Obesity paradox


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Obesity paradox

  1. 1. Reverse Epidemiology ofObesity in Kidney Disease Arun Chawla, MD Hofstra NSLIJ School of Medicine
  2. 2. Overview Conventional epidemiology Paradox Studies… Hypothesis to explain what we see Paradox within paradox
  3. 3. What we all know… for Now! In US, obesity is the second leading cause of preventable disease and death. Associated with ESRD - type 2 DM and hypertension. Epidemic growing and life expectancy being shortened.
  4. 4. Prevalence of overweight, obesity and extreme obesity among adults: United States, trends 1976-80 through 2005-2006NHANES December 2008
  5. 5. Associated co morbidities Type 2 DM  ESRD Hypertension  NASH CAD  VTE GDM  Cholelithiasis OSA  Depression Hyperlipidemia  Pulmonary Hypertension OA  CTS GERD  Infertility Gout  Breast/Colon cancer
  6. 6. Multivariate Relative Risk of Death from CVD, Cancer, and All Other Causesamong Men and Women Who Had Never Smoked and Who Had No History of Disease at Enrollment, According to BMI Calle E et al. Nejm 1999
  7. 7. Multivariate Relative Risk of Death from All Causes among Men and Women According to BMI, Smoking, and Disease Status Calle E et al. N Engl J Med 1999
  8. 8. So… Given the magnitude of the risks of obesity in the general population, it is important to clarify whether these risks apply to patients on dialysis, who have an overall cardiovascular risk at least 10 times greater than the general population.
  9. 9. First thoughts…. In 1982, Degoulet et al looked into 1453 subjects treated in 33 French dialysis centers over 5 years and noticed no increase in mortality with higher BMI. After 17 years came the first trial …
  10. 10. Influence of Excess Weight onMortality and Hospital Stay in 1346 Dialysis Patients Fleishmann and Salahudeen Kidney International 1999
  11. 11. Methods Cohort of 1346 HD patients in Mississippi Followed prospectively for 1 year for hospitalization and mortality. On dialysis for more than 90 days (avg. 4.3 yrs) 38% had BMI >27.5 13% had BMI <20 Normal considered BMI of 20-27.5
  12. 12. Kaplan-Meier Death Hazard Death Hazard 400 Survival in days Fleishmann et al. Kidney International 1999
  13. 13. Results Causes of death similar amongst three groups. For a unit increase in BMI>27.5 risk of dying showed RR reduction of 6% in the univariate model and 4% in the multivariate model. With 1 unit decrease of BMI below 20 the risk of death increased by 1.6 fold.
  14. 14. Hospital admission rate per year and Length of stay (LOS)…1.8 161.6 141.4 121.2 10 1 80.8 6 LOS0.60.4 40.2 2 0 0 Underweight Overweight underweight Normal Overweight Ad Rate weight
  15. 15. Conclusion Special attention to nutrition to achieve high end of normal BMI may help to reduce morbidity and mortality in hemodialysis patients. Results were significant even after adjustment for markers of nutrition like albumin, transferrin and creatinine.
  16. 16. Limitations Smaller sample size Mainly AA (88%) so no diversity Survival advantage not shown in Caucasians Short follow up BMI not the most accurate tool to comment on body composition.
  17. 17. Questions Fat mass or muscle mass? Mild versus severe obesity? Is high BMI only a short term advantage? Interactions of race or ethnicity?
  18. 18. Association of Body Size with Outcomes Among Patients Beginning Dialysis Johansen Et al Am J Clinical Nutrition 2004
  19. 19. Hypothesis Extremely high BMI would not be associated with increased survival time. If there were a survival advantage at higher BMI, it would be explained in part by the increased lean body mass (LBM) that usually accompanies high BMI.
  20. 20. Methods Data obtained from the USRDS and CMS. Mortality, hospitalisation and dialysis modality (i.e. HD or PD), for adult patients beginning dialysis between April 1, 1995, and November 30, 2000. Follow-up extended through November 30, 2001 with median follow up of 2 years.
  21. 21. FIGURE 1. Hazard ratios for death among men ({blacksquare}) and women ({square}) by category of BMI (A), Benn index (B), and estimated fat mass (C) Death Hazard Johansen, K. L et al. Am J Clin NutrCopyright ©2004 The American Society for Nutrition BMI 2004
  22. 22. Effects of adjustment for serum creatinine and creatinine index on the relation between BMI and survival Curve more steep!!!Death Hazard BMICopyright ©2004 The American Society for Nutrition Johansen, K. L et al. Am J Clin Nutr 2004
  23. 23. Conclusions BOTH THEIR HYPOTHESIS PROVED WRONG!!! Higher adiposity was associated with increased survival, even after adjustment for demographics, laboratory values, comorbidities, dialysis modality and even when adiposity was assessed by different methods. Pattern observed even for cardiovascular death Less evident with PD and Asians
  24. 24. Strengths Large sample size Complete data All racial and ethnic groups Longer follow upLimitations Observational design
  25. 25. Does weight gain help???? Association of Morbid Obesity and Weight Change Over Time with Cardiovascular Survival in Hemodialysis Population K.Kalantar-Zadeh et al Am J Kidney Diseases 2005
  26. 26. Methods Patients enrolled with DA Vita Inc Cohort on July 1, 2001 and subsequently patients were enrolled through June 30, 2003- Non-concurrent cohort. Data organized to form “8 quarterly mean values” to include 2 year observation period. 11 categories <18, >45 and then 9 in between 18 and 44.9
  27. 27. Data collected CBC, BUN, Hemoglobin, albumin, creatinine (muscle mass), dialysis dose and ferritin and TIBC (marker of nutrition). For each analysis, 3 models were examined based on the level of multivariate adjustment: (1) the unadjusted model (2) case-mix– adjusted models (3) case-mix and laboratory
  28. 28. 39.3 15.9K.Kalantar-Zadeh et al Am J Kidney Dis 2005
  29. 29. Fig 1. Time-dependent association between BMI and 2-year all-cause mortality in 54,535 MHD patients K.Kalantar-Zadeh et al Am J Kidney Dis 2005
  30. 30. Fig 2. Time-dependent association between BMI and 2-year cardiovascular mortality in 54,535 MHD patients K.Kalantar-Zadeh et al Am J Kidney Dis 2005
  31. 31. Fig 4. Association between the rate of weight change overtime and subsequent all-cause mortality in 46,629 HD patients p.068 P<.0001 K.Kalantar-Zadeh et al Am J Kidney Dis 2005
  32. 32. Fig 5. Association between the rate of weight change over time and cardiovascular mortality in 46,629 HD patients P .101 P<.001 K.Kalantar-Zadeh et al Am J Kidney Dis 2005
  33. 33. Conclusions Even after exhaustive adjustment for time varying laboratory markers, both all-cause and cardiovascular mortality showed decreasing rates across increasing BMI categories, even morbid obesity. Lower BMI at baseline consistently is found to be a strong predictor of elevated mortality. Weight loss was associated with increased CV and all-cause death, whereas weight gain showed a trend toward improved survival and reduced cardiovascular mortality.
  34. 34. Association Of Low Body Mass Index And WeightLoss With Increased Mortality In 14,065 Transplant- wait-listed Hemodialysis Patients K.Kalantar-Zadeh et al Circulation 2008
  35. 35. What we Now know…!Leavey SF, et al. Body mass index and mortality in ‘healthier’ as compared with sicker’ hemodialysis patients:results from the Dialysis Outcomes and Practice Patterns Study (DOPPS). Nephrology Dial Transplant 2001
  36. 36. ?????
  37. 37. Hypothesis
  38. 38. TNF- alpha receptors in obesity TNF-alpha is elevated in CHF and in dialysis patients and may contribute to cardiac injury through its pro-apoptotic and negative inotropic effects (2). Adipose tissue produces soluble TNF-alpha receptors, which may play a cardio-protective role. 2) Feldman et al. The role of tumor necrosis factor in the pathophysiology of heart failure. J Am Coll Cardiol 2000; 35:537– 44.
  39. 39. Neurohormonal alterations The lean subjects had significantly higher increases in plasma adrenaline and renin concentrations during treadmill testing, despite similar baseline values and a history of hypertension (3). Heightened sympathetic and renin-angiotensin activities are associated with a poor prognosis in heart failure and fluid overload states (such as those seen in dialysis patients). 3) Weber MA et al. Contrasting clinical properties and exercise responses in obese and lean hypertensive patients. J Am Coll Cardiol 2001;37:169 –74
  40. 40. Selection Bias
  41. 41. More stable hemodynamic status Despite having similar PCWP and cardiac indexes, overweight and obese patients with fluid overload tend to have higher systemic blood pressure values (1). Due to better tolerance larger proportion of obese and overweight patients take ACE inhibitors.1) Horwich et al . The relationship between obesity and mortality in patientswith heartfailure. J Am Coll Cardiol 2001;38:789 –9
  42. 42. Endotoxin-lipoprotein hypothesis Lower serum total cholesterol and lipoprotein concentrations are strongly and independently associated with impaired survival in dialysis (4). It reflects a richer pool of internal lipoproteins that can actively bind to and remove circulating endotoxins, which effectively retards inflammation and subsequent atherosclerosis (5). 4) Nishizawa et al. Paradox of risk factors for cardiovascular mortality in uremia: is a higher cholesterol level better for atherosclerosis in uremia? Am J Kidney Dis 2001;38 S4–7. 5) Niebauer J, et al. Endotoxin and immune activation in chronic heart failure: a prospective cohort study. Lancet 1999;353: 1838–42.
  43. 43. Malnutrition-inflammation complex syndrome – “Cachexia in slowmotion” Undernourished people more likely to develop PEM and slow to recover form illnesses and its complications. Increased release of IL-6 and TNF may suppress appetite (6), may cause muscle proteolysis and hypoalbuminemia, and may be involved in the processes that lead to atherosclerosis. Patients with lower albumin, low cholesterol, creatinine and homocysteine concentration might represent MICS making them prone to infection and inflammation and slower recovery. Nutritional Inflammatory hypothesis(6) Kalantar-Zadeh K. Appetite and inflammation, nutrition, anemia, and clinical outcome in hemodialysispatients. Am J Clin Nutr 2004.
  44. 44. Time discrepancies among competitive risk factors US population versus  Survival advantages that exist developing countries in obese dialysis patients may, in the short term, outweigh the harmful effects of these risk factors on CVD in the long term.  Dialysis patients, ironically, do not live long enough to die of the consequences of overnutrition!
  45. 45. Dialysis modality???? Body mass index, Dialysis Modality, and Survival: Analysis of the United States Renal Data System Dialysis Morbidity and Mortality Wave II Study ABBOTT et al KIDNEY INT. 2004
  46. 46. Methods Retrospective cohort study of the USRDS DMMS Wave II database. Patients who started dialysis in 1996 and were followed until October 2001 (5yrs) Outcome: Mortality Divided into 4 groups 1 (<21.9), 2 (21.9-24.9), 3 (25-29.9) and 4(>29.9)
  47. 47. Demographic and clinical variables PD patients- Less AA, younger, more renal transplant. Decreased prevalence of CAD, CHF, CVA, LVH on EKG, PVD, and cancer. Decreased Erythropoietin use Higher ACE, statins and beta blockers. No significant difference of BMI
  48. 48. PD patients In the lowest group were at an increased risk of mortality
  49. 49. Kaplan-Meier plot of patient survival by BMI HD survival 39.8% vs. 32.3% PD survival 38.7% vs. 40.4% ABBOTT et al KIDNEY INT. 65
  50. 50. Conclusions Low BMI was independently associated with higher mortality regardless of dialysis modality. HR for death in patients with BMI >30 was 0.89 for HD and significant while no such correlation in PD patients.
  51. 51. “Paradox within paradox” Adequacy of dialysis not known. Whether “uremic” and “inflammatory” malnutrition differ by dialysis modality has not been established. Changes in B.P, med use, lab values etc. wasn’t followed. 1.5–4.25% of dextrose in their peritoneal dialysate (often around the clock), which is estimated to be absorbed at 45%.
  52. 52. CKD patients??(1)Evans et al; Natural history of chronic reanl failure; Results from an unselected population cohort in(2)BMI and mortality in CKD. Madero et al Abstract JASN 2006(3)Reverse epidemiology in patients with CKD. Kovesdy etal. Seminars in Dialysis 2007