energy intake, decreases in energy expenditure or both. This review focuses on potential biobehavioral influences on adult weight gain, that is, specific eating behaviors that may contribute to weight gain through overeating. Macronutrient, energy density, fiber and glycemic index influences on energy regulation have recently been reviewed elsewhere (5 –14 ). National dietary and physical activity data provide strong evidence for a demographic shift toward an overall positive energy balance in the United States that has increased over the past few decades ( Fig. 1 ). Based on both food supply data and dietary survey data, energy intake is on the rise in all age groups from 2 to 90 y (15 ,16 ). As shown, national food supply data adjusted for spoilage and waste indicate that Americans consumed an average of 1.42 MJ/d (340 kcal/d) more in 1994 than in 1984 and 2.09 MJ/d (500 kcal/d) more than in 1977 (17 ). Self-reported energy intake also increased from 1977 to 1994–1996 by 0.54–0.79 MJ/d (130–190 kcal/d) (15 ,16 ) (data not shown). The figure also shows that the prevalence of overweight and obesity over the past 30 y (1 ,2 ) has increased along with per capita energy intake. In contrast, self-reported physical activity of adults remained fairly constant between 1990 and 1998 (18 ). [Note that physical activity data before 1990 were either tabulated differently or the specific questions differed and therefore are not readily comparable to the 1990–1998 data. However, those data also show that self-reported physical activity remained constant between 1985 and 1991 (19 ).] The overall differences between these energy intake and physical activity patterns suggest that U.S. trends in adult weight gain (3 ,4 ) may largely be attributed to overconsumption of energy. FIGURE 1 Left panel : Increase in the U.S. prevalence of overweight and obesity over the past 30 y. [Data are based on U.S. DHHS (1 ) and Flegal et al. (2 ).] Middle panel : Change over time in the United States per capita energy intake, adjusted for spoilage and waste. [Data from Putnam (17 ).] Right panel : Percentage of the U.S. population meeting physical activity recommendations (at least 5 d/wk moderate-intensity physical activity for 30 min, or at least 3 d/wk vigorous-intensity physical activity 20 min, or both) between 1990 and 1998. [Data from the National Center for Health Statistics (18 ).]
  • Role of Abdominal Obesity Most studies support the view that the metabolic syndrome that now confronts U.S. physicians in epidemic proportions is largely initiated by abdominal obesity. The four most crucial elements that link abdominal obesity to other features of the metabolic syndrome seem to be elevated FFA, reduction in circulating insulin-sensitizing adiponectin, peripheral-tissue resistance o the insulin-sensitizing actions of leptin, and enhanced macrophage infiltration in fat tissue with release of proinflammatory cytokines (Figure 1). Abdominal fat is unique in its metabolic features as compared with peripheral fat depots, exhibiting larger adipocytes that contain more triglyceride (TG) and exhibit greater insulin resistance than smaller adipocytes. Adipocyte resistance to the lipogenic effect of insulin yields higher basal rates of lipolysis with increased release of FFA into the portal venous system. This direct access to the liver (see below) may also contribute to the unique impact of visceral fat on energy homeostasis. Abdominal fat may also secrete less leptin than subcutaneous fat. Thus Cnop et al. (28), comparing lean-insulin resistant, lean insulinsensitive, and obese insulin-resistant adults, found that leptin levels correlated with increasing subcutaneous—but not visceral— fat mass, proposing yet another metabolic distinction between these two compartments. Abdominal fat mass expansion is also coupled with reciprocally reduced release of adiponectin, a multifunctional collagen-like molecule with potent capacity to stimulate fuel oxidation in peripheral tissues (25). Abdominal fat additionally expresses higher levels of reninangiotensin system components: increased angiotensinogen and increased angiotensin II (Ang II) AT1 receptors (38). Finally, epidemiologic studies confirm the unique significance of abdominal fat mass in predicting microalbuminuria, diabetes, and overall cardiovascular risk (39). It was this compelling evidence for a unique role of central or visceral obesity—in contradistinction to subcutaneous obesity—that prompted the NCEP ATPIII decision to specify abdominal obesity in the clinical definition of metabolic syndrome. These phenomena originating in abdominal adipose tissue generate the clinical picture that we recognize as metabolic syndrome. The roles of FFA excess and adiponectin deficiency are reviewed below in the context of their actions in individual organs; the role of leptin is addressed subsequently to compare and integrate prevailing views of how insulin resistance evolves.
  • Although excess abdominal fat serves to initiate dysfunctional energy homeostasis in multiple other organs, it eventually becomes also a target tissue. It is first a target of excess glucose in the vascular space. Increased glucose availability from liver-based gluconeogenesis and from muscle-based reduction in glucose uptake, together with pancreas-dependent hyperinsulinemia, promote lipogenesis in WAT (42) (Figure 1). This poses the disturbing possibility that abdominal obesity creates a self-perpetuating cycle. Excess cortisol activity is known to shift fat from peripheral (gluteal and subcutaneous) to central visceral depots and to mimic many aspects of metabolic syndrome. It thus is not surprising that glucocorticoid excess has been suspected in the cause of metabolic syndrome. Recently, increased activity (65) and a twofold increased expression of 11 hydroxy steroid dehydrogenase 1 (11HSD1) in adipose tissue of nondiabetic centrally obese women (66) were reported. This enzyme acts predominantly to convert inactive cortisone to the active cortisol form, thus generating glucocorticoid at a tissue level. Expression levels of 11HSD1 were directly proportional to waist circumference and insulin resistance (66). Supporting the relevance of locally generated glucocorticoid in WAT, transgenic mice overexpressing 11HSD1 in adipocytes faithfully reproduced the metabolic syndrome (67,68), whereas deficient mice were metabolically resistant to high-fat feeding (69). Once again, a change confined to fat tissue induces systemic metabolic dysregulation. Expanding WAT also becomes the primary target of an inflammatory process. Recent studies in mice and human adipose tissue elegantly documented this process and implicated the role of macrophage infiltration and macrophagederived inflammatory mediators in obesity and metabolic syndrome (30,31). Weisberg et al. (31) demonstrated that increasing body mass and increasing fat-cell volume ( i.e. , fat content) each correlates linearly with bone marrow– derived macrophage infiltration in WAT and linearly with increased expression of macrophage-linked proinflammatory genes (Figure 3). Xu et al. (30) similarly found that upregulated genes in WAT of obese mouse models were primarily inflammatory genes linked to macrophage infiltration/ activation; furthermore, in diet-induced obesity, this nflammatory process within WAT preceded insulin resistance. TNF- and IL-6 have been shown to induce insulin resistance in vitro and to contribute to insulin resistance in mouse models of obesity (36,70). This in situ inflammatory reaction within WAT therefore may induce/augment insulin resistance in the adipocytes per se , coming full circle in generation of multiorgan energy dysregulation. Renin-angiotensin system Adipocytes are known to produce angiotensinogen, renin, angiotensin-converting enzyme, and the angiotensin II receptor. Local conversion of angiotensinogen to angiotensin II stimulates adipocyte differentiation and lipogenesis in a paracrine or autocrine manner. Angiotensinogen in adipocytes is regulated by nutritional manipulations. Adipocyte angiotensinogen decreases with fasting and increases with refeeding. Conflicting reports have appeared regarding whether angiotensinogen is correlated with obesity: some reports describe decreased angiotensinogen expression in subcutaneous adipocytes of obese women, and others describe a positive correlation between body mass index and angiotensinogen expression in the subcutaneous adipose tissue of obese men [13]. Angiotensinogen expression is reported to be greater in omental than in subcutaneous fat. These discrepancies raise the question whether angiotensinogen expression displays a true sexual dimorphism or is related to body fat distribution. Tikhonoff and Staessen [13] have suggested a more complex picture in which variables such as gender, menopausal status, polymorphisms, underlying disease, and body fat depot may play a part. If adipose tissue contributes substantially to excess vasopressor activity via the renin-angiotensin axis, this could help explain the pathophysiology of obesity-related hypertension. To date, however, there is no known, clear syndrome of adipose deficiency or excess of these vasoactive peptides. Thus, it cannot be determined whether the renin-angiotensin system in adipose tissue is an endocrine, paracrine, or autocrine system.
  • Figure 3. Macrocyte infiltration in WAT correlates linearly with body mass index and adipocyte size/fat content (31). In human subcutaneous adipose tissues obtained from individuals with widely ranging body mass index (BMI), density of macrophage infiltration (measured by PCR quantification of CD68 mRNA, a specific macrophage marker) correlated linearly with BMI (a) and with average adipocyte cross-sectional area, an index of cell fat content (b). Squares and diamonds represent female and male subjects, respectively. (c and d) Representative photomicrographs of adipose tissue biopsies from obese (BMI 50.8 kg/m2; c) and lean (BMI 25.7 kg/m2; d) female subjects show the larger adipocyte area in obesity; arrows indicate F4/80/bone marrow–derived macrophages. In studies in agouti (Ay) and obese (Lepob) mice and in humans, adipose tissue macrophages accounted for virtually all of adipose TNF-, inducible nitric oxide synthase, and IL-6 expression (31), cytokines implicated in locally enhancing insulin resistance in WAT (Reproduced by permission from Weisberg SP, et al., J Clin Invest 112: 1796-1808, 2003). Although excess abdominal fat serves to initiate dysfunctional energy homeostasis in multiple other organs, it eventually becomes also a target tissue. It is first a target of excess glucose in the vascular space. Increased glucose availability from liver-based gluconeogenesis and from muscle-based reduction in glucose uptake, together with pancreas-dependent hyperinsulinemia, promote lipogenesis in WAT (42) (Figure 1). This poses the disturbing possibility that abdominal obesity creates a self-perpetuating cycle. Excess cortisol activity is known to shift fat from peripheral (gluteal and subcutaneous) to central visceral depots and to mimic many aspects of metabolic syndrome. It thus is not surprising that glucocorticoid excess has been suspected in the cause of metabolic syndrome. Recently, increased activity (65) and a twofold increased expression of 11 hydroxy steroid dehydrogenase 1 (11HSD1) in adipose tissue of nondiabetic centrally obese women (66) were reported. This enzyme acts predominantly to convert inactive cortisone to the active cortisol form, thus generating glucocorticoid at a tissue level. Expression levels of 11HSD1 were directly proportional to waist circumference and insulin resistance (66). Supporting the relevance of locally generated glucocorticoid in WAT, transgenic mice overexpressing 11HSD1 in adipocytes faithfully reproduced the metabolic syndrome (67,68), whereas deficient mice were metabolically resistant to high-fat feeding (69). Once again, a change confined to fat tissue induces systemic metabolic dysregulation. Expanding WAT also becomes the primary target of an inflammatory process. Recent studies in mice and human adipose tissue elegantly documented this process and implicated the role of macrophage infiltration and macrophage derived inflammatory mediators in obesity and metabolic syndrome (30,31). Weisberg et al. (31) demonstrated that increasing body mass and increasing fat-cell volume ( i.e. , fat content) each correlates linearly with bone marrow– derived macrophage infiltration in WAT and linearly with increased expression of macrophage-linked proinflammatory genes (Figure 3). Xu et al. (30) similarly found that upregulated genes in WAT of obese mouse models were primarily inflammatory genes linked to macrophage infiltration/ activation; furthermore, in diet-induced obesity, this nflammatory process within WAT preceded insulin resistance. TNF- and IL-6 have been shown to induce insulin resistance in vitro and to contribute to insulin resistance in mouse models of obesity (36,70). This in situ inflammatory reaction within WAT therefore may induce/augment insulin resistance in the adipocytes per se , coming full circle in generation of multiorgan energy dysregulation.
  • Dysregulation of the Peripheral and Adipose Tissue Endocannabinoid System in Human Abdominal Obesity The endocannabinoid system has been suspected to contribute to the association of visceral fat accumulation with metabolic diseases. We determined whether circulating endocannabinoids are related to visceral adipose tissue mass in lean, subcutaneous obese, and visceral obese subjects (10 men and 10 women in each group). We further measured expression of the cannabinoid type 1 ( CB1 ) receptor and fatty acid amide hydrolase ( FAAH ) genes in paired samples of subcutaneous and visceral adipose tissue in all 60 subjects. Circulating 2-arachidonoyl glycerol (2-AG) was significantly correlated with body fat ( r = 0.45, P = 0.03), visceral fat mass ( r = 0.44, P = 0.003), and fasting plasma insulin concentrations ( r = 0.41, P = 0.001) but negatively correlated to glucose infusion rate during clamp ( r = 0.39, P = 0.009). In visceral adipose tissue, CB1 mRNA expression was negatively correlated with visceral fat mass ( r = 0.32, P = 0.01), fasting insulin ( r = 0.48, P &lt; 0.001), and circulating 2-AG ( r = 0.5, P &lt; 0.001), whereas FAAH gene expression was negatively correlated with visceral fat mass ( r = 0.39, P = 0.01) and circulating 2-AG ( r = 0.77, P &lt; 0.001). Our findings suggest that abdominal fat accumulation is a critical correlate of the dysregulation of the peripheral endocannabinoid system in human obesity. Thus, the endocannabinoid system may represent a primary target for the treatment of abdominal obesity and associated metabolic changes. Endocannabinoids are lipid mediators derived from membrane phospholipids or triglycerides with complex effects on body weight and metabolic regulation ( 1 , 2 ). Several enzymes are involved in the synthesis and degradation of the two most important endocannabinoids, anandamide and 2-arachidonoyl glycerol (2-AG). At least two G-protein–coupled cannabinoid receptors ( CB1 and CB2 ) have been identified ( 3 , 4 ). Activation of central CB1 receptors clearly promotes food intake and weight gain ( 5 – 7 ). However, pharmacological blockade with rimonabant (SR141716) or genetic knockout of the CB1 receptor demonstrated that weight reduction under these conditions is only partly promoted by decreased food intake ( 8 – 10 ). These findings prompted the search for peripheral metabolic effects of endocannabinoids. CB1 receptors have now been identified in rodent liver ( 11 ), skeletal muscle ( 12 ), adipocytes ( 9 , 13 ), and pancreas ( 14 ). The potential role of peripheral CB1 receptors for metabolic regulation is further promoted by statistical analyses of clinical trials, suggesting that the influence of CB1 receptor blockade on adiponectin levels, lipids, and glucose homeostasis goes beyond the effect of weight loss alone ( 15 – 17 ). In general, endocannabinoid formation and signaling is dependent on external stimuli such as cellular stress, tissue damage, or metabolic challenges ( 3 , 4 ). However, recent findings point to profound changes in the regulation of the endocannabinoid system in obesity. Experimental data suggest that the endocannabinoid system is chronically activated in obesity or after high-fat feeding, both in the brain and in peripheral organs ( 18 – 20 ). Increased dietary supply of fatty acids, serving as endocannabinoid precursors, may be a possible mechanism, but decreased enzymatic degradation by the fatty acid amide hydrolase ( FAAH ) has been described in the liver of diet-induced obese mice ( 11 ). Recently, increased circulating levels of endocannabinoids and downregulation of subcutaneous adipose CB1 and FAAH gene expression in obese postmenopausal women were described ( 21 ). To better define the changes of endocannabinoid system regulation in human obesity, we determined whether elevated circulating endocannabinoids are related to visceral adipose tissue accumulation in 60 men and women with a wide range of obesity, fat distribution, insulin sensitivity, and glucose tolerance. We also measured expression of the CB1 and FAAH genes in paired samples of subcutaneous and visceral adipose tissue. CB1 and FAAH mRNA gene expression in adipose tissue depots. Gene expression analysis of 60 paired samples of visceral and subcutaneous adipose tissue revealed that mRNA expression of both CB1 and FAAH was higher in the visceral than in the subcutaneous fat depot ( Fig. 3 ). CB1 and FAAH gene expression was not different between men and women. CB1 and FAAH mRNA levels were significantly higher in adipose tissue of lean compared with obese subjects ( Fig. 3 ) but not different between the two fat distribution phenotypes. Significant correlations were found between visceral and subcutaneous mRNA levels, for both CB1 and FAAH ( Fig. 4 ). Correlations between endocannabinoid system gene expression and anthropometric and metabolic parameters are shown in Table 2 . These correlations were further analyzed by multivariate linear regression models, including age, sex, percent body fat, fasting plasma insulin, and plasma 2-AG levels as independent variables. These models revealed percent body fat and 2-AG as determinants of visceral CB1 gene expression and only circulating 2-AG as a predictor of visceral FAAH gene expression ( Table 3 ). Percent body fat and circulating 2-AG levels were also identified as the strongest predictors of subcutaneous CB1 and FAAH mRNA expression (data not shown FIG. 4. Correlation between visceral and subcutaneous adipose tissue gene expression of CB1 receptor ( A ) and FAAH ( B ) genes. Linear regression analysis with log-transformed data (1 AU = 1ag target gene/100 ng total RNA). Paired samples from visceral and subcutaneous adipose tissue were obtained from 60 Caucasian men ( n = 30) and women ( n = 30) and analyzed by real-time RT-PCR
  • Background: Understanding the natural history of obesity in a population may be a critical step toward developing effective interventions. Objective: To assess the development of body mass and examine the role of race or ethnicity, sex, and birth year in obesity onset in young U.S. adults. Design: Prospective cohort study. Setting: The National Longitudinal Survey of Youth 1979, a national sample with oversampling of minority ethnic groups. Participants: 9179 persons. Measurements: Body mass index (BMI) calculated from 12 self-reported height and weight samples recorded between 1981 and 1998. Logistic regression identified predictors of obesity at age 35 to 37 years. Cox proportional hazards models compared the incidence of obesity by ethnicity and birth year. Results: Overall, 26% of men and 28% of women were obese (BMI 30 kg/m2) by age 35 to 37 years. Race or ethnicity and baseline BMI were significant predictors of obesity . Obesity onset was 2.1 (95% CI, 1.6 to 2.7) times faster for black women and 1.5 (CI, 1.1 to 2.0) times faster for Hispanic women than for white women. The pattern for men differed: Overall, obesity developed most rapidly in Hispanic men, but relative rates of obesity onset for white men compared with black men varied according to age. The rate of obesity onset increased 26% to 28% over an 8-year span in birth year. Conclusions: Marked ethnic-based differences were found in rates of weight accumulation in young U.S. adults, with later birth cohorts experiencing earlier onset of obesity . To alter the course of obesity in the United States, interventions should target young adults, especially those of minority ethnic groups. Context Studies of the natural history of obesity have had limitations, such as cross-sectional design, short follow-up, or narrowly defined populations. Contribution Using data from a national study of Americans born between 1957 and 1964 and followed for nearly two decades, these investigators found that more than 25% were obese by age 35 years. Obesity developed most quickly in black women, with moderate rapidity in Hispanic women, and most slowly in white women. Hispanic men developed obesity more quickly than other men. More recent birth cohorts became obese faster than earlier cohorts. Implications Understanding ethnic differences in the age of onset and rate of progression of obesity may help efforts to prevent obesity . We analyzed relative rates of obesity onset in 1089 men and 1053 women who were 17 and 18 years of age and not obese. In women, Kaplan–Meier curves showed the same ethnic-based patterns seen in the BMI plots (Figure 3). Cox proportional hazards modeling quantified relative rates of obesity onset: Black women reached obesity 2.1 (CI, 1.6 to 2.7) times faster and Hispanic women reached obesity 1.5 (CI, 1.1 to 2.0) times faster than did white women. Hazard was proportional between Hispanic and white men, and the hazard ratio quantified obesity onset as 2.5 (CI, 1.9 to 3.3) times faster among Hispanic men. When obesity onset in black men was compared with that in white men, the proportional hazards assumption was violated. This can be seen from the Kaplan–Meier curves: Hispanic ethnicity was associated with the most rapid onset of obesity at all ages, but differences between black and white men in time to onset of obesity were age-dependent (Figure 3). Both black men and white men showed similar rates of obesity onset at the transition into adulthood, but obesity developed more rapidly in black men after approximately age 28 years. Because of the proportional hazards violation, black and white men were compared across two periods. From age 17 to 28 years, there was no significant difference between the time to onset of obesity between these two groups, whereas after age 28 years, obesity developed significantly more rapidly in black men (hazard ratio, 2.2 [CI, 1.5 to 3.4]). The proportional hazards assumption for the latter model was met by both graphical and Schoenfeld criteria. Figure 3. Kaplan–Meier survivor curves: time to obesity for 17- and 18-year-old men and women who were not obese.
  • Figure 3. Relation between the Change in Weight and the Relative Risk of Type 2 Diabetes, Hypertension, Coronary Heart Disease, and Cholelithiasis. Panel A shows these relations for change of weight from 18 years of age among women in the Nurses&apos; Health Study, initially 30 to 55 years of age, who were followed for up to 18 years. 18,19,20,21 Panel B shows the same relations for change of weight from 20 years of age among men in the Health Professionals Follow-up Study, initially 40 to 65 years of age, who were followed for up to 10 years A major limitation of standard weight guidelines is that a person initially at the low end of the range can gain as much as 15 or 20 kg (33 or 44 lb) and still remain within the recommended range. Much smaller gains in weight during adulthood, however, are associated with significantly increased risks of many chronic diseases (Figure 3). For example, as compared with women and men in the Nurses&apos; Health Study 18,19,20,21 and the Health Professionals Follow-up Study 22 who maintained their weight within 2 kg (4 lb) of their weight at 18 to 20 years of age, those who gained 5.0 to 9.9 kg (11 to 22 lb) had risks of coronary heart disease, hypertension, cholelithiasis, and type 2 diabetes that were 1.5 to 3 times as high. These increases in risk were greater with larger gains in weight.
  • Background Extreme obesity is recognized to be a risk factor for heart failure. It is unclear whether overweight and lesser degrees of obesity also pose a risk. Methods We investigated the relation between the body-mass index (the weight in kilograms divided by the square of the height in meters) and the incidence of heart failure among 5881 participants in the Framingham Heart Study (mean age, 55 years; 54 percent women). With the use of Cox proportional-hazards models, the body-mass index was evaluated both as a continuous variable and as a categorical variable (normal, 18.5 to 24.9; overweight, 25.0 to 29.9; and obese, 30.0 or more). Results During follow-up (mean, 14 years), heart failure developed in 496 subjects (258 women and 238 men). After adjustment for established risk factors, there was an increase in the risk of heart failure of 5 percent for men and 7 percent for women for each increment of 1 in body-mass index. As compared with subjects with a normal body-mass index, obese subjects had a doubling of the risk of heart failure. For women, the hazard ratio was 2.12 (95 percent confidence interval, 1.51 to 2.97); for men, the hazard ratio was 1.90 (95 percent confidence interval, 1.30 to 2.79). A graded increase in the risk of heart failure was observed across categories of body-mass index. The hazard ratios per increase in category were 1.46 in women (95 percent confidence interval, 1.23 to 1.72) and 1.37 in men (95 percent confidence interval, 1.13 to 1.67). Conclusions In our large, community-based sample, increased body-mass index was associated with an increased risk of heart failure. Given the high prevalence of obesity in the United States, strategies to promote optimal body weight may reduce the population burden of heart failure During a mean follow-up of 14 years (maximum, 21.8), heart failure developed in 496 participants (258 women and 238 men). The crude cumulative incidence (Figure 1) and the age-adjusted incidence rates (Table 2) of heart failure increased across categories of body-mass index for both men and women. Figure 1. Cumulative Incidence of Heart Failure According to Category of Body-Mass Index at the Base-Line Examination. The body-mass index was 18.5 to 24.9 in normal subjects, 25.0 to 29.9 in overweight subjects, and 30.0 or more in obese subjects.
  • In the most powerful analysis to date, Stevens et al. 17 studied, over a 10-year period, mortality rates among men and women in the large American Cancer Society cohort who had never smoked. After early deaths were eliminated, mortality increased linearly with increasing body-mass index from very lean to clearly obese at all ages up to 75 years; the association was weaker at older ages. For persons younger than 75, total mortality rates were 8 to 35 percent higher among those with body-mass indexes of 25 to 26.9 and 18 to 40 percent higher among those with body-mass indexes of 27.0 to 28.9 than among persons with body-mass indexes of 19 to 21.9 (Figure 1).
  • Background: Obesity increases the risk for hypertension, but the effects of modest long-term weight changes have not been precisely quantified. Objective: To investigate body mass index (BMI) and weight change in relation to risk for hypertension. Design: Cohort study. Setting: General community. Participants: Cohort of 82 473 U.S. female nurses 30 to 55 years of age followed every 2 years since 1976. The follow-up rate was 95%. Measurements: Primary risk factors examined were 1) BMI at age 18 years and midlife and 2) long-term and medium-term weight changes. The outcome was incident cases of hypertension. Results: By 1992, 16 395 incident cases of hypertension had been diagnosed. After adjustment for multiple covariates, BMI at 18 years of age and midlife were positively associated with occurrence of hypertension (P for trend &lt; 0.001). Long-term weight loss after 18 years of age was related to a significantly lower risk for hypertension, and weight gain dramatically increased the risk for hypertension (compared with weight change &lt; or = to 2 kg, multivariate relative risks were 0.85 for a loss of 5.0 to 9.9 kg, 0.74 for a loss &gt; or = to 10 kg, 1.74 for a gain of 5.0 to 9.9 kg, and 5.21 for a gain &gt; or = to 25.0 kg). Among women in the top tertile of baseline BMI at age 18 years, weight loss had a greater apparent benefit. The association between weight change and risk for hypertension was stronger in younger (&lt;45 years of age) than older women (&gt; or = to 55 years of age). Medium-term weight changes after 1976 showed similar relations to risk for hypertension. Conclusions: Excess weight and even modest adult weight gain substantially increase risk for hypertension. Weight loss reduces the risk for hypertension. To determine whether baseline BMI modified the relation between long-term weight change and risk for hypertension, we stratified the data by the tertiles of BMI at age 18 years (Figure 1). For women who were in the first and second tertiles of BMI at age 18 years (&lt;22 kg/m2), subsequent weight loss after age 18 years did not appreciably reduce risk for hypertension. However, subsequent weight gain was associated with a marked increase in risk compared with women who had stable weight. In contrast, for women who were in the highest tertile of BMI at age 18 years (&gt; or = to 22 kg/m2), subsequent weight loss substantially decreased risk for hypertension; the relative risks were 0.72 (CI, 0.62 to 0.84) for weight loss of 5.0 to 9.9 kg and 0.57 (CI, 0.48 to 0.67) for loss of 10 kg or more. Weight gain in this group was also associated with an increase in risk. Figure 1. Multivariate relative risk for hypertension according to weight change after age 18 years within strata of body mass index (BMI) at age 18 years. Adjusted for age, BMI (measured in kg/m2) at age 18 years, height, family history of myocardial infarction, parity, oral contraceptive use, menopausal status, postmenopausal use of hormones, and smoking status.
  • Leoncini et al studied the association of the insulin resistance syndrome and target organ damage in 354 adults (mean age 47 years) with untreated hypertension.1 The investigators used modified ATP III criteria, replacing waist circumference with BMI. • Microalbuminuria was defined as an albumin-to-creatinine ratio 2.38 to 19 mg/mmol (men) and 2.96 to 20 mg/mmol (women). Left ventricular (LV) hypertrophy was defined as an LV mass index &gt;51 g/m2.7 in both men and women. • There was significantly higher prevalence of microalbuminuria (P = 0.04) and LV hypertrophy (P = 0.003) in subjects with the insulin resistance syndrome compared to those without the metabolic syndrome. • Thus, subclinical as well as clinical cardiovascular (CV) disease appears to be accelerated in persons with the insulin resistance syndrome. 1. Leoncini G, Ratto E, Viazzi F, Vaccaro V, Parodi D, Parodi A, et al. Metabolic syndrome is associated with early signs of organ damage in nondiabetic, hypertensive patients. J Intern Med. 2005;257:454-460.
  • To summarize concepts to be detailed below, the evolution of metabolic syndrome seems to proceed not as a linear sequence of events but along a matrix of interconnected pathways that mediate interactions among multiple organs and also link these organs as a functional unit to regulate total-body energy homeostasis (Figure 1). Each organ/cell type is typically both a target and an effector within this matrix. Furthermore, disturbance within this matrix of pathways can be initiated by stimuli acting at any one of multiple sites in the matrix ( e.g. , in adipocytes, in hepatocytes, in skeletal myocytes), each ndependently capable of disturbing whole-body fuel homeostasis. However, initiation of metabolic syndrome by obesity,in keeping with the now-recognized role of adipose tissue as an endocrine organ, is characterized by powerful systemic stimuli that together impair energy homeostasis in multiple organs simultaneously, leaving no room for protective compensation. This multiplicityof pathways and targets likely explains the efficacy of obesity as the major generator ofmetabolic syndrome. Figure 1. Pathogenesis of obesity-initiated metabolic syndrome. Increased abdominal fat mass yields high circulating free fatty acids (FFA), which drives increased cellular FFA uptake. Reduced release of adiponectin from expanding abdominal white adipose tissue (WAT) reduces mitochondrial FA uptake/oxidation in multiple tissues. Despite increased release of leptin from WAT, which normally also enhances FA oxidation, tissue resistance to leptin further promotes cytosolic FA build-up. As a result, excess intracellular FA and its metabolites (fatty acyl CoA, diacylglyceride) accumulate, causing insulin resistance (see pathway, Figure 2). Organ-specific consequences include increased hepatic gluconeogenesis and reduced skeletal muscle glucose uptake; the latter raises plasma glucose content and stimulates pancreatic insulin release, and hyperinsulinemia ensues. The newly available glucose plus high insulin now comes back full circle to stimulate further WAT lipogenesis. Increasing fat cell size induces release of chemotactic molecules ( e.g. , monocyte chemoattractant protein-1) with macrophage infiltration plus TNF- and IL-6 generation. These cytokines generate an inflammatory reaction and enhance adipocyte insulin resistance in WAT. Role of Abdominal Obesity Most studies support the view that the metabolic syndrome that now confronts U.S. physicians in epidemic proportions is largely initiated by abdominal obesity. The four most crucial elements that link abdominal obesity to other features of the metabolic syndrome seem to be elevated FFA, reduction in circulating insulin-sensitizing adiponectin, peripheral-tissue resistance to the insulin-sensitizing actions of leptin, and enhanced macrophage infiltration in fat tissue with release of proinflammatory cytokines (Figure 1). Although excess abdominal fat serves to initiate dysfunctional energy homeostasis in multiple other organs, it eventually becomes also a target tissue. It is first a target of excess glucose in the vascular space. Increased glucose availability from liver-based gluconeogenesis and from muscle-based reduction in glucose uptake, together with pancreas-dependent hyperinsulinemia, promote lipogenesis in WAT (42) (Figure 1). This poses the disturbing possibility that abdominal obesity creates a self-perpetuating cycle. -
  • Figure 2. Insulin Signaling Pathways That Regulate Glucose Metabolism in Muscle Cells and Adipocytes. GLUT-4 is stored in intracellular vesicles. Insulin binds to its receptor in the plasma membrane, resulting in phosphorylation of the receptor and insulin-receptor substrates such as the IRS molecules. These substrates form complexes with docking proteins such as phosphoinositide-3 kinase at its 85-kd subunit (p85) by means of SH2 (Scr homology region 2) domains. Then p85 is constitutively bound to the catalytic subunit (p110). Activation of phosphoinositide-3 kinase is a major pathway in the mediation of insulinstimulated glucose transport and metabolism. It activates phosphoinositide-dependent kinases that participate in the activation of protein kinase B (also known as Akt) and atypical forms of protein kinase C (PKC). Exercise stimulates glucose transport by pathways that are independent of phosphoinositide-3 kinase and that may involve 5&apos;-AMP–activated kinase. Insulin-stimulated intracellular movement of GLUT-4 is initiated by the binding of insulin to the extracellular portion of the transmembrane insulin receptor (Fig. 2). Its binding activates tyrosine kinase phosphorylation at the intracellular portion of the receptor. The chief substrates for this tyrosine kinase include insulin-receptor–substrate molecules (IRS-1, IRS-2, IRS-3, and IRS-4), Gab-1 (Grb2 [growth factor receptor–bound protein 2]–associated binder 1), and SHC (Src and collagen-homologous protein). 16,17 In both adipocytes and skeletal muscle, subsequent activation of phosphoinositide-3 kinase is necessary for the stimulation of glucose transport by insulin 16,17 and is sufficient to induce at least partial translocation of GLUT-4 to the plasma membrane. 18-20 Activation of downstream protein serine–threonine kinases may also be involved. 21 Phosphoinositide-3 kinase also activates these other kinases by generating phosphatidylinositol lipid products in the lipid bilayer of cellular membranes. These lipids, in turn, bring into proximity and thereby activate key signaling molecules. In this way, a serine–threonine kinase called protein kinase B (or Akt) and phosphoinositide- dependent kinase 1 are brought together, 22 allowing the latter to phosphorylate and activate protein kinase B. Some isoforms of protein kinase C are also activated by insulin, and phosphoinositide- dependent protein kinase 1 may contribute to the activation of protein kinase C because it phosphorylates a site in the activation loop of protein kinase C. 23 -
  • keletal Muscle Increased circulating FFA also have an impact on skeletal muscle energy homeostasis. Skeletal muscle is normally a major site of glucose and FA uptake, accounting for the bulk of total-body glucose utilization and deriving 60% of resting energy from FA. As in the hepatocyte, increase in intramyocellular FA in skeletal muscle has been shown to impair insulin receptor signaling by PKC-dependent serine phosphorylation of IRS-1; this leads to reduced IRS-1 availability for tyrosine phosphorylation, reducing Glut 4 translocation to the myocyte plasma membrane with consequent reduction in glucose uptake (6). Secondarily, glucose-driven lipogenesis and glycogen synthesis in skeletal myocytes are also reduced. Accordingly, elevated circulating FFA contribute to insulin resistance in both liver and skeletal muscle. As in the hepatocyte, reduced adiponectin secretion secondary to increased visceral fat mass augments insulin resistance in skeletal muscle, also in part via reducing FA oxidation rate, further increasing intramyocellular FA content and impairing insulin action (48). Using magnetic resonance spectroscopy in insulin-resistant offspring of patients with type 2 diabetes, Petersen et al. (49) found evidence of a 30% reduction in mitochondrial oxidative phosphorylation together with impaired muscle FA oxidation and an 80% increase in intramyocellular lipid content. Overt diabetes has also been associated with impaired muscle oxidative capacity (50,51). Finally, the insulin resistance of aging is associated with impaired mitochondrial FA oxidative capacity in skeletal muscle (52). Impaired energy production is a particularly important consequence of insulin resistance in skeletal muscle. Diabetic and prediabetic patients have impaired maximal exercise capacity, reduced maximal oxygen consumption, and slower oxygen uptake at initiation of low-level exercise, potentially contributing to the fatigue and reduced physical activity typical of besity/insulin resistance (53). Exercise stimulates skeletal muscle oxidative enzymes and activates mitochondrial biogenesis (54). Inactivity would be predicted to reduce basal metabolic rate both by reducing muscle mass and by augmenting defective muscle energy production. The practical physical consequences of these skeletal muscle metabolic abnormalities have not yet been widely studied in metabolic syndrome but are likely to reinforce the vicious cycle of ongoing weight gain and sedentary lifestyle. Mechanism The long (2–4 h) delay between the rise in plasma FFAs and the appearance of insulin resistance seems more compatible with an indirect than a direct effect of FFA. For instance, FFAs may need to accumulate first as triglycerides inside muscle cells before they can interferewith insulin action. This notion is supported by studies in animals and humans that have shown a close relation between muscle fat content and insulin resistance, a relation which, in some studies, was even closer than the relation between plasma FFAs and insulin resistance [17–22]. Most of these studies could not differentiate between fat in adipocytes located between muscle fibers, fibers, ie , extramyocellular triglyceride, and fat located within muscle fibers, ie , intramyocellular triglyceride. Intramyocellular triglyceride can be quantitated with proton nuclear magnetic resonance spectroscopy [23,24]. The author used this technique recently to examine effects of acutely rising or falling plasma FFA levels on insulin stimulated glucose uptake and on intramyocellular triglyceride content in soleus muscle of healthy volunteers [25•]. The results showed that acute increases in plasma FFAs caused acute increases in intramyocellular triglyceride concomitant with the development of insulin resistance. However, this finding did not prove a causeand- effect relation between changes in intramyocellular triglyceride and changes in insulin resistance, because these could have been parallel and nonrelated events. The notion that FFAs produce insulin resistance via accumulation of intramyocellular triglyceride would be strengthened if it could be shown that increasing intramyocellular triglyceride increases insulin resistance irrespective of changes in plasma FFAs. How could intramyocellular triglyceride or FFAs cause insulin resistance? Intramyocellular triglyceride has been shown to be a metabolically active pool of fat [26–28] consisting of small oil droplets located close to mitochondria, providing fuel for oxidation [29,30]. As pointed out, it is unlikely that changes in fat and carbohydrate oxidation are the immediate reason for the development of insulin resistance, because these changes occurred long before inhibition of insulin stimulated glucose uptake [9–11,15]. On the other hand, it seems possible that excessive accumulation of intramyocellular triglyceride per se interferes with normal insulin action, or, alternatively, that an insulin resistance-causing signal is generated either from the intramyocellular triglyceride, or unrelated to the intramyocellular triglyceride, from an increase in plasma FFAs. Such a signal might be an increase in cytosolic long chain acyl-CoA concentration, which is likely to occur during lipid/heparin infusions [31]. This hypothesis is supported by the following observations: (1) long chain acyl-CoA and diacylglycerol are activators of protein kinase C [32•,33,34], (2) protein kinase C can inhibit insulin action via serine-threonine phosphorylation of the insulin receptor and of insulin receptor substitute (IRS)-1 [35–37], and (3) Griffin et al. [38] recently have shown blunting of insulin stimulated IRS-1 tyrosine phosphorylation and a fourfold increase in active (membrane bound) protein kinase C in lipid/heparin infused rats.
  • Effect of Excess FFA on Liver Intracellular FFA content is a function of substrate delivery from the plasma and FFA utilization (efflux into mitochondria for oxidation or cytosolic synthesis of intracellular lipids). With abdominal obesity, the increased FFA released into the portal vein from excess visceral fat lipolysis have direct acces to the liver. Because cellular FA uptake is substrate dependent, increased hepatocyte FFA uptake ensues (23). Elevated cytoplasmic FA content leads to hepatic insulin resistance. This process involves competition of FA and glucose for access to mitochondrial oxidative metabolism. The molecular mechanism was recently described by Shulman et al. (40,41) (Figure 2), wherein elevated intracellular fatty acyl CoA activates protein kinase C (PKC), causing phosphorylation of serine- 302 of insulin receptor substrate-1 (IRS-1). This renders IRS-1 unavailable for tyrosine phosphorylation by the activated insulin receptor and reduces all downstream actions of insulin. As a result, the fasting state is simulated and hepatocyte enzymatic machinery is shifted to favor enhanced hepatic gluconeogenesis at the expense of glycogen synthesis. The consequent increase in liver-derived glucose in plasma leads to hyperinsulinemia, a hallmark of metabolic syndrome in its earliest stage and a marker of insulin resistance. The capacity of the insulin-resistant liver to impair secondarily systemic energy homeostasis is illustrated by transgenic studies introducing an insulin-resistant form of the rate-limiting enzyme of liver gluconeogenesis: phosphoenolpyruvate carboxykinase (42). Creating isolated hepatic insulin resistance led to systemic hyperglycemia, hyperinsulinemia, and a moderate increase in fat mass (42). The last reflects WAT utilization of surplus circulating glucose for insulin-induced lipogenesis. In effect, this represents a redistribution of fuel away from the liver to adipose fat stores. These findings emphasize the potential for activating the abnormal metabolic matrix simply by inducing hepatic insulin resistance and also illustrate the dual role of the liver as target and effector in metabolic syndrome derangements. In dogs that were fed an isocaloric moderate-fat diet, striking visceral obesity was associated with marked reduction in the ability of insulin to suppress hepatic gluconeogenesis, even before any reduction in insulin-stimulated glucose uptake appeared; investigators concluded that hepatic insulin resistance plays a dominant role in the pathophysiologic cascade initiated by abdominal obesity (43). FFA overload also provides substrate for increased hepatic TG synthesis and for TG-rich VLDL assembly and secretion. Although details are beyond the scope of this review, the peripheral metabolism of these VLDL generate a small, dense form of highly atherogenic LDL [reviewed by Avramoglu et al. (44)] along with an increase in plasma TG. In addition, increased hepatic lipase activity in the insulin-resistant state reduces levels of protective HDL-2 cholesterol (45), which is essential to the transport of cholesterol from tissues back to the liver. Thus, hepatic insulin resistance, high plasma TG, and low plasma HDL are pathogenetically linked manifestations of altered lipid regulation in metabolic syndrome. Effect of Adiponectin Deficiency on Liver In addition to the effects of elevated FFA load, the energyrelated functions of the liver are profoundly affected by the reduced circulating levels of the adipokine adiponectin. The actions of this multifunctional protein are organ specific and uniformly insulin sensitizing. Adiponectin normally promotes insulin sensitivity in liver in part by enhancing FA oxidation (46); this reduces accumulation of cytoplasmic FA, thereby reducing intracellular FA levels and enhancing insulin action via IRS-1 availability to the insulin receptor. Second, like insulin, adiponectin normally suppresses hepatic gluconeogenic enzymes and induces glycogenetic enzymes. Increase in 5&apos;-AMP-activated kinase mediates these effects of adiponectin (46,47). Conversely, deficiency of adiponectin in states of abdominal obesity directly contributes to insulin resistance by further enhancing accumulation of intracellular FA and FA metabolites and by stimulating hepatic glucose output. The impact of insulin-sensitizing adipokines is apparent from transgenic mouse models that completely lack fat (and thus both adiponectin and leptin) (48). Animals are insulin resistant; the provision of physiologic levels of both adiponectin and leptin fully restores normal energy homeostasis, whereas either alone is only partially effective (48). These studies underscore theregulatory role of fat-derived adipokines and lend logic to the seeming paradox that either too little or too much adipose tissue can lead to insulin resistance (21).
  • NAFLD represents a spectrum of fatty liver disorders with remodeling changes ranging from hepatic steatosis to nonalcoholic steatohepatitis (NASH), fibrosis, cryptogenic cirrhosis, and end-stage liver disease (Figure 3).9 NASH is the most prevalent form of progressive liver disease in the United States, approaching 5% (currently thought to exceed that of hepatitis cirrhosis).10,11 NAFLD and NASH may be considered the hepatic component of the CMS and are strongly associated with CMS, obesity, and T2DM (Table II). Many authors have proposed that NASH be included as a clinical feature of the CMS and IR.12 14 The initial cellular remodeling consists of the intracellular hepatocyte accumulation of fat due to increased lipolysis and excessive generation of triglycerides and free fatty acids. This intracellular accumulation of fat is associated with enhanced oxidative stress and generation of ROS within the hepatocytes, while setting in motion a panoply of metabolic and intracellular and extracellular remodeling events within the liver. The hepatic stellate cell (a sinusoidal pericyte cell) is central to the underlying ECM accumulation and fibrosis. As a pericyte cell, it initially begins laying down ECM (types I and III collagen) adjacent to the hepatic sinusoids and may be responsible for a sinusoidal—endothelial cell hepatic parenchyma uncoupling both functionally and structurally. Over time, the stellate cells are responsible for the extensive remodeling within the liver, contributing to end-stage liver failure, which may necessitate liver transplantation (Figure 4). This process is discussed in detail by Ibdah and colleagues in this issue of JCMS . Figure 3. Metabolic hepatopathy in the cardiometabolic syndrome. Hematoxylin and eosin micrographs of liver in nonalcoholic fatty liver disease. A) Simple fatty liver; steatosis without inflammation or fibrosis; B) nonalcoholic steatohepatitis; C) fibrosis—cirrhosis (cryptogenic cirrhosis).
  • Increased prevalence of fatty liver in arterial hypertensive patients with normal liver enzymes: role of insulin resistance Background: The conditions associated with fatty liver disease presenting with normal liver enzymes and the mechanism involved in its development remain to be fully elucidated. Aims: The aim of the present study was to test the hypothesis that fatty liver with normal liver enzymes occurs more frequently in arterial hypertensive patients and to establish whether this condition is associated with insulin resistance. Patients: A total of 55 non-obese, non-diabetic, non-heavy alcohol drinking patients with arterial hypertensive and normal liver enzymes and 55 sex and age matched healthy subjects were enrolled into the study. Methods: Plasma metabolic parameters, body mass index, and the presence of fatty liver were investigated. Insulin resistance was estimated from plasma insulin and glucose as the homeostasis model assessment index. Stepwise logistic regression and multivariate regression analysis were used on the combined sample to identify variables independently associated with fatty liver and insulin resistance. Results: Hypertensive patients had a significantly higher prevalence of fatty liver (30.9% v 12.7%; p &lt; 0.041), higher insulin resistance (mean 2.27 (SD 1.81) v 1.56 (0.70); p = 0.022), and slightly higher body mass index (24.9 (3.0) v 24.0 (2.2); p = 0.043) than controls. Multivariate logistic regression identified insulin resistance (odds ratio 1.66 (95% confidence interval (CI) 1.03–2.52)) and body mass index (OR 1.22 (95% CI 1.00–1.49)) as factors independently associated with fatty liver. Multivariate regression analysis showed insulin resistance to be predicted by alanine transaminase (p = 0.002), presence of arterial hypertension (p = 0.029), and body mass index (p = 0.048). Conclusion: The higher prevalence of non-alcoholic fatty liver in non-obese hypertensive patients with normal liver enzymes appears to be related to increases in insulin resistance and body weight.
  • Increased prevalence of fatty liver in arterial hypertensive patients with normal liver enzymes: role of insulin resistance Background: The conditions associated with fatty liver disease presenting with normal liver enzymes and the mechanism involved in its development remain to be fully elucidated. Aims: The aim of the present study was to test the hypothesis that fatty liver with normal liver enzymes occurs more frequently in arterial hypertensive patients and to establish whether this condition is associated with insulin resistance. Patients: A total of 55 non-obese, non-diabetic, non-heavy alcohol drinking patients with arterial hypertensive and normal liver enzymes and 55 sex and age matched healthy subjects were enrolled into the study. Methods: Plasma metabolic parameters, body mass index, and the presence of fatty liver were investigated. Insulin resistance was estimated from plasma insulin and glucose as the homeostasis model assessment index. Stepwise logistic regression and multivariate regression analysis were used on the combined sample to identify variables independently associated with fatty liver and insulin resistance. Results: Hypertensive patients had a significantly higher prevalence of fatty liver (30.9% v 12.7%; p &lt; 0.041), higher insulin resistance (mean 2.27 (SD 1.81) v 1.56 (0.70); p = 0.022), and slightly higher body mass index (24.9 (3.0) v 24.0 (2.2); p = 0.043) than controls. Multivariate logistic regression identified insulin resistance (odds ratio 1.66 (95% confidence interval (CI) 1.03–2.52)) and body mass index (OR 1.22 (95% CI 1.00–1.49)) as factors independently associated with fatty liver. Multivariate regression analysis showed insulin resistance to be predicted by alanine transaminase (p = 0.002), presence of arterial hypertension (p = 0.029), and body mass index (p = 0.048). Conclusion: The higher prevalence of non-alcoholic fatty liver in non-obese hypertensive patients with normal liver enzymes appears to be related to increases in insulin resistance and body weight. Intragroup analysis showed glucose and insulin resistance (fig 1) to be higher in hypertensive patients with fatty liver (n=17) than in those with a normal liver (n=38) (plasma glucose 99.2 (11.4) v 88.6 (10.4) mg/l, respectively; p &lt; 0.001). Body mass index was also significantly higher (26.4 (2.6) v 24.3 (3.2) kg/m2, respectively; p=0.020) whereas the difference in ALT and plasma insulin between hypertensive patients with and without fatty liver did not reach statistical significance (24.3 (9.3) v 20.8 (7.5) IU/l, respectively (p=0.103) for ALT; and 11.9 (6.0) v 8.7 (6.5) mIU/ml, respectively (p=0.090) for insulin). The remaining parameters did not differ, although HDL cholesterol showed a clear trend to lower values and triglycerides to higher values in those with fatty liver (approximately p=0.07 in both). Insulin resistance (fig 1) and fasting serum insulin (9.6 (4.1) v 6.8 (2.2), respectively; p=0.008) were higher in healthy controls with fatty liver (n=7) in comparison with those with a normal liver (n=48). ALT was also significantly higher in controls with fatty liver (26.6 (6.6) v 19.5 (7.1) IU/l; p=0.017) despite being within the normal range in all subjects. Interestingly, body mass index was not significantly different in controls with fatty liver compared with those without (24.7 (1.9) v 23.9 (2.3) kg/m2, respectively; p=0.418). Glucose was also higher in controls with fatty liver (96.6 (12.6) v 86.1 (9.1) mg/l; p=0.010) whereas all other parameters were clearly not significantly different. Logistic regression selected insulin resistance (OR 1.61 (95% CI 1.03–2.52); p=0.037) and body mass index (OR 1.22 (95% CI 1.00–1.49); p=0.048) as factors independently associated with fatty liver. Moreover, ALT (p,0.001), hypertension (p=0.029), and body mass index (p=0.047) were found to be positively associated with insulin resistance in multivariate regression analysis Figure 1 Homeostasis model assessment index (HOMA), an indicator of insulin resistance. Values are mean (SD) in hypertensive (Hypert) patients and control subjects subgrouped according to presence or absence of fatty liver. p values indicate significance level of differences between subjects with and without fatty liver in hypertensive patients and in controls, as calculated using the unpaired t test.
  • Associations Between Liver Histology and Severity of the Metabolic Syndrome in Subjects With Nonalcoholic Fatty Liver Disease N onalcoholic fatty liver disease (NAFLD) is associated with a histopathological picture resembling alcohol-induced liver injury occurring in subjects who consume insignificant amounts of alcohol. In NAFLD, steatosis alone is associated with good prognosis, whereas nonalcoholic steatohepatitis (NASH) can progress to fibrosis and cirrhosis in up to 30% of cases, potentially leading to liver failure and hepatocellular carcinoma. The prevalence of NAFLD and NASH in the U.S. is estimated at is estimated at 10–20 and 2–3%, respectively (1–3). METHODS — Forty-six subjects with NAFLD were recruited from clinics. Diagnosis was based on the histological presence of macrovesicular steatosis, with or without lobular inflammation, hepatocellular degeneration, or fibrosis (8,9). Were negative for viral hepatitis, anti-nuclear antibody, anti–smooth muscle antibody, and anti-mitochondrial antibody and had normal iron and copper studies. All subjects consumed 14 standard drinks of alcohol per week (9). Nine male subjects and eight female subjects had preexisting type 2 diabetes, five managed their diabetes with diet alone, and 12 were taking metformin. Approval for the study was obtained from the institutional research ethics committee, and written informed consent was obtained Adiposity was assessed with BMI and dual-energy X-ray absorptiometry (DEXA) (Lunar DPX-L; Lunar, Madison, WI). Metabolic syndrome was defined by ATP III criteria (10). Each subject and their respective control was given a score of 1 for each feature of the metabolic syndrome, syndrome, for a maximum score of 5, with a score of ≥3 being diagnostic of the metabolic syndrome (10). Insulin resistance was estimated using the homeostasis model assessment (HOMA) for insulin resistance score with fasting insulin and glucose levels (11). A pathologist blinded to subject details scored liver biopsies, allotting a score from 0 to 4 for inflammation, steatosis, and fibrosis as previously described (12). For additional fibrosis assessment, all biopsies were stained with Masson’s Trichrome, percent fibrosis was calculated in triplicate by microscopy and image analysis (AIS, Toronto, ON, Canada), and data were expressed as mean percentages. Continuous variables were logn transformed, and correlations were assessed by Pearson’s correlations and stepwise regression. Correlations with categorical variables were analyzed using Spearman correlations. A P value &lt; 0.05 was considered statistically significant. RESULTS — Liver histopathology results were steatosis alone (10 subjects), NASH with fibrosis score of 0 (12 subjects), NASH/fibrosis score 1 (14 subjects), NASH/ fibrosis score 2 (5 subjects), and NASH/ fibrosis score 3 (5 subjects). None had cirrhosis (a score of 4 for fibrosis). Hepatic steatosis was associated with BMI ( r = 0.36, P = 0.02) and percentage trunk fat measured by DEXA ( r = 0.3, P = 0.05). Hepatic inflammation was only significantly associated with BMI ( r 0.35, P 0.02), and hepatic fibrosis was not correlated with any measure of adiposity. However, there were significant associations seen between hepatic inflammation, fibrosis, and features of the metabolic syndrome. Both inflammation and fibrosis correlated significantly with serum insulin, HOMA for insulin resistance, and ATP III score. Other measures of the metabolic syndrome analyzed individually did not correlate with hepatic fibrosis. The two measures of fibrosis (scored ranking and fibrosis percentage) were correlated ( r (scored ranking and fibrosis percentage) were correlated ( r (scored ranking and fibrosis percentage) were correlated ( r = 0.50, P &lt; 0.01), as were hepatic inflammation and fibrosis scores ( r = 0.42, p =&lt; 0.01). Serum aspartate aminotransferase, but not serum alanine aminotransferase, correlated significantly with hepatic inflammation and fibrosis. A total of 30 of 46 subjects (65%) had three or more features of the metabolic syndrome, 9 had two criteria, 6 had one criterion, and 1 had none. Subjects with the metabolic syndrome had a higher hepatic fibrosis score (3.3 vs. 1.6, P = 0.01) and a higher percentage fibrosis (0.40  0.10 vs. 0.18  0.03%, P = 0.02) than those without the metabolic syndrome. There was a significant increase in fibrosis as the number of features of the metabolic syndrome increased ( P = 0.014, ANOVA) (Fig. 1). By stepwise regression, sex, presence of diabetes, HOMA score, total fat by DEXA, serum alanine aminotransferase, and serum HDL were all associated with fibrosis, independent of age ( R 2 = 46.1%). Total fat mass was most highly correlated with fibrosis ( P = 0.001). This study suggests that use of ATP III–related guidelines in clinical practice might be of use both in screening for liver disease in those with the metabolic syndrome and in the selection of NAFLD patients at risk of progression of liver disease. These patients could be targeted for close observation, definitive histological investigation, and in the future, potential therapies such as insulin sensitizers. Assessment of the validity of such an approach warrants prospective study in a larger group.
  • Association Between Elevated Liver Enzymes and C-Reactive Protein: Possible Hepatic Contribution to Systemic Inflammation in the Metabolic Syndrome Objective —The objective of this study was to test whether the frequent association between liver enzyme elevations and various components of the metabolic syndrome is associated with higher C-reactive protein (CRP) levels. Methods and Results —Alanine aminotransferase (ALT), alkaline phosphatase (Alk-P), and high-sensitivity CRP were measured in 1740 subjects. Adjusted geometric mean CRP was calculated for subjects with normal and elevated ALT and for subjects with normal and elevated Alk-P, adjusting for age, sex, smoking, physical activity, body mass index, fasting glucose, triglycerides, the presence of hypertension and low HDL cholesterol, and use of aspirin or hormone replacement therapy. Adjusted CRP levels were higher in subjects with elevated ALT (2.21 versus 1.94 mg/L, P = 0.028) or elevated Alk-P (2.58 versus 1.66 mg/L, P &lt; 0.0001). Logistic regression showed that compared with subjects with normal liver function tests, the adjusted odds for high-risk CRP (3 mg/L) were significantly higher in subjects with elevated ALT (OR, 1.5; 95% CI, 1.2 to 1.9, P = 0.002) or elevated Alk-P (OR, 2.1; 95% CI, 1.7 to 2.6, P &lt; 0.0001). Conclusions —Elevations of liver enzymes are associated with higher CRP concentrations. Hepatic inflammation secondary to liver steatosis is a potential contributor to the low-grade inflammation associated with the metabolic syndrome. Arterial inflammation has emerged as central to the initiation and progression of atherosclerosis. Of the markers of inflammation, C-reactive protein (CRP) has been shown in multiple prospective studies to predict incident myocardial infarction, stroke, peripheral vascular disease, and sudden cardiac death.1,2 Obesity and the metabolic syndrome are associated with chronic inflammatory response, characterized by abnormal cytokine production, increased acute phase reactants, and activation of inflammatory signaling pathways.3 Recent studies have shown that elevated CRP is strongly associated with various characteristics of the metabolic syndrome.4–6 A growing body of evidence implicates adipose tissue as a major regulator of chronic low-grade inflammation in patients with the metabolic syndrome. Adipose tissue produces proinflammatory cytokines, such as tumor necrosis factor- and interleukin-6,3,5,7,8 and is considered an important source of basal production of interleukin-6, the chief stimulator of the production of CRP in the liver.9 Nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH) are highly prevalent diseases that accompany the epidemic of obesity and the metabolic syndrome.10–13 It is estimated that 25% of the American adult population has NAFLD.14 Many studies have shown a strong association between components of the metabolic syndrome and both NAFLD and NASH.10,12,13,15,16 Current understanding of the progression of NAFLD and NASH involves a “2-hit” hypothesis in which the initial metabolic disturbance causes steatosis and a second pathogenic stimulus causes oxidative stress, reactive oxygen species formation, and cytokine production.10,11,17,18 Thus it has been suggested that inflammatory processes that occur in the liver contribute to the systemic inflammation that characterizes the metabolic syndrome.19 Elevated serum alanine aminotransferase (ALT) levels is the most common liver abnormality in NAFLD and NASH, whereas alkaline phosphatase (Alk-P) and –glutamyltransferase are less frequently elevated.20 NAFLD is a common explanation for abnormal liver tests results and accounts for asymptomatic elevation of aminotransferase levels in up to 90% of cases.21 Although subjects with characteristics of the metabolic syndrome frequently have abnormal liver function tests,10,12,13,15,16 there are no data on the association between elevated liver function tests (a crude marker of NAFLD) and metabolic abnormalities in relation to markers of inflammation. The aim of this study was to examine the relationship between abnormal liver function tests and CRP levels in middle-aged subjects with characteristics of the metabolic syndrome. Adjusted geometric mean CRP levels were significantly higher in subjects with elevated ALT or elevated Alk-P (Figure 1). The analyses were repeated using continuous rather than dichotomous variables for all components of the metabolic syndrome (BMI, systolic blood pressure, triglycerides, HDL cholesterol, and fasting glucose). In the continuous variable models, the adjusted geometric mean CRP was also higher in patients with elevated ALT (2.21 versus 1.94 mg/L, P = 0.028) or elevated Alk-P (2.58 versus 1.66 mg/L, P &lt; 0.0001). Figure 1. Adjusted geometric mean CRP levels and 95% CIs according to liver function tests status. CRP levels were adjusted for age, sex, smoking status, physical activity, components of the metabolic syndrome (obesity, glucose intolerance, hypertension, low HDL-cholesterol, and high triglycerides), and use of HRT and aspirin using ANCOVA under a general linear model. Alk-P indicates alkaline phosphatase. Using the same models, we tested the significance of trends for increasing CRP levels across increasing quartiles of liver function tests. CRP levels increased with increasing quartiles of both ALT ( P for trend0.005) and Alk-P ( P for trend &lt; 0.0001). There was a significant increase in CRP levels wit increasing number of abnormal liver function tests. The adjusted geometric mean CRP was 1.78 mg/L (95% CI, 1.68 to 1.89) in subjects with normal ALT and Alk-P; 2.29 mg/L (95% CI, 2.12 to 2.48) in subjects with elevated ALT or Alk-P; and 2.75 mg/L (95% CI, 2.29 to 3.25) in subjects with both elevated ALT and Alk-P ( P for trend &lt; 0.0001).
  • Renal manifestations of the metabolic syndrome Metabolic syndrome has gained a great deal of attention because it is a precursor to type 2 diabetes and also because it increases cardiovascular disease (CVD) risk, even with levels of glycaemia below that used to define diabetes [1]. The connections between chronic kidney disease (CKD) and CVD are increasingly evident. Indicators of CKD, albuminuria (micro- or macro-) and loss of glomerular filtration rate (GFR) are independently associated with increased CVD risk in the general population, as well as high-risk subgroups [2–7]. These connections are exceedingly complex and include a number of shared traditional risk factors (notably diabetes and hypertension), development of non-traditional risk factors (anaemia, hyperparathyroidism with disordered mineral metabolism, high levels of homocysteine and others) and more severe atherosclerosis. Accordingly, relationships between indicators of CKD and metabolic syndrome have also gained increasing interest. Microalbuminuria is a clinical criterion for metabolic syndrome by the WHO classification [1]. The frequency of microalbuminuria increases across the spectrum from those with normal glucose tolerance (5–10%), to metabolic syndrome (12–20%), to type 2 diabetes (25–40%) [3,8–10]. CVD risk parallels the escalating frequency of microalbuminuria. Endothelial dysfunction is a hallmark of vascular injury associated with atherosclerosis. Loss of albumin in the urine is believed to reflect endothelial dysfunction expressed in the glomerulus that, in turn, reflects the status of the circulation at large. In support of this concept, elevated levels of albuminuria (even within the conventional normal range) are related directly to impaired brachial artery reactivity and to severity of angiographically defined coronary artery disease [11,12].
  • The aetiologies of endothelial dysfunction and CKD in metabolic syndrome are likely to be multifactorial. Prevalence or probability of microalbuminuria and/or low GFR is progressively amplified by increasing numbers of metabolic syndrome risk factors (Figure 1) [8,13,14]. Such observations have been made in a variety of different groups, including the general population, Native Americans, Australian Aboriginals and treated hypertensives, among others [8,13–16]. Associations were found between various individual risk factors or combinations and indicators of CKD, suggesting that the various components of metabolic syndrome have an important impact. Recent data indicate that fat itself, particularly in the abdomen, is a source of cytokines that produce endothelial damage [17]. In addition, adiponectin, an adipocytederived hormone that has antiatherogenic and antiinflammatory properties, is reduced in those with metabolic syndrome and increased intra-abdominal fat [17]. Fig. 1. Prevalence of CKD (estimated GFR &lt;60 ml/min/1.73m2) (top) and microalbuminuria (urinary albumin-to-creatinine ratio of 30–300 mg/g) (bottom) by number of metabolic syndrome components. Reproduced with permission from Annals of Internal Medicine taken from Chen et al. The metabolic syndrome and chronic kidney disease in US adults. Ann Intern Med 2004; 140: 167–174.
  • Insulin Resistance and the Cluster of Abnormalities Related to the Metabolic Syndrome Are Associated With Reduced Glomerular Filtration Rate in Patients With Type 2 Diabetes Two samples were studied. Selection criteria and clinical features of the first samples have been already published (8). Briefly, 731 type 2 diabetic patients (384 men and 347 women, age 61.4  10 years, diabetes duration10.8  9 years, HbA1c [A1C] 8.2  1.5%) were consecutively recruited at the Casa Sollievo della Sofferenza Institute in San Giovanni Rotondo. Standardized serum creatinine was measured by the modified kinetic Jaffe` reaction. Micro- or macroalbuminuria was diagnosed when albumin-to-creatinine ratio (ACR) was 2.5 mg/mmol in men and 3.5 mg/mmol in women; 216 patients (29%) had macroalbuminuria. Estimated GFR was calculated with the abbreviated MDRD (Modification of Diet inRenal Disease) formula (9). CKD was defined as estimated GFR &lt; 60 ml/min per 1.73m 2 . The homeostasis model assessment of insulin resistance index was calculated as fasting serum insulin (mU/ml) fasting plasma glucose (mmol/l)/22.5 (10). Besides diabetes, cardiovascular risk factors related to the metabolic syndrome such as arterial hypertension, dyslipidemia, and abdominal obesity were considered as reported (8). An individual MS-r score was then assigned ranging from 0 (diabetes only) to 3. In an independent sample of 86 patients with type 2 diabetes (68 men and 18 women, age 58.4  10 years, diabetes duration 10.6  7 years, A1C 8.2  1.5%) recruited at the Ospedali Riuniti of Bergamo and at the University of Padova, insulin sensitivity (by euglycemic hyperinsulinemic clamp) and GFR (by EDTA) were measured. RESULTS — In the 731 type 2 diabetic patients of the first sample, estimated GFR was 74.3 19 ml/min per 1.73 m2. A significant association was observed between estimated GFR and sex (being lower in women, P &lt; 0.001), duration of diabetes ( r = - 0.3, P &lt; 0.001), urinary ACR ( r = - 0.3 P &lt; 0.001), retinopathy (being lower in patients with retinopathy, P = 0.005), and smoking (being higher in smokers, P = 0.0001) but not with A1C ( r = 0.06, P = 0.1). When singly considered, arterial hypertension, dyslipidemia, and increased waist circumference (i.e., the three abnormalities related to the metabolic syndrome) were significantly associated with estimated GFR ( P values ranging from 0.002 to 0.00002). MS-r score was 0 in 17 (2.3%), 1 in 86 (11.8%), 2 in 289 (39.5%), and 3 in 339 (46.4%) type 2 diabetic patients; estimated GFR progressively and significantly decreased with increasing MS-r score (82  19, 76  18, and 71  18 ml/min per 1.73 m 2 in patients with score of 0–1, 2, and 3, respectively; P &lt; 0.001 by ANOVA) (Fig. 1). The association between MS-r score and estimated GFR was still significant after adjusting for several confounders including sex ( P = 0.001), duration of diabetes ( P &lt; 0.001), ACR ( P &lt; 0.0001), retinopathy ( P &lt; 0.001), and the four variables considered together ( P = 0.001). Age was not considered as a possible confounder for which to adjust because it is strongly correlated with duration of diabetes ( r = 0.41, P &lt; 0.00001) (i.e., collinearity), a finding that would have made it difficult to evaluate the independent effect of the two variables (11). The association between MS-r score and estimated GFR was also independent of other possible additional confounders, such as A1C ( P &lt; 0.0001), smoking ( P &lt; 0.0001), systolic ( P &lt; 0.0001) and diastolic ( P &lt; 0.0001) blood pressure, antidiabetic ( P &lt; 0.001) and antihypertensive ( P = 0.02) therapy, and treatment with ACE inhibitors and/or angiotensin II receptor antagonist ( P = .03). The MS-r score was significantly associated with estimated GFR in patients without ( n 508, P = 0.002) or with ( n 223, P &lt;&lt; 0.001) retinopathy. Of note, the association between estimated GFR and MS-r score was strengthened by the presence of retinopathy ( P for interaction between metabolic syndrome and retinopathy P = 0.005). As compared with aMS-r score of 0–1, both MS-r scores of 2 and 3 predicted the risk to be affected by CKD (odds ratio 3.4 [95% CI 1.3–9.2], P = 0.02 and 4.6 [95% CI 1.6 –12.4], P = 0.003, respectively, after adjusting for sex, duration of disease, ACR, and retinopathy). A significant inverse correlation was observed between estimated GFR and the homeostasis model assessment of insulin resistance index, a surrogate of insulin resistance ( r = - 0.2 P &lt; 0.001). In patients of the second sample, a significant correlation was observed between the glucose disposal rate value (at euglycemic clamp) and GFR (by EDTA) ( r = 0.30, P = 0.004; age and sex adjusted) CONCLUSIONS — In the present study we demonstrate that in patients with type 2 diabetes the cluster of abnormalities related to the metabolic syndrome is associated with reduced kidney function and increases the risk of being affected by CKD (i.e., a GFR 60 ml/min per 1.73m2). This association is independent of sex, duration of diabetes, urinaryACR, and retinopathy. A similar association has been recently reported in the general adult population (12) also including patients affected by type 2 diabetes. Whole-body insulin resistance on glucose handling is likely to underlie most, if not all, features of the metabolic syndrome (13), and this may explain the relationship between insulin sensitivity and kidney function we observed in this study. Since insulin resistance and all other features of the metabolic syndrome are established risk factors of cardiovascular disease, our data suggest that they could, in fact, represent the pathogenic link of the well-known association between advanced renal disease and cardiovascular mortality and morbidity observed in type 2 diabetic patients (14). -
  • Pathology and mechanisms Obesity is a defining characteristic of metabolic syndrome, which is increasingly recognized as a cause of CKD. Specific pathological features have been defined and termed ‘obesity-related glomerulopathy’ [18]. The primary features are glomerulomegaly (100% of cases), focal and segmental glomerulosclerosis (80% of cases) and increased mesangial matrix and cellularity (45% of cases) [18–20]. These features bear a striking resemblance to glomerulopathy induced by diabetes and/or hypertension. Similarly, the clinical course of obesity-related glomerulopathy appears to be progressive. After a mean follow-up of 27 months, 14% of patients reached a renal endpoint (doubling of serum creatinine or end-stage renal disease) in the series reported by Kambham et al. [18]. Of particular concern, obesity-related glomerulopathy has been observed in children as young as 3 years of age [20]. The consistent observation of glomerulomegaly underscores the likely importance of glomerular hyperfiltration mechanisms in the pathogenesis of obesity-related glomerulopathy. In the elegant studies of Chagnac et al. [21,22], characteristics of the obese patients, including subdiabetic levels of hyperglycaemia, were consistent with a diagnosis of metabolic syndrome. Their renal physiological studies demonstrated that values for GFR and renal plasma flow (RPF) exceeded those of lean controls by 50 and 30%, respectively, resulting in increased filtration fraction, an indirect indicator of glomerular hypertension [21]. The renal haemodynamic perturbations may be due, in part, to a higher intake of dietary protein in such individuals who consume an overall excess of nutrients. Interestingly, the studies were performed postprandially (after breakfast), consistent with nutrient-driven effects on GFR and RPF. Renal haemodynamics in obese individuals appear similar to those in early diabetes. In both types 1 and 2 diabetes, we found that an amino acid infusion designed to mimic a protein meal produced an augmented glomerular hyperfiltration response when patients were studied in the fasting state [23,24]. Since the plasma glucose was clamped at an ambient level of 200 mg/dl, the changes were not the result of fluctuating degrees of glycaemia. The augmented glomerular hyperfiltration response to the physiological amino acid stimulus was corrected by chronic (3 weeks) strict glycaemic control, but not by acute (36 h) normalization of glucose with insulin infusion or by hormonal blockade with octreotide (glucagon) or indomethacin (prostaglandins) [23–25]. These observations in diabetes have several important implication that may also apply in metabolic syndrome: (i) chronic hyperglycaemia is necessary, but not sufficient, to produce glomerular hyperfiltration; (ii) effects of nutrients (amino acids) on renal haemodynamics can be separated from those of glycaemia; (iii) the data point to dietary protein, acting through an increase in circulating amino acids, as a stimulus for augmented GFR; and (iv) although glucagon and vasodilatory prostaglandins can raise GFR and RPF, they do not appear to be the primary mediators of this renal haemodynamic response to amino acids. Perhaps most importantly, reduction of nutrient intake and weight loss corrects glomerular hyperfiltration in obese individuals. In a study of 17 morbidly obese patients [mean body mass index (BMI): 48 kg/m2] who lost an average of 48 kg at 1 year after bariatric surgery, postprandial GFR and RPF were significantly decreased and approached normal levels, even thoug the patients were still obese (mean BMI: 32 kg/m2) [22]. Of note, the decrease in GFR was predicted by reduction in glycaemia.
  • Renal Redox Stress and Remodeling   The CMS predisposes to an increased risk not only of CVD, T2DM, and stroke but also CKD and renal redox stress and remodeling.36,37 Diabetic nephropathy is the leading cause of end-stage renal failure and renal replacement therapy. Histopathologic changes consist of glomerular, renovascular, and tubulointerstitial ECM remodeling. The basic lesion is a pronounced thickening of the BM of glomerular capillaries, arterioles, and collecting tubules, and tubulointerstitial fibrosis. In addition, there are changes of inflammation, with monocyte-derived macrophages as well as mesangial cell hyperplasia and marked mesangial matrix expansion within the glomeruli (Figure 9). Glomerulosclerosis and atherosclerosis share many remodeling commonalities and have been previously compared.36 Since most patients who have diabetic nephropathy and CKD die of CVD, we must be aware of this parallel occurrence and treat both conditions in this high-risk patient population according to accepted guidelines. Within the glomerulus, there is also a differential expression of PGs—as in the intima. The heparan sulfate PGs are decreased and the chondroitin sulfate PGs are increased, with an associated loss of filtering function, resulting in an increased permeability to plasma proteins, resulting in turn in microalbuminuria and macroalbuminuria, which are associated with an increased risk of macrovascular disease and CV events. Microalbuminuria and macroalbuminuria reflect a generalized endotheliopathy associated with endothelial dysfunction. Early Renal Changes in the Zucker Obese Model. The Zucker obese rat parallels the human CMS patient very closely and is characterized by IR, the multiple A-FLIGHT-U metabolic toxicities, ROS, hypertension, impaired glucose tolerance, and the gradual development of overt T2DM. These changes are associated with significant remodeling of the glomerular filtration barrier interface (specifically the BM and podocyte) (Figure 10). The renal glomerular podocyte is a specialized endothelial pericyte and demonstrates the important supporting role of the pericyte throughout the systemic capillary beds in each of the end-organ complications (Table II). Microalbuminuria may be thought of as a manifestation of generalized endothelial dysfunction in both the systemic vasculature and in the renal glomerulus, resulting in microalbuminuria in the CMS. The microvascular leakage of proteins (specifically microalbumin) portends an increased risk for the future development of CKD, diabetic nephropathy, CVD, and stroke.38 The link between CMS and CKD is discussed in detail in the article by Bakris and colleagues in this issue of JCMS . Figure 9. Renal oxidation—reduction stress and remodeling in the cardiometabolic syndrome. Left: Normal renal capillary glomerular and tubulointerstitial structures. Transitioning to the center of the image is the mesangial stalk with mesangial cell hyperplasia (yellow) and mesangial expansion (pink), with loss of foot processes of the (blue) podocyte (also termed visceral epithelium) and increasing thickness of the glomerular basement membrane (BM) (red). Right: Increased capillary glomerular BM thickening (red) with atrophic podocytes and loss of foot processes of the podocyte (blue) to the capillary glomerular endothelial cell. Also depicts the tubulointerstitial fibrosis with expansion of the peritubular (blue) extracellular matrix (fibrosis), with an increased thickening of the tubular BM (red). Just below the efferent (blue) arteriole is depicted hyaline arteriolosclerosis and just above the afferent arteriole (red) is depicted hyperplastic arteriolosclerosis with its characteristic onion skin-like changes. The thickened BMs, arteriolar changes, and the mesangial expansion all are periodic acid-Schiff +, hyaline staining, and contain large amounts of type IV collagen with increased laminin and fibronectin with concurrent decreased amounts of heparan sulfate proteoglycan (perlecan).
  • y   Possible Pathways Linking IR and Hyperinsulinemia With CKD. Within the concept of the CMS, various aspects related to IR could potentially have deleterious effects on kidney function (Figure 1), apart from parameters that are already well known risk factors (e.g., hypertension, DM)7 for CKD. Moreover, IR and subsequent hyper-insulinemia appear to be an important link connecting the obesity epidemic with CKD8; numerous proinflammatory cytokines and hormones originating from adipose tissue, such as tumor necrosis factor-α, interleukin-6, leptin, and resistin are integral to the development of IR.9 In the natural history of the syndrome, resistance to insulin-stimulated glucose uptake would result in an increase in insulin levels to maintain glycemic control. This hyperinsulinemic state, which can precede the development of DM by many years, can have deleterious effects on end-organs such as the kidney and can lead to the development of several features of the syndrome.10 Recent experimental data suggest that the earliest evidence of structural change, glomerular hypertrophy, appears within the period of hyperinsulinemia and before the onset of DM.11 Several studies have examined the effects of compensatory hyperinsulinemia on the kidney. Most of them, however, focused on tubular function, where insulin has been repeatedly shown to have an important antinatriuretic effect that is preserved in insulin-resistant states.12,13 However, only a few studies have examined the effect of insulin on glomerular function. Glomerular Permeability. In regard to the impact of insulin on glomerular permeability to albumin and other proteins, most previous studies did not provide reliable data, as they were performed in small numbers of subjects, in an uncontrolled fashion, or with the use of insulin boluses.12 One study, however, simultaneously examined the acute effects of high or low insulin infusion during euglycemic, hyperinsulinemic conditions on systemic and renal vascular protein permeability in 12 normoalbuminuric type 2 diabetic patients and 12 healthy volunteers.14 Hyperinsulinemia was found to significantly increase UAE and clearance rates in patients by about 50%, but not in control subjects. These results suggest that insulin directly and selectively increases the urinary excretion of albumin in patients with DM without affecting systemic albumin permeability. Salt Sensitivity. The physiologic Na+-retaining action of insulin has led to the hypothesis that IR and hyperinsulinemia contribute to the development of salt sensitivity in hypertensive patients.15 In various studies, IR has been associated with salt sensitivity independently of the presence of obesity.16 On the other hand, diabetic patients with increased UAE have been shown to have greater salt sensitivity than patients with .17 In another recent study, investigators examined the associations of sensitivity of BP to salt intake, IR, and albuminuria.18 Subjects with normoalbuminuria showed no changes in switching from a low- to high-Na+ diet, but in microalbuminuric patients this switch resulted in increases in B P, UAE, renal plasma flow, and intraglomerular pressure. Since microalbuminuric patients also had significantly lower insulin sensitivity and intraglomerular pressure was positively related to UAE and inversely correlated with insulin sensitivity, the authors concluded that the contribution of IR to greater salt sensitivity in hypertension could be one mechanism leading to increased glomerular pressure and UAE. Glomerular Filtration Rate. Evidence supports a direct effect of insulin on glomerular filtration rate (GFR). In experimental animals, insulin produced a slight increase in GFR,19 possibly due to a direct vasodilatory effect. It is not known, however, what effect chronic hyperinsulinemia would have on GFR in insulin-resistant states, in which insulin-mediated vasodilatation is severely impaired and insulin-mediated sympathetic stimulation is unaffected, so that an imbalance in favor of systemic vasoconstriction occurs.13,20 The associated renal vasoconstriction may be associated with a shift toward a reduction in renal plasma flow and GFR. However, it is plausible that reduced Na+ excretion capacity and renal plasma flow associated with hyperinsulinemia would lead to reduced Na+ delivery to sites proximal to the macula densa, which, in turn, would induce afferent vasodilation and glomerular hyperfiltration. Future studies are needed to elucidate these maladaptive mechanisms. Cell Proliferation. Studies have shown that important maladaptive events in the progression of diabetic nephropathy are early mesangial cell proliferation, increased growth factor expression, and extracellular matrix expansion.21 Although insulin has long been known to increase proliferation of vascular cells, the discovery of insulin-like growth factors (IGF) has added a great deal to our understanding of insulin mitogenic actions. IGF-1 is a very important growth hormone produced by vascular smooth muscle cells under a variety of stimuli, among which is insulin.22 Elevated insulin levels, such as those present in insulin-resistant states, can promote vascular cell proliferation through action on the IGF-1 receptor.10 Human glomerular mesangial cells both secrete IGF-1 and possess IGF-1 receptors.23 Further, physiologic concentrations of IGF-1 and pharmacologic concentrations of insulin can induce mesangial cell growth.24,25 Moreover, in vitro studies have shown that insulin markedly increases the rate of protein synthesis from mesangial cells and can alter the type of interstitial and basement membrane collagens they excrete. In particular, elevated insulin concentrations cause mesangial cells to synthesize predominantly collagens type I and III, and not collagen type IV, only the latter of which resembles normal extracellular matrix composition. Mesangial cells cultured in the presence of insulin failed to synthesize the normal collagen pattern after withdrawal of insulin. This suggests insulin can evoke a mesangial phenotypic change.25 Apart from a direct proliferative effect and its impact on IGF-1, insulin may interfere with the progression of kidney disease through its effect on other growth factors, such as transforming growth factor-β (TGF-β). In vitro studies have previously shown that insulin significantly increases TGF-β1 production not only from mesangial cells,26 but also from proximal renal tubular cells—and thus contributes to extracellular matrix production.27 Moreover, TGF-β1 and IGF-1 were found to increase the expression and activity, respectively, of another profibrotic cytokine, connective tissue growth factor, which also enhances fibrosis, a process important in the pathogenesis of diabetic nephropathy.28 Renin-Angiotensin-Aldosterone System (RAAS). In parallel to the above, there is mounting evidence that insulin resistance/hyperinsulinemia contributes to abnormalities in the systemic and intrarenal RAAS. In vivo, hyperinsulinemia significantly increases both the aldosterone and pressor responses to angiotensin II (Ang II).29 Previous data suggest that insulin is necessary for Ang II-induced contraction of mesangial cells in vitro, providing a possible link between insulin and Ang II-mediated renal injury.30 Ang II alone has a slight effect on TGF-β1 and collagen protein production from cultured mesangial cells, but this effect is multiplied by the addition of insulin.26 Endothelial Dysfunction. Insulin has previously been shown to stimulate endothelin-1 (ET-1) production from endothelial cells in vitro31 and to raise plasma ET-1 levels in vivo in both healthy and insulin-resistant persons.32 Epidemiologic studies have also shown increased plasma ET-1 and significant associations with markers of IR in insulin-resistant states.33 Previous case-control studies reported an increase in ET-1 levels in patients with DM or hypertension and MAU,34 suggesting an involvement of ET-1 in the development of nephropathy in these patients. In experimental studies, apart from being a potent vasoconstrictor of the renal vasculature, ET-1 has been shown to have mitogenic effects on mesangial cells.35 Among others, ET-1 stimulates protein kinase C-regulated phospholipase D, which hydrolyzes phospholipid substrates and induces the generation of phosphatidic acids that stimulate proliferation in mesangial cells.35 Taking into account the growing evidence of the involvement of endothelin peptides in various tissue functions, an important effect on the progression of kidney disease should not be excluded. In healthy subjects, insulin promotes endothelium-dependent vasodilatation through NO release but, in individuals with IR, this action is impaired.20 The cause of this endothelial dysfunction in insulin-resistant states is not well understood; hypotheses include a primary defect in the endothelium causing both IR and impaired vasodilatation,20 or hyperinsulinemia leading to blunted endothelial function.36 Endothelial dysfunction is central in the development of vascular complications of DM and often occurs in concert with MAU.5 However, the role of renal endothelial function and NO availability in the progression of diabetic CKD is more complex and not entirely clear. Recent evidence suggests that abnormalities in NO production modulate renal structure and function. Early nephropathy in DM is associated with increased intrarenal NO production, whereas advanced nephropathy associated with severe proteinuria and renal function decline is related to a state of progressive NO deficiency caused by various factors.37 From this perspective, IR could promote CKD progression through renal endothelial dysfunction, but many aspects of this association need to be further elucidated. Figure 1. Possible mechanisms linking insulin resistance and compensatory hyperinsulinemia with chronic kidney disease. increased; IGF-1=insulin-like growth factor-1; RAS=renin-angiotensin system; ATI=angiotensin type 1; TGF-β=transforming growth factor-β; ET-1=endothelin-1; =decreased
  • Pancreas The pancreas is the ultimate arbiter of insulin availability, determining the point at which overt diabetes will occur. Early in the course of metabolic syndrome, hepatic gluconeogenesis stimulates the pancreas to hypersecrete insulin, yielding normoglycemic hyperinsulinemia. Increased FFA uptake by pancreatic cells also increases glucose-induced insulin secretion and modifies expression of peroxisome proliferator–activated receptor-(PPAR-), glucokinase, and Glut 2 transporter (23). In the spontaneously obese captive rhesus monkey, hyperinsulinemia is sustained and progressively increases, eventually falling as overt hyperglycemia appears (55). Once hyperglycemia ensues, insulin-secreting cells become targets of glucotoxicity: reduction in insulin-stimulated insulin secretion, late increase in mitochondrial free-radical production, and lipid overload–induced apoptosis (lipotoxicity; see also below) with progressive loss of cell mass (56 –58). Adverse effects of hyperinsulinemia per se on organ structure and function in the prehyperglycemic phase of metabolic syndrome are not well defined but are relevant to establishing optimum timing of intervention. It is worthy of emphasis that lifestyle interventions that reduce hyperglycemia can markedly decrease progression to overt diabetes (59).
  • Hemostatic Risk Factors and Insulin Sensitivity, Regional Body Fat Distribution, and the Metabolic Syndrome Disturbances in the thrombotic and fibrinolytic systems are a feature of insulin resistance, obesity, and the metabolic syndrome. However, there are few studies in which these relationships have been explored in mainly asymptomatic individuals using sophisticated measures of insulin sensitivity and regional adiposity. Variables of the hemostatic system were measured in 106 men (aged 32–68 yr; body mass index, 20–34 kg/m2). Insulin sensitivity was measured by minimal model analysis and regional adiposity by dual energy x-ray absorptiometry. Clustering of intercorrelated variables was assessed by the statistical technique of factor analysis. Plasma levels of procoagulant factors VII and X, anticoagulant proteins C and S, and plasminogen activator inhibitor-1 correlated positively with total and percent central body fat (r = 0.25–0.38; P &lt; 0.05) and negatively with insulin sensitivity (except protein S; r = –0.24 to –0.35; P &lt; 0.05). On factor analysis, procoagulant factors VII and X, proteins C and S, and plasminogen activator inhibitor-1 were components of the cluster of variables that explained the greatest proportion of the variance in the data (39.2%). Other variables included in this cluster were those typical of the metabolic syndrome and also serum -glutamyl transferase activity. These results suggest that factors VII and X and proteins C and S are features of the intercorrelated disturbances of the metabolic syndrome. Associations with adiposity and liver enzyme activity suggest the involvement of hepatic fat deposition. Hemostatic Risk Factors and Insulin Sensitivity, Regional Body Fat Distribution, and the Metabolic Syndrome Disturbances in the thrombotic and fibrinolytic systems are a feature of insulin resistance, obesity, and the metabolic syndrome. However, there are few studies in which these relationships have been explored in mainly asymptomatic individuals using sophisticated measures of insulin sensitivity and regional adiposity. Variables of the hemostatic system were measured in 106 men (aged 32–68 yr; body mass index, 20–34 kg/m2). Insulin sensitivity was measured by minimal model analysis and regional adiposity by dual energy x-ray absorptiometry. Clustering of intercorrelated variables was assessed by the statistical technique of factor analysis. Plasma levels of procoagulant factors VII and X, anticoagulant proteins C and S, and plasminogen activator inhibitor-1 correlated positively with total and percent central body fat (r = 0.25–0.38; P &lt; 0.05) and negatively with insulin sensitivity (except protein S; r = –0.24 to –0.35; P &lt; 0.05). On factor analysis, procoagulant factors VII and X, proteins C and S, and plasminogen activator inhibitor-1 were components of the cluster of variables that explained the greatest proportion of the variance in the data (39.2%). Other variables included in this cluster were those typical of the metabolic syndrome and also serum -glutamyl transferase activity. These results suggest that factors VII and X and proteins C and S are features of the intercorrelated disturbances of the metabolic syndrome. Associations with adiposity and liver enzyme activity suggest the involvement of hepatic fat deposition. The working clinical definitions of the metabolic syndrome do not include any hemostatic system measure despite significant associations having been reported between such measures and the insulin sensitivity, body fat, inflammation and lipid components of the syndrome. Studies in which a direct measure of insulin sensitivity has been related to hemostatic system measures are rare (11) as are studies incorporating both a broad range of hemostatic factors and metabolic syndrome components (12, 13). The Heart Disease and Diabetes Risk Factors in a Screened Cohort Study (HDDRISC) is an open, occupational cohort study, which has included an unusually detailed range of risk factor measurements. In the present analysis, we have employed a validated measure of insulin sensitivity, derived from minimal model analysis of the iv glucose tolerance test (IVGTT), as well as direct measures of total and regional body fat. Relationships have been investigated between these variables and a range of measures of the thrombotic and fibrinolytic systems in 106 consecutively studied men, free of diabetes mellitus, who were attending a company health screening program. Clustering of hemostatic measures with a range of lipid, lipoprotein, liver function, and subclinical inflammation measures has been explored using factor analysis. Design The HDDRISC is a cohort study of metabolic risk factors for the development of coronary heart disease and diabetes (14, 15). The study began in 1971 and derives from a company health program, in the course of which participants received a range of metabolic, clinical, and laboratory measurements. From 1986 on, participants in the program underwent IVGTT. The present analysis concerns the 106 consecutively studied male recruits who, between 1992 and 1995, underwent IVGTT and also had measurements of a range of hemostatic factors. Written, informed consent to the study was obtained in each case, and local research ethics committee approval was given. Procedures Participants were instructed to consume more than 200 g/d carbohydrate in their diet for the previous 3 d as preparation for the IVGTT, to have fasted overnight (&gt;12 h), and to have taken only water and refrained from cigarette smoking on the morning of their test. Height and weight were measured, and a clinical history was taken. An indwelling cannula was inserted into an antecubital vein in each arm. With the volunteer semirecumbent, blood samples were taken for fasting plasma and serum measurements. All samples were kept on ice before separation of plasma or serum, which took place within 1 h of the sample being taken. Samples for routine biochemical measurements were stored at 4 C before analysis. Plasma samples for measurement of insulin and hemostatic factors were frozen immediately. An iv glucose injection was then given [0.5 g glucose/kg body weight as a 50% (wt/vol) solution of dextrose, given over 3 min] via the cannula in the opposite arm to the sampling arm. Blood samples (10 ml) were then taken at 3, 5, 7, 10, 15, 20, 30, 45, 60, 75, 90, 120, 150, and 180 min for measurement of plasma glucose, insulin, and C peptide. Regional distribution of fat and lean tissue was measured by dual energy x-ray absorptiometry (DXA) using a whole body scanner (DPX; Lunar Radiation Corp., Madison, WI). Total body fat mass was recorded and central and peripheral fat masses were measured using manually determined regions of interest defined by anatomical bone landmarks, as previously described (16). The precision of total fat mass, based on repeated measurements in volunteers, was 2.9% (17). Laboratory measurements Plasma levels of fibrinogen, factors VII and X, and proteins C and S were measured by prothrombin time-based nephelometry on an automated coagulation analyzer (ACL 100, Instrumentation Laboratory, Lexington, MA). Antithrombin III and plasminogen were measured by enzymatic methods (Chromogenix, Mölndal, Sweden) using a discrete clinical analyzer (Cobas MIRA, Roche, Basel, Switzerland). Tissue plasminogen activator (tPA) and plasminogen activator inhibitor-1 (PAI-1) activities were measured by manual enzymatic methods (Chromogenix), and fibrinopeptide A was determined by RIA (Byk-Sangtec, Dietzenbach, Germany). Plasma glucose and insulin, and serum total cholesterol, triglycerides, high density lipoprotein (HDL) and low density lipoprotein (LDL) cholesterol concentrations were measured as described previously (14). Participants also received a full blood count and liver function test profile, including white cell count, erythrocyte sedimentation rate (ESR), serum uric acid, globulin and albumin concentrations, and serum - glutamyl transferase (GGT) activity. Quality control was continuously monitored with commercially available lyophilized sera and by participation in national schemes. Between-batch assay coefficients of variation were: fibrinogen, 7%; factor VII, 7%; factor X, 4%; protein C, 8%; protein S, 11%; antithrombin III, 8% plasminogen, 5%; PAI-1, 22%; tPA, 17%; fibrinopeptide A, 13%; plasma glucose, 3%; plasma insulin, 6%; serum cholesterol and triglycerides, 2%; HDL cholesterol, 4%; serum uric acid, 4%, globulin and albumin concentrations, 5%; and serum GGT activity, 3%. IVGTT modeling analysis Insulin sensitivity was determined using the minimal model of glucose disappearance described by Bergman et al. (18). The relatively high glucose dose (0.5 g/kg) we employed provides for a high rate of model identification (19), and we have validated our procedure, without recourse to augmentation of insulin concentrations by tolbutamide or insulin injection, against the reference euglycemic clamp technique (r = 0.92) (20). ß-Cell function was estimated according to the minimal model of posthepatic insulin delivery described by Toffolo et al. (21), which returns measures of the sensitivity of first phase ( 1) and late phase ( 2) plasma insulin delivery to glucose. For a model analysis to be acceptable, parameter estimates were required to be positive and have parameter coefficients of variation less than 100%. Data analysis Fasting plasma glucose and insulin concentrations were expressed as the mean of two fasting measurements made within 10 min of each other before commencement of the IVGTT. Mean fasting glucose (MFG) and insulin (MFI) concentrations were used to estimate insulin resistance and ß-cell function according to the homeostasis model assessment (HOMA) method (22). The HOMA index of insulin resistance (HOMA-IR) was estimated as (MFG x MFI)/22.5, and the HOMA index of ß-cell function (HOMA-B) was estimated as (20 x MFI)/(MFG – 3.5) (units of glucose concentration, millimoles per liter; units of insulin concentration, milliunits per liter). The glucose elimination rate during the IVGTT was expressed as the k value ( i.e. the slope of the regression line for the natural log of the IVGTT glucose concentrations between 20 and 60 min). The net increment in postload insulin concentrations above the fasting insulin level was calculated as the area under the curve using the trapezium rule. Cigarette smoking was categorized as: never smoked, ex-smoker, or less than five, 5–14, or 15–24 cigarettes/d. Alcohol intake was expressed as units consumed per week [a unit of alcohol approximates 10 ml or 8 g pure ethanol and is the amount contained in a half-pint (284 ml) of beer, a single glass (125 ml) of table wine, or a single measure (25 ml) of spirits]. Alcohol intake was also expressed categorically as never, light irregular, or less than 28, 28–56, or more than 56 U/wk. Exercise habit was expressed as none, moderate, or aerobic. DXA-derived central and peripheral fat masses were expressed as a percentage of total fat mass. Statistical analyses were carried out using STATA 8 (Stata Corp., College Station, TX). For subsequent parametric statistical analysis, insulin sensitivity measures and HOMA-B were square root-transformed; otherwise, measures were log-transformed, as appropriate, to normalize their distributions. One-way ANOVA was used to detect significant variation in hemostatic variables according to cigarette smoking, alcohol intake, and exercise habit. Pearson correlation was used to explore univariate associations between hemostatic and other continuous variables, and multiple linear regression was used to confirm the independence of significant associations detected. Factor analysis was used to detect clustering of intercorrelated variables. Factor analysis supposes that the existence of a large number of highly intercorrelated variables reflects variation in a more limited number of underlying variables or factors. Measured variables are related to the resulting factors by their so-called loading, which is equivalent to the correlation coefficient between the variable and the factor. The variables with the highest loadings are then the measures that are of greatest importance in interpreting the nature of the underlying factor responsible for their covariation. A number of different procedures are available for factor analysis. In the present study factor analysis employed a principal factors analysis, followed by varimax rotation, as previously described for this cohort (23). Blood pressure was included in factor analysis as mean arterial pressure (calculated as: [(2 x diastolic blood pressure) + systolic blood pressure]/3) to avoid the emergence of a single, uninformative factor consisting of only the highly correlated systolic and diastolic blood pressure measurements. Only factors with eigen values greater than 1 were considered. Potential factor components were considered to be features of a given factor if their loading on that factor was 0.30 or more (15). These findings suggest that in addition to PAI-1, elevated levels of the procoagulant factors VII and X and anticoagulant proteins C and S are features of the intercorrelated disturbances of the metabolic syndrome. FIG. 1. Scattergrams of factor VII and X coagulant activities on metabolic syndrome factor scores in 106 men. Factor loadings were 0.38 and 0.42 for factors VII and X, respectively
  • The Insulin Resistance Atherosclerosis Study found that, irrespective of sex, age and ethnicity, PAI-1 levels significantly increased with decreasing glucose tolerance. Mean PAI-1 antigen levels were 19, 27 and 33 ng/mL in people with NGT, IGT and type 2 diabetes, respectively. Reference: Festa A et al. Arterioscler Thromb Vasc Biol 1999; 19 :562–568.
  • Figure 1. Mean levels of log CRP (SE represented by bars) adjusted for age, sex, ethnicity, clinic, and smoking status according to number of metabolic disorders (0 to 4), including (1) dyslipidemia (high triglyceride &gt;2.27 mmol/L [200 mg/dL] and/or low HDL: men 0.91 mmol/L [35 mg/dL] and women 1.16 mmol/L [45 mg/dL]), (2) upper body adiposity ( 75th percentile for waist circumferences: men=103.0 cm and women=99.3 cm), (3) insulin resistance (&lt;25th percentile for SI: &lt;0.88x10-4 min-1 · µU-1 · mL-1 or in 47 subjects without frequently sampled intravenous glucose tolerance test, 75th percentile for fasting insulin: 114 pmol/L), and (4) hypertension (systolic blood pressure of 140 mm Hg and/or diastolic blood pressure of 90 mm Hg or current use of antihypertensive medication). All comparisons, P =0.0001, except for 2 versus 4 ( P &lt;0.005) and 3 versus 4 ( P =NS).
  • Background— Inflammation (assessed by C-reactive protein [CRP]) and the metabolic syndrome (MetS) are associated with cardiovascular disease (CVD), but population-based data are limited. Methods and Results— We assessed the cross-sectional relations of CRP to the MetS (National Cholesterol Education Program Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults, Adult Treatment Panel III definition) in 3037 subjects (1681 women; mean age, 54 years) and the utility of CRP and the MetS to predict new CVD events (n=189) over 7 years. MetS ( 3 of 5 traits) was present in 24% of subjects; mean age-adjusted CRP levels for those with 0, 1, 2, 3, 4, or 5 MetS traits were 2.2, 3.5, 4.2, 6.0, or 6.6 mg/L, respectively ( P trend &lt;0.0001). In persons with MetS, age-adjusted CRP levels were higher in women than men (7.8 versus 4.6 mg/L; P &lt;0.0001). MetS and baseline CRP were individually related to CVD events (for MetS: age-sex-adjusted hazard ratio [HR], 2.1; 95% CI, 1.5 to 2.8; for highest versus lowest CRP quartile: HR, 2.2; 95% CI, 1.4 to 3.5). Greater risk of CVD persisted for MetS and CRP even after adjustment in a model including age, sex, MetS (HR, 1.8; 95% CI, 1.4 to 2.5), and CRP (HR, 1.9; 95% CI, 1.2 to 2.9). The c-statistic associated with the age- and sex-adjusted model including CRP was 0.72; including MetS, 0.74; and including CRP and MetS, 0.74. Conclusions— Elevated CRP levels are related to insulin resistance and the presence of the MetS, especially in women. Although discrimination of subjects at risk of CVD events using both MetS and CRP is not better than using either phenotype alone, both CRP and MetS are independent predictors of new CVD events A highly significant relationship was observed between the number of components of the MetS present and mean age-adjusted CRP levels (the Figure). As shown, there was a significant gender interaction; age-adjusted CRP levels were significantly higher in women than in men when 2 components of the MetS were present (all P &lt;0.02). Almost identical results were obtained in subgroups of older and younger men and women and when men were compared with women not using HRT, indicating that this relationship was not due to HRT use. The relationship between CRP level and the number of features of the MetS was also unaltered when BMI &gt;30 kg/m2 instead of using waist circumference thresholds suggested by the NCEP was used to define obesity (not shown). Age-adjusted mean (SE) levels in women and men according to number of components of the metabolic syndrome A highly significant relationship was observed between the number of components of the MetS present and mean age-adjusted CRP levels (the Figure). As shown, there was a significant gender interaction; age-adjusted CRP levels were significantly higher in women than in men when ≥ 2 components of the MetS were present (all P &lt; 0.02). Almost identi cal results were obtained in subgroups of older and younger men and women and when men were compared with women not using HRT, indicating that this relationship was not due to HRT use. The relationship between CRP level and the number of features of the MetS was also unaltered when BMI 30 kg/m2 instead of using waist circumference thresholds suggested by the NCEP was used to define obesity (not shown). Genetic and environmental factors contribute to the pathogenesis of the MetS, but of the modifiable risk factors, obesity and physical inactivity are the most important. The cross-sectional relations of CRP and features of the MetS in this and other studies1–3,7–16 suggest that inflammation is strongly associated with insulin resistance and the MetS,1 and it supports the hypothesis that inflammation plays an impotant role in the pathogenesis of diabetes1,2 and atherosclerosis as originally suggested by Pickup and Crook.19a An important link between these conditions and obesity could be the proinflammatory cytokines produced by adipose tissue suchas tumor necrosis factor-and interleukin-6. These cytokines can influence insulin resistance and glucose uptake,20,21 promote hepatic fatty acid synthesis,22 and increase hepatic CRP production.23 Here, we have shown in a population-based cohort that age-adjusted CRP levels were strongly related to the individual components of the NCEP ATP III MetS and that this relationship was stronger in women than in men. Our results are in keeping with those from the Mexico City Diabetes Study in which CRP levels were more strongly related to insulin resistance and features of the MetS in women.1 That study also showed that CRP levels predicted the development of the MetS and diabetes in women but not in men. Two more recent reports have shown that in a cross-sectional analysis, markers of inflammation, including CRP, were more strongly related to insulin resistance and/or the NCEP MetS in women than in men.3,29 r- TABLE 1. Clinical and Biochemical Characteristics -
  • • Festa et al studied the association of new-onset diabetes and inflammatory activity in 1047 individuals free from diabetes at baseline. 1 •  Over a 5-year follow-up, subjects who developed diabetes had significantly higher baseline levels of CRP and PAI-1 (P &lt; 0.001 for upper vs lower quartiles). A similar trend was observed for fibrinogen (P = 0.06). These findings support the hypothesis that inflammation has an important role in the pathogenesis of both diabetes and atherosclerosis. 1. Festa A, D’Agostino R Jr, Tracy RP, Haffner SM. Elevated levels of acute-phase proteins and plasminogen activator inhibitor-1 predict the development of type 2 diabetes: The insulin resistance atherosclerosis study. Diabetes. 2002;51:1131-1137.
  • Myocardial Redox Stress and Remodeling in CMS Diastolic dysfunction relates to an excessive and stiffened ECM associated with cellular myocyte remodeling hypertrophy, which results in a thickened myocardial wall and impaired ventricular relaxation. This is in contrast to systolic dysfunction, which is associated predominately with an impaired ejection fraction. Diastolic dysfunction is an abnormality seen early in the CMS, and this abnormality is associated with reduced insulin responsiveness and increased oxidative stress.3,4 Congestive heart failure may develop acutely following a myocardial infarction/recurrent ischemic events or insidiously over time in association with the CMS (Figure 2).5 Figure 2. Putative mechanisms for myocardial remodeling in the cardiometabolic syndrome (CMS). Interstitial extracellular matrix (ECM) consisting of types I and III collagen and elastin with fibrosis is indicated by blue. Matrix metalloproteinases and activation of oxidation—reduction stress act upon endomysial struts and ECM leading to myocardial remodeling. Hyperinsulinemia, hyperamylinemia, and hyperproinsulinemia activate a local tissue renin-angiotensin-aldosterone system (RAAS) and local reactive oxygen species (ROS) production through nicotinamide adenine dinucleotide phosphate reduced (NAD[P]H) oxidase. Advanced glycation end products (AGEs) which are indicated by red colorization overlying blue might also contribute to diastolic dysfunction. Pericapillary fibrosis (PCF) has also been implicated in myocardial dysfunction as well as other end-organs affected by CMS. Myofibroblasts (MyoFB) represented in green contribute to excessive ECM and fibrotic changes within the myocardium. Ang II=angiotensin II; TGF=transforming growth factor; AT=angiotensin receptor type
  • Intimal Redox Stress and ECM Remodeling in Vasculature In CMS there is both microvessel disease within the intima (adventitia-derived vasa vasorum) and macrovessel disease (accelerated atherosclerosis) located at the same sites of vulnerable atherosclerotic plaques within the arterial vessel wall (Figure 5).15 This angiogenic intimal pathology is very similar to diabetic retinopathy and plays an important role in destabilizing vulnerable atherosclerotic plaques via intimal intraplaque hemorrhage. Neovascularization is the most powerful independent predictor of plaque rupture ( p =0.001), followed by disruption of the internal elastic lamina ( p =0.01) and fibrous cap thinness ( p =0.02).16 Atherogenesis occurs in the intima and contains fibrillar macromolecules such as collagens, proteoglycans (PGs), hyaluronan, and extracellular glycans and PGs within the intima and result in the accumulation of atherogenic lipoproteins.18 Atherosclerotic lesions in CMS constantly undergo remodeling and may assume multiple types of different multidomain proteins. The negatively charged glycosaminoglycans attached to PGs are responsible for the retention of lipoproteins in early atherogenesis.17 CMS and the A-FLIGHT-U toxicities are believed to alter these glycosaminoplaques, as outlined in the American Heart Association&apos;s classification (types I—VIII).19 There are at least two major types of plaque morphology in sudden death and acute coronary syndromes: plaque rupture and plaque erosion (Figure 5). Plaque rupture is associated with a large lipid core, a thin fibrous cap, macrophage inflammatory changes at the shoulder, decreased vascular smooth muscle cells in the fibrous cap, and plaque vasa vasorum angiogenesis, while plaque erosion is associated with endothelial denudation and formation of thrombosis without rupture of the plaque and is accompanied by an increase in vascular smooth muscle cells and subendothelial PG matrix accumulation. Another common lesion in CMS is the complicated lesion with multi-layering of the atherosclerotic lesion and luminal stenosis (Figure 5). Differential regulation of the PGs versican and hyaluronan (with its CD44 integrin) in the subendothelial—intima space in plaque erosion, promoting increased vascular smooth muscle cells and increased synthesis of PGs (specifically hyaluronan and CD44), may interfere with the endothelial cell&apos;s adhering to its basement membrane (BM). In addition, hyaluronan and CD44 have been shown to mediate the adhesion of platelets to hyaluronan, which could accelerate thrombus formation.20 Further, calcification proteins (thrombospondin-1 and matrix gamma carboxyglutamic acid protein) within the ECM predispose to plaque remodeling. A fundamental abnormality in this process is an increased redox signaling system via ROS, which recruits vascular inflammatory cells, culminating in cellular and ECM remodeling.21 Figure 5. Atherothrombosis in the cardiometabolic syndrome. A) Vulnerable plaque rupture; B) vulnerable plaque erosion; C) complicated multi-layering plaque; D) malignant-like angiogenesis—neovascularization of the media and intima reminiscent of the neovascularization found in diabetic retinopat
  • CMS and Heart Failure Many of the components of the CMS are known risk factors for the development of nonischemic heart failure. Diabetes, insulin resistance, obesity, and hypertension, in particular, are known to be risk factors for the development of increased left ventricular mass and diastolic and systolic dysfunction.23–28 It is thought that insulin resistance can precede and contribute to heart failure.29,30 Excessive calorie intake, one of the most important features leading to CMS, may contribute to insulin resistance and cardiac dysfunction through a variety of mechanisms, including a process that has been demonstrated in animal models known as “lipotoxicity.” In lipotoxicity, excessive fatty acid accumulation in the heart and other nonadipose tissues over time leads to apoptosis, or programmed cell death.31 Free radical production also may contribute to insulin resistance-related cardiac dysfunction.32 Conversely, heart failure may precede and contribute to insulin resistance due to increased sympathetic output, loss of skeletal muscle mass, and/or increased cytokine production.33 Regardless of whether insulin resistance is an antecedent or consequence of heart failure, its presence has important implications for the metabolism of substrates and, hence, for the generation of energy for contractile function of the heart. For example, in young women with abdominal obesity, insulin resistance was an independent predictor of increased myocardial fatty acid uptake, utilization, and oxidation as quantified using positron emission tomography.34 Myocardial oxygen consumption increased and efficiency decreased (a hallmark of heart failure) as obesity increased.34 Other evidence from humans that supports the theory that insulin resistance and its attendant increase in free fatty acids are detrimental to the heart include magnetic resonance spectroscopy data correlating increasing obesity with triglyceride accumulation in human myocardium; this increased triglyceride was related to impaired cardiac contractility.35 In addition, patients with type 2 diabetes, who often have CMS, also appear to have detrimentally altered energy production, manifested by decreased adenosine triphosphate production.36 These data all fit with the hypothesis that excessive fatty acid delivery to the myocardium may contribute to decreased function exclusive of coronary artery disease. There are other studies in humans that do not support this hypothesis, possibly due to differences in patient population, study numbers, radiolabeled tracers, and kinetic modeling.37,38 Nevertheless, the general notion that altered myocardial metabolism may contribute to cardiac dysfunction in patients with heart failure and CMS (or one or more of its components) is gaining wider acceptance, and provides a novel paradigm for designing new metabolic treatments for cardiac failure associated with CMS.29,39–41
  • Objective: Congestive heart failure (CHF) has been associated with insulin resistance, but few studies have examined its relationship with metabolic syndrome (MetS). Little is known about whether insulin resistance explains the association between MetS and CHF. Design: Population-based, cross-sectional surveys. Setting: Third National Health and Nutrition Examination Survey (NHANES III). Participants: Data from 5549 men and non-pregnant women aged &gt;40 years in NHANES III were analysed. Results: About 4% of men and 3% of women had CHF between 1988 and 1994 in the US. The age-adjusted prevalence of CHF was significantly higher in African Americans (4.1%), in Mexican Americans (8.5%) and in those of other ethnic origin (6.7%) than in white people (2.5%). People with MetS had nearly twice the likelihood of self-reported CHF (adjusted odds ratio 1.8; 95% confidence interval 1.1 to 3.0) after adjustment for demographic and conventional risk factors such as sex, ethnicity, age, smoking, total cholesterol, left ventricular hypertrophy, and probable or possible myocardial infarction determined by electrocardiography. However,this association was attenuated after further adjustment for insulin resistance as measured by the homoeostasismodel assessment (HOMA). .90% of the association between MetS and CHF was explained by the HOMA. Conclusions: MetS was associated with about a twofold increased likelihood of self-reported CHF and it may serve as a surrogate indicator for the association between insulin resistance and CHF. RESULTS Age-adjusted prevalence of CHF We observed significant differences between men and women in the prevalence of CHF among people aged 70–79 years (p=0.04), among those with three components of MetS by the IDF definition (p=0.04), and among those with the highestquartile of total cholesterol (p=0.05) or with LVH (p,0.01; table 1). Mexican Americans had a significantly higherprevalence of CHF than white people (p,0.01). We found a significantly increasing trend in the prevalence of CHF for thenumber of components of MetS (p,0.01). People with diabetes, LVH, or probable or possible myocardial infarction had a higher prevalence of CHF than their counterparts (all p &lt; 0.01). People with the highest quartile of the HOMA had a higher prevalence of CHF than those who had the lowest quartile (p, &lt; 0.001). We found significant differences in the prevalence of CHF between people with and without MetS among African American women (p,0.01) by the NCEP ATP III definition, and among white men (p,0.01) and African American women (p=0.03) by the IDF definition (fig 1). Figure 1 Age-adjusted percentages of congestive heart failure (CHF) among participants from the Third National Health and Nutrition Examination Survey (age &gt;40 years) by metabolic syndrome (MetS), sex, and race or ethnicity, 1988–94; n = 4922 with complete data for both CHF and MetS. IDF, International Diabetic Federation; NCEP ATP III, National Cholesterol Education Program Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults.
  • Sudden Cardiac Death. Although there is limited information linking CMS with ventricular arrhythmias, they are a known cause of sudden death, and some components of CMS—hypertension (with related left ventricular hypertrophy) and obesity—have been associated bwith sudden cardiac death.45 There are also biomarkers of sudden cardiac death that are common among patients with CMS. Among the best markers of increased sudden death risk in hypertension is left ventricular hypertrophy, which can be measured using standard two-dimensional echocardiographic imaging.46 Concentric remodeling and frank hypertrophy are also common complications of obesity, diabetes, and CMS.25,47 Another biomarker that has been correlated with an increased risk of sudden death and that is common in patients with diabetes, hypertension, and CMS is low heart rate variability. 48–51 Heart rate variability is a noninvasive parameter for studying the autonomic function of the heart. Small changes in the interval between normal heartbeats are mediated by both the parasympathetic and sympathetic systems. A low heart rate variability, suggesting decreased autonomic control of the heart, may contribute to the increased risk of sudden death. Given the associations between CMS or its components and known markers of sudden cardiac death, it follows that CMS may also be linked with an increased risk of sudden death.
  • Data are limited regarding prevalence and prognostic significance of subclinical cardiovascular disease (CVD) inindividuals with metabolic syndrome (MetS). We investigated prevalence of subclinical CVD in 1,945 Framingham Offspring Study participants (mean age 58 years; 59% women) using electrocardiography, echocardiography, carotid ultrasound, ankle-brachial blood pressure, and urinary albumin excretion. We prospectively evaluated the incidence of CVD associated with MetS and diabetes according to presence versus absence of subclinical disease. Cross-sectionally, 51% of 581 participants with MetS had subclinical disease in at least one test, a frequency higher than individuals without MetS (multivariable-adjusted odds ratio 2.06 [95% CI 1.67–2.55]; P &lt; 0.0001). On follow- up (mean 7.2 years), 139 individuals developed overt CVD, including 59 with MetS (10.2%). Overall, MetS was associated with increased CVD risk (multivariable adjusted hazards ratio [HR] 1.61 [95% CI 1.12–2.33]). Participants with MetS and subclinical disease experienced increased risk of overt CVD (2.67 [1.62–4.41] compared with those without MetS, diabetes, or subclinical disease), whereas the association of MetS with CVD risk was attenuated in absence of subclinical disease (HR 1.59 [95% CI 0.87–2.90]). A similar attenuation of CVD risk in absence of subclinical disease was observed also for diabetes. Subclinical disease was a significant predictor of overt CVD in participants without MetS or diabetes (1.93 [1.15–3.24]). In our community-based sample, individuals with MetS have a high prevalence of subclinical atherosclerosis that likely contributes to the increased risk of overt CVD associated with the condition.
  • Data are limited regarding prevalence and prognostic significance of subclinical cardiovascular disease (CVD) inindividuals with metabolic syndrome (MetS). We investigated prevalence of subclinical CVD in 1,945 Framingham Offspring Study participants (mean age 58 years; 59% women) using electrocardiography, echocardiography, carotid ultrasound, ankle-brachial blood pressure, and urinary albumin excretion. We prospectively evaluated the incidence of CVD associated with MetS and diabetes according to presence versus absence of subclinical disease. Cross-sectionally, 51% of 581 participants with MetS had subclinical disease in at least one test, a frequency higher than individuals without MetS (multivariable-adjusted odds ratio 2.06 [95% CI 1.67–2.55]; P &lt; 0.0001). On follow- up (mean 7.2 years), 139 individuals developed overt CVD, including 59 with MetS (10.2%). Overall, MetS was associated with increased CVD risk (multivariable adjusted hazards ratio [HR] 1.61 [95% CI 1.12–2.33]). Participants with MetS and subclinical disease experienced increased risk of overt CVD (2.67 [1.62–4.41] compared with those without MetS, diabetes, or subclinical disease), whereas the association of MetS with CVD risk was attenuated in absence of subclinical disease (HR 1.59 [95% CI 0.87–2.90]). A similar attenuation of CVD risk in absence of subclinical disease was observed also for diabetes. Subclinical disease was a significant predictor of overt CVD in participants without MetS or diabetes (1.93 [1.15–3.24]). In our community-based sample, individuals with MetS have a high prevalence of subclinical atherosclerosis that likely contributes to the increased risk of overt CVD associated with the condition. Subclinical vascular disease in participants with prevalent MetS or diabetes. The prevalence of subclinical disease in the three groups is shown in Table 2 ( lower half ). Individuals with MetS or diabetes had a higher prevalence of subclinical disease (Table 2). The prevalence of subclinical disease increased with age in both sexes in all three groups (Fig. 1). In age- and sex-adjusted logistic regression models, MetS and diabetes were both strongly and significantly associated with the presence of electrocardiographic and echocardiographic LVH, increased carotid IMT and stenosis, and microalbuminuria (Table 3). Diabetes was associated positively with left ventricular systolic dysfunction and higher prevalence of a low ankle-brachial index, whereas MetS was not significantly associated. Overall, MetS was associated with a twofold odds and diabetes with an over fourfold odds of having at least one subclinical disease abnormality compared with individuals without MetS or diabetes . FIG. 1. Prevalence of any subclinical atherosclerosis or target organ damage in individuals without diabetes or MetS, those with MetS (but no diabetes), and participants with diabetes, by age .
  • &lt; The Metabolic Syndrome , Insulin Resistance, and Cardiovascular Risk in Diabetic and Nondiabetic Patients Context: The contribution of insulin resistance per se to the vascular risk conferred by the metabolic syndrome (MetS) is not known; conversely, it is uncertain whether insulin resistance confers vascular risk beyond the entity of the MetS. Objective: The objective of this study was to investigate the impact of the MetS (Adult Treatment Panel III criteria) and insulin resistance (as estimated by the homeostasis model assessment index) on the incidence of vascular events. Design and Patients: This was a prospective cohort study enrolling 750 consecutive patients undergoing coronary angiography for the evaluation of coronary artery disease. Setting: The study was performed at a tertiary care clinical research center. Main Outcome Measure: The main outcome measure was the incidence of vascular events over 2.3 yr. Results: Both the MetS and insulin resistance predicted vascular events after controlling for non-MetS risk factors [hazard ratio (HR), 2.74 (95% confidence interval, 1.71–4.39; P &lt; 0.001) and 1.51 (1.24–1.84; P &lt; 0.001), respectively]. After additional adjustment for insulin resistance, the MetS remained significantly predictive of vascular events [HR, 2.69 (1.57–4.64); P &lt; 0.001], and conversely, insulin resistance remained significantly predictive of vascular events despite adjustment for the MetS [standardized HR, 1.41 (1.14–1.75); P = 0.002]. Additional adjustment for the presence of type 2 diabetes revealed that both the MetS [adjusted HR, 2.57 (1.47–4.51); P = 0.001] and homeostasis model assessment of insulin resistance [standardized adjusted HR, 1.37 (1.09–1.73); P = 0.007] significantly predicted vascular events independent from diabetes status. Conclusions: Both the MetS and insulin resistance are strong and mutually independent predictors of vascular risk among angiographed coronary patients. Subgroup analyses Figure 3A depicts the results of important subgroup analyses. The MetS proved significantly predictive for vascular events among both men and women [adjusted HR, 2.45 (1.47– 4.11; P = 0.001) and 5.03 (1.46 –17.03; P = 0.010), respectively]. Furthermore, the MetS was significantly predictive for vascular events among patients with type 2 diabetes as well as among nondiabetic patients [adjusted HR, 4.51 (1.29 –15.75; P = 0.018) and 2.37 (1.32– 4.25; P = 0.004, respectively]. Among patients with significant coronary stenoses of 50% or more, the MetS was a significant predictor of vascular events [adjusted HR, 2.42 (1.48 –3.97); P &lt; 0.001]. The adjusted HR for patients without significant CAD [2.28 (0.50 –10.34); P = 0.285] was not significantly different from the HR for those with significant CAD (for interaction of MetS CAD, P = 0.982). However, because of the low absolute number of end points in patients without significant CAD, the confidence interval was wide. In subgroup analyses with respect to both gender and the presence of significant coronary stenoses of 50% or more at baseline, the MetS proved significantly predictive for vascular events among men with significant stenoses [n 350; adjusted HR, 2.18(1.28 –3.72); P = 0.004]; among women with significant stenoses (n 106), the adjusted HR was 3.95 (0.96 –15.87; P = 0.053). FIG. 3. A, AdjustedHRsfor the incidence of vascular events in patients with the MetS for the total cohort and for study subgroups. B, Adjusted HRs according to categories of the MetS score; the MetS score is defined as the number of MetS traits. Increase in vascular risk with increasing number of MetS traits The incidence of vascular events increased with an increasing MetS score. For patients with zero through five MetS risk factors, the respective incidence rates were 8.8, 12.0, 10.7, 14.1, 25.0, and 32.3% ( P trend &lt; 0.001). Compared with patients without any MetS risk factor, the HRs adjusted for non-MetS risk factors were 2.03 (0.68–6.06), 2.10 (0.69–6.38), 3.04 (0.99– 9.33), 7.34 (2.47–21.85), and 14.37 (4.21– 49.12)] for patients with one through five MetS risk factors (Fig. 3B; through the categories of the MetS score P trend 0.001).
  • The Metabolic Syndrome , Insulin Resistance, and Cardiovascular Risk in Diabetic and Nondiabetic Patients Context: The contribution of insulin resistance per se to the vascular risk conferred by the metabolic syndrome (MetS) is not known; conversely, it is uncertain whether insulin resistance confers vascular risk beyond the entity of the MetS. Objective: The objective of this study was to investigate the impact of the MetS (Adult Treatment Panel III criteria) and insulin resistance (as estimated by the homeostasis model assessment index) on the incidence of vascular events. Design and Patients: This was a prospective cohort study enrolling 750 consecutive patients undergoing coronary angiography for the evaluation of coronary artery disease. Setting: The study was performed at a tertiary care clinical research center. Main Outcome Measure: The main outcome measure was the incidence of vascular events over 2.3 yr. Results: Both the MetS and insulin resistance predicted vascular events after controlling for non-MetS risk factors [hazard ratio (HR), 2.74 (95% confidence interval, 1.71–4.39; P &lt; 0.001) and 1.51 (1.24–1.84; P &lt; 0.001), respectively]. After additional adjustment for insulin resistance, the MetS remained significantly predictive of vascular events [HR, 2.69 (1.57–4.64); P &lt; 0.001], and conversely, insulin resistance remained significantly predictive of vascular events despite adjustment for the MetS [standardized HR, 1.41 (1.14–1.75); P = 0.002]. Additional adjustment for the presence of type 2 diabetes revealed that both the MetS [adjusted HR, 2.57 (1.47–4.51); P = 0.001] and homeostasis model assessment of insulin resistance [standardized adjusted HR, 1.37 (1.09–1.73); P = 0.007] significantly predicted vascular events independent from diabetes status. Conclusions: Both the MetS and insulin resistance are strong and mutually independent predictors of vascular risk among angiographed coronary patients. FIG. 2. Event-free survival in patients with and without the MetS. The survival curves show the incidence of vascular events in patients with the MetS and in those without the MetS ( P &lt; 0.001). Solid line , No MetS; broken line , MetS. Incidence of vascular events During a mean sd follow-up time of 2.3  0.4 yr, we recorded 95 vascular end points (encompassing 26 coronary deaths, three fatal ischemic strokes, 10 nonfatal myocardial infarctions, 10 nonfatal ischemic strokes, 15 coronary artery bypass graftings, 14 percutaneous coronary interventions, and 17 revascularizations at the carotid or peripheral arteries). The incidence of vascular events was higher in men (n 509) than in women (n 241; 16.9% vs. 8.9%; P = 0.006), it was higher in patients with type 2 diabetes (n 164) than in nondiabetic patients (n 586; 21.9% vs. 12.2%; P = 0.003), and it was higher among patients with significant coronary artery stenoses of 50% or more at baseline (n 456) than in patients without such lesions (n 294; 20.4% vs. 4.7%; P &lt; 0.001). Event-free survival was significantly lower ( P &lt; 0.001) in patients with the MetS than in patients without the MetS (Fig. 2). In Cox regression analysis, adjusting for age, gender, smoking, BMI, and LDL cholesterol, the MetS proved independently predictive for the incidence of vascular events [adjusted HR, 2.74 (95% confidence interval, 1.71– 4.39); P &lt; 0.001].
  • Hoorn: MetS and 10-Year Cardiovascular Disease Risk The dramatic rise in obesity and its implications for diabetes and cardiovascular disease (CVD) led the World Health Organization (WHO) to publish a working definition of the metabolic syndrome (MetS). Since then, additional definitions of MetS have been proposed by the National Cholesterol Education Program (NCEP), the European Group for the Study of Insulin Resistance (EGIR), and the American College of Endocrinology (ACE). While all the definitions are similar, there are differences in cutoff points for various variables and in the measure of obesity. Regardless, the predictive value of these definitions in a clinical setting is still unclear. The definitions were compared for their potential agreement and their predictive value for total mortality and for fatal and nonfatal CVD, using a subpopulation consisting of 615 men and 749 women (aged 50-75 years) with or without diabetes, from the Hoorn study: a Dutch cohort study of diabetes and its complications. Hazard ratios (shown in the slide) identify the NCEP definition as associated with an approximately twofold increase in fatal CVD in men and nonfatal CVD in women. Hazard ratios using the other definitions were slightly lower. Overall, risk increased with the number of risk factors (data not shown). The authors conclude that MetS, regardless of definition, is associated with an approximate twofold increase of incident CV morbidity and mortality. However, in a clinical practice, taking into account the number of individual risk factors may be more informative than using a definition of MetS. Dekker JM et al. Circulation . 2005;112:666-673.
  • ARIC: Association Between MetS Components and CHD Risk The metabolic syndrome (MetS) has been characterized as a constellation of metabolic and nonmetabolic disorders that are related to impaired insulin sensitivity and contribute to a higher risk for developing type 2 diabetes and cardiovascular disease (CVD). Previous studies using either the National Cholesterol Education Program Third Adult Treatment Panel Report (NCEP ATP III) or the World Health Organization definition of MetS have shown that the presence of MetS doubles the risk for CVD. The current study questioned the generalizability of findings from prior studies based on their predominant use of white men or of individuals with a family history of type 2 diabetes or lack of CVD morbidity data. In the current study, the association between the NCEP-defined MetS and CVD mortality and morbidity was assessed over 11 years of follow-up in participants of the Atherosclerosis Risk in Communities (ARIC) study, a biracial cohort of white and black men and women between 45 and 64 years of age. As shown in the slide, elevated blood pressure and low levels of HDL-C were most strongly associated with coronary heart disease (CHD). Application of a lower threshold for impaired fasting glucose (100 mg/dL) to the MetS definition did not significantly alter hazard ratios (data not shown). The investigators suggest that MetS may be an early manifestation of the shared factors between diabetes and CVD. Hence, treatment effective against MetS may prove beneficial for diabetes as well. McNeill AM et al. Diabetes Care . 2005;28:385-390.
  • Weight loss is considered to be one of the key therapeutic interventions to limit the risks associated with the metabolic syndrome. Indeed, weight loss has been shown to reduce the incidence of the metabolic syndrome and significantly improve control of blood pressure, lipids, and glucose -- all central features of the syndrome. However, achieving and sustaining weight loss, particularly in obese patients with the metabolic syndrome, has been difficult if not impossible with nonpharmacologic, nonsurgical therapies. As such, medical therapies to assist with weight management have been actively sought. Exciting reports from this year&apos;s ADA meeting provided an early look at the potential of several classes of pharmacologic therapies to assist in efforts to achieve sustained weight reductions. Rimonabant is a unique compound that exerts its effects through the endocannabinoid receptor system. Results from a double-blind, placebo-controlled study with rimonabant in patients with diabetes were presented. In overweight and obese patients with type 2 diabetes, rimonabant therapy resulted in significant weight loss (averaging approximately 5% of body weight) and demonstrated improvements in several cardiovascular and metabolic risk factors characteristic of the metabolic syndrome. Rimonabant therapy resulted in a reduction in A1C levels, blood pressure, waist circumference, body weight, and triglycerides. Modest increases in HDL-C were also observed. Among patients treated with rimonabant, 68% lowered A1C below 7%.[18] Other compounds known to reduce body weight in the setting of diabetes include the incretin mimetics. Compounds such as exenatide mimic the effects of the native gut peptide glucagon-like peptide (GLP)-1. Exenatide was recently approved as adjunctive treatment for the treatment of type 2 diabetes. The results of open-label extension studies (82-104 weeks of therapy) with this unique GLP-1 agonist demonstrated significant reductions in both triglycerides and diastolic blood pressure and an increase in HDL-C. These changes were accompanied by an average 1.2% reduction in A1C and continued weight loss totaling &gt; 4 kg on average after the 82 weeks of treatment.[19] Whether exenatide or other compounds that work via the GLP-1 system will have a greater role in either weight management or the treatment of the metabolic syndrome remains to be determined. Many of the syndrome characteristicsare acknowledged to be closely related to insulin resistance or hyperinsulinemiaand their correlates. Yet at the same time it is unknown whether treating “insulin resistance” itself would be of value in preventing CVD in all, or a subset, of metabolic syndrome patients. Although some studies suggest that the newer insulin- sensitizing agents (i.e., thiazolidinediones) improve glycemic control, reduce CVD risk factors, and generally result in a beneficial CVD profile (154 –161), at the time of writing no controlledstudies have shown that thiazolidinediones reduce CVD events even in the setting of diabetes, although one major trial thatwill help address this issue will be reported shortly (i.e., the PROactive [ProspectivePioglitazone Clinical Trial in Macrovascular Events] study); studies using metformin are equivocal. Since thiazolidinediones affect a wide variety of parameters, even favorable trial results will not prove that reducing insulin resistance itself is the critical factor. Moreover, even if positive trial evidence were to emerge relatively soon, other important issues have been identified (162), such as how will insulin resistance be measured, what is the cut point to begin treatment, and is the target population only patients similar to those included in the trials? Thus, our knowledge base is such that we cannot yet contemplate drug treatment for insulin resistance, let alone the metabolic syndrome. Other modifiers of insulin resistance A. -Glucosidase inhibitors 1. Mechanism of action. These agents delay digestion of complex carbohydrates and disaccharides (starch, dextrin, sucrose) to absorbable monosaccharides by reversibly inhibiting -glucosidases within the intestinal brush border (glucoamylase, sucrase, maltase, and isomaltase). This leads to a reduction of glucose absorption and, subsequently, the rise of postprandial hyperglycemia is attenuated. The currently available -glucosidase inhibitors are acarbose, miglitol, and voglibose. Extensive and excellent reviews about their pharmacology have been published (175, 176, 177, 178, 179, 180, 181, 182, 183). 2. Effect of -glucosidase inhibitors on hyperglycemia in patients with type 2 diabetes mellitus. The effect of monotherapy with -glucosidase inhibitors (usually 100 mg three times daily) on postprandial hyperglycemia is well documented in numerous randomized placebo-controlled studies, and the decrease of postprandial glycemia averages about 3 mmol/liter (184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203). The effect of -glucosidase inhibitors on fasting plasma glucose levels is less pronounced and averages -1.3 mmol/liter. The overall effect of -glucosidase inhibitors on glycemia of diet-pretreated subjects with type 2 diabetes, as determined by HbA1c-measurements, averages 0.9% (range, 0.6–1.4), as recently reviewed by Lebovitz (204). Addition of acarbose to type 2 diabetic subjects pretreated with insulin, metformin, or sulfonylureas causes a reduction of HbA1c levels between 0.5 and 0.8%. This beneficial effect seems to last for at least 3 yr as has been recently shown in the UKPDS study. During the last 3 yr of this long-term trial, 379 patients were additionally treated with acarbose in a placebo-controlled design. This resulted in a mean reduction of the HbA1c by 0.5% in the group of patients who still took acarbose after 3 yr. This significant effect was sustained over the 3-yr time period (205). However, at 3 yr a significant lower proportion of patients were taking acarbose compared with placebo (39 vs. 58%), the main reasons for noncompliance being flatulence and diarrhea. Intention to treat analysis showed that all patients allocated to acarbose, compared with placebo, had 0.2% significantly lower HbA1c at 3 yr (205). 3. Effect of -glucosidase-inhibitors on insulin sensitivity. Eight randomized placebo-controlled studies have been published examining the effect of -glucosidase inhibitors on insulin sensitivity in patients with IGT or type 2 diabetes mellitus (Table 2 ). In subjects with IGT, Chiasson et al. (206) demonstrated that acarbose (100 mg three times daily) for 4 months caused a 21% decrease in steady-state plasma glucose (SSPG) during an insulin suppression test using somatostatin, glucose, and insulin infusions. Similar results were obtained by Laube et al. (207), who reported that 12 weeks of acarbose treatment (100 mg three times daily) increased steady-state glucose infusion rate (SSGIR) by 45%. In addition, Shinozaki et al. (208) treated subjects with IGT with a different glucosidase inhibitor, voglibose (0.2 mg three times daily), for 12 weeks, and showed that SSPG levels decreased significantly after voglibose treatment. Thus, these data suggest that -glucosidase inhibitors improve insulin sensitivity in subjects with IGT and hyperinsulinemia possibly secondary to an amelioration of glucose-induced insulin resistance by reducing hyperglycemia in the postprandial period. In contrast to studies in subjects with IGT, studies examining the effect of -glucosidase inhibitors on insulin sensitivity in patients with type 2 diabetes showed no amelioration of insulin resistance despite decreased postprandial glycemia (209, 210, 211, 212, 213). Thus, these data are in support of the notion that -glucosidase inhibitors improve insulin sensitivity in subjects with IGT but have no effect on insulin sensitivity in subjects with overt type 2 diabetes. 4. -Glucosidase inhibitors in type 2 diabetes prevention studies. Currently, three type 2 diabetes prevention trials examining the effect of acarbose on the conversion rate from IGT to type 2 diabetes are under way. The Early Diabetes Intervention Trial (EDIT), the Dutch Acarbose Intervention Trial (DAISI), and the Study to Prevent NIDDM (STOP-NIDDM). The STOP-NIDDM is the largest trial including more than 1,400 IGT-subjects recruited until February 1998. The study has a randomized double-blind placebo-controlled design, and the recently published preliminary screening data (214) provide interesting information on the population under study. In a preliminary subset of 3,919 screened subjects, preselected by known risk factors to develop type 2 diabetes (BMI &gt; 27 kg/m2, history of diabetes, hypertension, dyslipidemia, and gestational diabetes in women) 13.3% had previously undetected diabetes and 17.3% had IGT. A total of 1.418 IGT subjects identified during the screening procedure were included in the study for a predictive median follow-up period of 3.9 yr. The results will be available by 2002, and it will be interesting to see whether treatment with acarbose is able to decrease the conversion rate of IGT to manifest type 2 diabetes mellitus in a higher proportion than nonpharmacological intervention protocols including dietary advice and exercise in the Da Qing study (215). In addition, two other multicenter studies are investigating the effect of diet, increased physical activity or metformin [Diabetes Prevention Program (DPP)] and diet, increased physical activity and sulfonylurea [Fasting Hyperglycemia Study (216)] to prevent type 2 diabetes mellitus. The results of these long-term studies will be available between 2002 and 2004. 5. Adverse effects of -glucosidase inhibitors. -Glucosidase inhibitors have not been associated with life-threatening adverse effects, possibly due to the low systemic absorption. a. Gastrointestinal adverse effects. The major adverse effects associated with acarbose therapy are gastrointestinal complaints, including flatulence and abdominal discomfort, resulting from malabsorption and consequently increased fermentation of carbohydrates. Depending on the acarbose dosage used (300–900 mg/day), the frequency of gastrointestinal effects was as high as 56–76% (placebo, 32–37%) in earlier studies (217). When the new recommendations for use of -glucosidase inhibitors were considered in the study protocols (low acarbose starting dose of 50 mg/day, slow increase of dosage over weeks, maximum dose 100 mg three times daily), the incidence of gastrointestinal adverse effects were reported to be as low as 7.5% (203). Furthermore, it has been shown that the incidence of gastrointestinal side effects decreased during long-term treatment (218). b. Systemic effects. The systemic availability of nonmetabolized acarbose is reported to be 0.5–1.7% (217, 219, 220). Due to the low systemic absorption of acarbose, systemic effects are rare. However, liver transaminase elevations [defined as treatment-induced increases of alanine aminotransferase (ALT) and/or aspartate aminotransferase (AST) &gt; 1.8-fold the upper limit of the normal range] were documented in 3.8% of acarbose recipients (placebo 0.9%) in the early studies carried out in the United States, using high acarbose daily dosage (900 mg/day) (221). Animal studies on ethanolinduced hepatotoxicity revealed that high-dose acarbose treatment augmented ethanol-induced hepatotoxicity (222). However, in all major acarbose trials using 100 mg three times daily as the maximum dose, hepatic transaminase elevations were extremely rare (203, 223) and in the five cases published, transaminase levels were reversible upon withdrawal of the drug (224, 225, 226, 227). Furthermore, a recent study from Japan demonstrated that acarbose treatment in patients with chronic liver disease and diabetes mellitus was effective and caused no significant alterations in hepatic transaminase levels after 8 weeks of treatment (228). Recently, it has been reported that acarbose induced a generalized erythema multiforme in a middle-aged Japanese type 2 diabetic patient (229). 6. Guidelines for the clinical use of -glucosidase inhibitors. a. Selection of the most appropriate patients. Postprandial hyperglycemia represents a major metabolic disturbance of carbohydrate metabolism in IGT and early phase type 2 diabetic subjects. Since -glucosidase inhibitors decrease postprandial glycemia these patients are suitable candidates for treatment with -glucosidase inhibitors, provided that the individual therapeutic goal was not achieved by dietary advice and increased physical activity. In type 2 diabetic patients suffering predominantly from fasting hyperglycemia, glucosidase inhibitors are less effective but may be used in combination with other antihyperglycemic agents, such as metformin, sulfonylureas, or insulin. The results of the UKPDS have shown that combination therapy using these drugs is effective and safe over at least 3 yr. In patients remaining on their allocated therapy, the HbA1c-difference at 3 yr was 0.5% lower in the acarbose study group compared with placebo (205). B. Biguanides 1. Introduction. There is now a large body of data documenting the clinical efficacy of metformin in the treatment of type 2 diabetes (230), and most of its clinical, pharmacological, and basic cellular aspects have been addressed in several excellent reviews published during the past 20 yr (231, 232, 233, 234, 235, 236, 237, 238). Recently, the UKPDS showed that metformin is particularly effective in overweight type 2 diabetic subjects, a condition usually characterized by insulin resistance (239). Moreover, in essentially all clinical studies the improvement of hyperglycemia with metformin occurred in the presence of unaltered or reduced plasma insulin concentrations (e.g., Refs. 240, 241). Taken collectively, these findings indicate the potential of metformin as an insulin-sensitizing or insulin-mimetic drug, which is the focus of the following. Despite almost 40 yr of research, the precise cellular mechanism of metformin action is still not entirely understood. Several cellular mechanisms have been described but a single unifying site of action, such as a receptor, an enzyme, or a transcription factor, has yet to be identified. Nevertheless, it is generally undisputed that metformin has no effect on the pancreatic ß-cell in stimulating insulin secretion (234). Mild increases in glucose-stimulated insulin secretion after metformin treatment (242) are thought to be the result of reduced glucose toxicity on the ß-cell secondary to improved glycemic control (243). 2. Mechanisms of action in humans. a. Glucose production. Accelerated endogenous glucose production is thought to be a key factor in the development of fasting hyperglycemia in type 2 diabetes (244, 245). In patients with type 2 diabetes, metformin has been shown to inhibit endogenous glucose production in most studies (246, 247, 248, 249, 250, 251, 252). This could be accounted for largely by inhibition of gluconeogenesis (247), although an additional inhibitory effect of metformin on glycogen breakdown is likely (247, 248). The observation in many studies that, in the basal postabsorptive state, overall glucose disposal (metabolic plasma clearance rate of glucose) did not change while endogenous glucose production decreased (246, 247, 248, 251, 252, 253) suggests that the improvement in glycemic control is largely attributable to the effect of metformin on glucose production. b. Peripheral glucose metabolism. Many (246, 249, 251, 252, 254, 255, 256), but not all, studies (248, 250, 253, 257) using the hyperinsulinemic-euglycemic clamp technique have shown a metformin-induced increase in insulin-stimulated glucose disposal in patients with type 2 diabetes. Since muscle represents a major site of insulin-mediated glucose uptake (244, 258), metformin must, either directly or via indirect mechanisms, have an insulin-like or insulin-sensitizing effect on this tissue. In humans, the increase in insulin-stimulated glucose disposal is mostly accounted for by nonoxidative pathways (252, 255, 259). Nonoxidative glucose metabolism includes storage as glycogen, conversion to lactate, and incorporation into triglycerides. While no effect on lactate production is observed (247, 248), implications on net triglyceride synthesis cannot be drawn. Nevertheless, it appears reasonable to propose that in human muscle glucose transport and, possibly as a consequence, glycogen synthesis are the major targets of metformin action in the insulin-stimulated state. However, in the basal state, metformin had no effect on glucose clearance or whole-body glucose oxidation, although the proportion of glucose turnover undergoing oxidation was increased (247). Moreover, forearm glucose uptake in the postabsorptive state was not significantly altered (247). c. Metabolic effects independent of improved glycemia. The interpretation of the above experiments is limited by the fact that treatment with metformin was always accompanied by improvement in glycemic control and sometimes also by reduction of body weight. It cannot be excluded, therefore, that the effects on endogenous glucose production and glucose disposal, at least in part, were secondary to reduced glucose toxicity (243) and/or weight loss (260) rather than metformin per se. Only four studies have examined the metabolic actions of metformin in the absence of any changes in glycemic control or body weight. In one study, 1 g of metformin was administered acutely to patients with type 2 diabetes; after 12 h no effect on insulin-stimulated glucose disposal was seen while the excessive endogenous glucose production in the basal state was significantly reduced (253). This suggests that in patients with type 2 diabetes, improvement in insulin-stimulated glucose disposal is predominantly due to alleviation of glucose toxicity while endogenous glucose production is immediately affected by metformin. In another study, lean, normal glucose-tolerant, insulin-resistant first-degree relatives of patients with type 2 diabetes acutely received 1 g of metformin and the opposite effect was observed (259). In subjects with IGT, 6-week metformin treatment improved basal (HOMA) but not insulin-stimulated glucose disposal or glucose oxidation (261). In this study both fasting glucose and insulin decreased significantly. In android obese subjects with IGT, increased insulin sensitivity (using an iv glucose tolerance) was observed after only 2 days of metformin treatment (1,700 mg/day) (262). In obese women with the polycystic ovary syndrome (PCOS) 6 months treatment with metformin also significantly improved insulin-stimulated glucose disposal (263, 264). In another study in obese women with PCOS, the decrease in serum insulin levels was associated with an increased ovulatory response to clomiphene (265). Glucose production was not assessed in the latter study. These apparent discrepancies could be explained by differences in the type of insulin resistance. In the highly selected group of lean, first-degree relatives and women with PCOS, mechanisms may contribute to insulin resistance that are different than those in garden-variety type 2 diabetes in which insulin resistance is predominantly the result of obesity and longstanding hyperglycemia. Moreover, the reduction in endogenous glucose production after metformin treatment may only be seen in subjects in whom it was increased to begin with, such as patients with type 2 diabetes. The latter is supported by observations showing that metformin alone does not cause hypoglycemia or lower blood glucose in nondiabetic subjects (266, 267). The effect of metformin on endogenous glucose production in nondiabetic humans has not yet been studied. Additional evidence for improved insulin action comes from studies combining insulin therapy and metformin. It was shown that requirements of exogenous insulin are reduced (by 30%) by addition of metformin in obese patients with type 2 diabetes (268, 269, 270) and in some patients with type 1 diabetes in whom glycemic control was unaltered (271, 272, 273). d. Other mechanisms of action. It has been suggested that part of the antihyperglycemic effect of metformin is due to decreased release of FFA from adipose tissue and/or decreased lipid oxidation (253, 274). However, reduced FFA levels after metformin treatment have been shown in some (251, 257, 274) but not all studies (247, 248, 259). Moreover, in vitro studies have shown that metformin does not enhance the antilipolytic action of insulin on adipose tissue (275). Only two studies have examined FFA turnover using isotope techniques and found either no difference (247) or a 17% reduction (255) after metformin treatment. In the latter study, the effect was seen in the basal state but not in the insulin-stimulated state in which FFA flux was largely suppressed. Thus, the metformin effect on peripheral glucose uptake may, at least in part, be mediated by suppression of FFA and lipid oxidation. In contrast, a causal relationship with endogenous glucose production is unlikely, since distinctly greater reductions in circulating FFA levels with acipimox failed to lower glucose production (276, 277). Evidence for other proposed mechanisms of metformin action is less convincing. Increased intestinal utilization of glucose has been suggested by animal studies (278, 279, 280). More recently, in vivo treatment with metformin increased gene expression of the energy-dependent sodium-glucose cotransporter (SGLT1) in rat intestine (281). However, such a mechanism has not been confirmed in humans (250). e. Weight loss. Unlike other pharmacological therapies for type 2 diabetes (sulfonylureas, insulin), metformin treatment is not associated with weight gain. Clinical studies have consistently shown either a small but significant decrease in body weight (240, 251) or a significantly smaller increase in body weight compared with other forms of treatment (268). One study has shown that weight loss during metformin treatment was largely accounted for by loss of adipose tissue (247). This was explained by differential effects of metformin on adipose tissue and muscle. While metformin improves insulin sensitivity in muscle, it does not affect the antilipolytic action of insulin on adipose tissue (282). The overall effect of metformin on body weight is attributed to a reduction in caloric intake (268, 283) rather than an increase in energy expenditure (247, 253, 284). Since reduction in body weight per se reduces insulin resistance, this may also represent a mechanism by which metformin improves insulin resistance. To summarize, the partly divergent observations from the numerous metabolic studies regarding metformin’s effect on muscle and liver (Table 3, A and B ) may reflect different mechanisms of metformin action in the basal vs. the insulin-stimulated state. In the basal, postabsorptive state, the improvement of fasting hyperglycemia is mostly due to a decrease of the accelerated endogenous glucose production. This results from inhibition of both gluconeogenesis and glycogen breakdown. Direct or indirect effects on regulatory enzymes are likely to be involved. No data are available for suppression of glucose production during experimental hyperinsulinemia. However, the fact that reduction in basal glucose production occurs in the presence of lower or unaltered insulin levels suggests that glucose production in liver and kidney (285, 286) is more sensitive to the restrictive action of insulin after treatment with metformin. In the insulin-stimulated state during the clamp, peripheral glucose disposal is increased even in the absence of improved fasting glycemia, indicating a reduction in insulin resistance. This is thought to be mainly a result of enhanced glucose transport and storage in muscle. The effect on glucose transport is most likely due to a potentiation of insulin-stimulated translocation of glucose transporters and an increase in their intrinsic activity (287, 288). Glycogen synthesis is increased as a result of stimulatory effects of metformin on the signaling chain to activation of glycogen synthase. Moreover, the in vivo effect on muscle may, in part, be due to a reduction in FFA oxidation. Finally, in insulin-resistant subjects the effect on muscle appears to be more pronounced, suggesting a reversal of insulin resistance rather than a mere improvement in insulin sensitivity. 3. Clinical efficacy of metformin in patients with type 2 diabetes mellitus. a. Glycemic control. The glucose-lowering effect of metformin, monotherapy or in combination, has been extensively reviewed (231, 232, 233). In a recent meta-analysis (230), all randomized, controlled clinical trials comparing metformin with placebo (239, 240, 252, 289, 290, 291, 292, 293, 294) and sulfonylurea (239, 240, 295, 296, 297, 298, 299, 300, 301) were evaluated. The weighted mean difference between metformin and placebo after treatment (median treatment duration, 4.5 months) for fasting blood glucose was -2.0 mM and for HbA1c -0.9%. Body weight was not significantly changed after treatment. Sulfonylureas and metformin lowered blood glucose (-2.0 and -1.8 mM, respectively) and HbA1c (-1.1 and -1.3%, respectively) equally (median treatment duration, 6 months). However, whereas after sulfonylurea treatment body weight increased by 2.9 kg, there was a decrease of 1.2 kg after metformin. In a retrospective study of 9,875 patients with type 2 diabetes mellitus who attended a large health maintenance organization, metformin treatment improved the mean HbA1c by 1.41% over a 20-month period (302). Among obese patients treated by intensive blood glucose control within the UKPDS, metformin showed a significantly greater effect than chlorpropamide, glibenclamide, or insulin for any diabetes-related endpoint, all-cause mortality, and stroke (239). In summary, metformin is as effective as sulfonylureas in improving glycemic control but, especially in overweight/obese patients, advantageous with respect to body weight, diabetes-related endpoints, and frequency of hypoglycemia. b. Lipid profile and cardiovascular system. In addition to improving glycemic control, metformin has been shown to reduce serum lipid levels. Metformin treatment results in a moderate (10–20%) reduction in circulating triglyceride levels, particularly in patients with marked hypertriglyceridemia and hyperglycemia (247, 257, 303), but also in nondiabetic subjects (304, 305). This has been attributed to a reduction in hepatic very low density lipoprotein (VLDL) synthesis (257, 292, 306). Small (5–10%) decreases in total circulating cholesterol have also been reported (286, 289, 290, 291) that were essentially attributed to reductions in low density lipoprotein (LDL) levels (307, 308, 309) since high-density lipoprotein (HDL) cholesterol levels were either increased (304) or unchanged (309). In addition to the improvement of the lipid profile, metformin appears to have potentially beneficial hemostaseological effects. Fibrinolysis is increased (305, 307, 308) and the fibrinolysis inhibitor plasminogen-activator inhibitor 1 (PAI1) is decreased (292, 305, 310). Moreover, a decrease in platelet aggregability and density has been demonstrated (296, 311). These additional effects of metformin, which have been extensively reviewed elsewhere (231, 232), may explain the advantage of metformin over sulfonylurea or insulin treatment with respect to macrovascular endpoints shown in the UKPDS (239). c. Combination therapies: metformin plus sulfonylureas and metformin plus insulin. Metformin is also used in combination with other antihyperglycemic agents. Because of its unique mechanisms of action, a synergistic effect on glycemic control has been observed in combination with sulfonylureas (e.g., Refs. 240, 312, 313), troglitazone (Ref. 314 and see next chapter), and insulin where a dose-sparing effect was consistently demonstrated (268, 269, 270, 314, 315, 316). Interestingly, in patients in whom sulfonylurea therapy has failed to satisfactory glycemic control, the combination of bedtime NPH-insulin with metformin was advantageous compared with other combinations (316). In contrast to insulin alone, insulin plus sulfonylurea, and sulfonylurea alone, when bedtime NPH-insulin was combined with metformin, a decrease in HBA1c was achieved without significant weight gain (315, 316). 4. Adverse effects. While mild gastrointestinal disturbances are the most common side effects, lactic acidosis, although rare, is the most serious side effect of metformin treatment (317). In 9,875 patients one case of probable lactic acidosis was observed in 20 treatment months (302). The incidence of lactic acidosis is 10 to 20 times lower than with phenformin. This is explained by the necessity to hydroxylate phenformin before renal excretion, a step that is genetically defective in 10% of whites (318, 319). Metformin, in contrast, is excreted unmetabolized. In addition, in contrast to phenformin (320), metformin neither increases peripheral lactate production nor decreases lactate oxidation (247, 248), making lactate accumulation unlikely. One study investigating individual cases of metformin-associated lactic acidosis showed that in these patients metformin should never have been started or should have been discontinued with the onset of acute illness (321). Thus, strict adherence to the exclusion criteria of metformin treatment (renal and hepatic disease, cardiac or respiratory insufficiency, severe infection, alcohol abuse, history of lactic acidosis, pregnancy, use of intravenous radiographic contrast; reviewed in Refs. 213, 216) should minimize the risk of metformin-induced lactic acidosis. 5. Guidelines for the clinical use of metformin. As recently reviewed (231) metformin or sulfonylurea therapy can be initiated when patients with NIDDM continue to have hyperglycemia despite diet and exercise. Metformin appears to be the drug of choice to start pharmacological treatment in insulin-resistant and overweight/obese diabetic subjects (239, 322). However, since the antihyperglycemic effects of metformin are similar in lean and obese subjects, it can also be recommended as first-line treatment in the absence of obesity. Addition of metformin to sulfonylureas in patients with secondary sulfonylurea failure appears reasonable in view of their synergistic mechanisms of action and has been shown to improve glycemic control. Furthermore, especially in overweight/obese patients, the addition of metformin to insulin is advantageous compared with insulin alone (507). Finally, metformin is not recommended for patients with type 1 diabetes, or in insulin-resistant states in the absence of overt type 2 diabetes. However, metformin is currently under investigation as an agent to prevent type 2 diabetes in subjects with IGT as one of the three arms (vs. diet and intensive life-style modification) of the Diabetes Prevention Program (322), but it is not yet approved for use in subjects with IGT. C. Thiazolidinediones 1. Introduction. The thiazolidinediones are a new class of hypoglycemic agents that were originally developed in the early 1980s in Japan as antioxidants (323). Soon after the synthesis of the first thiazolidinedione, ciglitazone, the blood glucose-lowering potential of these compounds was observed in animals, with particularly pronounced effects in animals with genetic insulin resistance such as the KK, db/db, and ob/ob mice, and fa/fa rats (324, 325, 326). The observation that glycemia improved in the absence of increasing insulin and the lack of effect in insulin-deficient animals (327) led to the conclusion that thiazolidinediones improved insulin resistance and resulted in the nickname &quot;insulin sensitizers.&quot; However, due to an unacceptable side effect profile, ciglitazone and, later, englitazone never proceeded to human studies. Troglitazone became the first thiazolidinedione available for clinical use and was released in 1997 in the United States and Japan followed by rosiglitazone and pioglitazone (both marketed in 1999 in the United States). In Europe, except for the United Kingdom, where it was available for a few months, troglitazone has not been approved and, due to an untoward risk-benefit ratio (hepatotoxic side effects), was withdrawn from the US market by the Food and Drug Administration (FDA) in March 2000. Thus, at the present time rosiglitazone and pioglitazone are the two members of the thiazolidinedione class available for clinical use in some countries including the United States, Japan, and Europe. Since the ma
  • Background In creased physical activity is related to reduced risk of cardiovascular disease, possibly because it leads to improvement in the lipoprote in profile. However, the amount of exercise tra inin g required for optimal benefit is unknown. In a prospective, randomized study, we in vestigated the effects of the amount and in tensity of exercise on lipoprote in s. Methods A total of 111 sedentary, overweight men and women with mild-to-moderate dyslipidemia were randomly assigned to participate for six months in a control group or for approximately eight months in one of three exercise groups: high-amount–high- in tensity exercise, the caloric equivalent of jogg in g 20 mi (32.0 km) per week at 65 to 80 percent of peak oxygen consumption; low-amount–high- in tensity exercise, the equivalent of jogg in g 12 mi (19.2 km) per week at 65 to 80 percent of peak oxygen consumption; or low-amount–moderate- in tensity exercise, the equivalent of walk in g 12 mi per week at 40 to 55 percent of peak oxygen consumption. Subjects were encouraged to ma in ta in their base-l in e body weight. The 84 subjects who complied with these guidel in es served as the basis for the ma in analysis. Detailed lipoprote in profil in g was performed by nuclear magnetic resonance spectroscopy with verification by measurement of cholesterol in lipoprote in subfractions. Results There was a beneficial effect of exercise on a variety of lipid and lipoprote in variables, seen most clearly with the high amount of high- in tensity exercise. The high amount of exercise resulted in greater improvements than did the lower amounts of exercise ( in 10 of 11 lipoprote in variables) and was always superior to the control condition (11 of 11 variables). Both lower-amount exercise groups always had better responses than the control group (22 of 22 comparisons). Conclusions The highest amount of weekly exercise, with m in imal weight change, had widespread beneficial effects on the lipoprote in profile. The improvements were related to the amount of activity and not to the in tensity of exercise or improvement in fitness. Exercise tra inin g had no significant effect on the total cholesterol or LDL cholesterol concentrations (data not shown). It did, however, have important effects on the concentrations of LDL subfractions (Figure 1). High-amount–high- in tensity exercise significantly reduced the concentrations of LDL and small LDL particles and in creased the average size of LDL particles. The IDL cholesterol concentration decreased nonsignificantly with in creas in g exercise levels. Although both low-amount groups had improvements in these variables as compared with controls, only the effect on the size of LDL particles was significant for the low-amount–high- in tensity group. There was a progressive effect of the amount of exercise on all four of the variables shown in Figure 1. Figure 1. Comparison of the Effects of Three Different Exercise Programs with Those in a Control Group on Mean Changes in the Concentration of Small Low-Density Lipoprote in (LDL) Cholesterol (Panel A), the Concentration of LDL Particles (Panel B), the Average Size of LDL Particles (Panel C), and the Concentration of In termediate-Density Lipoprote in (IDL) Cholesterol (Panel D). Subjects in the control group ma in ta in ed their normal diet and level of physical activity for six months. In the exercise groups, the amount and in tensity of exercise were gradually in creased to the prescribed level over the course of one to three months, after which exercise was ma in ta in ed at the prescribed level for six months. Low-amount–moderate- in tensity exercise represents the caloric equivalent of walk in g approximately 12 mi per week at 40 to 55 percent of peak oxygen consumption; low-amount–high- in tensity exercise represents the same amount of exercise at 65 to 80 percent of peak oxygen consumption. High-amount–high- in tensity exercise represents the caloric equivalent of jogg in g approximately 20 mi per week at 65 to 80 percent of oxygen consumption. Values shown represent means of in dividual change scores. I bars represent the standard errors. To convert the values for cholesterol to millimoles per liter, multiply by 0.02586.
  • Mixed Noradrenergic–Serotonergic Agents Sibutramine (Meridia), an inhibitor of both norepinephrine reuptake and serotonin reuptake that also weakly inhibits dopamine reuptake (Figure 1), is approved by the FDA for weight loss and weight maintenance in conjunction with a reduced-calorie diet.7 Sibutramine is given in a dose of 10 to 15 mg once daily and may be given in a 5-mg dose to patients who do not tolerate the 10-mg dose. Unlike fenfluramine and dexfenfluramine, it does not induce serotonin release, and has not been implicated in the development of valvular heart disease.44,45 Over a six-month period, subjects who follow a reduced-calorie diet and receive sibutramine typically lose 5 to 8 percent of their preintervention body weight, as compared with 1 to 4 percent among subjects who receive placebo.46,47,48,49 Sibutramine-induced reductions in weight appear to be largely maintained for periods of up to one year and remain significantly greater than those observed in patients who receive placebo.50 Published studies with up to two years of data are now available. The Sibutramine Trial of Obesity Reduction and Maintenance followed 605 European adults who took 10 mg of sibutramine daily for 6 months, after which 467 participants who had lost more than 5 percent of their preintervention body weight were randomly assigned to continue to receive sibutramine or to receive placebo for 18 months.51 Although weight was regained in both groups during the second year of follow-up, weight losses were significantly greater among those who received sibutramine for the full two years of the trial. More than 25 percent of those who continued to take sibutramine maintained their reduced weight for the entire observation period. As in most studies of weight-loss medications, the large numbers of dropouts in both the study-drug group and the placebo group limit the generalizability of the findings.51 By the end of the study, the dose of sibutramine had been increased to 20 mg, a dose higher than is approved in the United States, in 52 percent of the subjects taking the medication. However, 86 percent of the subjects who did not regain any of the weight they had lost were taking no more than 15 mg of sibutramine daily. Sibutramine may also increase weight loss and improve maintenance of reduced weight in subjects who have previously lost weight with a very-low-calorie diet.52 Side effects of sibutramine include increases in blood pressure and pulse; although these are usually mild, they lead to the discontinuation of sibutramine in up to 5 percent of patients.47,48,50,51 In general, reductions in blood pressure in those who lose weight with sibutramine are less than the reductions in blood pressure seen with similar weight loss obtained with other treatments.47 Adverse reactions also include dry mouth, headache, insomnia, and constipation.45 Commensurate with weight loss, other metabolic risk factors improve; these include hyperlipidemia and hyperuricemia, as well as glycemic control and plasma insulin levels in patients with type 2 diabetes.46,48,50,51,53
  • The only FDA-approved medication for obesity that reduces nutrient absorption is orlistat (Xenical), which acts by binding to gastrointestinal lipases in the lumen of the gut, preventing hydrolysis of dietary fat (triglycerides) into absorbable free fatty acids and monoacylglycerols (Figure 2). Patients who take 120 mg of orlistat with or up to one hour after meals excrete in the stool approximately one third of the dietary fat they ingest, thereby reducing calorie and fat intake. In double-blind, placebo-controlled trials, orlistat had moderate efficacy for weight loss in adults. Orlistat-treated subjects who completed trials lasting one year lost approximately 9 percent of their preintervention body weight, as compared with 5.8 percent among those who took placebo.54 Orlistat has also been found to slow the rate of regain of weight during a second year of use; orlistat-treated subjects regained less weight during the second year than placebo-treated subjects did (a regain of 35.2 percent vs. 62.4 percent, a difference of about 2.5 kg).55,56,57 In these long-term studies, orlistat-treated patients also had moderate decreases in diastolic blood pressure, insulin levels while fasting, and total cholesterol and low-density lipoprotein cholesterol, with a small cholesterol-lowering effect that was independent of weight loss. Orlistat induced small reductions in body weight in patients with type 2 diabetes that were nevertheless significantly greater than those that occurred in such patients who received placebo (losses of 6.2 kg vs. 4.3 kg); orlistat also led to improvement in glycosylated hemoglobin values and a decreased requirement for sulfonylurea drugs.58 Orlistat appears to have similar efficacy regardless of whether it is prescribed in a primary care or a specialized treatment setting.59 Side effects of orlistat include flatulence with discharge, fecal urgency, fecal incontinence, steatorrhea, oily spotting, and increased frequency of defecation. These side effects are usually mild to moderate, and generally decrease in frequency with ongoing treatment. However, such side effects lead to discontinuation in nearly 9 percent of patients, as compared with a rate of discontinuation of 5 percent among patients treated with placebo.7,54 Orlistat also decreases absorption of fat-soluble vitamins, primarily vitamin D, an effect that can be counteracted by daily administration of a multivitamin at least two hours before or after a dose of orlistat.7
  • Endocannabinoid Blockade for Improving Glycemic Controland Lipids in Patients with Type 2 Diabetes Mellitus Rimonabant significantly reduces weight and waist circumference and improves dyslipidemias in overweight and obese patients without diabetes mellitus. Numerous other metabolic changes, including reduced prevalence of the metabolic syndrome and associated cardiovascular disease (CVD) risk factors, reduced fasting glucose, and elevated adiponectin, have been demonstrated with the administration of rimonabant. The Rimonabant-in-Obesity (RIO)–Diabetes trial studied the safety and efficacy of rimonabant in overweight and obese patients with type 2 diabetes who were treated with metformin or sulfonylureas. RIO-Diabetes was a 1-year, randomized, double-blind, placebo-controlled, parallel-group study of 1,047 overweight/obese patients with type 2 diabetes in 151 centers in 11 countries. The body mass index of participants ranged from 27 to 40. Glycosylated hemoglobin (HbA1c) at screening ranged from 6.5% to 10.0%. All patients were receiving either metformin or sulfonylurea therapy and were asked to follow a hypocaloric diet (600 kcal/day deficit [1 kcal 4.2 kJ]) for the duration of the trial. After a 4-week placebo plus diet run-in period, patients were randomized to receive placebo or rimonabant 5 mg or 20 mg once daily. At 1 year, absolute change in weight from baseline in the intention-to-treat, last observation carried forward analysis of the rimonabant 5 mg and 20 mg groups, respectively, was loss of 2.3 kg and 5.3 kg compared with 1.4 kg in the placebo group ( P 0.013 and P 0.001, respectively). Waist circumference was significantly decreased in the rimonabant 5-mg and 20-mg groups by 2.9 cm and 5.2 cm compared with 1.9 cm in the placebo group ( P 0.034 and P 0.001, respectively). HbA1c reductions of 0.1% and 0.6% were significant in the rimonabant 5-mg and 20-mg groups ( P 0.034 and P 0.001, respectively). Some 57% of the improvements in HbA1c and high-density lipoprotein cholesterol could not be attributed to observed weight loss. Compared with placebo, rimonabant 20 mg also demonstrated significant improvements in the prevalence of metabolic syndrome and improvement in its constituents, as well as systolic blood pressure and C-reactive protein levels (assay by ICON Laboratories, Farmingdale, NY and Dublin, Ireland). Rimonabant is the first selective cannabinoid1 blocker studied for type 2 diabetes and associated CVD risk factor therapy. Its ability to improve the numerous metabolic pathologies associated with diabetes and CVD risk and concomitantly to reduce weight and waist circumference introduces a strongly positive new dynamic in type 2 diabetes treatment. Its multifactorial mechanisms warrant further investigation and may provide insights into other pathologies. © 2007 Elsevier Inc. All rights reserved Rimonabant in the Management of Multiple Cardiometabolic Risk Factors Rimonabant is a selective cannabinoid-1 (CB1) blocker that has demonstrated efficacy in the treatment of overweight and obesity, dyslipidemia, and CVD risk factors ( Figure 3 ).23,24 The newly discovered endocannabinoid (EC) and CB1 receptor system modulates food intake, energy balance, and body composition through both central and peripheral effects that improve glucose and lipid metabolism. 16,25-28 CB1 receptors are expressed centrally in several areas of the brain and peripherally in organs and tissue, including the autonomic nervous system, the liver, skeletal muscle, the gastrointestinal system, and adipose tissue.23,29 EC system stimulation in peripheral fat cells promotes lipogenesis and inhibits adiponectin production. Adiponectin, a cytokine derived from peripheral fat cells, has antidiabetic and antiatherosclerotic effects.26,30,31 CB1 receptor blockade by rimonabant reduces food intake and induces weight loss, alters fat metabolism to therapeutically affect lipid profiles, and induces expression of the adiponectin gene.26,30-33 The results of phase 3 clinical trials of rimonabant have demonstrated its safety and efficacy in the treatment of overweight, obesity, and dyslipidemia and in the reduction of multiple CVD risk factors.23,24 There are 4 major rimonabant trials in total (RIO-Europe, RIO-North America, RIO-Lipids, and RIO-Diabetes).16,23,24,34 Three of the four, all of which have reported favorable safety and efficacy results, excluded diabetes patients. This article concerns the RIODiabetes trial and focuses on the safety and efficacy of rimonabant in the treatment of overweight and obese patients with type 2 diabetes.
  • Effects of Rimonabant on Metabolic Risk Factors in Overweight Patients with Dyslipidemia background Rimonabant, a selective cannabinoid-1 receptor (CB 1 ) blocker, has been shown to reduce body weight and improve cardiovascular risk factors in obese patients. The Rimonabant in Obesity–Lipids (RIO-Lipids) study examined the effects of rimonabant onmetabolic risk factors, including adiponectin levels, in high-risk patients who are overweightor obese and have dyslipidemia. methods We randomly assigned 1036 overweight or obese patients (body-mass index [the weight in kilograms divided by the square of the height in meters], 27 to 40) with untreated dyslipidemia (triglyceride levels &gt;1.69 to 7.90 mmol per liter, or a ratio of cholesterol to high-density lipoprotein [HDL] cholesterol of &gt;4.5 among women and &gt;5 among men) to double-blinded therapy with either placebo or rimonabant at a dose of 5 mg or 20 mg daily for 12 months in addition to a hypocaloric diet. results The rates of completion of the study were 62.6 percent, 60.3 percent, and 63.9 percent in the placebo group, the group receiving 5 mg of rimonabant, and the group receiving 20 mg of rimonabant, respectively. The most frequent adverse events resulting in discontinuation of the drug were depression, anxiety, and nausea. As compared with placebo, rimonabant at a dose of 20 mg was associated with a significant (P&lt;0.001) mean weight loss (repeated-measures method, .6.7±0.5 kg, and last-observation-carriedforward analyses, .5.4±0.4 kg), reduction in waist circumference (repeated-measures method, .5.8±0.5 cm, and last-observation-carried-forward analyses, .4.7±0.5 cm), increase in HDL cholesterol (repeated-measures method, +10.0±1.6 percent, and lastobservation- carried-forward analyses, +8.1±1.5 percent), and reduction in triglycerides (repeated-measures method, .13.0±3.5 percent, and last-observation-carriedforward analyses, .12.4±3.2 percent). Rimonabant at a dose of 20 mg also resulted in an increase in plasma adiponectin levels (repeated-measures method, 57.7 percent, and last-observation-carried-forward analyses, 46.2 percent; P&lt;0.001), for a change that was partly independent of weight loss alone. conclusions Selective CB 1-receptor blockade with rimonabant significantly reduces body weight and waist circumference and improves the profile of several metabolic risk factors in high-risk patients who are overweight or obese and have an atherogenic dyslipidemia. After a weight loss of approximately 2 kg in each group during the run-in period (Table 1), the placebo group had a further decline of 2.3 kg over the next 12 months, as compared with a weight loss of 4.2 kg and 8.6 kg in the group receiving 5 mg of rimonabant and the group receiving 20 mg of rimonabant, respectively (Table 2) (P&lt;0.001 for both doses). Weight loss was generally greater among patients who completed the 12-month study. In the overall population, the proportion of patients who had a weight loss equal to or greater than 5 percent was 19.5 percent in the placebo group and 58.4 percent in the group receiving 20 mg of rimonabant (P&lt;0.001), whereas the proportion of those who had a weight loss equal to or greater than 10 percent was 7.2 percent in the placebo group and 32.6 percent in the group receiving 20 mg of rimonabant (P&lt;0.001). Weight loss occurred during the first 9 months of the study period, after which body weight stabilized until the end of the 12th month without evidence of regain (Fig. 1A). Changes in waist circumference showed a similar dose response (Table 2) and temporal pattern (Fig. 1B). The caloric restriction during the four-week run-in period produced reductions of 5.3±37.9 percent in triglycerides, 4.9±17.2 percent in LDL cholesterol, and 3.6±11.9 percent in HDL cholesterol, which resulted in a 0.11±0.76 decrease in the total cholesterol:HDL cholesterol ratio (Table 1). During treatment, triglycerides remained stable in boththe placebo group and the group receiving 5 mg of rimonabant but fell an additional 15.8±38.0 percent percentin the group receiving 20 mg of rimonabant (P&lt;0.001) (Table 2 and Fig. 1C). HDL cholesterol increased in a dose-dependent fashion, achieving an increase of 15.6±15.3 percent from baseline in the group receiving 5 mg of rimonabant (P=0.017) and of 23.4±21.8 percent in the group receiving 20 mg of rimonabant (P&lt;0.001) (Table 2 and Fig. 1D). Although there was no change in levels of LDL cholesterol, the distribution of LDL particles shifted toward larger size in the group receiving 20 mg of rimonabant, as compared with placebo, with a difference of 1.1 . in the peak size of LDL particles (P=0.008) and a 4.6 percent lower proportion of small LDL particles (P=0.007)(Table 2). Changes in levels of HDL cholesteroltranslated into a dose-dependent reduction in the total cholesterol:HDL cholesterol ratio of –15.2 percent with 20 mg of rimonabant, which was greater than with placebo (P&lt;0.001) (Table 2). Levelsof fasting plasma insulin, the one-hour and twohour plasma glucose and insulin levels, and the insulin and glucose AUCs during the 75-g oral glucose- tolerance test decreased significantly in the group receiving 20 mg of rimonabant (Fig. 2A and 2B; P=0.011 to &lt;0.001). Figure 1. Effect of Placebo or Rimonabant for 52 Weeks on Body Weight, Waist Circumference, Plasma Triglyceride Levels, and High-Density Lipoprotein (HDL) Cholesterol Levels. Body weight and waist circumference were measured at randomization (week 0) and every four weeks thereafter untilweek 52, and plasma HDL cholesterol and triglyceride levels were measured at randomization (week 0) and every three months thereafter until week 52. Values are shown as means ±SE for all patients for whom measurements were taken at each visit (lines); P values were obtained after the repeated-measures analysis. P values correspond to the mean difference between the rimonabant groups and the placebo group.
  • Does pharmacologically induced weight loss improve cardiovascular outcome? Impact of anti-obesity agents on cardiovascular risk factors Currently available anti-obesity drugs sibutramine and orlistat, and the soon to be available rimonabant, all produce similar degrees of clinically meaningful weight loss and weight loss maintenance, even though they differ considerably in their mode of action. Pharmacologically induced weight loss has a beneficial impact on a number of metabolic and cardiovascular risk factors, such as glucose homeostasis, blood pressure, central adiposity, and dyslipidaemia. In some cases, these effects appear to be over and above that explained by weight loss. These effects are important if obese patients are to be treated not just for their weight but also to reduce co-existing metabolic and cardiovascular risk factors. Key Words: Anti-obesity drugs • Cardiovascular risk • HDL-cholesterol • Metabolic risk • Obesity • Orlistat • Rimonabant • Risk factors • Sibutramine • Weight loss Central adiposity Two-year studies with both sibutramine and orlistat 4,17 and the 1-year study with rimonabant 10 show patients achieve marked decreases in waist circumference, an index of visceral adiposity ( Figure 7 ). Confirmation of the fat redistribution that occurs with anti-obesity treatment is provided by a subset of the STORM study, in which computed tomography measurements were taken in the first 6 months. This shows that following treatment with sibutramine, there is a marked and preferential decrease in visceral fat of around 24% ( P &lt;0.001 compared with baseline) and a reduction of 17% in subcutaneous fat ( P &lt;0.001 compared with baseline). 18 Figure 7  Effect on waist circumference in patients who have completed placebo-controlled trials of sibutramine, 4 orlistat, 17 and rimonabant. 10
  • Figure 1. Estimated Number of Bariatric Operations Performed in the United States, 1992–2003. Data are from the American Society for Bariatric Surgery.
  • Background Weight loss is associated with short-term amelioration and prevention of metabolic and cardiovascular risk, but whether these benefits persist over time is unknown. Methods The prospective, controlled Swedish Obese Subjects Study involved obese subjects who underwent gastric surgery and contemporaneously matched, conventionally treated obese control subjects. We now report follow-up data for subjects (mean age, 48 years; mean body-mass index, 41) who had been enrolled for at least 2 years (4047 subjects) or 10 years (1703 subjects) before the analysis (January 1, 2004). The follow-up rate for laboratory examinations was 86.6 percent at 2 years and 74.5 percent at 10 years. Results After two years, the weight had increased by 0.1 percent in the control group and had decreased by 23.4 percent in the surgery group (P&lt;0.001). After 10 years, the weight had increased by 1.6 percent and decreased by 16.1 percent, respectively (P&lt;0.001). Energy intake was lower and the proportion of physically active subjects higher in the surgery group than in the control group throughout the observation period. Two- and 10-year rates of recovery from diabetes, hypertriglyceridemia, low levels of high-density lipoprotein cholesterol, hypertension, and hyperuricemia were more favorable in the surgery group than in the control group, whereas recovery from hypercholesterolemia did not differ between the groups. The surgery group had lower 2- and 10-year incidence rates of diabetes, hypertriglyceridemia, and hyperuricemia than the control group; differences between the groups in the incidence of hypercholesterolemia and hypertension were undetectable. Conclusions As compared with conventional therapy, bariatric surgery appears to be a viable option for the treatment of severe obesity, resulting in long-term weight loss, improved lifestyle, and, except for hypercholesterolemia, amelioration in risk factors that were elevated at baseline The changes in weight among the subjects followed for 10 years are shown in Figure 1. Weight change was maximal after six months in the control group (mean [±SD] change, –1±6 percent [where the minus sign denotes a decrease]) and maximal after one year in the three surgical subgroups (gastric bypass, –38±7 percent; vertical banded gastroplasty, –26±9 percent; and banding, –21±10 percent). After two years, weight had increased by 0.1 percent in the control group and had decreased by 23.4 percent in the surgical group (P&lt;0.001) (Table 2). After 10 years, the controls had increased by 1.6±12 percent over the inclusion weight, whereas the maintained weight change was –25±11 percent in the gastric-bypass subgroup, –16.5±11 percent in the subgroup that underwent vertical banded gastroplasty, and –13.2±13 percent in the banding subgroup. The average weight changes in the entire group of surgically treated subjects are listed in Table 2 and were almost identical to those shown in the curve for the subgroup that underwent vertical banded gastroplasty (Figure 1). The fractions of subjects who, after completing 10 years of the study, had a loss of less than 5 percent of their initial weight were 72.7 percent (control group), 8.8 percent (gastric-bypass subgroup), 13.8 percent (vertical-banded-gastroplasty subgroup), and 25.0 percent (banding subgroup). The fractions of subjects achieving 20 percent weight loss or more over the 10-year period were 3.8 percent (control group), 73.5 percent (gastric-bypass subgroup), 35.2 percent (vertical-banded-gastroplasty subgroup), and 27.6 percent (banding subgroup). Figure 1. Weight Changes among Subjects in the SOS Study over a 10-Year Period. All data are for subjects who completed 10 years of the study. The average weight change in the entire group of surgically treated subjects was almost identical to that in the subgroup of subjects who underwent vertical banded gastroplasty. The I bars represent the 95 percent confidence intervals.
  • Background Weight loss is associated with short-term amelioration and prevention of metabolic and cardiovascular risk, but whether these benefits persist over time is unknown. Methods The prospective, controlled Swedish Obese Subjects Study involved obese subjects who underwent gastric surgery and contemporaneously matched, conventionally treated obese control subjects. We now report follow-up data for subjects (mean age, 48 years; mean body-mass index, 41) who had been enrolled for at least 2 years (4047 subjects) or 10 years (1703 subjects) before the analysis (January 1, 2004). The follow-up rate for laboratory examinations was 86.6 percent at 2 years and 74.5 percent at 10 years. Results After two years, the weight had increased by 0.1 percent in the control group and had decreased by 23.4 percent in the surgery group (P&lt;0.001). After 10 years, the weight had increased by 1.6 percent and decreased by 16.1 percent, respectively (P&lt;0.001). Energy intake was lower and the proportion of physically active subjects higher in the surgery group than in the control group throughout the observation period. Two- and 10-year rates of recovery from diabetes, hypertriglyceridemia, low levels of high-density lipoprotein cholesterol, hypertension, and hyperuricemia were more favorable in the surgery group than in the control group, whereas recovery from hypercholesterolemia did not differ between the groups. The surgery group had lower 2- and 10-year incidence rates of diabetes, hypertriglyceridemia, and hyperuricemia than the control group; differences between the groups in the incidence of hypercholesterolemia and hypertension were undetectable. Conclusions As compared with conventional therapy, bariatric surgery appears to be a viable option for the treatment of severe obesity, resulting in long-term weight loss, improved lifestyle, and, except for hypercholesterolemia, amelioration in risk factors that were elevated at baseline Mean energy intake at the time of inclusion in the intervention study was 2882 kcal per day among the surgically treated subjects, as compared with 2526 kcal per day among the controls. As Figure 2 and Table 2 indicate, the baseline adjusted energy intake was significantly lower in the surgery group than in the control group over the 10-year period. Similarly, the fraction of subjects physically active during leisure time was higher in the surgery group over the 10-year period, and the fraction of those physically active during work time was higher in the surgery group for the first 6 years of the intervention. Figure 2. Lifestyle Changes among the Subjects in the SOS Study over a 10-Year Period. Mean energy intake (in kilocalories per day) (Panel A) and the percentage of subjects who were physically active during leisure time and at work (Panels B and C, respectively) are shown. Energy intake and the proportion of active subjects at baseline (year 0) are unadjusted values, whereas the values during the follow-up have been adjusted for sex, age, body-mass index, and energy intake or physical activity at baseline. All data are from subjects who completed 10 years of the study. The numbers of subjects at each time point are the same as those shown in Figure 1. Asterisks denote P&lt;0.01 and daggers P&lt;0.05 for the comparison between the groups (by tests for equality). I bars represent the 95 percent confidence intervals.
  • Background Weight loss is associated with short-term amelioration and prevention of metabolic and cardiovascular risk, but whether these benefits persist over time is unknown. Methods The prospective, controlled Swedish Obese Subjects Study involved obese subjects who underwent gastric surgery and contemporaneously matched, conventionally treated obese control subjects. We now report follow-up data for subjects (mean age, 48 years; mean body-mass index, 41) who had been enrolled for at least 2 years (4047 subjects) or 10 years (1703 subjects) before the analysis (January 1, 2004). The follow-up rate for laboratory examinations was 86.6 percent at 2 years and 74.5 percent at 10 years. Results After two years, the weight had increased by 0.1 percent in the control group and had decreased by 23.4 percent in the surgery group (P&lt;0.001). After 10 years, the weight had increased by 1.6 percent and decreased by 16.1 percent, respectively (P&lt;0.001). Energy intake was lower and the proportion of physically active subjects higher in the surgery group than in the control group throughout the observation period. Two- and 10-year rates of recovery from diabetes, hypertriglyceridemia, low levels of high-density lipoprotein cholesterol, hypertension, and hyperuricemia were more favorable in the surgery group than in the control group, whereas recovery from hypercholesterolemia did not differ between the groups. The surgery group had lower 2- and 10-year incidence rates of diabetes, hypertriglyceridemia, and hyperuricemia than the control group; differences between the groups in the incidence of hypercholesterolemia and hypertension were undetectable. Conclusions As compared with conventional therapy, bariatric surgery appears to be a viable option for the treatment of severe obesity, resulting in long-term weight loss, improved lifestyle, and, except for hypercholesterolemia, amelioration in risk factors that were elevated at baseline Mean energy intake at the time of inclusion in the intervention study was 2882 kcal per day among the surgically treated subjects, as compared with 2526 kcal per day among the controls. As Figure 2 and Table 2 indicate, the baseline adjusted energy intake was significantly lower in the surgery group than in the control group over the 10-year period. Similarly, the fraction of subjects physically active during leisure time was higher in the surgery group over the 10-year period, and the fraction of those physically active during work time was higher in the surgery group for the first 6 years of the intervention. Figure 2. Lifestyle Changes among the Subjects in the SOS Study over a 10-Year Period. Mean energy intake (in kilocalories per day) (Panel A) and the percentage of subjects who were physically active during leisure time and at work (Panels B and C, respectively) are shown. Energy intake and the proportion of active subjects at baseline (year 0) are unadjusted values, whereas the values during the follow-up have been adjusted for sex, age, body-mass index, and energy intake or physical activity at baseline. All data are from subjects who completed 10 years of the study. The numbers of subjects at each time point are the same as those shown in Figure 1. Asterisks denote P&lt;0.01 and daggers P&lt;0.05 for the comparison between the groups (by tests for equality). I bars represent the 95 percent confidence intervals.
  • Background Weight loss is associated with short-term amelioration and prevention of metabolic and cardiovascular risk, but whether these benefits persist over time is unknown. Methods The prospective, controlled Swedish Obese Subjects Study involved obese subjects who underwent gastric surgery and contemporaneously matched, conventionally treated obese control subjects. We now report follow-up data for subjects (mean age, 48 years; mean body-mass index, 41) who had been enrolled for at least 2 years (4047 subjects) or 10 years (1703 subjects) before the analysis (January 1, 2004). The follow-up rate for laboratory examinations was 86.6 percent at 2 years and 74.5 percent at 10 years. Results After two years, the weight had increased by 0.1 percent in the control group and had decreased by 23.4 percent in the surgery group (P&lt;0.001). After 10 years, the weight had increased by 1.6 percent and decreased by 16.1 percent, respectively (P&lt;0.001). Energy intake was lower and the proportion of physically active subjects higher in the surgery group than in the control group throughout the observation period. Two- and 10-year rates of recovery from diabetes, hypertriglyceridemia, low levels of high-density lipoprotein cholesterol, hypertension, and hyperuricemia were more favorable in the surgery group than in the control group, whereas recovery from hypercholesterolemia did not differ between the groups. The surgery group had lower 2- and 10-year incidence rates of diabetes, hypertriglyceridemia, and hyperuricemia than the control group; differences between the groups in the incidence of hypercholesterolemia and hypertension were undetectable. Conclusions As compared with conventional therapy, bariatric surgery appears to be a viable option for the treatment of severe obesity, resulting in long-term weight loss, improved lifestyle, and, except for hypercholesterolemia, amelioration in risk factors that were elevated at baseline Mean energy intake at the time of inclusion in the intervention study was 2882 kcal per day among the surgically treated subjects, as compared with 2526 kcal per day among the controls. As Figure 2 and Table 2 indicate, the baseline adjusted energy intake was significantly lower in the surgery group than in the control group over the 10-year period. Similarly, the fraction of subjects physically active during leisure time was higher in the surgery group over the 10-year period, and the fraction of those physically active during work time was higher in the surgery group for the first 6 years of the intervention. Figure 2. Lifestyle Changes among the Subjects in the SOS Study over a 10-Year Period. Mean energy intake (in kilocalories per day) (Panel A) and the percentage of subjects who were physically active during leisure time and at work (Panels B and C, respectively) are shown. Energy intake and the proportion of active subjects at baseline (year 0) are unadjusted values, whereas the values during the follow-up have been adjusted for sex, age, body-mass index, and energy intake or physical activity at baseline. All data are from subjects who completed 10 years of the study. The numbers of subjects at each time point are the same as those shown in Figure 1. Asterisks denote P&lt;0.01 and daggers P&lt;0.05 for the comparison between the groups (by tests for equality). I bars represent the 95 percent confidence intervals.
  • Background Obesity is associated with increased mortality. Weight loss improves cardiovascular risk factors, but no prospective interventional studies have reported whether weight loss decreases overall mortality. In fact, many observational studies suggest that weight reduction is associated with increased mortality. Methods The prospective, controlled Swedish Obese Subjects study involved 4047 obese subjects. Of these subjects, 2010 underwent bariatric surgery (surgery group) and 2037 received conventional treatment (matched control group). We report on overall mortality during an average of 10.9 years of follow-up. At the time of the analysis (November 1, 2005), vital status was known for all but three subjects (follow-up rate, 99.9%). Results The average weight change in control subjects was less than ±2% during the period of up to 15 years during which weights were recorded. Maximum weight losses in the surgical subgroups were observed after 1 to 2 years: gastric bypass, 32%; vertical-banded gastroplasty, 25%; and banding, 20%. After 10 years, the weight losses from baseline were stabilized at 25%, 16%, and 14%, respectively. There were 129 deaths in the control group and 101 deaths in the surgery group. The unadjusted overall hazard ratio was 0.76 in the surgery group (P=0.04), as compared with the control group, and the hazard ratio adjusted for sex, age, and risk factors was 0.71 (P=0.01). The most common causes of death were myocardial infarction (control group, 25 subjects; surgery group, 13 subjects) and cancer (control group, 47; surgery group, 29). Conclusions Bariatric surgery for severe obesity is associated with long-term weight loss and decreased overall mortality Figure 1. Mean Percent Weight Change during a 15-Year Period in the Control Group and the Surgery Group, According to the Method of Bariatric Surgery. I bars denote 95% confidence intervals igure 2. Unadjusted Cumulative Mortality. The hazard ratio for subjects who underwent bariatric surgery, as compared with control subjects, was 0.76 (95% confidence interval, 0.59 to 0.99; P=0.04), with 129 deaths in the control group and 101 in the surgery group
  • Background Weight loss is associated with short-term amelioration and prevention of metabolic and cardiovascular risk, but whether these benefits persist over time is unknown. Methods The prospective, controlled Swedish Obese Subjects Study involved obese subjects who underwent gastric surgery and contemporaneously matched, conventionally treated obese control subjects. We now report follow-up data for subjects (mean age, 48 years; mean body-mass index, 41) who had been enrolled for at least 2 years (4047 subjects) or 10 years (1703 subjects) before the analysis (January 1, 2004). The follow-up rate for laboratory examinations was 86.6 percent at 2 years and 74.5 percent at 10 years. Results After two years, the weight had increased by 0.1 percent in the control group and had decreased by 23.4 percent in the surgery group (P&lt;0.001). After 10 years, the weight had increased by 1.6 percent and decreased by 16.1 percent, respectively (P&lt;0.001). Energy intake was lower and the proportion of physically active subjects higher in the surgery group than in the control group throughout the observation period. Two- and 10-year rates of recovery from diabetes, hypertriglyceridemia, low levels of high-density lipoprotein cholesterol, hypertension, and hyperuricemia were more favorable in the surgery group than in the control group, whereas recovery from hypercholesterolemia did not differ between the groups. The surgery group had lower 2- and 10-year incidence rates of diabetes, hypertriglyceridemia, and hyperuricemia than the control group; differences between the groups in the incidence of hypercholesterolemia and hypertension were undetectable. Conclusions As compared with conventional therapy, bariatric surgery appears to be a viable option for the treatment of severe obesity, resulting in long-term weight loss, improved lifestyle, and, except for hypercholesterolemia, amelioration in risk factors that were elevated at baseline Effects on Incidence of and Recovery from Risk Conditions As Figure 3 shows, the incidence rates of hypertriglyceridemia, diabetes, and hyperuricemia were markedly lower in the surgically treated group than in the control group after 2 and 10 years. The incidence of low HDL cholesterol was significantly lower in the surgical group after 2 years but not after 10 years. The incidence of hypertension and hypercholesterolemia did not differ between the groups over the 2- and 10-year periods (Figure 3). Figure 3. Incidence of Diabetes, Lipid Disturbances, Hypertension, and Hyperuricemia among Subjects in the SOS Study over 2- and 10-Year Periods. Data are for subjects who completed 2 years and 10 years of the study. The bars and the values above the bars indicate unadjusted incidence rates; I bars represent the corresponding 95 percent confidence intervals (CIs). The odds ratios, 95 percent CIs for the odds ratios, and P values have been adjusted for sex, age, and body-mass index at the time of inclusion in the intervention study
  • Background Weight loss is associated with short-term amelioration and prevention of metabolic and cardiovascular risk, but whether these benefits persist over time is unknown. Methods The prospective, controlled Swedish Obese Subjects Study involved obese subjects who underwent gastric surgery and contemporaneously matched, conventionally treated obese control subjects. We now report follow-up data for subjects (mean age, 48 years; mean body-mass index, 41) who had been enrolled for at least 2 years (4047 subjects) or 10 years (1703 subjects) before the analysis (January 1, 2004). The follow-up rate for laboratory examinations was 86.6 percent at 2 years and 74.5 percent at 10 years. Results After two years, the weight had increased by 0.1 percent in the control group and had decreased by 23.4 percent in the surgery group (P&lt;0.001). After 10 years, the weight had increased by 1.6 percent and decreased by 16.1 percent, respectively (P&lt;0.001). Energy intake was lower and the proportion of physically active subjects higher in the surgery group than in the control group throughout the observation period. Two- and 10-year rates of recovery from diabetes, hypertriglyceridemia, low levels of high-density lipoprotein cholesterol, hypertension, and hyperuricemia were more favorable in the surgery group than in the control group, whereas recovery from hypercholesterolemia did not differ between the groups. The surgery group had lower 2- and 10-year incidence rates of diabetes, hypertriglyceridemia, and hyperuricemia than the control group; differences between the groups in the incidence of hypercholesterolemia and hypertension were undetectable. Conclusions As compared with conventional therapy, bariatric surgery appears to be a viable option for the treatment of severe obesity, resulting in long-term weight loss, improved lifestyle, and, except for hypercholesterolemia, amelioration in risk factors that were elevated at baseline Effects on Incidence of and Recovery from Risk Conditions As Figure 3 shows, the incidence rates of hypertriglyceridemia, diabetes, and hyperuricemia were markedly lower in the surgically treated group than in the control group after 2 and 10 years. The incidence of low HDL cholesterol was significantly lower in the surgical group after 2 years but not after 10 years. The incidence of hypertension and hypercholesterolemia did not differ between the groups over the 2- and 10-year periods (Figure 3). Figure 3. Incidence of Diabetes, Lipid Disturbances, Hypertension, and Hyperuricemia among Subjects in the SOS Study over 2- and 10-Year Periods. Data are for subjects who completed 2 years and 10 years of the study. The bars and the values above the bars indicate unadjusted incidence rates; I bars represent the corresponding 95 percent confidence intervals (CIs). The odds ratios, 95 percent CIs for the odds ratios, and P values have been adjusted for sex, age, and body-mass index at the time of inclusion in the intervention study
  • Background Weight loss is associated with short-term amelioration and prevention of metabolic and cardiovascular risk, but whether these benefits persist over time is unknown. Methods The prospective, controlled Swedish Obese Subjects Study involved obese subjects who underwent gastric surgery and contemporaneously matched, conventionally treated obese control subjects. We now report follow-up data for subjects (mean age, 48 years; mean body-mass index, 41) who had been enrolled for at least 2 years (4047 subjects) or 10 years (1703 subjects) before the analysis (January 1, 2004). The follow-up rate for laboratory examinations was 86.6 percent at 2 years and 74.5 percent at 10 years. Results After two years, the weight had increased by 0.1 percent in the control group and had decreased by 23.4 percent in the surgery group (P&lt;0.001). After 10 years, the weight had increased by 1.6 percent and decreased by 16.1 percent, respectively (P&lt;0.001). Energy intake was lower and the proportion of physically active subjects higher in the surgery group than in the control group throughout the observation period. Two- and 10-year rates of recovery from diabetes, hypertriglyceridemia, low levels of high-density lipoprotein cholesterol, hypertension, and hyperuricemia were more favorable in the surgery group than in the control group, whereas recovery from hypercholesterolemia did not differ between the groups. The surgery group had lower 2- and 10-year incidence rates of diabetes, hypertriglyceridemia, and hyperuricemia than the control group; differences between the groups in the incidence of hypercholesterolemia and hypertension were undetectable. Conclusions As compared with conventional therapy, bariatric surgery appears to be a viable option for the treatment of severe obesity, resulting in long-term weight loss, improved lifestyle, and, except for hypercholesterolemia, amelioration in risk factors that were elevated at baseline Recovery from hypertension, diabetes, hypertriglyceridemia, a low HDL cholesterol level, and hyperuricemia was more frequent in the surgical group than in the control group, both at 2 and 10 years (Figure 4). The rates of recovery from hypercholesterolemia did not differ between the two groups after either 2 or 10 years. Figure 4. Recovery from Diabetes, Lipid Disturbances, Hypertension, and Hyperuricemia over 2 and 10 Years in Surgically Treated Subjects and Their Obese Controls. Data are for subjects who completed 2 years and 10 years of the study. The bars and the values above the bars indicate unadjusted rates of recovery; I bars represent the corresponding 95 percent confidence intervals (CIs). The odds ratios, 95 percent CIs for the odds ratios, and P values have been adjusted for sex, age, and body-mass index at the time of inclusion in the intervention
  • Background Weight loss is associated with short-term amelioration and prevention of metabolic and cardiovascular risk, but whether these benefits persist over time is unknown. Methods The prospective, controlled Swedish Obese Subjects Study involved obese subjects who underwent gastric surgery and contemporaneously matched, conventionally treated obese control subjects. We now report follow-up data for subjects (mean age, 48 years; mean body-mass index, 41) who had been enrolled for at least 2 years (4047 subjects) or 10 years (1703 subjects) before the analysis (January 1, 2004). The follow-up rate for laboratory examinations was 86.6 percent at 2 years and 74.5 percent at 10 years. Results After two years, the weight had increased by 0.1 percent in the control group and had decreased by 23.4 percent in the surgery group (P&lt;0.001). After 10 years, the weight had increased by 1.6 percent and decreased by 16.1 percent, respectively (P&lt;0.001). Energy intake was lower and the proportion of physically active subjects higher in the surgery group than in the control group throughout the observation period. Two- and 10-year rates of recovery from diabetes, hypertriglyceridemia, low levels of high-density lipoprotein cholesterol, hypertension, and hyperuricemia were more favorable in the surgery group than in the control group, whereas recovery from hypercholesterolemia did not differ between the groups. The surgery group had lower 2- and 10-year incidence rates of diabetes, hypertriglyceridemia, and hyperuricemia than the control group; differences between the groups in the incidence of hypercholesterolemia and hypertension were undetectable. Conclusions As compared with conventional therapy, bariatric surgery appears to be a viable option for the treatment of severe obesity, resulting in long-term weight loss, improved lifestyle, and, except for hypercholesterolemia, amelioration in risk factors that were elevated at baseline Recovery from hypertension, diabetes, hypertriglyceridemia, a low HDL cholesterol level, and hyperuricemia was more frequent in the surgical group than in the control group, both at 2 and 10 years (Figure 4). The rates of recovery from hypercholesterolemia did not differ between the two groups after either 2 or 10 years. Figure 4. Recovery from Diabetes, Lipid Disturbances, Hypertension, and Hyperuricemia over 2 and 10 Years in Surgically Treated Subjects and Their Obese Controls. Data are for subjects who completed 2 years and 10 years of the study. The bars and the values above the bars indicate unadjusted rates of recovery; I bars represent the corresponding 95 percent confidence intervals (CIs). The odds ratios, 95 percent CIs for the odds ratios, and P values have been adjusted for sex, age, and body-mass index at the time of inclusion in the intervention
  • Endocannabinoid Blockade for Improving Glycemic Controland Lipids in Patients with Type 2 Diabetes Mellitus Rimonabant significantly reduces weight and waist circumference and improves dyslipidemias in overweight and obese patients without diabetes mellitus. Numerous other metabolic changes, including reduced prevalence of the metabolic syndrome and associated cardiovascular disease (CVD) risk factors, reduced fasting glucose, and elevated adiponectin, have been demonstrated with the administration of rimonabant. The Rimonabant-in-Obesity (RIO)–Diabetes trial studied the safety and efficacy of rimonabant in overweight and obese patients with type 2 diabetes who were treated with metformin or sulfonylureas. RIO-Diabetes was a 1-year, randomized, double-blind, placebo-controlled, parallel-group study of 1,047 overweight/obese patients with type 2 diabetes in 151 centers in 11 countries. The body mass index of participants ranged from 27 to 40. Glycosylated hemoglobin (HbA1c) at screening ranged from 6.5% to 10.0%. All patients were receiving either metformin or sulfonylurea therapy and were asked to follow a hypocaloric diet (600 kcal/day deficit [1 kcal 4.2 kJ]) for the duration of the trial. After a 4-week placebo plus diet run-in period, patients were randomized to receive placebo or rimonabant 5 mg or 20 mg once daily. At 1 year, absolute change in weight from baseline in the intention-to-treat, last observation carried forward analysis of the rimonabant 5 mg and 20 mg groups, respectively, was loss of 2.3 kg and 5.3 kg compared with 1.4 kg in the placebo group ( P 0.013 and P 0.001, respectively). Waist circumference was significantly decreased in the rimonabant 5-mg and 20-mg groups by 2.9 cm and 5.2 cm compared with 1.9 cm in the placebo group ( P 0.034 and P 0.001, respectively). HbA1c reductions of 0.1% and 0.6% were significant in the rimonabant 5-mg and 20-mg groups ( P 0.034 and P 0.001, respectively). Some 57% of the improvements in HbA1c and high-density lipoprotein cholesterol could not be attributed to observed weight loss. Compared with placebo, rimonabant 20 mg also demonstrated significant improvements in the prevalence of metabolic syndrome and improvement in its constituents, as well as systolic blood pressure and C-reactive protein levels (assay by ICON Laboratories, Farmingdale, NY and Dublin, Ireland). Rimonabant is the first selective cannabinoid1 blocker studied for type 2 diabetes and associated CVD risk factor therapy. Its ability to improve the numerous metabolic pathologies associated with diabetes and CVD risk and concomitantly to reduce weight and waist circumference introduces a strongly positive new dynamic in type 2 diabetes treatment. Its multifactorial mechanisms warrant further investigation and may provide insights into other pathologies. © 2007 Elsevier Inc. All rights reserved Goals of the International Diabetes Federation (IDF) and American Diabetes Association (ADA) are slightly different for HbA1c, at &lt; 6.5% and &lt; 7.0%, respectively. In the respective ITT, LOCF rimonabant 20-mg populations, ç42.9% were able to achieve IDF goals and 52.7% achieved ADA goals ( Figure 5 ). Baseline HbA1c in the placebo and rimonabant 5-mg and 20-mg groups was 7.2%, 7.3%, and 7.3%, respectively.16 Also, 11.9% of the rimonabant 20-mg group required downward adjustmentof their antidiabetic medication compared with 12.8% of placebo patients who required an upward adjustment. HDL cholesterol was significantly improved in the rimonabant 20-mg group (15.4%) compared with placebo (7.1%), reflecting a difference of 8.4% ( P &lt; 0.001). Of the total increase in HDL cholesterol, 57% could not be attributed to observed weight loss, again raising speculation about the further metabolic effects of rimonabant and the therapeutic gap in available type 2 diabetes therapies. Compared with placebo, HDL cholesterol levels in the rimonabant 5-mg group were relatively improved (9.2%), but the difference did not reach statistical significance. Effects of rimonabant on TG levels proved similar. In the placebo group, TG levels increased by 7.3%, whereas in the rimonabant 20-mg group TG levels decreased by 9.1%, for a difference of 16.4% between these groups.16 Supine systolic blood pressure was significantly reduced in the rimonabant 5-mg and 20-mg groups compared with placebo ( P = 0.043 and P = 0.020, respectively). There was a slight trend, although not a significant difference, in supine diastolic blood pressure in these groups. Compared with placebo, CRP levels were significantly reduced in the rimonabant 20-mg group ( P = 0.024) and were also reduced, although not significantly, in the rimonabant 5-mg group. Leptin levels compared with placebo were signifi- cantly reduced in both the rimonabant 5-mg and 20-mg groups ( P = 0.03 and P &lt; 0.001). The prevalence of the metabolic syndrome decreased in all 3 treatment arms. In the placebo and rimonabant 5-mg and 20-mg groups, respectively, metabolic syndrome prevalence decreased by 7.6%, 13.8%, and 18.9%. Compared with placebo, this reduction in prevalence reached statistical significance in the rimonabant 20-mg group ( P = 0.007). Waist circumference was also significantly reduced in the rimonabant 5-mg group by 2.9 cm and in the 20-mg group by 5.2 cm compared with 1.9 cm in the placebo group ( P == 0.016 and P &lt; 0.001, respectively).
  • Therapeutic Options for Modifying Cardiometabolic Risk Factors CANNABINOID1 BLOCKADE: A NOVEL PHARMACOLOGIC APPROACH TO WEIGHT LOSS AND CARDIOMETABOLIC RISK The endocannabinoid (EC) system appears to play a role inthe central and peripheral regulation of body weight and energy balance.20 Animal studies have demonstrated that administration of cannabinoid agonists increased food intake, 21 whereas administration of a cannabinoid1 (CB1) receptor blocker decreased food intake.22 A recent study appears to document overactivity of the peripheral EC system.23 In obese versus lean women, circulating levels of 2 endogenous cannabinoids, anandamide and 1/2-arachidonoylglycerol, were increased by 35% and 52% ( P &lt; 0.05), respectively.23 In addition, fatty acid amide hydrolase (FAAH) gene expression in adipose tissue was reduced in the obese women and may contribute to the excess levels of circulating ECs because FAAH is the primary enzyme involved in the degradation of anandamide. These markers of EC system activity were not affected by a 5% weight loss. These findings support the concept that activity of the ECs may be greater in obese women than in lean women. In 4 pivotal phase 3 clinical trials, rimonabant, the first selective CB1 receptor blocker, was shown to reduce weight and waist circumference and improve numerous cardiovascular and metabolic risk factors in 6,000 patients. In all 4 of the Rimonabant In Obesity/Overweight (RIO) trials (the RIO-Europe Study, the RIO–North America Study, the RIO-Lipids Study, and the RIO-Diabetes Study), rimonabant was associated with an increased HDL cholesterol and decreased waist circumference and triglycerides ( Figure 3 ).20,24 –26 The results of the 2-year, 72-center RIO–North America study showed that rimonabant 20 mg/day not only reduced body weight and cardiometabolic risk factors but also prevented weight regain.24 During the first year, the percentage of patients treated with rimonabant 20 mg who experienced a 5% weight loss was 48.6%, compared with 20% for patients given placebo, whereas the percentage achieving a 10% weight loss was 25.2%, compared with 8.5% for placebo-treated patients ( P &lt; 0.001). Levels of HDL cholesterol increased and levels of fasting insulin and triglycerides decreased in patients receiving rimonabant 20 mg.24 Of importance, the trial also addressed the efficacy of rimonabant in preventing weight regain. Overweight or obese patients (N 3,045) were randomized to receive rimonabant (5 mg or 20 mg) or placebo for 1 year and then rerandomized to receive either the same dose of rimonabant or placebo for an additional year.24 When patients initially treated with 5 mg or 20 mg of rimonabant for 1 year were switched to placebo in the second year of the trial, they regained most of the weight they had lost. By contrast, the patients who continued treatment with rimonabant 20 mg maintained a mean SEM weight loss from baseline of 7.4 ± 0.4 kg ( Figure 4 ).24 The same pattern was observed for waist circumference.24
  • Slide 27. Effects of Weight Loss in Obese Women on IL-6, TNF-alpha, and CRP In another study looking at the effects of weight loss, in obese women, on IL-6, TNF-alpha, and CRP, these investigators confirmed the previous study. As you can see in the far right-hand panel, CRP was reduced a little, although it wasn&apos;t statistically significantly different. But IL-6, shown in the first panel, was significantly reduced; IL-6 is an inflammatory marker that&apos;s being increasingly used to evaluate patients with CKD, as well as people with cardiovascular disease who don&apos;t have overt kidney disease.
  • • The Diabetes Prevention Program Research Group reported on the association of insulin sensitivity and secretion and incident diabetes. 1 • As shown, subjects with the lowest baseline levels of insulin sensitivity or secretion were at highest risk for progression to diabetes. These findings support the hypothesis that reduction in both insulin sensitivity and insulin secretion (ie, β-cell function) contribute to incident diabetes. 1. The Diabetes Prevention Program Research Group. Role of insulin secretion and sensitivity in the evolution of type 2 diabetes in the Diabetes Prevention Program: Effects of lifestyle intervention and metformin. Diabetes. 2005;54:2404-2414. Role of Insulin Secretion and Sensitivity in the Evolution of Type 2 Diabetes in the Diabetes Prevention Program Prediction of diabetes by multiple metabolic factors Baseline insulin sensitivity and insulin secretion. Figure 5 shows the hazard rates for progression to diabetes when participants were grouped by tertiles of baseline insulin secretion (using either CIR or IGR) and sensitivity (using 1/fasting insulin or ISI). Hazard rates for diabetes were lowest among the lifestyle participants, intermediate among the metformin participants, and highest among the placebo participants. Using either set of estimates, low insulin sensitivity and low insulin secretion at baseline predicted higher diabetes risk in all treatment arms. In general, these treatment effects were seen regardless of the categories of sensitivity and secretion, except in the lowest risk categories (high insulin sensitivity and secretion), in which hazard rates were uniformly low.
  • Background: The metabolic syndrome is a high-risk state for diabetes and cardiovascular disease. Little is known about its prevalence and prevention in those with impaired glucose tolerance. Objective: To determine the prevalence of the metabolic syndrome at baseline in the Diabetes Prevention Program and the effect of intensive lifestyle intervention and metformin therapy on the syndrome’s incidence and resolution. Design: Randomized, controlled clinical trial. Setting: Research and community-based centers. Participants: Participants had impaired glucose tolerance (World Health Organization criteria plus fasting plasma glucose level &gt;5.3 mmol/L [&gt;95 mg/dL]) and were followed for a mean of 3.2 years after random assignment to intensive lifestyle intervention,metformin therapy, or placebo. Interventions: Metformin, 850 mg twice daily, or intensive lifestyle intervention designed to achieve and maintain a 7% weight loss and 150 minutes of exercise per week. Measurements: The metabolic syndrome was defined as having 3 or more characteristics (waist circumference; blood pressure; and levels of high-density lipoprotein cholesterol, triglycerides, and fasting plasma glucose) that met criteria from the National Cholesterol Education Program Adult Treatment Panel III. Results: Fifty-three percent of participants (n 1711) had the metabolic syndrome at baseline; incidence did not vary substantially by age. However, low levels of high-density lipoprotein cholesterol predominated in younger participants (age 25 to 44 years), and high blood pressure predominated in older participants (age 60 to 82 years). In life-table analyses (log-rank test), incidence of the metabolic syndrome was reduced by 41% in the lifestyle group (P&lt; 0.001) and by 17% in the metformin group (P 0.03) compared with placebo. Three-year cumulative incidences were 51%, 45%, and 34% in the placebo, metformin, and lifestyle groups, respectively. There was no significant heterogeneity by ethnic group. Limitations: The study involved a volunteer group with impaired glucose tolerance, which limits generalizability. Conclusions: The metabolic syndrome affected approximately half of the participants in the Diabetes Prevention Program at baseline. Both lifestyle intervention and metformin therapy reduced the development of the syndrome in the remaining participants. Incidence of the Metabolic Syndrome among Participants without the Syndrome at Baseline Figure 2 separately shows the cumulative incidence of the metabolic syndrome by time since randomization for the 3 intervention groups after exclusion of baseline cases. The incidence is highest in the placebo group. By 3 years, 53% of the participants in the placebo group (260 of 490) had acquired the metabolic syndrome compared with 47% (236 of 503) in the metformin group and 38% (201 of 530) in the lifestyle group. The cumulative incidence overall (per 100 person-years) was 61% for the placebo group, 50% for the metformin group, and 38% for the lifestyle group. In a proportional hazards analysis, lifestyle intervention yielded a reduction of 41% (95% CI, 28% to 52%) in incidence of the metabolic syndrome compared with placebo ( P &lt; 0.001) and a significant 29% (CI, 13% to 42%)ç reduction compared with metformin ( P &lt; 0.001), which itself yielded a 17% (CI, 0% to 31%) lower incidence than placebo ( P = 0.03). Redefining the metabolic syndrome by using the criterion of a glucose level greater than 110 mg/dL (6.2 mmol/L) yielded very similar results. Threeyear incidences were 40%, 33%, and 27% for the placebo, metformin, and lifestyle groups, respectively. No major adverse events were noted, but musculoskeletal problems were more common in the lifestyle group and gastrointestinal symptoms were more common in the metformin group and less common in the lifestyle group than in the group receiving placebo. We also examined whether the efficacy of the 2 interventions differed by age, sex, ethnicity, or baseline fasting insulin level. Lifestyle intervention was least effective in those 25 to 44 years of age. Lifestyle intervention was effective compared with placebo in both men and women ( P &lt; 0.001), but more so in men (64% vs. 37%) ( P = 0.02 for heterogeneity). Metformin was not effective compared with placebo in women ( P &gt; 0.2) but was effective in men ( P = 0.002), again demonstrating significant heterogeneity ( P = 0.02). Although numbers became too small for definitive results when divided according to ethnicity, it appears that risk reduction compared with placebo was greater for the lifestyle group than for the metformin group in both white persons (50% vs. 12%, respectively) and Hispanic persons (57% vs. 2%, respectively). In African Americans (42% vs. 29%) and Native Americans (43% vs. 42%), efficacy for the lifestyle group and the metformin group appeared more similar, while for Asian Americans, metformin showed a nonsignificantly greater reduction than intensive lifestyle intervention (62% vs. 30%). The efficacy of neither lifestyle nor metformin showed significant heterogeneity across the 5 ethnic groups. Finally, although lifestyle was effective compared with placebo regardless of baseline fasting insulin level, metformin showed significant heterogeneity ( P = 0.03). Metformin was most effective in participants with a fasting insulin level of 136 pmol/L or less and had no effect in those with fasting insulin levels of 144 to 215 pmol/L or greater than 125 pmol/L compared with placebo. Resolution of the Metabolic Syndrome among Participants Who Had the Syndrome at Baseline Figure 3 shows the cumulative incidence of resolution of the metabolic syndrome (that is, no longer meeting the criteria) by treatment group. Although the pattern is somewhat similar to that seen for incidence of the metabolic syndrome, with the best result at 3 years occurring in the lifestyle group and an intermediary effect noted for metformin, the differences are less striking. By the log-rank test, only lifestyle showed a significant effect compared with placebo ( P = 0.002). Nevertheless, prevalence at 3 years did vary significantly by treatment group ( P &lt; 0.001): Eighteen percent of the placebo group, 23% of the metformin group, and 38% of the lifestyle group no longer had the syndrome.
  • Background: The metabolic syndrome is a high-risk state for diabetes and cardiovascular disease. Little is known about its prevalence and prevention in those with impaired glucose tolerance. Objective: To determine the prevalence of the metabolic syndrome at baseline in the Diabetes Prevention Program and the effect of intensive lifestyle intervention and metformin therapy on the syndrome’s incidence and resolution. Design: Randomized, controlled clinical trial. Setting: Research and community-based centers. Participants: Participants had impaired glucose tolerance (World Health Organization criteria plus fasting plasma glucose level &gt; 5.3 mmol/L [ &gt; 95 mg/dL]) and were followed for a mean of 3.2 years after random assignment to intensive lifestyle intervention,metformin therapy, or placebo. Interventions: Metformin, 850 mg twice daily, or intensive lifestyle intervention designed to achieve and maintain a 7% weight loss and 150 minutes of exercise per week. Measurements: The metabolic syndrome was defined as having 3 or more characteristics (waist circumference; blood pressure; and levels of high-density lipoprotein cholesterol, triglycerides, and fasting plasma glucose) that met criteria from the National Cholesterol Education Program Adult Treatment Panel III. Results: Fifty-three percent of participants (n 1711) had the metabolic syndrome at baseline; incidence did not vary substantially by age. However, low levels of high-density lipoprotein cholesterol predominated in younger participants (age 25 to 44 years), and high blood pressure predominated in older participants (age 60 to 82 years). In life-table analyses (log-rank test), incidence of the metabolic syndrome was reduced by 41% in the lifestyle group (P&lt; 0.001) and by 17% in the metformin group (P 0.03) compared with placebo. Three-year cumulative incidences were 51%, 45%, and 34% in the placebo, metformin, and lifestyle groups, respectively. There was no significant heterogeneity by ethnic group. Limitations: The study involved a volunteer group with impaired glucose tolerance, which limits generalizability. Conclusions: The metabolic syndrome affected approximately half of the participants in the Diabetes Prevention Program at baseline. Both lifestyle intervention and metformin therapy reduced the development of the syndrome in the remaining participants. Incidence of Metabolic Syndrome Components among Participants Not Meeting Criteria at Baseline To examine the effect of the interventions on individual components, life-table analyses were performed for those who did not met the criteria at baseline. The incidence of specific components over time compared with placebo suggests that lifestyle reduces the incidence of all components except HDL cholesterol level, while metformin is effective only in reducing the incidence of waist circumference and fasting glucose level ( Table 3 ). Prevalence of Metabolic Syndrome Components among Participants Meeting Criteria at Baseline Also shown in Table 3 is the effect of the interventions on each component among participants who met the specific component criterion at baseline. A slightly different pattern emerges, that is, both interventions decreased the prevalence of low levels of HDL cholesterol, increased waist circumference, and fasting glucose level, while intensive lifestyle intervention also lowered the prevalence of increased blood pressure and triglyceride levels. Prevalence of the Metabolic Syndrome in All Participants at 3 Years The prevalence of the metabolic syndrome in all participants increased from 55% at baseline to 61% after 3 years in the placebo group ( P 0.003) and from 54% to 55% in the metformin group ( P 0.2). In the lifestyle group, overall prevalence decreased from 51% to 43% ( P 0.001).
  • Síndrome metabólico y riesgo cardiovascular

    1. 1. SÍNDROME METABÓLICO, OBESIDAD Y RIESGO CARDIOMETABOLICO Dr. Denis O. Granados Doña Curso de actualización Síndrome Metabólico Managua 20 de octubre 2007
    2. 2. SÍNDROME METABÓLICO Hiperglucemia Hiperinsulinemia Dislipidemia Inflamación Resistencia a laHipertensión Hipercoagulación insulinaHigado graso de origen Mucroalbuminuria no alcoholico Obesidad
    3. 3. SINDROME METABÓLICO Y RIESGO DE ECV A 10 AÑOS 2.5 2.25 2.05 Tasa de riesgo ajustada por 1.98 1.91 1.88 2 1.68 H 1.5 M 1.18 edad 1 0.76 0.5 0 Mortalidad ECV fatal ECV o ECV fatal fatal mas no fatalCirculation 2005; 112: 666-673
    4. 4. FACTORES ASOCIADOS AL SÍNDROME • Factores de riesgo metabólicos – Dislipidemia aterogénica – Hipertensión – Hiperglucemia – Estado protrombótico – Estado proinflamatorio • Factores de riesgo subyacentes – Obesidad abdominal – Resistencia a la insulina • Otras condiciones asociadas – Envejecimiento, inactividad física, desequilibrio hormonalCirculation 2005;112:0000-0000
    5. 5. PREDISPOSICIÓN A SD METABOLICO GENERADO POR OBESIDAD• Edad – Disminución de la masa y la capacidad oxidativa muscular – Pérdida de la termogénesis inducida por T3• Estilos de vida protectores – Ejercicio, dieta mediterránea – Dietas con índice glucémico bajo y consumo de granos enteros• Ambiente fetal• Factores genéticos J Am Soc Nephrol 2004, 15: 2775–2791
    6. 6. RAIZ DEL PROBLEMA• Sobrepeso/obesidad• Inactividad física• Factores genéticos – Alteraciones en la homeostasis de la energía• Elementos centrales – Sobrecarga exógena de energía – Acumulación ectópica de lípidos en células no adiposas – Resistencia a la insulina J Am Soc Nephrol 2004, 15: 2775–2791
    7. 7. INTERRELACIÓN ENTRE LOS COMPONENTES DEL SÍNDROME METABÓLICO Síndrome Metabólico Adiposidad visceral Resistencia a la insulina •Intolerancia a la glucosa •Grados bajos de inflamación •Hipertensión •Secreción alterada de •Dislipidemia adiponectina •Microalbuminuria •Desequilibrio en hemostasis y fibrinolisis (PAI-1) ENFERMEDAD CARDIOVASCULAR Y DMT2Nature 444, 875-880 (14 December 2006)
    8. 8. VALORACIÓN DEL RIESGO CARDIOMETABÓLICO GLOBAL Síndrome LDL Síndrome LDL metabólico HDL metabólico HDL HTA DMT2 HTA DMT2 + Edad Hombre = Edad Hombre Tabaco Otros. Otros. Tabaco Genes Genes Nuevo Riesgo global de ECV Riesgo cardiometabólico FRCV derivado de FR tradicionales globalNature 444, 881-887 (14 December 2006)
    9. 9. FACTORES DE TOXICIDAD METABÓLICA A Toxicidad de NAD(P)H oxidasa por activación Angiotensina II → ERO (ROS) Toxicidad por Amilina (hperamilinemia) polipeptido amiloide de los islotes Toxicidadd por productos finales de glicosilación/fructosilación avanzada (AGE) Envejecimiento (Aging) Reserva Antioxidante comprometida; ausencia de red antioxidante. Arginina dimetil asimétrica Toxicidad por Adipocitoquinas/adiposo Albuminuria, microalbuminuria F Toxicidad por ácidos grasos libres (Free faty acids) L Toxicidad por Lípidos/Leptina I Toxicidad por Insulina (toxicidad por Insulinemia/hiperproinsulinemia) Toxicidad por Inflamación G Glucotoxicidad H Toxicidad por Hipertensión /Homocisteína/ PCR alta sensibilidad (HS-CRP) T Toxicidad por Triglicéridos U Toxicidad por acido Urico xantinoxidasa Desacoplamiento función eNOS-estructura tisular endotelial (Uncoupling)JCMS. 2006;1:16–24
    10. 10. IMPACTO EN MORTALIDAD • Second National Health and Nutrition Examination Survey (NHANES II) • 6225 personas seguimiento 13.3 años. • 1.5-3 riesgo de ECV comparado con controles. Mortalidad Mortalidad CV Mortalidad coronaria total Sin ECV 2.02 1.82 1.14 preexistente Con ECV 4.19 3.14 1.87 preexistente Ajustado por edad, sexo y factores de riesgoJCMS. 2006;1:25–28
    11. 11. OBESIDAD
    12. 12. PRESPECTIVA HISTÓRICA DE OBESIDADNature 404, 635-643 (6 April 2000)
    13. 13. CIRCUNFERENCIA DE CINTURA Y GRASA INTRABDOMINALBMJ 2001;322: 716-720
    14. 14. RELACIÓN ENTRE IMC O GRASA INTRABDOMINAL Y SENSIBILIDAD A LA INSULINALarsen: Williams Textbook of Endocrinology, 10th ed., Copyright © 2003 Saunders, AnImprint of Elsevier
    15. 15. FACTORES QUE INFLUENCIAN EL DESARROLLO DE OBESIDAD Genes Síndromes Genes monogéncos susceptibles Obesidad Tasa metabólica Cultura Ejercicio Alimentos Factores ambientalesNature 404, 635-643 (6 April 2000)
    16. 16. ACTIVIDAD HABITUAL DE ACUERDO A AÑO ESCOLAR Y RAZAN Engl J Med 2002; 347:709-715
    17. 17. PARTICIPACIÓN EN ACTIVIDAD FÍSICA > 50 MIN/SEM NHIS BRFSS 35 30 25 Porcentaje 20 15 10 5 0 18-29 30-44 45-64J. Nutr. 2002 132: 3826-3829
    18. 18. CAMBIOS EN EL TIEMPO EN LAINGESTIÓN CALORICA PER CÁPITAData from the National Center for Health Statistics
    19. 19. ELEMENTOS QUE RELACIONAN LA OBESIDAD CENTRAL A OTROS COMPONENTES DEL SÍNDROME• ↑ AGL∀ ↓ adiponectina• Resistencia a la acción sensibilizadora a la insulina de la leptina• Infiltración de macrófagos en el tejido adiposo con liberación de citokinasJ Am Soc Nephrol 2004, 15: 2775–2791
    20. 20. ACTIVDAD DEL TEJIDO ADIPOSO Leptina Adiponectina Angiotensinógeno FNT-α Resistina IL-6 PAI-1 AdipsinaCurr Opin Endocrinol Diabetes 2003; 10:317–321
    21. 21. INFILTRACIÓN DE MACRÓFAGOS EN TEJIDO ADIPOSOJ Am Soc Nephrol 2004, 15: 2775–2791
    22. 22. CORRELACIÓN ENTRE TEJIDO ADIPOSO VICERAL Y SUBCUTÁNEO Y LA EXPRESIÓN DE GENES CB1 Y FAAHDiabetes 55:3053-3060, 2006
    23. 23. PROBABILIDAD DE PERMANECER SIN OBESIDADAnn Intern Med, Jun 2002; 136: 857 - 864
    24. 24. CAMBIOS EN EL PESO Y RR DE COMORBILIDADESN Engl J Med 1999; 341:427-434
    25. 25. PROPORCIÓN DE DIABETES ATRIBUIBLE AL SOBREPESO
    26. 26. INCIDENCIA ACUMULATIVA DE ICC DE ACUERDO A IMCN Engl J Med 2002; 347:305-313
    27. 27. EDAD, IMC Y RIESGO DE MUERTEN Engl J Med 1999; 341:427-434
    28. 28. RR PARA HTA DE ACUERDO A CAMBIOS EN EL PESO DESPUES DE LOS 18 AÑOSAnn Intern Med1998; 128: 81 - 88
    29. 29. LA RESISTENCIA A LA INSULINA INCREMENTA EL RIESGO DE DAÑO A ORGANO BLANCO EN LA HIPERTENSIÓN N= 354 con HTA no tratada Microalbuminuria HVI 59 P = 0.003 60 50 40 40 % de pacientes P = 0.04 40 30 20 10 10 0 Sin RI Con RI J Intern Med 2005; 257: 454-60
    30. 30. NUEVOS COMPONENETES
    31. 31. SÍNDROME METABÓLICO ORIGINADO POR OBESIDADJ Am Soc Nephrol 2004, 15: 2775–2791
    32. 32. RESISTENCIA A LOS EFECTOS DE INSULINA SOBRE LOS GLUCOTRANSPORTADORES• Mutaciones en transportadores de glucosa• Alteraciones especifica para tejidos en la producción de GLUT-4• Defectos en la translocación intracelular de GLUT-4• Defectos en las vías de señalización• Factores paracrinos :AGL, FNT-α, vía de la hexosaminaN Eng J M ed, 1999; 341::248-257
    33. 33. RESISTENCIA A LA INSULINA EFECTOS EN MÚSCULO ESQUELÉTICO • ↑ AGL – Alteración señalización de receptor por la fosforilación serina dependiente de CPK del IRS1 → reducción de la translocación de GLUT 4 ↓ glucogénesis y lipogénesis derivadas de glucosa ∀ ↓ Adiponectina ↓ oxidación de AG (~ 30%)→ ↑AG intramiocelular (~ 89%)J Am Soc Nephrol 2004, 15: 2775–2791Curr Opin Endocrinol Diabetes 2001, 8:235–239
    34. 34. RESISTENCIA A LA INSULINA EFECTOS EN HÍGADO Efecto de AGL Déficit de adiponectina – Resistencia a la insulina. ↓ oxidación de AG Compiten AGL con • ↑ AG intracelular glucosa para acceso a oxidación mitocondrial – No supresión de enzimas – La insulina no suprime la gluconeogénicas producción hepática de • Acumulación glucosa intracelular de AG y – Provee sustrato para sus metabolitos síntesis de Tg → VLDL → • Estimulación de la LDL pequeñas y densas producción hepática – ↑ actividad de lipasa de glucosa hepáticaJ Am Soc Nephrol 2004, 15: 2775–2791
    35. 35. HEPATOPATÍA METABÓLICA NAFLD/NASH • Enfermedad hepática de orígen no alcohólica (NAFLD) – Esteatosis hepática, esteatohepatitis no alcohólica (NASH), fibrosis, cirrosis criptogénica, enfermedad hepática terminal. • NASH es la enfermedad hepática progresiva mas prevalente en EE UU (∼ 5%). • NAFLD/NASH componente hepático del SM, fuertemente asociados a obesidad y DMT2JCMS. 2006;1:16–24
    36. 36. ESTEATOSIS HEPÁTICA• Concentraciones de plasmáticas de enzimas hepáticas normales 10-15% de la población general.• Estetohepatitis progresa a fibrosis y cirrosis ~ 30%.• Monitoreo periódico de enzimas hepáticas.• Obesidad, alcohol, diabetes, hepatitis viral crónica (C).Gut 2004;53:1020–1023.
    37. 37. RI EN HIPERTENSOS CON ESTEATOSIS HEPATICA 3.5 p < 0.05 3 p < 0.05 2.5 HOMA 2 1.5 1 0.5 0 HTA HTA no Hígado Hígado Hígado hígado graso normal graso graso control controlGut 2004;53:1020–1023.
    38. 38. FIBROSIS HEPÁTICA VS SCORE ATP III 10.0% n=8 9.0% P= 0.014 ANOVA 8.0% % de fibrosis hepática 7.0% 6.0% 5.0% n=9 7.44 4.0% 3.0% n=12 n=17 2.0% 3.64 1.0% 1.85 2.29 0.0% Score 0-2 Score 3 Score 4 Score 5Diabetes Care 2005; 28: 122- 1224
    39. 39. CONCENTRACIONES DE PCR mg/dL SEGÚN STATUS DE PRUEBAS HEPÁTICAS Media geométrica ajustada PCR (mg/dL) p=0.002 3 2.58 2.21 2.5 1.94 1.66 2 1.5 1 0.5 0 Normal Alterada Normal Alterada ALT Fosfatasa AlcalinaArterioscler. Thromb. Vasc. Biol. 2005;25;193-197
    40. 40. MICROALBUMINURIA• Cinco al 10% en tolerancia normal a la glucosa• Doce a 20% en sindrome metabólico.• Veinticinco a 40% en DM tipo 2.• Refleja disfunción endotelial a nivel generalNephrol Dial Transplant (2005) 20: 861–864
    41. 41. PREVALENCIA DE ALTERACIONES LA FUNCIÓNRENAL EN RELACIÓN A COMPONENTES DE SD METABÓLICO TFG menor de 60 mL/min/1.73 m2 Relación albúmina/creatinina 30-300 mg/g 10 9.2 25 20.1 % de prevalencia 8 7 20 14.6 6 4.9 15 9.8 4 2.9 10 6.8 4.9 2 0.9 5 3 0.3 0 0 0 1 2 3 4 5 0 1 2 3 4 5 Factores de riesgo de Síndrome Metabólico Factores de riesgo de Síndrome MetabólicoNephrol Dial Transplant (2005) 20: 861–864
    42. 42. TFG SEGÚN SCORE ATP III TFG (mL/min/1.73 m2) 90 85 80 75 70 65 60 0-1 2 3 n=103 n=289 n=339 Score de Síndrome MetabólicoDiabetes Care 2006; 29: 432-434
    43. 43. MECANISMOS FISIOPATOLÓGICOS• Glomerulopatía asociada a la obesidad.• Glomerulomegalia inicial (100% de los casos).• Glomeruloesclerosis focal y segmentaria (80% de los casos).• Aumento de la celularidad y matriz mesangial (45% de los casos)• Se puede observar en niños de 3 años.Nephrol Dial Transplant (2005) 20: 861–864
    44. 44. ALTERACIONES RENALES • Remodelación glomerular, tubulointersticial, MEC • Lesión básica engrosamiento de la MB – Capilar glomerular – Arteriolas – Tubulos colectores – Fibrosis tubulointersticial • ↓ heparán sulfato, ↑ condroitin sulfato – ↑ permeabilidad aJCMS. 2006;1:16–24 proteinas
    45. 45. MECANISMOS QUE IMPLICAN A LA RESISTENCIA A LAINSULINA E HIPERINSULINEMIA COMPENSADORA CON ERC Resistencia a la insulina Hiperinsulinemia ↑ permeabilidad glomerular a la albúmina ↑ Excreción urinaria de albúmina Regulación a la alta del ↑ de la proliferación de células del SRA mesangio ↑ IGF-1 ↑ síntesis de proteína de matriz extracelular ↑ proliferación de células del ↑ expresión del receptor AT1 mesangio ↑ acción de Ang II ↓ apoptosis de células del mesagio ↑ TGF-β ↑ producción y acción de ET-1 ↑proliferación de células ↑ proliferación de del mesangio células del mesangio ↑ síntesis de proteína de matriz extracelular ↓ producción de ON Disfunción ↑ Stress oxidativo endotelialJCMS. 2006;1:58–65
    46. 46. EFECTOS EN PANCREAS• Gluconeogénesis hepática estimula la hipersecreción de insulina (hiperinsulinemia normoglucemica)• ↑ AGL dentro de la célula β – ↑ secreción de insulina estimulada por glucosa – Modificaciones en PPAR, glucoquinasa y Glut 2• Hiperglucemia → glucotoxicidad ↓ producción de insulina, ↑ producción de radicales libres, apotosis inducida por sobrecarga de lípidosJ Am Soc Nephrol 2004, 15: 2775–2791
    47. 47. ACTIVIDAD COAGULANTE Y SÍNDROME METABÓLICO FACTOR VII (% ACTIVIDAD) FACTOR X (% ACTIVIDAD) SCORE SD METABÓLICO SCORE SD METABÓLICOJ. Clin. Endocrinol. Metab. 2005; 90: 190 - 197
    48. 48. PAI-1 EN DMT2 35 * 30 * PAI-1 antígeno (ng/mL) 25 * 20 15 10 5 0 Tolerancia Tolerancia a la Diabetes tipo 2 normal a la glucosa alterada Error de barras = SEn = 1,551 glucosa*p < 0.001para todas las comparaciones Arterioscler Thromb Vasc Biol 1999; 19:562–568.
    49. 49. PCR Y SINDROME METABÓLICO 1.8 Valores medios (SE) Log PCR 1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 0 0 1 2 3 4 Número de alteraciones metabólicasCirculation. 2000;102:42
    50. 50. PCR Y COMPONENTES DEL SD METABÓLICO 10 9 8 M 7 PCR mg/dL 6 H 5 4 3 2 1 0 n=495 n=287 n=459 n=354 n=288 n=320 n=219 n=219 n=140 n=125 0 1 2 3 4 o mas Numero de componentes de Síndrome MetabólicoCirculation. 2004;110:380-385
    51. 51. INFLAMACIÓN MECANISMOCONTRIBUYENTE AL DESARROLLO DE DMN = 1047 25 20 % Incidencia P = 0.06 P = 0.001 P = 0.001 15 10 5 0 Fibrinogeno PCR PAI-1 Quartiles de proteínas inflamatorias 1 2 3 4 Festa A et al. Diabetes. 2002;51:1131-7.
    52. 52. ALTERACIONES MIOCARDICAS • Disfunción diastólica asociada a exceso y rigidéz de la MEC. – Hallazgo temprano en SM. • Asociado a resistencia a la insulina y stress oxidativo. • ICC se puede presentar por IAM/CIC o de forma insidiosa.JCMS. 2006;1:16–24
    53. 53. REMODELACIÓN DE LA MICROVASCULATURA • Enfermedad microvascular (vasa vasorum derivados de la adventicia), • Intima angiogénica • Neovascularización predictor independiente de ruptura de la placa (p=0.001), disrrupción de la lámina elástica interna (p=0.01), grosor de la capa fibrosa (p=0.02)JCMS. 2006;1:16–24
    54. 54. SINDROME METABÓLICO E ICC • RI → lipotoxicidad – Acumulación de AGL → apoptosis – Producción de radicales libres • ICC precede y contribuye a RI: – ↑ actividad simpática – Pérdida de masa muscular – Producción de citokinas Circulation. 2002;105:1861–1870. Diabetes. 1995;44:863–870. Int J Obes Relat Metab Disord. 2001;25:378–388.JCMS. 2006;1:25–28
    55. 55. ICC NHANES III > 40 AÑOS Definición ATP III Definición IDFJ Epidemiol Community Health 2007;61:67–73. No SM SM
    56. 56. ALTERACIONES ELECTROFISIOLÓGICAS • Fibrilación y flutter – Asociados a obesidad – El riesgo ↑ en hombres 1.08 y mujeres 1.06 por incremento unitario en el IMC. • Asociación con arritmias ventriculares (HTA e HVI) • SM asociado a a tasa baja de variabilidad cardiaca. Am J Hypertens. 1998;11:523–531. Diabetes Care. 998;21:2116–2122 Diabetes Res Clin Pract. 2004;64:51–58.JCMS. 2006;1:25–28
    57. 57. ENFERMEDAD CARDIOVASCULAR SUBCLÍNICA • Ventriculo izquierdo: – hipertrofia (EKG, Eco), disfunción sistólica (Eco) • Carótidas – ↑ grosor intima-media (US) – Incrementos extremos grosor I-M carótida común ≥ 1 mm – Estenosis de carotidea ≥ 25% • Indice tobillo-brazo ≤ 0.9 • Disfunción endotelial glomerular – Relación albúmina/creatinina ≥ 25 µg/mg hombres y ≥ 35 µg/mg mujeresDiabetes 2007 56:1718–1726,
    58. 58. PREVALENCIA DE ATEROESCLEROSIS SUBCLÍNICA O DAÑO A ORGANO BLANCO Sin SM SM DM 100 > 60 años < 60 años 86 90 % prevalencia enfermedad 80 75 70 63 64 57 54 subclínica 60 48 50 43 42 36 40 30 22 23 20 10 0 Hombre Mujer Hombre MujerDiabetes 2007 56:1718–1726,
    59. 59. RIESGO DE INCIDENCIA DE EVENTOS CV EN SÍNDROME METABÓLICO Según número de Cohorte Total componentes de síndrome metabólicoJ Clin Endocrinol Metab 2005; 90:5698–5703
    60. 60. EVENTOS CARDIOVASCULARES CON Y SIN SINDROME METABÓLICO J Clin Endocrinol Metab 2005; 90:5698–5703
    61. 61. SÍNDROME METABÓLICO Y RIESGO CARDIOVASCULAR A 10 AÑOS Mortalidad ECV fatal ECV no fatal ECV fatal + por todas las no fatal causasHombresNECP 1.98* 2.25* 1.88* 1.91*WHO 1.29 1.45 1.43 1.45*EGIR 1.58* 1.86 1.48 1.49*ACE 1.53* 1.80 1.20 1.30MujeresNECP 1.18 0.76 2.05* 1.68*WHO 1.01 0.98 1.48 1.31EGIR 0.87 0.90 1.53 1.34ACE 1.29 1.57 1.98* 1.84*Circulation 2005; 112: 666-673 * Estadisticamente significativo
    62. 62. ASOCIACIÓN ENTRE COMPONENTES DEL SINDROME METABÓLICO Y RIESGO EC ARIC Riesgo de EC (11 años de seguimientoComponentes Mujeres (n=6881) Hombres (n=5208)T/A aumentada 2.89 (2.18-3,80) 1.55 (1.32-1.83)HDL disminuido 1.70 (1.30-2.22) 1.59 (1.34-1.88)TG aumentados 1.22 (0.84-1.50) 1.00 (0.84-1.19)Glucosa de ayuna 0.99 (0.69-1.42) 1.13 (0.91-1.39)aumentadaCircunferencia de 1.05 (0.79-1.39) 0.93 (0.78-1.11)cintura aumentadaCirculation 2005;28:385-390
    63. 63. TRATAMIENTO DE LOS FR EN ESTILOS DE VIDA PARA PREVENCIÓN A LARGO PLAZO DE ECVA O PREVENCIÓN/TRATAMIENTO DE DMT2 Blancos de la terapia y objetivos Recomendaciones terapéuticos Obesidad abdominal Alentar consistentemente mantenimiento/reducción de peso Objetivo: Reducir el peso corporal en 7%- atravez e un adecuado equilibrio de actividad física, reducción 10% durante los primeros años de la calórica y programas de modificación de conductas, cuando terapia. Continuar la perdida de peso con el esté indicado para mantener circunferencia de cintura < 90 cm objetivo de alcanzar peso ideal (IMC ≤ 25) en hombres y < 80 en la mujer. Alentar inicialmente una reducción lenta de peso ~7%-10% a partir del basal. Aún las perdidas pequeñas de peso se asocian a benficios en la salud Inactividad física En pacientes con ECV establecida, valorar el riesgo con una Objetivo: actividad física regular moderada- intensa al menos 30 minutos continua/intermitentemente (preferiblemente 60 min) 5/d/semana. Preferiblemente todos los días. Circulation 2005;112:0000-0000
    64. 64. TRATAMIENTO DE LOS FR METABOLICOS PARA PREVENCIÓN A LARGO PLAZO DE ECVA O PREVENCIÓN/TRATAMIENTO DE DMT2 Blancos de la terapia y objetivos Recomendaciones terapéuticos Dislipedemia aterogéncia Objetivo primario: LDL Si LDL está elevado dar a la reducción de LDL prioridad sobre otros Reducir LDL a las concentraciones parametros lipídicos. Alcanzar los objetivos LDL de acuerdo a la metas de ATP III cateegoría de riesgo del paciene: Riesgo alto: < 100 mg/dL (opcional < 70 mg/dL para riesgo alto) Riesgo moderadamente alto < 130 mg/dL (opcional < 100 mg/dL) Riesgo moderado: < 130 mg/dL Riesgo bajo: < 160 mg/dL Objetivo secundario: Colesterol no-HDL Si Si os TG ≥ 200 mg/dL, el objetivo de colesterol no para Si os TG ≥ 200 mg/dL disminuir colesterol no HDL para cada categoría de riesfo es 30 mg/dl mas colesterol no HDL a las lato que para LDL. Si TG ≥ 200 mg/dL después de alcanzar las concentraciones metas ATP III (después metas LDL considerar terapia adicional para alanzar la meta de de alcanzar las metas de LDL) colesterol no HDL. Objetivo terciario: HDL Si el HDL permanece disminuido luego de alcanzar las metas no Si HDL < 40 mg/dLH y < 50 mg/dL M HDL hay que intensifica la terapia de cambios en los estilos de vida despues de alcanzar el objetivo no HDL y utilizar fármacos para llegar a las metas dependiendo de la aumentar HDL lo mas posible con categoría de riesgo. terapia estandar para dislipidemia terogénica .Circulation 2005;112:0000-0000
    65. 65. TRATAMIENTO DE LOS FR METABOLICOS PARA PREVENCIÓN ALARGO PLAZO DE ECVA O PREVENCIÓN/TRATAMIENTO DE DMT2Blancos de la terapia y objetivos RecomendacionesterapéuticosHipertensión T/A ≥ 120/80: iniciar o mantener cambios en los estilos deObjetivo: reducir la T/A a < 140/90 mmHg vida vía reducción de peso, aumentar actividad física,(<130/80 si hay diabetes). Reducir T/A al moderar consumo de alcohol, restricción de sodio, consumomáximo posible con cambios en estilos de de frutas frescas, vegetales, lácteos descremados en todosvida . los pacientes con síndrome metabólico. T/A ≥ 140/90 (≥ 130/90 si hay DM) añador farmacos para alcanzar la meta si es necesario.Hiperglucemia Para glucosa de ayuno alterada: alentar la reducción de pesoPara glucosa de ayuno alterada: retardar la y actividad física.progresión a DM. Para DMT2: cambios en los estilos de vida y fármacos paraPara DM: HbA1C < 7% alcanzar la meta de HbA1C < 7%.Estado protrombótico Pacientes de riesgo alto: iniciar y continuar dosis bajas deReducir factores de riesgo trombóticos y aspirina. Considerar clopidogrel si la aspirina estáfibrinolíticos contraindicada.Estado proinflamatorio No hay recomendaciones mas allá de los cambios en los estilos de vida.Circulation 2005;112:0000-0000
    66. 66. BLANCOS DEL TRATAMIENTO • Obesidad Central – Cambios en los estilos de vida, rimonabant, exenatide, sibutramina, orlistat, cirugía bariátrica • Resistencia a la insulina – Cambios en los estilos de vida, Metformina, TZD • Intolerancia a la glucosa – Cambios en los estilos de vida, Metformina, TZD, inhibidores de α glucosidasas • Hipertensión – IECA, ARA II • Dislipidemia – Cambios en los estilos de vida, Fibratos, estatinas, TZD, ácido nicotinico • Estado protrombótico – ASA, ClopidogrelSymposium: is insulin resistance a relevant treatment target? Program andabstracts of the 65th Scientific Sessions of the American DiabetesAssociation; June 10-14, 2005; San Diego, California.Endocrine Reviews 2000; 21 (6): 585-618 JAMA 2001; 285: 2486-2497
    67. 67. Intervenciones en obesidad
    68. 68. EFECTO DE DIFERENTESPROGRAMAS DE EJERCICION Engl J Med 2002; 347:1483-1492
    69. 69. MECANISMO DE ACCIÓN DE SIBUTRAMINAN Engl J Med 2002; 346: 591-602
    70. 70. MECANISMO DE ACCIÓN DE ORLISTATN Engl J Med 2002; 346: 591-602
    71. 71. BLOQUEO DEL RECEPTOR CB1 Sitio de acción Mecanísmo Objetivos ↓ Ingestión de alimentos Peso corporal Circunferencia de cintura ↑Adiponectina Dislipidemia ↓ Lipogenesis Resistencia a la insulina ↑ Captación de glucosa Resistencia a la inulina ↓ Lipogénesis Resistencia a la insulina ↑ Señales de saciedad Peso corporalThe American Journal of Medicine (2007) Vol 120 (2A), S18–S28
    72. 72. RIMONABANT VS PLACEBON Engl J Med 2005;353:2121-34.
    73. 73. PORCENTAJE DE PACIENTES QUE ALCANZAN 5% DE PERDIDA DE PESO CORPORAL EN UN AÑO CON DIFERENTES TRATAMIENTOS 70% 60% 67 63 50% 50 40% P P 30% P 31 30 20% 24 10% 0% Sibutramina Orlistat 120 Rimonabant 15 mg/d mg TID 20 mg/dEuropean Heart Journal Supplements (2005) 7 (Supplement L), L32–L38
    74. 74. ESTUDIOS CONTROLADOS CON PLACEBO Sibutramina Orlistat Rimonabant 1a 2a 1a 2a 1a -0 -1 -7.9 -11.9 -4.5 -9.2 -4.7 -6.2 -3.1 -5.1 -4.5 -8.5 -2 -3 -4 -5 -6 -7 -8 P=NS P < 0.005 -9 -10 -11 P < 0.001 P < 0.001 -12 Placebo -13 P < 0.05 -14CEF E European Heart Journal Supplements (2005) 7 (Supplement L), L32–L38
    75. 75. TRATAMIENTO CRITERIOS PARA CIRUGIA BARIÁTRICA• Peso corporal > 100% del peso corporal ideal.• Presencia de comorbilidad grave.• Fracasos para perder peso en programas de reducción de peso supervisados no quirúrgicos.• No debe haber psicosis, abuso de sustancias o depresión no controlada.Mayo Clin Proc Jun 1997 Vol 72
    76. 76. CIRUGÍA BARIÁTRICA EN USA 1992-2003N Engl J Med 2004; 350:1075-1079
    77. 77. CAMBIOS EN EL PESO EN SUJETOS CON CIRUGÍA BARIÁTRICA N Engl J Med 2004; 351:2683-2693
    78. 78. PROMEDIO DE CONSUMO DEKILOCALORÍAS /DÍA EN PERSONAS CON CIRUGÍA BARIÁTRICAN Engl J Med 2004; 351:2683-2693
    79. 79. ACTIVIDAD FÍSICA DURANTE EL TIEMPO LIBRE EN SUJETOS CON CIRUGÍA BARIÁTRICAN Engl J Med 2004; 351:2683-2693
    80. 80. ACTIVIDAD FÍSICA EN EL TRABAJO EN SUJETOS CON CIRUGÍA BARIÁTRICAN Engl J Med 2004; 351:2683-2693
    81. 81. CAMBIOS EN EL PESO Y SOBREVIVENCIAN Engl J Med 2007 ;357:741
    82. 82. INCIDENCIA DE DISLIPIDEMIA ENSUJETOS CON CIRUGÍA BARIÁTRICAN Engl J Med 2004; 351:2683-2693
    83. 83. INCIDENCIA DIABETES, HTA EHIPERURICEMIA EN SUJETOS CON CIRUGÍA BARIÁTRICA N Engl J Med 2004; 351:2683-2693
    84. 84. RECUPERACIÓN DE DISLIPIDEMIA ENSUJETOS CON CIRUGÍA BARIÁTRICA N Engl J Med 2004; 351:2683-2693
    85. 85. RECUPERACIÓN DE DIABETES, HTA EHIPERURICEMIA EN SUJETOS CON CIRUGÍA BARIÁTRICA N Engl J Med 2004; 351:2683-2693
    86. 86. META DE HbA1C DESPUES DE 1 AÑO RIO-DIABETES Meta IDF < 6.5% Meta ADA < 7% P < 0.001 P < 0.001 60 50 % de pacientes Rimonabant 40 20 mg n=315 Rimonabant 42.9 20 mg n=315 52.7 30 20 Placebo Placebo n=317 26.8 n=317 20.8 10 0The American Journal of Medicine (2007) Vol 120 (2A), S18–S28
    87. 87. CAMBIOS EN PARAMETROS DEL SINDROME METABÓLICO Cambios en circunferencia de RIO Porcentaje de cambio en HDL mg/dL cintura 0 20 NA 15 -1 10 % cambio HDL -2 5 7.2* 8.9* 8.1* 8.3 & E Centimetros 0 -3 -5 -4 -3.6 -3.3 & -10 L -5 * -4.2 -15 *p < 0.001 ^ -4.7 *p < 0.001 * -20 &p < 0.0001 -6 &p < 0.0001 D 10 0.5 *p < 0.001 0 5 &p < 0.0001 % cambio triglicéridos -0.5 Cambio en mmHg 0 -1 -0.2 NS -1.5 -5 -2 -1.2 -10 -13.2 -15.1 -12.4 -16.4 -2.5 NS -1.7 * * * & -3 * -15 -2.4 -3.5 * p < 0.05 * -20 -4 Cambio en triglicéridos mg/dL Cambios en PAS mmHgThe American Journal of Medicine (2007) Vol 120 (3A), S26–S34
    88. 88. EFECTO DE LA PERDIDA DE PESO SOBRE IL-6, FNT-α Y PCR EN MUJERES OBESAS Después de una dieta muy baja en calorías (reducción media de IMC 2.1 k/m2; reducción de grasa corporal de 4 kg) Antes de dieta Después de dieta PCR 3 7 2.5 6 2 5 pg/mL 4 mg/dL 1.5 3 1 2 0.5 1 0 0 IL-6 FNT-α Antes de dieta Despues de dietaJ Clin Endocrinol Metab 2000; 85: 3336-3342
    89. 89. PREVENCIÓN
    90. 90. DPP: AL MEJORAR LA SENSIBILIDAD A LA INSULINA SE PREVIENE DMN = 3234 30 Placebo Metformin Lifestyle Insulin 25 secretion (IGR) 20Diabetes Low hazard Medium rate 15 High (per100 pyr) 10 5 Insulin secretion (IGR) 0 Low Medium High Low Medium High Low Medium Highpyr = person years Insulin sensitivity (1/fasting insulin)IGR = insulin-to-glucose ratioDPP = Diabetes Prevention Program DPP Research Group. Diabetes. 2005;54:2404-14.
    91. 91. PREVENCIÓN DEL SÍNDROME METABÓLICO DPP Metformina 850 mg BID o Cambios intensivos en estilos de vida para mantener 7% de perdida de peso y 150 minutos de ejercicio semanal Resolución de SM Incidencia de SMAnn Intern Med. 2005;142:611-619.
    92. 92. PREVENCIÓN DE SÍNDROME METABÓLICOIncidencia y Prevalencia (3 años) de componentes de Síndrome Metabólico en DPP • Después de 3 años la prevalencia de SM ↑ de 55% a 61 % ( P= 0.003) en el grupo placebo ↑ 54% a 55% (P >0.2) en grupo metformina ↓ 51% a 43% (P < 0.001) en grupo con cambios en estilo de vida Ann Intern Med. 2005;142:611-619.
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