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Adipose tissue innate immunity & inflammation - a nutrigenomics perspective of the metabolic syndrome
1. Adipose tissue, innate immunity and inflammation – a nutrigenomics perspective of the metabolic syndrome http://twitter.com/nutrigenomics Michael MüllerNetherlands Nutrigenomics Centre & Nutrition, Metabolism and Genomics GroupDivision of Human Nutrition, Wageningen University
2. I will talk about Our challenges: What is healthy What is Nutrigenomics? The metabolic syndrome The deadly sins Good fat / bad fat (tissue) Modern nutritional science & early biomarkers NASH & the role of the adipose tissue Dietary saturated fat can induce pro-inflammatory responses We have different phenotypes: Personalized health Saturated fat can be “killing” (in transgenic mice) Summary & recommendations
6. Our “paleolithic” genes + modern diets Paleolithic era Modern Times 1.200.000 Generations between feast en famine 2-3 Generations in energy abundance % Energy % Energy 100 100 Grain Milk/-products Isolated Carbohydrates Isolated Fat/OilAlcohol Low-fat meatChicken Eggs Fish 50 Meat Chicken Fish 50 Fruit Vegetables (carrots) Nuts Honey Fruit Vegetables Beans 0 0
9. What is the metabolic syndrome? The metabolic syndrome is characterized by a group of metabolic risk factors in one person: Central obesity (excessive fat tissue in and around the abdomen) Atherogenic dyslipidemia (blood fat disorders — mainly high triglycerides and low HDL cholesterol — that foster plaque buildups in artery walls) Raised blood pressure (130/85 mm Hg or higher) Insulin resistance or glucose intolerance (the body can’t properly use insulin or blood sugar) Prothrombotic state (e.g., high fibrinogen or plasminogen activator inhibitor in the blood) Proinflammatory state (e.g., elevated high-sensitivity C-reactive protein in the blood)
13. Normal Type 2 Diabetes Visceral Fat Distribution:Normal vs Type 2 Diabetes
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15. We need a new nutritional science Insulin ± oral agents Oral combination Oral monotherapy Diet & exercise Complex Disease 100 Different & similar targets 80 Pharma 60 DISEASE STATE (%) 40 Nutrition 20 0 TIME (months/years) HomeostasisHealth L. Afman & M. Müller J Am DietAssoc. 2006;106:569-576.
16. Late biomarkersof disease Earlybiomarkersof disease Onset of disease Biomarkers of earlydiseasestate Single marker vsmultimarker profiles Disease Pharma Nutrition Early biomarkers in human nutrition research healthy
17. Organ-specific gene expression signatures of the early phase (metabolic stress) & the late phase of metabolic syndrome 1 2 3 4 10 16 Weeks WAT 1 2 3 4 10 16 Weeks Muscle 1 2 3 4 10 16 Weeks Liver 1 2 3 4 10 16 Weeks Intestine Healthy Unhealthy Healthy Unhealthy Healthy Unhealthy Healthy Unhealthy
25. Role of macrophages in lipid metabolism (JBC 2008; Cell Metabolism 2010)hepatic steatosis steatohepatitis (NASH) & fibrosiscirrhosis
26. Study: Interaction between WAT and liver tissue essential for NASH/NAFLD in C57Bl/6 mice Objective: Nonalcoholic fatty liver disease (NAFLD) is strongly linked to obesity and diabetes, suggesting an important role of adipose tissue in the pathogenesis of NAFLD. Here we aimed to investigate the interaction between adipose tissue and liver in NAFLD, and identify potential early plasma markers that predict NASH.
36. Plasma proteins as early predictive biomarker for NASH in C57Bl/6 mice Multivariate analysis of association of protein plasma concentrations with final liver triglyceride content
37. Conclusions Our data support the existence of a tight relationship between adipose tissue dysfunction and NASH pathogenesis. It points to several novel potential predictive biomarkers for NASH. Duval C, Thissen U, Keshtkar S, Accart B, Stienstra R, Boekschoten MV, Roskams T, Kersten S, Müller M. Adipose tissue dysfunction signals progression of hepatic steatosis towards nonalcoholic steatohepatitis in C57BL/6 mice. Diabetes. 2010;59:3181-91.
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39. Human nutrigenomics study Dietary fat and inflammation in adipose tissue Change in diet composition ? Van Dijk et al. AJCN 2009 de Luca, C and Olefsky JM, Nature Medicine 12, 41 - 42 (2006)
45. ‘Obese-linked’ pro-inflammatory gene expression profile by SFAs MUFA diet SFA diet The SFA-rich diet: Induces a pro-inflammatory obese-linked gene expression profile Decreases expression and plasma level of the anti-inflammatory cytokine adiponectin “Personal Transcriptomes” Van Dijk et al. AJCN 2009
46. Humanstudy:Plasma Protein Profiling Reveals Protein Clusters Related to BMI and Insulin Levels in Middle-Aged Overweight Subjects AIM Associate plasma protein profiles with BMI Identifypotential marker profile of earlydisease state . PLoS One. 2010 Dec 23;5(12):e14422
47. Measurements RulesBasedMedicine (Austin, USA) Multiplex immunoassay In total 124 proteinsmeasured Involved in diseases, inflammation, endothelialfunction and metabolism . PLoS One. 2010 Dec 23;5(12):e14422
48. We are different: improved phenotyping necessary to reveal phenotype clusters . PLoS One. 2010 Dec 23;5(12):e14422
49. Conclusion We identified clusters of plasma proteins associated with BMI and insulin in a healthy population. These clusters included earlier identified biomarkers for obesity-related disease as well as potential new biomarkers. These plasma protein clusters could have potential applications for improved phenotypic characterization of volunteers in nutritional intervention studies or as biomarkers in the early detection in obesity-linked disease development and progression. van DijkSJ, Feskens EJM, Heidema AG, Bos MB, van de Rest O, Geleijnse JM, de Groot CPGM, Müller M, Afman LA. Plasma Protein Profiling Reveals Protein Clusters Related to BMI and Insulin Levels in Middle-Aged Overweight Subjects. PLoS One. 2010 Dec 23;5(12):e14422
57. MLN-resident macrophages are protected from the pro-inflammatory effect of saturated fatty acids via expression of Angptl4, which is strongly induced by chyle and fatty acids and which via inhibition of LPL prevents lipolysis of chylomicron-TG.
58. In the absence of this protective mechanism, feeding a diet rich in saturated fat rapidly leads to enhanced lipid uptake into MLN-resident macrophages, triggering foam cell formation and a massive inflammatory response.Lichtenstein et al. Cell Metab. 2010
60. Pharma is not the (only) solution:Eat foods rich in challenging food bioactives Drugs A B C PPARg PPARb PPARa Receptor C3 C2 C1 Fatty acids F C6 C5 C4 Multiple targets
61. Summary You are what you eat => during life all events will leave their (epigenetic) traces on our genome, some are irreversible => ageing Disease phenotypes such as obesity, metabolic syndrome, diabetes are largely related to our “gluttony / sloth” lifestyles and modern convenient (“fast”) foods => unhealthy ageing. NASH is the liver phenotype of the metabolic syndrome and appears early in the progression towards diabetes or CVD. There is a tight relationship between adipose tissue dysfunction and NASH pathogenesis. Chronic overconsumption of saturated fat or lipogenic precursors (starch, sugars) induces non-resolving low-grade pro-inflammatory state largely caused by the innate immune system. => choose the right lifestyle & food pattern (diverse & anti-inflammatory), eat less & exercise more (at least for 30 minutes/day).
62. 2 Meals a day, work as long as possible & embrace challenge Walter Breuning (1896 - 2011)
63. Sander KerstenLinda SandersonNatasha Georgiadi Mark BouwensLydia Afman Guido Hooiveld Meike Bunger Philip de Groot Mark Boekschoten Nicole de Wit Mohammad Ohid Ullah Christian Trautwein Folkert Kuipers Ben van Ommen + many more
Editor's Notes
Inflammation has been associated with many disease phenotypes including steatohepatitis or diabetes. This relationship is in particular when inflammation is chronic or non-resolving. There is an interaction between metabolism and inflammation with positive or negative consequences with respect to organ and systemic health.In my talk I will briefly discuss two unpublished studies, one investigating the important interaction of WAT and liver in particular under conditions of diet-induced obesity. Organ-specific macrophages in WAT and liver play an crucial role in progressing organ-specific inflammatory phenotypes. In the second study we found very interesting interaction between dietary fat and macrophages in mesenteric lymph nodes that are exposed postprandially to very high concentrations of chylomicrons. We used a k.o. mouse for ANGPTL4 and could show that chronic consumption of saturated fat can be deadly.
Haematoxylin and eosin staining (D) and oil red O staining (E) of representative liver sections of the 4 subgroups
(Immuno)histochemical staining confirms enhanced inflammation and early fibrosis in HFH miceImmunohistochemical staining of macrophage activation in representative liver section of HFL and HFH mice using antibody against the specific macrophagemarker Cd68Collagen staining using fast green FCF/sirius red F3B. Staining of stellate cell activation using antibody against GFAP.
- Number of genes up- or down-regulated in the various subgroups in comparison to the LFL mice, as determined by Affymetrix GeneChip analysis. Genes with a p-value below 0.05 were considered significantly regulated. - Heat map showing changes in expression of selected genes involved in lipid metabolism, inflammation and fibrosis in liver. Changes in gene expression of selected genes as determined by real-time quantitative PCR. Mean expression in LFL mice was set at 100%. Error bars reflect standard deviation. Bars with different letters are statistically different (P<0.05 according to Student’s t-test). Number of mice per group: n=4 (LFL, HFL, HFH), n=6 (LFH).
Haematoxylin and eosin staining of representative adipose tissue sections. Immunohistochemical staining of macrophages using antibody against Cd68. Collagen staining using fast green FCF/sirius red F3B.
Adipose tissue mRNA expression of a selected group of genes was determined by quantitative real-time PCR after 21 weeks of dietary intervention. Mean expression in LFL mice was set at 100%. Error bars reflect standard deviation. * = significantly different from HFL mice according to Student’s t-test (P<0.05). Number of mice per group: n=4 (LFL, HFL, HFH), n=6 (LFH).
. A) Plasma concentration of haptoglobin, TIMP-1, IL-1β, leptin and insulin were determined by multiplex assay at specific time points during the 21 weeks of dietary intervention after a 6h fast. White squares: LFL, Light grey squares: LFH, dark grey squares: HFL, black squares: HFH. Error bars reflect standard deviation. * = significantly different from HFL mice according to Student’s t-test (P<0.05). Number of mice per group: n=4 (LFL, HFL, HFH), n=6 (LFH).
Graphs illustrating the result of multivariate analysis showing the association of protein plasma concentrations at various time points with final liver triglyceride content. Significant proteins display an inverse RSD value higher than 2 (bold line indicates the inverse RSD threshold value of 2).RSD = Relative standard deviation.