Non-targeted metabolomics combined with genetic analyses identifies bile acid synthesis and phospholipid metabolism as being associated with incident type 2 diabetes

Aims/hypothesisIdentification of novel biomarkers for type 2 diabetes and their genetic determinants could lead to improved understanding of causal pathways and improve risk prediction.MethodsIn this study, we used data from non-targeted metabolomics performed using liquid chromatography coupled with tandem mass spectrometry in three Swedish cohorts (Uppsala Longitudinal Study of Adult Men [ULSAM], n = 1138; Prospective Investigation of the Vasculature in Uppsala Seniors [PIVUS], n = 970; TwinGene, n = 1630). Metabolites associated with impaired fasting glucose (IFG) and/or prevalent type 2 diabetes were assessed for associations with incident type 2 diabetes in the three cohorts followed by replication attempts in the Cooperative Health Research in the Region of Augsburg (KORA) S4 cohort (n = 855). Assessment of the association of metabolite-regulating genetic variants with type 2 diabetes was done using data from a meta-analysis of genome-wide association studies.ResultsOut of 5961 investigated metabolic features, 1120 were associated with prevalent type 2 diabetes and IFG and 70 were annotated to metabolites and replicated in the three cohorts. Fifteen metabolites were associated with incident type 2 diabetes in the four cohorts combined (358 events) following adjustment for age, sex, BMI, waist circumference and fasting glucose. Novel findings included associations of higher values of the bile acid deoxycholic acid and monoacylglyceride 18:2 and lower concentrations of cortisol with type 2 diabetes risk. However, adding metabolites to an existing risk score improved model fit only marginally. A genetic variant within the CYP7A1 locus, encoding the rate-limiting enzyme in bile acid synthesis, was found to be associated with lower concentrations of deoxycholic acid, higher concentrations of LDL-cholesterol and lower type 2 diabetes risk. Variants in or near SGPP1, GCKR and FADS1/2 were associated with diabetes-associated phospholipids and type 2 diabetes.Conclusions/interpretationWe found evidence that the metabolism of bile acids and phospholipids shares some common genetic origin with type 2 diabetes.Access to research materialsMetabolomics data have been deposited in the Metabolights database, with accession numbers MTBLS93 (TwinGene), MTBLS124 (ULSAM) and MTBLS90 (PIVUS).

[1]  A. Morris,et al.  Sphingosine 1-Phosphate (S1P) Regulates Glucose-stimulated Insulin Secretion in Pancreatic Beta Cells* , 2012, The Journal of Biological Chemistry.

[2]  G. Rossi,et al.  Diagnosis and Classification of Diabetes Mellitus The information that follows is based largely on the reports of the Expert Committee on the Diagnosis and Classification of Diabetes (Diabetes Care 20:1183–1197, 1997, and Diabetes Care 26:3160–3167, 2003). , 2008, Diabetes Care.

[3]  N. de Pedro,et al.  The Farnesoid X Receptor Controls Gene Expression in a Ligand- and Promoter-selective Fashion* , 2004, Journal of Biological Chemistry.

[4]  Gabi Kastenmüller,et al.  Biochemical insights from population studies with genetics and metabolomics. , 2016, Archives of biochemistry and biophysics.

[5]  A. Morris,et al.  1 SPHINGOSINE-1-PHOSPHATE ( S 1 P ) REGULATES GLUCOSE-STIMULATED INSULIN SECRETION IN PANCREATIC BETA CELLS , 2012 .

[6]  P. Meikle,et al.  Plasma Lysophosphatidylcholine Levels Are Reduced in Obesity and Type 2 Diabetes , 2012, PloS one.

[7]  R. Abagyan,et al.  XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. , 2006, Analytical chemistry.

[8]  H. Löwel,et al.  High prevalence of undiagnosed diabetes mellitus in Southern Germany: Target populations for efficient screening. The KORA survey 2000 , 2003, Diabetologia.

[9]  V. Mootha,et al.  Metabolite profiles and the risk of developing diabetes , 2011, Nature Network Boston.

[10]  Stephen Burgess,et al.  Mendelian randomization: where are we now and where are we going? , 2015, International journal of epidemiology.

[11]  R. Mägi,et al.  Using Genetic Variants to Assess the Relationship Between Circulating Lipids and Type 2 Diabetes , 2015, Diabetes.

[12]  S. Loke,et al.  Colesevelam for type 2 diabetes mellitus. , 2012, The Cochrane database of systematic reviews.

[13]  Jingyuan Fu,et al.  Pleiotropic Effects of Lipid Genes on Plasma Glucose, HbA1c, and HOMA-IR Levels , 2014, Diabetes.

[14]  Large-scale non-targeted metabolomic profiling in three human population-based studies , 2014, bioRxiv.

[15]  Hynek Pikhart,et al.  HMG-coenzyme A reductase inhibition, type 2 diabetes, and bodyweight: evidence from genetic analysis and randomised trials , 2015, The Lancet.

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

[17]  Tamara S. Roman,et al.  New genetic loci link adipose and insulin biology to body fat distribution , 2014, Nature.

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

[19]  Ralph B D'Agostino,et al.  Prediction of incident diabetes mellitus in middle-aged adults: the Framingham Offspring Study. , 2007, Archives of internal medicine.

[20]  Nigel W. Hardy,et al.  Proposed minimum reporting standards for chemical analysis , 2007, Metabolomics.

[21]  M. Johannesson,et al.  The Swedish Twin Registry: Establishment of a Biobank and Other Recent Developments , 2012, Twin Research and Human Genetics.

[22]  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.

[23]  D. Accili,et al.  Insulin Resistance in Humans Is Associated With Increased Plasma 12-Hydroxylated Bile Acids , 2013 .

[24]  Tanya M. Teslovich,et al.  Discovery and refinement of loci associated with lipid levels , 2013, Nature Genetics.

[25]  Anders Larsson,et al.  Large-scale Metabolomic Profiling Identifies Novel Biomarkers for Incident Coronary Heart Disease , 2014, PLoS genetics.

[26]  R Holle,et al.  KORA - A Research Platform for Population Based Health Research , 2005, Gesundheitswesen (Bundesverband der Arzte des Offentlichen Gesundheitsdienstes (Germany)).

[27]  Christian Gieger,et al.  New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk , 2010, Nature Genetics.

[28]  Lars Lind,et al.  A Comparison of Three Different Methods to Evaluate Endothelium-Dependent Vasodilation in the Elderly: The Prospective Investigation of the Vasculature in Uppsala Seniors (PIVUS) Study , 2005, Arteriosclerosis, thrombosis, and vascular biology.

[29]  Tanya M. Teslovich,et al.  Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes , 2012, Nature Genetics.

[30]  M. Nauck,et al.  Incretin-based therapies: where will we be 50 years from now? , 2015, Diabetologia.

[31]  F. Harrell,et al.  Evaluating the yield of medical tests. , 1982, JAMA.

[32]  M. Pirinen,et al.  Genome-wide study for circulating metabolites identifies 62 loci and reveals novel systemic effects of LPA , 2016, Nature Communications.

[33]  J. Degen,et al.  Functional characterization of enzymes catalyzing ceramide phosphoethanolamine biosynthesis in mice[S] , 2015, Journal of Lipid Research.

[34]  H. Hedstrand.  A study of middle-aged men with particular reference to risk factors for cardiovascular disease. , 1975, Upsala journal of medical sciences. Supplement.

[35]  C. Gieger,et al.  Genetics of human metabolism: an update , 2015, Human molecular genetics.

[36]  Yurii S. Aulchenko,et al.  Genome-Wide Association Study Identifies Novel Loci Associated with Circulating Phospho- and Sphingolipid Concentrations , 2012, PLoS genetics.

[37]  John P. Overington,et al.  An atlas of genetic influences on human blood metabolites , 2014, Nature Genetics.

[38]  W. Guan,et al.  Genome-Wide Association Study Identifies Novel Loci Associated With Concentrations of Four Plasma Phospholipid Fatty Acids in the De Novo Lipogenesis Pathway: Results From the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium , 2013, Circulation. Cardiovascular genetics.

[39]  Christian Gieger,et al.  Metabolic Footprint of Diabetes: A Multiplatform Metabolomics Study in an Epidemiological Setting , 2010, PloS one.

[40]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[41]  Tanya M. Teslovich,et al.  Biological, Clinical, and Population Relevance of 95 Loci for Blood Lipids , 2010, Nature.

[42]  Ross M. Fraser,et al.  Genetic studies of body mass index yield new insights for obesity biology , 2015, Nature.

[43]  S. Milstien,et al.  Sphingosine-1-phosphate phosphohydrolase in regulation of sphingolipid metabolism and apoptosis , 2002, The Journal of cell biology.

[44]  Patrik Magnusson,et al.  American Journal of Epidemiology Practice of Epidemiology Risk Prediction Measures for Case-cohort and Nested Case-control Designs: an Application to Cardiovascular Disease , 2022 .