Metabolomics insights into early type 2 diabetes pathogenesis and detection in individuals with normal fasting glucose

Jordi Merino; Aaron Leong; Ching-Ti Liu; Bianca Porneala; Geoffrey A Walford; Marcin von Grotthuss; Thomas J Wang; Jason Flannick; Josée Dupuis; Daniel Levy; Robert E Gerszten; Jose C Florez; James B Meigs

doi:10.1007/s00125-018-4599-x

Metabolomics insights into early type 2 diabetes pathogenesis and detection in individuals with normal fasting glucose

Diabetologia. 2018 Jun;61(6):1315-1324. doi: 10.1007/s00125-018-4599-x. Epub 2018 Apr 6.

Authors

Jordi Merino^{1

2}, Aaron Leong^{2

3}, Ching-Ti Liu⁴, Bianca Porneala³, Geoffrey A Walford^{1

2}, Marcin von Grotthuss², Thomas J Wang⁵, Jason Flannick^{1

2}, Josée Dupuis^{4

6}, Daniel Levy^{6

7}, Robert E Gerszten^{8

9}, Jose C Florez^{1

2

10}, James B Meigs^{11

12

13}

Affiliations

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.

Keywords: Metabolomics; Normoglycaemia; Type 2 diabetes pathophysiology; Type 2 diabetes prediction.

Publication types

Observational Study
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Adult
Aged
Area Under Curve
Blood Glucose / metabolism*
Computational Biology
Diabetes Mellitus, Type 2 / blood*
Diabetes Mellitus, Type 2 / metabolism
Female
Glycated Hemoglobin / metabolism*
Glycine / metabolism
Humans
Incidence
Male
Mendelian Randomization Analysis
Metabolomics*
Middle Aged
Phenylalanine / metabolism
Prospective Studies
ROC Curve
Risk Factors
Tandem Mass Spectrometry
Taurine / metabolism

Substances

Blood Glucose
Glycated Hemoglobin A
Taurine
Phenylalanine
Glycine

Abstract

Publication types

MeSH terms

Substances

Grants and funding