Tove Fall1,2, Samira Salihovic3,4, Stefan Brandmaier5,6, Christoph Nowak3,4, Andrea Ganna3,4,7,8,9, Stefan Gustafsson3,4, Corey D Broeckling10, Jessica E Prenni10,11, Gabi Kastenmüller12, Annette Peters6,13,14, Patrik K Magnusson9, Rui Wang-Sattler5,6,14, Vilmantas Giedraitis15, Christian Berne16, Christian Gieger5,6,14, Nancy L Pedersen9, Erik Ingelsson3,4,17, Lars Lind16. 1. Department of Medical Sciences, Molecular Epidemiology, Uppsala University, Box 1115, S - 751 41, Uppsala, Sweden. tove.fall@medsci.uu.se. 2. Science for Life Laboratory, Uppsala University, Uppsala, Sweden. tove.fall@medsci.uu.se. 3. Department of Medical Sciences, Molecular Epidemiology, Uppsala University, Box 1115, S - 751 41, Uppsala, Sweden. 4. Science for Life Laboratory, Uppsala University, Uppsala, Sweden. 5. Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany. 6. Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany. 7. Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA. 8. Analytical and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA. 9. Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. 10. Proteomics and Metabolomics Facility, Colorado State University, Fort Collins, CO, USA. 11. Department of Biochemistry and Molecular Biology, Colorado State University, Fort Collins, CO, USA. 12. Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany. 13. Department of Environmental Health, Harvard School of Public Health, Boston, MA, USA. 14. German Center for Diabetes Research (DZD), München-Neuherberg, Germany. 15. Department of Public Health and Caring Sciences, Uppsala University, Uppsala, Sweden. 16. Department of Medical Sciences, Uppsala University, Uppsala, Sweden. 17. Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Abstract
AIMS/HYPOTHESIS: Identification of novel biomarkers for type 2 diabetes and their genetic determinants could lead to improved understanding of causal pathways and improve risk prediction. METHODS: In 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. RESULTS: Out 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/ INTERPRETATION: We found evidence that the metabolism of bile acids and phospholipids shares some common genetic origin with type 2 diabetes. ACCESS TO RESEARCH MATERIALS: Metabolomics data have been deposited in the Metabolights database, with accession numbers MTBLS93 (TwinGene), MTBLS124 (ULSAM) and MTBLS90 (PIVUS).
AIMS/HYPOTHESIS: Identification of novel biomarkers for type 2 diabetes and their genetic determinants could lead to improved understanding of causal pathways and improve risk prediction. METHODS: In 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. RESULTS: Out 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 aciddeoxycholic 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/ INTERPRETATION: We found evidence that the metabolism of bile acids and phospholipids shares some common genetic origin with type 2 diabetes. ACCESS TO RESEARCH MATERIALS: Metabolomics data have been deposited in the Metabolights database, with accession numbers MTBLS93 (TwinGene), MTBLS124 (ULSAM) and MTBLS90 (PIVUS).
Entities:
Keywords:
Genetic; Metabolomics; Prediction; Type 2 diabetes
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