Plasma metabolomics identified novel metabolites associated with risk of type 2 diabetes in two prospective cohorts of Chinese adults.

BACKGROUND Metabolomics studies in Caucasians have identified a number of novel metabolites in association with the risk of type 2 diabetes (T2D). However, few prospective metabolomic studies are available in Chinese populations. In the present study, we sought to identify novel metabolites consistently associated with incident T2D in two independent cohorts of Chinese adults. METHODS We performed targeted metabolomics (52 metabolites) of fasting plasma samples by liquid chromatography-mass spectrometry in two prospective case-control studies nested within the Dongfeng-Tongji (DFTJ) cohort and Jiangsu Non-communicable Disease (JSNCD) cohort. After following for 4.61 ± 0.15 and 7.57 ± 1.13 years, respectively, 1039 and 520 eligible participants developed incident T2D in these two cohorts, and controls were 1:1 matched with cases by age (± 5 years) and sex. Multivariate conditional logistic regression models were constructed to identify metabolites associated with future T2D risk in both cohorts. RESULTS We identified four metabolites consistently associated with an increased risk of developing T2D in the two cohorts, including alanine, phenylalanine, tyrosine and palmitoylcarnitine. In the meta-analysis of two cohorts, the odds ratios (95% confidence intervals, CIs) comparing extreme quartiles were 1.79 (1.32-2.42) for alanine, 1.91 (1.41-2.60) for phenylalanine, 1.85 (1.37-2.48) for tyrosine and 1.63 (1.21-2.20) for palmitoylcarnitine (all Ptrend ≤ 0.01). CONCLUSIONS We confirmed the association of alanine, phenylalanine and tyrosine with future T2D risk and further identified palmitoylcarnitine as a novel metabolic marker of incident T2D in two prospective cohorts of Chinese adults. Our findings might provide new aetiological insight into the development of T2D.

[1]  Thomas J. Wang,et al.  Metabolite Profiles of Diabetes Incidence and Intervention Response in the Diabetes Prevention Program , 2016, Diabetes.

[2]  R. Wellard,et al.  The use of metabolomics to monitor simultaneous changes in metabolic variables following supramaximal low volume high intensity exercise , 2015, Metabolomics.

[3]  C. Matthews,et al.  Plasma metabolomic profiles in association with type 2 diabetes risk and prevalence in Chinese adults , 2015, Metabolomics.

[4]  Wanchang Lin,et al.  Untargeted metabolic profiling identifies altered serum metabolites of type 2 diabetes mellitus in a prospective, nested case control study. , 2015, Clinical chemistry.

[5]  Sophie V. Eastwood,et al.  Diabetes risk and amino acid profiles: cross-sectional and prospective analyses of ethnicity, amino acids and diabetes in a South Asian and European cohort from the SABRE (Southall And Brent REvisited) Study , 2015, Diabetologia.

[6]  Anup M Oommen,et al.  Investigating correlations in the altered metabolic profiles of obese and diabetic subjects in a South Indian Asian population using an NMR-based metabolomic approach. , 2015, Molecular bioSystems.

[7]  R. McPherson,et al.  Acylcarnitines: potential implications for skeletal muscle insulin resistance , 2015, FASEB journal : official publication of the Federation of American Societies for Experimental Biology.

[8]  Dean P. Jones,et al.  Novel Metabolic Markers for the Risk of Diabetes Development in American Indians , 2014, Diabetes Care.

[9]  Albert Koulman,et al.  Differences in the prospective association between individual plasma phospholipid saturated fatty acids and incident type 2 diabetes: the EPIC-InterAct case-cohort study , 2014, The lancet. Diabetes & endocrinology.

[10]  C. Parikh,et al.  Key concepts and limitations of statistical methods for evaluating biomarkers of kidney disease. , 2014, Journal of the American Society of Nephrology : JASN.

[11]  Xiu-ying Zhang,et al.  Human serum acylcarnitine profiles in different glucose tolerance states. , 2014, Diabetes research and clinical practice.

[12]  M. Stumvoll,et al.  Serum Levels of Acylcarnitines Are Altered in Prediabetic Conditions , 2013, PloS one.

[13]  R. Gerszten,et al.  A Plasma Long‐Chain Acylcarnitine Predicts Cardiovascular Mortality in Incident Dialysis Patients , 2013, Journal of the American Heart Association.

[14]  Yong Zhou,et al.  Obesity and diabetes related plasma amino acid alterations. , 2013, Clinical biochemistry.

[15]  R. Vasan,et al.  2-Aminoadipic acid is a biomarker for diabetes risk. , 2013, The Journal of clinical investigation.

[16]  Jiang He,et al.  Prevalence and control of diabetes in Chinese adults. , 2013, JAMA.

[17]  E. Boerwinkle,et al.  Associations between metabolomic compounds and incident heart failure among African Americans: the ARIC Study. , 2013, American journal of epidemiology.

[18]  F. Hu,et al.  Cohort Profile: the Dongfeng-Tongji cohort study of retired workers. , 2013, International journal of epidemiology.

[19]  A. Peters,et al.  Identification of Serum Metabolites Associated With Risk of Type 2 Diabetes Using a Targeted Metabolomic Approach , 2013, Diabetes.

[20]  David S. Wishart,et al.  HMDB 3.0—The Human Metabolome Database in 2013 , 2012, Nucleic Acids Res..

[21]  Christian Gieger,et al.  Novel biomarkers for pre-diabetes identified by metabolomics , 2012, Molecular systems biology.

[22]  Jussi Paananen,et al.  Hyperglycemia and a Common Variant of GCKR Are Associated With the Levels of Eight Amino Acids in 9,369 Finnish Men , 2012, Diabetes.

[23]  Tuija Tammelin,et al.  Metabolic Signatures of Insulin Resistance in 7,098 Young Adults , 2012, Diabetes.

[24]  T. Lehtimäki,et al.  Circulating Metabolite Predictors of Glycemia in Middle-Aged Men and Women , 2012, Diabetes Care.

[25]  Duo Li,et al.  Serum levels of polyunsaturated fatty acids are low in Chinese men with metabolic syndrome, whereas serum levels of saturated fatty acids, zinc, and magnesium are high. , 2012, Nutrition research.

[26]  P. Marquez,et al.  Toward a healthy and harmonious life in China : stemming the rising tide of non-communicable diseases , 2011 .

[27]  Joshua D. Knowles,et al.  Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry , 2011, Nature Protocols.

[28]  Frank B. Hu,et al.  Globalization of Diabetes , 2011, Diabetes Care.

[29]  S. Carr,et al.  Lipid profiling identifies a triacylglycerol signature of insulin resistance and improves diabetes prediction in humans. , 2011, The Journal of clinical investigation.

[30]  V. Mootha,et al.  Metabolite profiles and the risk of developing diabetes , 2011, Nature Medicine.

[31]  V. Basevi Diagnosis and Classification of Diabetes Mellitus , 2011, Diabetes Care.

[32]  S. Sharp,et al.  Fatty acids measured in plasma and erythrocyte-membrane phospholipids and derived by food-frequency questionnaire and the risk of new-onset type 2 diabetes: a pilot study in the European Prospective Investigation into Cancer and Nutrition (EPIC)-Norfolk cohort. , 2010, The American journal of clinical nutrition.

[33]  F. Toledo,et al.  Increased Levels of Plasma Acylcarnitines in Obesity and Type 2 Diabetes and Identification of a Marker of Glucolipotoxicity , 2010, Obesity.

[34]  E. Fukusaki,et al.  Serum metabolomics as a novel diagnostic approach for pancreatic cancer , 2010, Metabolomics.

[35]  E. Tai,et al.  Insulin resistance is associated with a metabolic profile of altered protein metabolism in Chinese and Asian-Indian men , 2010, Diabetologia.

[36]  Brett R. Wenner,et al.  Metabolomics Applied to Diabetes Research , 2009, Diabetes.

[37]  Svati H Shah,et al.  A branched-chain amino acid-related metabolic signature that differentiates obese and lean humans and contributes to insulin resistance. , 2009, Cell metabolism.

[38]  Ralph A. DeFronzo,et al.  From the Triumvirate to the Ominous Octet: A New Paradigm for the Treatment of Type 2 Diabetes Mellitus , 2009, Diabetes.

[39]  N. Sattar,et al.  Novel biochemical risk factors for type 2 diabetes: pathogenic insights or prediction possibilities? , 2008, Diabetologia.

[40]  M. Pencina,et al.  Evaluating the added predictive ability of a new marker: From area under the ROC curve to reclassification and beyond , 2008, Statistics in medicine.

[41]  Yu Li,et al.  Identification of IRS-1 Ser-1101 as a target of S6K1 in nutrient- and obesity-induced insulin resistance , 2007, Proceedings of the National Academy of Sciences.

[42]  D. Matthews An overview of phenylalanine and tyrosine kinetics in humans. , 2007, The Journal of nutrition.

[43]  Ying Zhang,et al.  HMDB: the Human Metabolome Database , 2007, Nucleic Acids Res..

[44]  E. Boyko,et al.  Type 2 diabetes prevalence in Asian Americans: results of a national health survey. , 2004, Diabetes care.

[45]  J. Pankow,et al.  Plasma fatty acid composition and incidence of diabetes in middle-aged adults: the Atherosclerosis Risk in Communities (ARIC) Study. , 2003, The American journal of clinical nutrition.

[46]  Peter Nowotny,et al.  Mechanism of amino acid-induced skeletal muscle insulin resistance in humans. , 2002, Diabetes.

[47]  Piero Rinaldo,et al.  Fatty acid oxidation disorders. , 2002, Annual review of physiology.

[48]  G. Paolisso,et al.  Glucose handling, diabetes and ageing. , 1995, Hormone research.

[49]  R. Sherwin,et al.  Amino acid and protein metabolism in diabetes mellitus. , 1977, Archives of internal medicine.

[50]  F. Snyder Lipid Metabolism in Mammals , 1977, Monographs in Lipid Research.

[51]  W. A. Müller,et al.  The effect of alanine on glucagon secretion. , 1971, The Journal of clinical investigation.

[52]  H. Munro,et al.  Mammalian protein metabolism , 1964 .