Systems Epidemiology: A New Direction in Nutrition and Metabolic Disease Research
Tóm tắt
Từ khóa
Tài liệu tham khảo
Hu FB. Metabolic profiling of diabetes: from black-box epidemiology to systems epidemiology. Clin Chem. 2011;57(9):1224–6.
Haring R, Wallaschofski H: Diving through the "-omics": the case for deep phenotyping and systems epidemiology. Omics 2012.
Lund E, Dumeaux V. Systems epidemiology in cancer. Cancer Epidemiol Biomarkers Prev. 2008;17(11):2954–7.
Bictash M, Ebbels TM, Chan Q, et al. Opening up the "Black Box": metabolic phenotyping and metabolome-wide association studies in epidemiology. J Clin Epidemiol. 2010;63(9):970–9.
Keusch GT. What do -omics mean for the science and policy of the nutritional sciences? Am J Clin Nutr. 2006;83(2):520S–2.
•• Morris AP, Voight BF, Teslovich TM, et al. Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat Genet. 2012;44(9):981–90. This article is the latest in a series of successful genome-wide association studies of type 2 diabetes that demonstrate the power of an agnostic system-wide approach to susceptibility loci discovery.
Ericson U, Sonestedt E, Gullberg B, et al. High intakes of protein and processed meat associate with increased incidence of type 2 diabetes. Br J Nutr. 2013;109(6):1143–53.
Hu FB, Manson JE, Stampfer MJ, et al. Diet, lifestyle, and the risk of type 2 diabetes mellitus in women. N Engl J Med. 2001;345(11):790–7.
Djousse L, Gaziano JM, Buring JE, et al. Dietary omega-3 fatty acids and fish consumption and risk of type 2 diabetes. Am J Clin Nutr. 2011;93(1):143–50.
van Dam RM, Hu FB. Coffee consumption and risk of type 2 diabetes: a systematic review. JAMA. 2005;294(1):97–104.
Koppes LL, Dekker JM, Hendriks HF, et al. Moderate alcohol consumption lowers the risk of type 2 diabetes: a meta-analysis of prospective observational studies. Diabetes Care. 2005;28(3):719–25.
Jiang R, Manson JE, Meigs JB, et al. Body iron stores in relation to risk of type 2 diabetes in apparently healthy women. JAMA. 2004;291(6):711–7.
Nettleton JA, Hivert MF, Lemaitre RN, et al. Meta-analysis investigating associations between healthy diet and fasting glucose and insulin levels and modification by loci associated with glucose homeostasis in data from 15 cohorts. Am J Epidemiol. 2013;177(2):103–15.
• Cornelis MC, Hu FB. Gene-Environment Interactions in the Development of Type 2 Diabetes: Recent Progress and Continuing Challenges. Annu Rev Nutr. 2012;32:245–59. This article discusses recent progress, continuing challenges, evolving approaches, and recommendations for future efforts pertaining to studies of gene-environment interactions of type 2 diabetes.
Vimaleswaran KS, Berry DJ, Lu C, et al. Causal relationship between obesity and vitamin D status: bi-directional Mendelian randomization analysis of multiple cohorts. PLoS Med. 2013;10(2):e1001383.
Franks PW, Pearson E, Florez JC. Gene-environment and gene-treatment interactions in type 2 diabetes: progress, pitfalls, and prospects. Diabetes Care. 2013;36(5):1413–21.
•• Qi Q, Chu AY, Kang JH, et al. Sugar-sweetened beverages and genetic risk of obesity. N Engl J Med. 2012;367(15):1387–96. This article tests and validates an interaction between a genetic score of established obesity risk loci and sugar-sweetened beverages for risk of obesity.
• Herder C, Karakas M, Koenig W. Biomarkers for the prediction of type 2 diabetes and cardiovascular disease. Clin Pharmacol Ther. 2011;90(1):52–66. This article provides an exhaustive review of system-level studies for prediction of type 2 diabetes and cardiovascular disease.
Cornelis MC, Monda KL, Yu K, et al. Genome-wide meta-analysis identifies regions on 7p21 (AHR) and 15q24 (CYP1A2) as determinants of habitual caffeine consumption. PLoS Genet. 2011;7(4):e1002033.
Agrawal A, Freedman ND, Cheng YC, et al. Measuring alcohol consumption for genomic meta-analyses of alcohol intake: opportunities and challenges. Am J Clin Nutr. 2012;95(3):539–47.
Meng Q, Makinen VP, Luk H, et al. Systems Biology Approaches and Applications in Obesity, Diabetes, and Cardiovascular Diseases. Curr Cardiovas Risk Rep. 2013;7(1):73–83.
Afman LA, Muller M. Human nutrigenomics of gene regulation by dietary fatty acids. Prog Lipid Res. 2012;51(1):63–70.
Takamura T, Honda M, Sakai Y, et al. Gene expression profiles in peripheral blood mononuclear cells reflect the pathophysiology of type 2 diabetes. Biochem Biophys Res Commun. 2007;361(2):379–84.
Zampetaki A, Kiechl S, Drozdov I, et al. Plasma microRNA profiling reveals loss of endothelial miR-126 and other microRNAs in type 2 diabetes. Circ Res. 2010;107(6):810–7.
Wang S, Aurora AB, Johnson BA, et al. The endothelial-specific microRNA miR-126 governs vascular integrity and angiogenesis. Dev Cell. 2008;15(2):261–71.
Camargo A, Ruano J, Fernandez JM, et al. Gene expression changes in mononuclear cells in patients with metabolic syndrome after acute intake of phenol-rich virgin olive oil. BMC Genomics. 2010;11:253.
Bouwens M, Grootte Bromhaar M, Jansen J, et al. Postprandial dietary lipid-specific effects on human peripheral blood mononuclear cell gene expression profiles. Am J Clin Nutr. 2010;91(1):208–17.
van Dijk SJ, Feskens EJ, Bos MB, et al. A saturated fatty acid-rich diet induces an obesity-linked proinflammatory gene expression profile in adipose tissue of subjects at risk of metabolic syndrome. Am J Clin Nutr. 2009;90(6):1656–64.
Bouwens M, van de Rest O, Dellschaft N, et al. Fish-oil supplementation induces antiinflammatory gene expression profiles in human blood mononuclear cells. Am J Clin Nutr. 2009;90(2):415–24.
Kabir M, Skurnik G, Naour N, et al. Treatment for 2 mo with n 3 polyunsaturated fatty acids reduces adiposity and some atherogenic factors but does not improve insulin sensitivity in women with type 2 diabetes: a randomized controlled study. Am J Clin Nutr. 2007;86(6):1670–9.
Kallio P, Kolehmainen M, Laaksonen DE, et al. Dietary carbohydrate modification induces alterations in gene expression in abdominal subcutaneous adipose tissue in persons with the metabolic syndrome: the FUNGENUT Study. Am J Clin Nutr. 2007;85(5):1417–27.
Di Caro S, Tao H, Grillo A, et al. Effects of Lactobacillus GG on genes expression pattern in small bowel mucosa. Dig Liver Dis. 2005;37(5):320–9.
van Oostrom O, de Kleijn DP, Fledderus JO, et al. Folic acid supplementation normalizes the endothelial progenitor cell transcriptome of patients with type 1 diabetes: a case–control pilot study. Cardiovasc Diabetol. 2009;8:47.
Khymenets O, Fito M, Covas MI, et al. Mononuclear cell transcriptome response after sustained virgin olive oil consumption in humans: an exploratory nutrigenomics study. Omics. 2009;13(1):7–19.
Safdar A, Yardley NJ, Snow R, et al. Global and targeted gene expression and protein content in skeletal muscle of young men following short-term creatine monohydrate supplementation. Physiol Genomics. 2008;32(2):219–28.
Tsavachidou D, McDonnell TJ, Wen S, et al. Selenium and vitamin E: cell type- and intervention-specific tissue effects in prostate cancer. J Natl Cancer Inst. 2009;101(5):306–20.
Linnane AW, Kopsidas G, Zhang C, et al. Cellular redox activity of coenzyme Q10: effect of CoQ10 supplementation on human skeletal muscle. Free Radic Res. 2002;36(4):445–53.
Niculescu MD, Pop EA, Fischer LM, et al. Dietary isoflavones differentially induce gene expression changes in lymphocytes from postmenopausal women who form equol as compared with those who do not. J Nutr Biochem. 2007;18(6):380–90.
Crujeiras AB, Parra D, Milagro FI, et al. Differential expression of oxidative stress and inflammation related genes in peripheral blood mononuclear cells in response to a low-calorie diet: a nutrigenomics study. Omics. 2008;12(4):251–61.
Dahlman I, Linder K, Arvidsson Nordstrom E, et al. Changes in adipose tissue gene expression with energy-restricted diets in obese women. Am J Clin Nutr. 2005;81(6):1275–85.
Ong KR, Sims AH, Harvie M, et al. Biomarkers of dietary energy restriction in women at increased risk of breast cancer. Canc Prev Res (Philadelphia, Pa). 2009;2(8):720–31.
Capel F, Viguerie N, Vega N, et al. Contribution of energy restriction and macronutrient composition to changes in adipose tissue gene expression during dietary weight-loss programs in obese women. J Clin Endocrinol Metab. 2008;93(11):4315–22.
van Erk MJ, Blom WA, van Ommen B, et al. High-protein and high-carbohydrate breakfasts differentially change the transcriptome of human blood cells. Am J Clin Nutr. 2006;84(5):1233–41.
Diamant M, Blaak EE, de Vos WM. Do nutrient-gut-microbiota interactions play a role in human obesity, insulin resistance and type 2 diabetes? Obes Rev. 2011;12(4):272–81.
Wu X, Ma C, Han L, et al. Molecular characterisation of the faecal microbiota in patients with type II diabetes. Curr Microbiol. 2010;61(1):69–78.
Kalliomaki M, Collado MC, Salminen S, et al. Early differences in fecal microbiota composition in children may predict overweight. Am J Clin Nutr. 2008;87(3):534–8.
Fukushima Y, Kasuga M, Nakao K, et al. Effects of coffee on inflammatory cytokine gene expression in mice fed high-fat diets. J Agric Food Chem. 2009;57(23):11100–5.
Schulze MB, Hoffmann K, Manson JE, et al. Dietary pattern, inflammation, and incidence of type 2 diabetes in women. Am J Clin Nutr. 2005;82(3):675–84. quiz 714–675.
• Chadt A, Yeo GS, Al-Hasani H. Nutrition-/diet-induced changes in gene expression in pancreatic beta-cells. Diabetes Obes Metab. 2012;14 Suppl 3:57–67. This article reviews the application of targeted and genome-wide approaches to diet-induced changes in gene expression in type 2 diabetes target tissue and supports the important utility of experimental studies to address scientific questions that cannot be feasibly answered by human studies.
Anderson NL, Polanski M, Pieper R, et al. The human plasma proteome: a nonredundant list developed by combination of four separate sources. Mol Cell Proteomics. 2004;3(4):311–26.
Wittwer J, Rubio-Aliaga I, Hoeft B, et al. Nutrigenomics in human intervention studies: current status, lessons learned and future perspectives. Mol Nutr Food Res. 2011;55(3):341–58.
Considine RV, Sinha MK, Heiman ML, et al. Serum immunoreactive-leptin concentrations in normal-weight and obese humans. N Engl J Med. 1996;334(5):292–5.
Hotta K, Funahashi T, Arita Y, et al. Plasma concentrations of a novel, adipose-specific protein, adiponectin, in type 2 diabetic patients. Arterioscler Thromb Vasc Biol. 2000;20(6):1595–9.
Norata GD, Ongari M, Garlaschelli K, et al. Plasma resistin levels correlate with determinants of the metabolic syndrome. Eur J Endocrinol. 2007;156(2):279–84.
Vozarova B, Weyer C, Hanson K, et al. Circulating interleukin-6 in relation to adiposity, insulin action, and insulin secretion. Obes Res. 2001;9(7):414–7.
Jiang M, Jia L, Jiang W, et al. Protein disregulation in red blood cell membranes of type 2 diabetic patients. Biochem Biophys Res Commun. 2003;309(1):196–200.
Zhang R, Barker L, Pinchev D, et al. Mining biomarkers in human sera using proteomic tools. Proteomics. 2004;4(1):244–56.
Hwang H, Bowen BP, Lefort N, et al. Proteomics analysis of human skeletal muscle reveals novel abnormalities in obesity and type 2 diabetes. Diabetes. 2010;59(1):33–42.
Boden G, Duan X, Homko C, et al. Increase in endoplasmic reticulum stress-related proteins and genes in adipose tissue of obese, insulin-resistant individuals. Diabetes. 2008;57(9):2438–44.
Fuchs D, Vafeiadou K, Hall WL, et al. Proteomic biomarkers of peripheral blood mononuclear cells obtained from postmenopausal women undergoing an intervention with soy isoflavones. Am J Clin Nutr. 2007;86(5):1369–75.
Fuchs D, Piller R, Linseisen J, et al. The human peripheral blood mononuclear cell proteome responds to a dietary flaxseed-intervention and proteins identified suggest a protective effect in atherosclerosis. Proteomics. 2007;7(18):3278–88.
de Roos B, Geelen A, Ross K, et al. Identification of potential serum biomarkers of inflammation and lipid modulation that are altered by fish oil supplementation in healthy volunteers. Proteomics. 2008;8(10):1965–74.
Duthie SJ, Horgan G, de Roos B, et al. Blood folate status and expression of proteins involved in immune function, inflammation, and coagulation: biochemical and proteomic changes in the plasma of humans in response to long-term synthetic folic acid supplementation. J Proteome Res. 2010;9(4):1941–50.
Bakker GC, van Erk MJ, Pellis L, et al. An antiinflammatory dietary mix modulates inflammation and oxidative and metabolic stress in overweight men: a nutrigenomics approach. Am J Clin Nutr. 2010;91(4):1044–59.
Esposito K, Marfella R, Ciotola M, et al. Effect of a mediterranean-style diet on endothelial dysfunction and markers of vascular inflammation in the metabolic syndrome: a randomized trial. JAMA. 2004;292(12):1440–6.
Nettleton JA, Matijevic N, Follis JL, et al. Associations between dietary patterns and flow cytometry-measured biomarkers of inflammation and cellular activation in the Atherosclerosis Risk in Communities (ARIC) Carotid Artery MRI Study. Atherosclerosis. 2010;212(1):260–7.
Lopez-Garcia E, Schulze MB, Fung TT, et al. Major dietary patterns are related to plasma concentrations of markers of inflammation and endothelial dysfunction. Am J Clin Nutr. 2004;80(4):1029–35.
Garcia-Bailo B, Brenner DR, Nielsen D, et al. Dietary patterns and ethnicity are associated with distinct plasma proteomic groups. Am J Clin Nutr. 2012;95(2):352–61.
Norheim F, Gjelstad IM, Hjorth M, et al. Molecular nutrition research: the modern way of performing nutritional science. Nutr. 2012;4(12):1898–944.
Rezzi S, Ramadan Z, Fay LB, et al. Nutritional metabonomics: applications and perspectives. J Proteome Res. 2007;6(2):513–25.
Wishart DS. Human Metabolome Database: completing the 'human parts list'. Pharmacogenomics. 2007;8(7):683–6.
The Human Metabolome Project [ www.metabolomics.ca ].
Scalbert A, Brennan L, Fiehn O, et al. Mass-spectrometry-based metabolomics: limitations and recommendations for future progress with particular focus on nutrition research. Metabolomics. 2009;5(4):435–58.
• Koek MM, Jellema RH, van der Greef J, et al. Quantitative metabolomics based on gas chromatography mass spectrometry: status and perspectives. Metabolomics. 2011;7(3):307–28. This article provides technological and conceptual challenges as well as progress in the broader field of metabolomics.
Gall WE, Beebe K, Lawton KA, et al. alpha-hydroxybutyrate is an early biomarker of insulin resistance and glucose intolerance in a nondiabetic population. PLoS One. 2010;5(5):e10883.
Xu F, Tavintharan S, Sum CF et al.: Metabolic Signature Shift in Type 2 Diabetes Mellitus Revealed by Mass Spectrometry-based Metabolomics. J Clin Endocrinol Metab 2013.
Mihalik SJ, Goodpaster BH, Kelley DE, et al. Increased levels of plasma acylcarnitines in obesity and type 2 diabetes and identification of a marker of glucolipotoxicity. Obes (Silver Spring). 2010;18(9):1695–700.
Rhee EP, Cheng S, Larson MG, et al. Lipid profiling identifies a triacylglycerol signature of insulin resistance and improves diabetes prediction in humans. J Clin Invest. 2011;121(4):1402–11.
•• 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. 2013;62(2):639–48. This article describes the largest prospective metabolomic study of type 2 diabetes conducted to date.
Suhre K, Meisinger C, Doring A, et al. Metabolic footprint of diabetes: a multiplatform metabolomics study in an epidemiological setting. PLoS One. 2010;5(11):e13953.
Wang TJ, Larson MG, Vasan RS, et al. Metabolite profiles and the risk of developing diabetes. Nat Med. 2011;17(4):448–53.
Wurtz P, Makinen VP, Soininen P et al.: Metabolic Signatures of Insulin Resistance in 7,098 Young Adults. Diabetes 2012.
Wurtz P, Tiainen M, Makinen VP, et al. Circulating metabolite predictors of glycemia in middle-aged men and women. Diabetes Care. 2012;35(8):1749–56.
Newgard CB, An J, Bain JR, et al. A branched-chain amino acid-related metabolic signature that differentiates obese and lean humans and contributes to insulin resistance. Cell Metabol. 2009;9(4):311–26.
Shah SH, Crosslin DR, Haynes CS, et al. Branched-chain amino acid levels are associated with improvement in insulin resistance with weight loss. Diabetologia. 2012;55(2):321–30.
Cheng S, Rhee EP, Larson MG et al.: Metabolite Profiling Identifies Pathways Associated with Metabolic Risk in Humans. Circulation 2012.
Huffman KM, Shah SH, Stevens RD, et al. Relationships between circulating metabolic intermediates and insulin action in overweight to obese, inactive men and women. Diabetes Care. 2009;32(9):1678–83.
Mihalik SJ, Michaliszyn SF, de Las Heras J, et al. Metabolomic Profiling of Fatty Acid and Amino Acid Metabolism in Youth With Obesity and Type 2 Diabetes: Evidence for enhanced mitochondrial oxidation. Diabetes Care. 2012;35(3):605–11.
Boyle KE, Canham JP, Consitt LA, et al. A high-fat diet elicits differential responses in genes coordinating oxidative metabolism in skeletal muscle of lean and obese individuals. J Clin Endocrinol Metab. 2011;96(3):775–81.
Shah SH, Svetkey LP, Newgard CB. Branching out for detection of type 2 diabetes. Cell Metabol. 2011;13(5):491–2.
Jakobsen LH, Kondrup J, Zellner M, et al. Effect of a high protein meat diet on muscle and cognitive functions: a randomised controlled dietary intervention trial in healthy men. Clin Nutr (Edinburgh, Scotland). 2010;30(3):303–11.
Koeth RA, Wang Z, Levison BS, et al. Intestinal microbiota metabolism of l-carnitine, a nutrient in red meat, promotes atherosclerosis. Nat Med. 2013;19(5):576–85.
•• Wang Z, Klipfell E, Bennett BJ, et al. Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease. Nature. 2011;472(7341):57–63. This article presents the first of a series of investigations confirming an important role of gut flora metabolism (potentially modified by diet) in cardiovascular disease development.
Tang WH, Wang Z, Levison BS, et al. Intestinal microbial metabolism of phosphatidylcholine and cardiovascular risk. N Engl J Med. 2013;368(17):1575–84.
Bennett BJ, de Aguiar Vallim TQ, Wang Z, et al. Trimethylamine-N-oxide, a metabolite associated with atherosclerosis, exhibits complex genetic and dietary regulation. Cell Metabol. 2013;17(1):49–60.
Pellis L, van Erk MJ, van Ommen B, et al. Plasma metabolomics and proteomics profiling after a postprandial challenge reveal subtle diet effects on human metabolic status. Metabolomics. 2012;8(2):347–59.
Lankinen M, Schwab U, Gopalacharyulu PV, et al. Dietary carbohydrate modification alters serum metabolic profiles in individuals with the metabolic syndrome. Nutr Metab Cardiovasc Dis. 2010;20(4):249–57.
• Krug S, Kastenmuller G, Stuckler F et al.: The dynamic range of the human metabolome revealed by challenges. Faseb J 2012. This article presents important insight to the potential application of metabolomics for nutrition research in a clinical and population setting.
Altmaier E, Kastenmuller G, Romisch-Margl W, et al. Questionnaire-based self-reported nutrition habits associate with serum metabolism as revealed by quantitative targeted metabolomics. Eur J Epidemiol. 2011;26(2):145–56.
Solanky KS, Bailey NJ, Beckwith-Hall BM, et al. Biofluid 1H NMR-based metabonomic techniques in nutrition research - metabolic effects of dietary isoflavones in humans. J Nutr Biochem. 2005;16(4):236–44.
Stella C, Beckwith-Hall B, Cloarec O, et al. Susceptibility of human metabolic phenotypes to dietary modulation. J Proteome Res. 2006;5(10):2780–8.
Walsh MC, Brennan L, Pujos-Guillot E, et al. Influence of acute phytochemical intake on human urinary metabolomic profiles. Am J Clin Nutr. 2007;86(6):1687–93.
Schwab U, Seppanen-Laakso T, Yetukuri L, et al. Triacylglycerol fatty acid composition in diet-induced weight loss in subjects with abnormal glucose metabolism–the GENOBIN study. PLoS One. 2008;3(7):e2630.
Chorell E, Moritz T, Branth S, et al. Predictive metabolomics evaluation of nutrition-modulated metabolic stress responses in human blood serum during the early recovery phase of strenuous physical exercise. J Proteome Res. 2009;8(6):2966–77.
Redeuil K, Smarrito-Menozzi C, Guy P et al.: Identification of novel circulating coffee metabolites in human plasma by liquid chromatography-mass spectrometry. J Chromatogr A 2011.
Nagy K, Redeuil K, Williamson G, et al. First identification of dimethoxycinnamic acids in human plasma after coffee intake by liquid chromatography-mass spectrometry. J Chromatogr A. 2011;1218(3):491–7.
Stalmach A, Mullen W, Barron D, et al. Metabolite profiling of hydroxycinnamate derivatives in plasma and urine after the ingestion of coffee by humans: identification of biomarkers of coffee consumption. Drug Metab Dispos. 2009;37(8):1749–58.
Kempf K, Herder C, Erlund I, et al. Effects of coffee consumption on subclinical inflammation and other risk factors for type 2 diabetes: a clinical trial. Am J Clin Nutr. 2010;91(4):950–7.
Daykin CA, Van Duynhoven JP, Groenewegen A, et al. Nuclear magnetic resonance spectroscopic based studies of the metabolism of black tea polyphenols in humans. J Agric Food Chem. 2005;53(5):1428–34.
van Velzen EJ, Westerhuis JA, van Duynhoven JP, et al. Phenotyping tea consumers by nutrikinetic analysis of polyphenolic end-metabolites. J Proteome Res. 2009;8(7):3317–30.
Martin FP, Rezzi S, Pere-Trepat E, et al. Metabolic effects of dark chocolate consumption on energy, gut microbiota, and stress-related metabolism in free-living subjects. J Proteome Res. 2009;8(12):5568–79.
Llorach R, Urpi-Sarda M, Tulipani S et al.: Metabolomic fingerprint in patients at high risk of cardiovascular disease by cocoa intervention. Mol Nutr Food Res 2013.
Tulipani S, Llorach R, Jauregui O, et al. Metabolomics unveils urinary changes in subjects with metabolic syndrome following 12-week nut consumption. J Proteome Res. 2011;10(11):5047–58.
Johansson-Persson A, Barri T, Ulmius M, et al. LC-QTOF/MS metabolomic profiles in human plasma after a 5-week high dietary fiber intake. Anal Bioanal Chem. 2013;405(14):4799–809.
McCombie G, Browning LM, Titman CM, et al. omega-3 oil intake during weight loss in obese women results in remodelling of plasma triglyceride and fatty acids. Metabolomics. 2009;5(3):363–74.
Altmaier E, Kastenmuller G, Romisch-Margl W, et al. Variation in the human lipidome associated with coffee consumption as revealed by quantitative targeted metabolomics. Mol Nutr Food Res. 2009;53(11):1357–65.
Adams SH, Hoppel CL, Lok KH, et al. Plasma acylcarnitine profiles suggest incomplete long-chain fatty acid beta-oxidation and altered tricarboxylic acid cycle activity in type 2 diabetic African-American women. J Nutr. 2009;139(6):1073–81.
Fiehn O, Garvey WT, Newman JW, et al. Plasma metabolomic profiles reflective of glucose homeostasis in non-diabetic and type 2 diabetic obese African-American women. PLoS One. 2010;5(12):e15234.
Kim JY, Park JY, Kim OY, et al. Metabolic profiling of plasma in overweight/obese and lean men using ultra performance liquid chromatography and Q-TOF mass spectrometry (UPLC-Q-TOF MS). J Proteome Res. 2010;9(9):4368–75.
Menni C, Zhai G, Macgregor A, et al. Targeted metabolomics profiles are strongly correlated with nutritional patterns in women. Metabolomics. 2013;9(2):506–14.
• Heinzmann SS, Merrifield CA, Rezzi S, et al. Stability and robustness of human metabolic phenotypes in response to sequential food challenges. J Proteome Res. 2012;11(2):643–55. This article presents important insight to the potential application of metabolomics for nutrition research in a clinical and population setting.
Demirkan A, van Duijn CM, Ugocsai P, et al. Genome-wide association study identifies novel loci associated with circulating phospho- and sphingolipid concentrations. PLoS Genet. 2012;8(2):e1002490.
Small KS, Hedman AK, Grundberg E, et al. Identification of an imprinted master trans regulator at the KLF14 locus related to multiple metabolic phenotypes. Nat Genet. 2011;43(6):561–4.
Zhong H, Yang X, Kaplan LM, et al. Integrating pathway analysis and genetics of gene expression for genome-wide association studies. Am J Hum Genet. 2010;86(4):581–91.
Greenawalt DM, Sieberts SK, Cornelis MC, et al. Integrating genetic association, genetics of gene expression, and single nucleotide polymorphism set analysis to identify susceptibility Loci for type 2 diabetes mellitus. Am J Epidemiol. 2012;176(5):423–30.
Yang X. Use of functional genomics to identify candidate genes underlying human genetic association studies of vascular diseases. Arterioscler Thromb Vasc Biol. 2012;32(2):216–22.
Robinette SL, Holmes E, Nicholson JK, et al. Genetic determinants of metabolism in health and disease: from biochemical genetics to genome-wide associations. Genome Med. 2012;4(4):30.
•• Chen R, Mias GI, Li-Pook-Than J, et al. Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell. 2012;148(6):1293–307. This article features the first “integrative personal omics profile” (iPOP) by combining several omic-profiles from a single individual over a 14-month period.
Inouye M, Kettunen J, Soininen P, et al. Metabonomic, transcriptomic, and genomic variation of a population cohort. Mol Syst Biol. 2010;6:441.
Dodd KW, Guenther PM, Freedman LS, et al. Statistical methods for estimating usual intake of nutrients and foods: a review of the theory. J Am Diet Assoc. 2006;106(10):1640–50.
• Sampson JN, Boca SM, Shu XO, et al. Metabolomics in epidemiology: sources of variability in metabolite measurements and implications. Cancer Epidemiol Biomarkers Prev. 2013;22(4):631–40. This article discusses important limitations of metabolomics applied to a population setting.
Kettunen J, Tukiainen T, Sarin AP, et al. Genome-wide association study identifies multiple loci influencing human serum metabolite levels. Nat Genet. 2012;44(3):269–76.
Xie W, Wood AR, Lyssenko V, et al. Genetic variants associated with glycine metabolism and their role in insulin sensitivity and type 2 diabetes. Diabetes. 2013;62(6):2141–50.
Ferrucci L, Perry JR, Matteini A, et al. Common variation in the beta-carotene 15,15'-monooxygenase 1 gene affects circulating levels of carotenoids: a genome-wide association study. Am J Hum Genet. 2009;84(2):123–33.
Kapur K, Johnson T, Beckmann ND, et al. Genome-wide meta-analysis for serum calcium identifies significantly associated SNPs near the calcium-sensing receptor (CASR) gene. PLoS Genet. 2010;6(7):e1001035.
O'Seaghdha CM, Yang Q, Glazer NL, et al. Common variants in the calcium-sensing receptor gene are associated with total serum calcium levels. Hum Mol Genet. 2010;19(21):4296–303.
Kutalik Z, Benyamin B, Bergmann S, et al. Genome-wide association study identifies two loci strongly affecting transferrin glycosylation. Hum Mol Genet. 2011;20(18):3710–7.
McLaren CE, Garner CP, Constantine CC, et al. Genome-wide association study identifies genetic loci associated with iron deficiency. PLoS One. 2011;6(3):e17390.
Benyamin B, McRae AF, Zhu G, et al. Variants in TF and HFE explain approximately 40% of genetic variation in serum-transferrin levels. Am J Hum Genet. 2009;84(1):60–5.
Pichler I, Minelli C, Sanna S, et al. Identification of a common variant in the TFR2 gene implicated in the physiological regulation of serum iron levels. Hum Mol Genet. 2011;20(6):1232–40.
Benyamin B, Ferreira MA, Willemsen G, et al. Common variants in TMPRSS6 are associated with iron status and erythrocyte volume. Nat Genet. 2009;41(11):1173–5.
Tanaka T, Roy CN, Yao W, et al. A genome-wide association analysis of serum iron concentrations. Blood. 2010;115(1):94–6.
Oexle K, Ried JS, Hicks AA, et al. Novel association to the proprotein convertase PCSK7 gene locus revealed by analysing soluble transferrin receptor (sTfR) levels. Hum Mol Genet. 2011;20(5):1042–7.
Meyer TE, Verwoert GC, Hwang SJ et al.: Genome-wide association studies of serum magnesium, potassium, and sodium concentrations identify six Loci influencing serum magnesium levels. PLoS Genet 2010; 6(8).
Mondul AM, Yu K, Wheeler W, et al. Genome-wide association study of circulating retinol levels. Hum Mol Genet. 2011;20(23):4724–31.
Hazra A, Kraft P, Selhub J, et al. Common variants of FUT2 are associated with plasma vitamin B12 levels. Nat Genet. 2008;40(10):1160–2.
Tanaka T, Scheet P, Giusti B, et al. Genome-wide association study of vitamin B6, vitamin B12, folate, and homocysteine blood concentrations. Am J Hum Genet. 2009;84(4):477–82.
Lin X, Lu D, Gao Y, et al. Genome-wide association study identifies novel loci associated with serum level of vitamin B12 in Chinese men. Hum Mol Genet. 2012;21(11):2610–7.
Ahn J, Yu K, Stolzenberg-Solomon R, et al. Genome-wide association study of circulating vitamin D levels. Hum Mol Genet. 2010;19(13):2739–45.
Wang TJ, Zhang F, Richards JB, et al. Common genetic determinants of vitamin D insufficiency: a genome-wide association study. Lancet. 2010;376(9736):180–8.
Lasky-Su J, Lange N, Brehm JM, et al. Genome-wide association analysis of circulating vitamin D levels in children with asthma. Hum Genet. 2012;131(9):1495–505.
Major JM, Yu K, Chung CC, et al. Genome-wide association study identifies three common variants associated with serologic response to vitamin E supplementation in men. J Nutr. 2012;142(5):866–71.
Major JM, Yu K, Wheeler W, et al. Genome-wide association study identifies common variants associated with circulating vitamin E levels. Hum Mol Genet. 2011;20(19):3876–83.
Baik I, Cho NH, Kim SH, et al. Genome-wide association studies identify genetic loci related to alcohol consumption in Korean men. Am J Clin Nutr. 2011;93(4):809–16.
Takeuchi F, Isono M, Nabika T, et al. Confirmation of ALDH2 as a Major locus of drinking behavior and of its variants regulating multiple metabolic phenotypes in a Japanese population. Circ J. 2011;75(4):911–8.
Yang X, Lu X, Wang L, et al. Common variants at 12q24 are associated with drinking behavior in Han Chinese. Am J Clin Nutr. 2013;97(3):545–51.
Schumann G, Coin LJ, Lourdusamy A, et al. Genome-wide association and genetic functional studies identify autism susceptibility candidate 2 gene (AUTS2) in the regulation of alcohol consumption. Proc Natl Acad Sci U S A. 2011;108(17):7119–24.
Sulem P, Gudbjartsson DF, Geller F et al.: Sequence variants at CYP1A1-CYP1A2 and AHR associate with coffee consumption. Hum Mol Genet 2011.
Amin N, Byrne E, Johnson J et al.: Genome-wide association analysis of coffee drinking suggests association with CYP1A1/CYP1A2 and NRCAM. Molecular psychiatry 2011.
Chu AY, Workalemahu T, Paynter NP et al.: Novel locus including FGF21 is associated with dietary macronutrient intake. Hum Mol Genet 2013.
