Identification of serum metabolites associated with risk of type 2 diabetes using a targeted metabolomic approach

Anna Floegel, Norbert Stefan, Zhonghao Yu, Kristin Mühlenbruch, Dagmar Drogan, Hans-Georg Joost, Andreas Fritsche, Hans-Ulrich Häring, Martin Hrabě de Angelis, Annette Peters, Michael Roden, Cornelia Prehn, Rui Wang-Sattler, Thomas Illig, Matthias B Schulze, Jerzy Adamski, Heiner Boeing, Tobias Pischon, Anna Floegel, Norbert Stefan, Zhonghao Yu, Kristin Mühlenbruch, Dagmar Drogan, Hans-Georg Joost, Andreas Fritsche, Hans-Ulrich Häring, Martin Hrabě de Angelis, Annette Peters, Michael Roden, Cornelia Prehn, Rui Wang-Sattler, Thomas Illig, Matthias B Schulze, Jerzy Adamski, Heiner Boeing, Tobias Pischon

Abstract

Metabolomic discovery of biomarkers of type 2 diabetes (T2D) risk may reveal etiological pathways and help to identify individuals at risk for disease. We prospectively investigated the association between serum metabolites measured by targeted metabolomics and risk of T2D in the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam (27,548 adults) among all incident cases of T2D (n = 800, mean follow-up 7 years) and a randomly drawn subcohort (n = 2,282). Flow injection analysis tandem mass spectrometry was used to quantify 163 metabolites, including acylcarnitines, amino acids, hexose, and phospholipids, in baseline serum samples. Serum hexose; phenylalanine; and diacyl-phosphatidylcholines C32:1, C36:1, C38:3, and C40:5 were independently associated with increased risk of T2D and serum glycine; sphingomyelin C16:1; acyl-alkyl-phosphatidylcholines C34:3, C40:6, C42:5, C44:4, and C44:5; and lysophosphatidylcholine C18:2 with decreased risk. Variance of the metabolites was largely explained by two metabolite factors with opposing risk associations (factor 1 relative risk in extreme quintiles 0.31 [95% CI 0.21-0.44], factor 2 3.82 [2.64-5.52]). The metabolites significantly improved T2D prediction compared with established risk factors. They were further linked to insulin sensitivity and secretion in the Tübingen Family study and were partly replicated in the independent KORA (Cooperative Health Research in the Region of Augsburg) cohort. The data indicate that metabolic alterations, including sugar metabolites, amino acids, and choline-containing phospholipids, are associated early on with a higher risk of T2D.

Figures

FIG. 1.
FIG. 1.
Two metabolite factors associated with risk of T2D. Presented is a two-dimensional factor loading plot obtained from PCA. For simple interpretation, metabolites that cluster together in the plot are related to one another. Metabolites presented in blue are associated with decreased risk of T2D, whereas metabolites presented in red are associated with increased risk of T2D. More specifically, the factor loadings represent the correlation coefficients of individual metabolites with corresponding metabolite factors and may range from −1 to 1. They were identified by PCA based on the correlation matrix of all metabolites significantly associated with risk of T2D in the EPIC-Potsdam study. An orthogonal varimax rotation was used, and two factors were retained because they accounted for >50% of the observed variance. a, acyl; aa, diacyl; ae, acyl-alkyl; C3, propionylcarnitine; Gly, glycine; H1, hexose; PC, phosphatidylcholine; Phe, phenylalanine; SM, sphingomyelin; Trp, tryptophan; Tyr, tyrosine; Val; valine; xLeu, isoleucine.
FIG. 2.
FIG. 2.
Relative contribution of metabolites to predict T2D in EPIC-Potsdam. Presented are ROC curves comparing different multivariable-adjusted models to predict T2D, including the DRS, the identified metabolites, glucose (Glc), and HbA1c. The DRS (16) combines information on several diabetes risk factors, such as diet, lifestyle, and anthropometry, to estimate risk of developing T2D. The DRS is computed according to the following formula: DRS = (7.4 × waist circumference [cm]) − (2.4 × height [cm]) + (4.3 × age [years]) + (46 × hypertension [self-report]) + (49 × red meat [each 150 g/day]) – (9 × whole-grain bread [each 50 g/day]) – (4 × coffee [each 150 g/day]) – (20 × moderate alcohol [between 10 and 40 g/day]) – (2 × physical activity [h/week]) + (24 × former smoker) + (64 × current heavy smoker [≥ 20 cigarettes/day]). Metabolites are hexose; phenylalanine; glycine; sphingomyelin C16:1; diacyl-phosphatidylcholines C32:1, C36:1, C38:3, and C40:5; acyl-alkyl-phosphatidylcholines C34:3, C40:6, C42:5, C44:4, and C44:5; and lysophosphatidylcholine C18:2.
FIG. 3.
FIG. 3.
Examples of metabolites associated with risk of T2D. ▲Metabolites with an increased risk (hexose, phenylalanine, and diacyl-phosphatidylcholines [PCs] C32:1, C36:1, C38:3, and C40:5). *Metabolites with a decreased risk (glycine; sphingomyelin C16:1; acyl-alkyl-PCs C34:3, C40:6, C42:5, C44:4, and C44:5; and lysophosphatidylcholine C18:2). Note that the mass spectrometric assay used does not distinguish molecular lipids and sugar types among hexoses. Therefore, formulas are given for a molecule corresponding to molecular mass and composition. Positions of double bonds and chain length may vary if more than one acid residue is present. Arrows represent many reactions, and key intermediates are given in the brackets. aa, diacyl; ae, acyl-alkyl. (A high-quality color representation of this figure is available in the online issue.)

References

    1. DeFronzo RA. Banting Lecture. From the triumvirate to the ominous octet: a new paradigm for the treatment of type 2 diabetes mellitus. Diabetes 2009;58:773–795
    1. Peters AL, Davidson MB, Schriger DL, Hasselblad V, Meta-analysis Research Group on the Diagnosis of Diabetes Using Glycated Hemoglobin Levels A clinical approach for the diagnosis of diabetes mellitus: an analysis using glycosylated hemoglobin levels. JAMA 1996;276:1246–1252
    1. Herder C, Baumert J, Zierer A, et al. Immunological and cardiometabolic risk factors in the prediction of type 2 diabetes and coronary events: MONICA/KORA Augsburg case-cohort study. PLoS ONE 2011;6:e19852.
    1. Abu-Qamar M, Wilson A. Evidence-based decision-making: the case for diabetes care. Int J Evid Based Healthc 2007;5:254–260
    1. Swellam M, Sayed Mahmoud And M, Abdel-Fatah Ali A. Clinical implications of adiponectin and inflammatory biomarkers in type 2 diabetes mellitus. Dis Markers 2009;27:269–278
    1. Li S, Shin HJ, Ding EL, van Dam RM. Adiponectin levels and risk of type 2 diabetes: a systematic review and meta-analysis. JAMA 2009;302:179–188
    1. Ford ES, Schulze MB, Bergmann MM, Thamer C, Joost HG, Boeing H. Liver enzymes and incident diabetes: findings from the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam Study. Diabetes Care 2008;31:1138–1143
    1. Stefan N, Fritsche A, Weikert C, et al. Plasma fetuin-A levels and the risk of type 2 diabetes. Diabetes 2008;57:2762–2767
    1. Bain JR, Stevens RD, Wenner BR, Ilkayeva O, Muoio DM, Newgard CB. Metabolomics applied to diabetes research: moving from information to knowledge. Diabetes 2009;58:2429–2443
    1. 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 Metab 2009;9:311–326
    1. Wopereis S, Rubingh CM, van Erk MJ, et al. Metabolic profiling of the response to an oral glucose tolerance test detects subtle metabolic changes. PLoS ONE 2009;4:e4525.
    1. Fiehn O, Garvey WT, Newman JW, Lok KH, Hoppel CL, Adams SH. Plasma metabolomic profiles reflective of glucose homeostasis in non-diabetic and type 2 diabetic obese African-American women. PLoS ONE 2010;5:e15234.
    1. Suhre K, Meisinger C, Döring A, et al. Metabolic footprint of diabetes: a multiplatform metabolomics study in an epidemiological setting. PLoS ONE 2010;5:e13953.
    1. 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;139:1073–1081
    1. Hu FB. Metabolic profiling of diabetes: from black-box epidemiology to systems epidemiology. Clin Chem 2011;57:1224–1226
    1. 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
    1. Boeing H, Korfmann A, Bergmann MM. Recruitment procedures of EPIC-Germany. European Investigation into Cancer and Nutrition. Ann Nutr Metab 1999;43:205–215
    1. Kroke A, Bergmann MM, Lotze G, Jeckel A, Klipstein-Grobusch K, Boeing H. Measures of quality control in the German component of the EPIC study. European Prospective Investigation into Cancer and Nutrition. Ann Nutr Metab 1999;43:216–224
    1. Boeing H, Wahrendorf J, Becker N. EPIC-Germany—a source for studies into diet and risk of chronic diseases. European Investigation into Cancer and Nutrition. Ann Nutr Metab 1999;43:195–204
    1. Weikert C, Stefan N, Schulze MB, et al. Plasma fetuin-A levels and the risk of myocardial infarction and ischemic stroke. Circulation 2008;118:2555–2562
    1. Montonen J, Drogan D, Joost HG, et al. Estimation of the contribution of biomarkers of different metabolic pathways to risk of type 2 diabetes. Eur J Epidemiol 2011;26:29–38
    1. Bergmann MM, Bussas U, Boeing H. Follow-up procedures in EPIC-Germany—data quality aspects. European Prospective Investigation into Cancer and Nutrition. Ann Nutr Metab 1999;43:225–234
    1. Schienkiewitz A, Schulze MB, Hoffmann K, Kroke A, Boeing H. Body mass index history and risk of type 2 diabetes: results from the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam Study. Am J Clin Nutr 2006;84:427–433
    1. Rothman KJ, Greenland S, Lash TL. Case-control studies. In Modern epidemiology. 3rd ed. Rothman KJ, Greenland S, Eds. Philadelphia, Lippincott-Williams & Wilkins, 2008, p. 111–127
    1. Meisinger C, Strassburger K, Heier M, et al. Prevalence of undiagnosed diabetes and impaired glucose regulation in 35-59-year-old individuals in Southern Germany: the KORA F4 Study. Diabet Med 2010;27:360–362
    1. Rathmann W, Strassburger K, Heier M, et al. Incidence of type 2 diabetes in the elderly German population and the effect of clinical and lifestyle risk factors: KORA S4/F4 cohort study. Diabet Med 2009;26:1212–1219
    1. Kowall B, Rathmann W, Strassburger K, Meisinger C, Holle R, Mielck A. Socioeconomic status is not associated with type 2 diabetes incidence in an elderly population in Germany: KORA S4/F4 cohort study. J Epidemiol Community Health 2011;65:606–612
    1. Stefan N, Kantartzis K, Machann J, et al. Identification and characterization of metabolically benign obesity in humans. Arch Intern Med 2008;168:1609–1616
    1. Matsuda M, DeFronzo RA. Insulin sensitivity indices obtained from oral glucose tolerance testing: comparison with the euglycemic insulin clamp. Diabetes Care 1999;22:1462–1470
    1. Römisch-Margl W, Prehn C, Bogumil R, Röhring C, Suhre K, Adamski J. Procedure for tissue sample preparation and metabolite extraction for high throughput targeted metabolomics. Metabolomics 11 March 2011 [Epub ahead of print]
    1. Floegel A, Drogan D, Wang-Sattler R, et al. Reliability of serum metabolite concentrations over a 4-month period using a targeted metabolomic approach. PLoS ONE 2011;6:e21103.
    1. Prentice RL. Design issues in cohort studies. Stat Methods Med Res 1995;4:273–292
    1. Holm S. A simple sequentially rejective multiple test procedure. Scand J Stat 1979;6:65–70
    1. Hardy RJ, Thompson SG. Detecting and describing heterogeneity in meta-analysis. Stat Med 1998;17:841–856
    1. Wang TJ, Larson MG, Vasan RS, et al. Metabolite profiles and the risk of developing diabetes. Nat Med 2011;17:448–453
    1. DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 1988;44:837–845
    1. Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. BMJ 2003;327:557–560
    1. Hsieh FY, Lavori PW. Sample-size calculations for the Cox proportional hazards regression model with nonbinary covariates. Control Clin Trials 2000;21:552–560
    1. Ruderman NB. Muscle amino acid metabolism and gluconeogenesis. Annu Rev Med 1975;26:245–258
    1. Sekhar RV, McKay SV, Patel SG, et al. Glutathione synthesis is diminished in patients with uncontrolled diabetes and restored by dietary supplementation with cysteine and glycine. Diabetes Care 2011;34:162–167
    1. Koves TR, Ussher JR, Noland RC, et al. Mitochondrial overload and incomplete fatty acid oxidation contribute to skeletal muscle insulin resistance. Cell Metab 2008;7:45–56
    1. Muoio DM, Neufer PD. Lipid-induced mitochondrial stress and insulin action in muscle. Cell Metab 2012;15:595–605
    1. Vianey-Liaud C, Divry P, Gregersen N, Mathieu M. The inborn errors of mitochondrial fatty acid oxidation. J Inherit Metab Dis 1987;10(Suppl 1):159–200
    1. She P, Van Horn C, Reid T, Hutson SM, Cooney RN, Lynch CJ. Obesity-related elevations in plasma leucine are associated with alterations in enzymes involved in branched-chain amino acid metabolism. Am J Physiol Endocrinol Metab 2007;293:E1552–E1563
    1. Tremblay F, Brûlé S, Hee Um S, et al. Identification of IRS-1 Ser-1101 as a target of S6K1 in nutrient- and obesity-induced insulin resistance. Proc Natl Acad Sci U S A 2007;104:14056–14061
    1. Montonen J, Järvinen R, Knekt P, Heliövaara M, Reunanen A. Consumption of sweetened beverages and intakes of fructose and glucose predict type 2 diabetes occurrence. J Nutr 2007;137:1447–1454
    1. Magnusson CD, Haraldsson GG. Ether lipids. Chem Phys Lipids 2011;164:315–340
    1. Cole LK, Vance JE, Vance DE. Phosphatidylcholine biosynthesis and lipoprotein metabolism. Biochim Biophys Acta 2012;182:754–761
    1. Quehenberger O, Dennis EA. The human plasma lipidome. N Engl J Med 2011;365:1812–1823
    1. Wallner S, Schmitz G. Plasmalogens the neglected regulatory and scavenging lipid species. Chem Phys Lipids 2011;164:573–589
    1. Raubenheimer PJ, Nyirenda MJ, Walker BR. A choline-deficient diet exacerbates fatty liver but attenuates insulin resistance and glucose intolerance in mice fed a high-fat diet. Diabetes 2006;55:2015–2020
    1. Jacobs RL, Devlin C, Tabas I, Vance DE. Targeted deletion of hepatic CTP:phosphocholine cytidylyltransferase alpha in mice decreases plasma high density and very low density lipoproteins. J Biol Chem 2004;279:47402–47410
    1. Pietiläinen KH, Sysi-Aho M, Rissanen A, et al. Acquired obesity is associated with changes in the serum lipidomic profile independent of genetic effects—a monozygotic twin study. PLoS ONE 2007;2:e218.
    1. 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
    1. 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;121:1402–1411
    1. Yu Z, Kastenmüller G, He Y, et al. Differences between human plasma and serum metabolite profiles. PLoS ONE 2011;6:e21230.

Source: PubMed

3
Abonnere