Jordi Merino1,2, Aaron Leong2,3, Ching-Ti Liu4, Bianca Porneala3, Geoffrey A Walford1,2, Marcin von Grotthuss2, Thomas J Wang5, Jason Flannick1,2, Josée Dupuis4,6, Daniel Levy6,7, Robert E Gerszten8,9, Jose C Florez1,2,10, James B Meigs11,12,13. 1. Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA. 2. Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA. 3. Division of General Internal Medicine, Massachusetts General Hospital, 100 Cambridge St, Boston, MA, 02114, USA. 4. Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA. 5. Division of Cardiovascular Medicine, Vanderbilt University, Nashville, TN, USA. 6. The Framingham Heart Study, National Heart, Lung and Blood Institute, National Institutes of Health, Framingham, MA, USA. 7. The Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, NIH, Bethesda, MD, USA. 8. Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA. 9. Broad Institute of MIT and Harvard Program in Metabolism, Cambridge, MA, USA. 10. Department of Medicine, Harvard Medical School, Boston, MA, USA. 11. Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA. JMEIGS@mgh.harvard.edu. 12. Division of General Internal Medicine, Massachusetts General Hospital, 100 Cambridge St, Boston, MA, 02114, USA. JMEIGS@mgh.harvard.edu. 13. Department of Medicine, Harvard Medical School, Boston, MA, USA. JMEIGS@mgh.harvard.edu.
Abstract
AIMS/HYPOTHESIS: Identifying the metabolite profile of individuals with normal fasting glucose (NFG [<5.55 mmol/l]) who progressed to type 2 diabetes may give novel insights into early type 2 diabetes disease interception and detection. METHODS: We conducted a population-based prospective study among 1150 Framingham Heart Study Offspring cohort participants, age 40-65 years, with NFG. Plasma metabolites were profiled by LC-MS/MS. Penalised regression models were used to select measured metabolites for type 2 diabetes incidence classification (training dataset) and to internally validate the discriminatory capability of selected metabolites beyond conventional type 2 diabetes risk factors (testing dataset). RESULTS: Over a follow-up period of 20 years, 95 individuals with NFG developed type 2 diabetes. Nineteen metabolites were selected repeatedly in the training dataset for type 2 diabetes incidence classification and were found to improve type 2 diabetes risk prediction beyond conventional type 2 diabetes risk factors (AUC was 0.81 for risk factors vs 0.90 for risk factors + metabolites, p = 1.1 × 10-4). Using pathway enrichment analysis, the nitrogen metabolism pathway, which includes three prioritised metabolites (glycine, taurine and phenylalanine), was significantly enriched for association with type 2 diabetes risk at the false discovery rate of 5% (p = 0.047). In adjusted Cox proportional hazard models, the type 2 diabetes risk per 1 SD increase in glycine, taurine and phenylalanine was 0.65 (95% CI 0.54, 0.78), 0.73 (95% CI 0.59, 0.9) and 1.35 (95% CI 1.11, 1.65), respectively. Mendelian randomisation demonstrated a similar relationship for type 2 diabetes risk per 1 SD genetically increased glycine (OR 0.89 [95% CI 0.8, 0.99]) and phenylalanine (OR 1.6 [95% CI 1.08, 2.4]). CONCLUSIONS/ INTERPRETATION: In individuals with NFG, information from a discrete set of 19 metabolites improved prediction of type 2 diabetes beyond conventional risk factors. In addition, the nitrogen metabolism pathway and its components emerged as a potential effector of earliest stages of type 2 diabetes pathophysiology.
AIMS/HYPOTHESIS: Identifying the metabolite profile of individuals with normal fasting glucose (NFG [<5.55 mmol/l]) who progressed to type 2 diabetes may give novel insights into early type 2 diabetes disease interception and detection. METHODS: We conducted a population-based prospective study among 1150 Framingham Heart Study Offspring cohort participants, age 40-65 years, with NFG. Plasma metabolites were profiled by LC-MS/MS. Penalised regression models were used to select measured metabolites for type 2 diabetes incidence classification (training dataset) and to internally validate the discriminatory capability of selected metabolites beyond conventional type 2 diabetes risk factors (testing dataset). RESULTS: Over a follow-up period of 20 years, 95 individuals with NFG developed type 2 diabetes. Nineteen metabolites were selected repeatedly in the training dataset for type 2 diabetes incidence classification and were found to improve type 2 diabetes risk prediction beyond conventional type 2 diabetes risk factors (AUC was 0.81 for risk factors vs 0.90 for risk factors + metabolites, p = 1.1 × 10-4). Using pathway enrichment analysis, the nitrogen metabolism pathway, which includes three prioritised metabolites (glycine, taurine and phenylalanine), was significantly enriched for association with type 2 diabetes risk at the false discovery rate of 5% (p = 0.047). In adjusted Cox proportional hazard models, the type 2 diabetes risk per 1 SD increase in glycine, taurine and phenylalanine was 0.65 (95% CI 0.54, 0.78), 0.73 (95% CI 0.59, 0.9) and 1.35 (95% CI 1.11, 1.65), respectively. Mendelian randomisation demonstrated a similar relationship for type 2 diabetes risk per 1 SD genetically increased glycine (OR 0.89 [95% CI 0.8, 0.99]) and phenylalanine (OR 1.6 [95% CI 1.08, 2.4]). CONCLUSIONS/ INTERPRETATION: In individuals with NFG, information from a discrete set of 19 metabolites improved prediction of type 2 diabetes beyond conventional risk factors. In addition, the nitrogen metabolism pathway and its components emerged as a potential effector of earliest stages of type 2 diabetes pathophysiology.
Entities:
Keywords:
Metabolomics; Normoglycaemia; Type 2 diabetes pathophysiology; Type 2 diabetes prediction
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