TY - JOUR
T1 - Metabolomics insights into early type 2 diabetes pathogenesis and detection in individuals with normal fasting glucose
AU - Merino, Jordi
AU - Leong, Aaron
AU - Liu, Ching Ti
AU - Porneala, Bianca
AU - Walford, Geoffrey A.
AU - von Grotthuss, Marcin
AU - Wang, Thomas J.
AU - Flannick, Jason
AU - Dupuis, Josée
AU - Levy, Daniel
AU - Gerszten, Robert E.
AU - Florez, Jose C.
AU - Meigs, James B.
N1 - Funding Information:
Funding This work was partially supported by the National Heart, Lung and Blood Institute’s FHS (contract no. N01-HC-25195 and HHSN268201500001I) and its contract with Affymetrix, Inc. for genotyping (contract no. N02-HL-6-4278) and metabolomic services (R01-HL081572) and supported by U01 DK078616 and NIDDK K24DK080140 (JBM). JM was supported by a postdoctoral fellowship funded by the European Commission Horizon 2020 program and Marie Skłodowska-Curie actions (H2020-MSCA-IF-2015-703787). JCF is a Massachusetts General Hospital Research Scholar and is supported by NIDDK K24 DK110550.
Funding Information:
This research was conducted in part using data and resources from the FHS of the National Heart Lung and Blood Institute of the National Institutes of Health and Boston University School of Medicine. The analyses reflect intellectual input and resource development from the FHS investigators participating in the SNP Health Association Resource (SHARe) project. The authors wish to thank the GoT2D Consortium for access to their data. JM, GAW, CTL, JD, JCF and JBM participated in the design and conception of the study. JM, BP, MG and JF acquired and analysed the data. All authors participated in the interpretation of data, drafting of the manuscript and its revisions and approved the final version. JM and JBM are the guarantors of this work and, as such, had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. JCF has received consulting honoraria from Boehringer-Ingelheim, Merck and Intarcia Therapeutics. All other authors declare that there is no duality of interest associated with their contribution to this manuscript.
Publisher Copyright:
© 2018, Springer-Verlag GmbH Germany, part of Springer Nature.
PY - 2018/6/1
Y1 - 2018/6/1
N2 - 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.
AB - 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.
KW - Metabolomics
KW - Normoglycaemia
KW - Type 2 diabetes pathophysiology
KW - Type 2 diabetes prediction
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U2 - 10.1007/s00125-018-4599-x
DO - 10.1007/s00125-018-4599-x
M3 - Article
C2 - 29626220
AN - SCOPUS:85045033940
SN - 0012-186X
VL - 61
SP - 1315
EP - 1324
JO - Diabetologia
JF - Diabetologia
IS - 6
ER -