Molekulare Grundlagen des Zusammenhangs zwischen Krankheitsentstehung und Ernährung: Was kann die Grundlagenforschung für Prävention und Therapie leisten?

Zusammenfassung

Die experimentelle Forschung kann durch Identifikation von Mechanismen, Suszeptibilitätsgenen und Biomarkern zur Validierung und Verbesserung von Präventions- und Interventionsstrategien beitragen. Eine Personalisierung der Ernährungsempfehlungen mit genetischen Markern ist zurzeit nicht möglich, kann sich aber an konventionellen Risikofaktoren und Biomarkern orientieren. Mit genetischen Biomarkern lässt sich die Kausalität von epidemiologischen Assoziationen belegen (sog. Mendelian Randomization), in Zukunft möglicherweise auch die von ernährungsbezogenen Faktoren. Genetische Biomarker können zudem biologische Plausibilität von Präventions- und Interventionsstrategien belegen.

Abstract

Experimental research identifies mechanisms, susceptibility genes and biomarkers for nutrition-associated diseases, thereby contributing to the validation and improvement of prevention and intervention strategies. At present, the personalization of nutritional recommendations is not possible with genetic marker but can be guided by conventional risk factors and biomarkers. However, genetic markers allow the inference of causality of associations between biomarkers and disease endpoints by Mendelian randomization. In the future, genetic markers may also help to establish causality of associations between nutrients and disease endpoints. In addition, elucidation of the genetic basis of nutrition-associated diseases can provide biological plausibility of preventive and therapeutic strategies.

Schlüsselwörter

personalisierte Ernährung - Genvarianten - Biomarker - Mendelian Randomization - Prävention

Keywords

personalized nutrition - gene variants - biomarkers - Mendelian randomization - prevention

• Literatur

• 1 Ford ES, Bergmann MM, Kröger J et al. Healthy living is the best revenge: findings from the European Prospective Investigation Into Cancer and Nutrition-Potsdam study. Arch Intern Med 2009; 169: 1355-1362
• 2 Kaput J, Ordovas JM, Ferguson L et al. The case for strategic international alliances to harness nutritional genomics for public and personal health. Br J Nutr 2005; 94: 623-632
• 3 Joost HG, Gibney MJ, Cashman KD et al. Personalised nutrition: status and perspectives. Br J Nutr 2007; 98: 26-31
• 4 Speliotes EK, Willer CJ, Berndt SI et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat Genet 2010; 42: 937-948
• 5 Wheeler E, Barroso I. Genome-wide association studies and type 2 diabetes. Brief Funct Genomics 2011; 10: 52-60
• 6 Chen R, Mias GI, Li-Pook-Than J et al. Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell 2012; 148: 1293-1307
• 7 Heid IM, Jackson AU, Randall JC et al. Meta-analysis identifies 13 new loci associated with waist-hip ratio and reveals sexual dimorphism in the genetic basis of fat distribution. Nat Genet 2010; 42: 949-960
• 8 Schulze MB, Weikert C, Pischon T et al. Use of multiple metabolic and genetic markers to improve the prediction of type 2 diabetes: the EPIC-Potsdam Study. Diabetes Care 2009; 32: 2116-2119
• 9 Fisher E, Boeing H, Fritsche A et al. Whole-grain consumption and transcription factor-7-like 2 ( TCF7L2) rs7903146: gene-diet interaction in modulating type 2 diabetes risk. Br J Nutr 2009; 101: 478-481
• 10 Heni M, Herzberg-Schäfer S, Machicao F et al. Dietary fiber intake modulates the association between variants in TCF7L2 and weight loss during a lifestyle intervention. Diabetes Care 2012; 35: e24
• 11 Buijsse B, Simmons RK, Griffin SJ et al. Risk assessment tools for identifying individuals at risk of developing type 2 diabetes. Epidemiol Rev 2011; 33: 46-62
• 12 Schulze MB, Hoffmann K, Boeing H et al. An accurate risk score based on anthropometric, dietary, and lifestyle factors to predict the development of type 2 diabetes. Diabetes Care 2007; 30: 510-515
• 13 Heidemann C, Boeing H, Pischon T et al. Association of a diabetes risk score with risk of myocardial infarction, stroke, specific types of cancer, and mortality: a prospective study in the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam cohort. Eur J Epidemiol 2009; 24: 281-288
• 14 Kröger J, Zietemann V, Enzenbach C et al. Erythrocyte membrane phospholipid fatty acids, desaturase activity, and dietary fatty acids in relation to risk of type 2 diabetes in the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam Study. Am J Clin Nutr 2011; 93: 127-142
• 15 Floegel A, Stefan N, Yu Z et al. Identification of Serum Metabolites Associated with Risk of Type 2 Diabetes Using a Targeted Metabolomic Approach. Diabetes 2012; (im Druck)
• 16 Sleiman PM, Grant SF. Mendelian randomization in the era of genomewide association studies. Clin Chem 2010; 56: 723-728
• 17 Linsel-Nitschke P, Götz A, Erdmann J et al. Lifelong reduction of LDL-cholesterol related to a common variant in the LDL-receptor gene decreases the risk of coronary artery disease – a Mendelian Randomisation study. PLoS One 2008; 3: e2986
• 18 Sarwar N, Sandhu MS, Ricketts SL et al. Triglyceride-mediated pathways and coronary disease: collaborative analysis of 101 studies. Lancet 2010; 375: 1634-1639
• 19 Fisher E, Stefan N, Saar K et al. Association of AHSG gene polymorphisms with fetuin-A plasma levels and cardiovascular diseases in the EPIC-Potsdam study. Circ Cardiovasc Genet 2009; 2: 607-613
• 20 Voight BF, Peloso GM, Orho-Melander M et al. Plasma HDL cholesterol and risk of myocardial infarction: a mendelian randomisation study. Lancet 2012; [Epub ahead of print]
• 21 Knowler WC, Barrett-Connor E, Fowler SE et al. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med 2002; 346: 393-403
• 22 Poitout V, Robertson RP. Glucolipotoxicity: fuel excess and beta-cell dysfunction. Endocr Rev 2008; 29: 351-366
• 23 Kluth O, Mirhashemi F, Scherneck S et al. Dissociation of lipotoxicity and glucotoxicity in a mouse model of obesity associated diabetes: role of forkhead box O1 (FOXO1) in glucose-induced beta cell failure. Diabetologia 2011; 54: 605-616
• 24 Weir GC, Marselli L, Marchetti P et al. Towards better understanding of the contributions of overwork and glucotoxicity to the beta-cell inadequacy of type 2 diabetes. Diabetes Obes Metab 2009; 11 (Suppl. 04) 82-90
• 25 Unger RH, Clark GO, Scherer PE et al. Lipid homeostasis, lipotoxicity and the metabolic syndrome. Biochim Biophys Acta 2010; 1801: 209-214
• 26 Chadt A, Leicht K, Deshmukh A et al. Tbc1d1 mutation in lean mouse strain confers leanness and protects from diet-induced obesity. Nat Genet 2008; 40: 1354-1359
• 27 Scherneck S, Nestler M, Vogel H et al. Positional cloning of zinc finger domain transcription factor Zfp69, a candidate gene for obesity-associated diabetes contributed by mouse locus Nidd/SJL. PLoS Genet 2009; 5: e1000541
• 28 Vogel H, Scherneck S, Kanzleiter T et al. Loss of function of Ifi202b by a microdeletion on chromosome 1 of C57BL/6J mice suppresses 11β-hydroxysteroid dehydrogenase type 1 expression and development of obesity. Hum Mol Genet 2012; 21: 3845-3857
• 29 Donath MY, Shoelson SE. Type 2 diabetes as an inflammatory disease. Nat Rev Immunol 2011; 11: 98-107