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Metabolite Signatures of Metabolic Risk Factors and their Longitudinal Changes
Xiaoyan Yin1, Subha Subramanian1, Christine M Willinger1
1Framingham Heart Study (X.Y., S.S., C.M.W., G.C., P.C., B.H.C., S.-J.H., C.S.F., C.J.O., M.G.L., D.L.), Framingham, Massachusetts 01702; Boston University Department of Mathematics and School of Public Health (X.Y., M.G.L.), Boston, Massachusetts 02118; Population Sciences Branch (S.S., C.M.W., G.C., P.C., B.H.C., S.-J.H., C.S.F., C.J.O., D.L.), Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892; BG Medicine, Inc (P.J., X.L., P.M., N.G., A.A.), Waltham, Massachusetts 02451; Department of Medicine (C.S.F.), Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts 02115; Department of Medicine (C.J.O.), Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts 02114; Mount Sinai School of Medicine (V.F.), New York, New York 10029; Centro Nacional de Investigaciones Cardiovasculares (V.F.), 28029 Madrid, Spain; TNO Triskelion BV, Inc (I.B.-P.), 3704 HE Zeist, The Netherlands; Institute of Medical Research A Lanari-IDIM, University of Buenos Aires (S.C.S., C.J.P.), National Scientific and Technical Research Council, Ciudad Autónoma de Buenos Aires C11428, Argentina; and Boston University School of Medicine (D.L.), Boston, Massachusetts 02118.
Context:
Metabolic dysregulation underlies key metabolic risk factors—obesity, dyslipidemia, and dysglycemia.
Objective:
To uncover mechanistic links between metabolomic dysregulation and metabolic risk by testing metabolite associations with risk factors cross-sectionally and with risk factor changes over time.
Design:
Cross-sectional—discovery samples (n = 650; age, 36–69 years) from the Framingham Heart Study (FHS) and replication samples (n = 670; age, 61–76 years) from the BioImage Study, both following a factorial design sampled from high vs low strata of body mass index, lipids, and glucose. Longitudinal—FHS participants (n = 554) with 5–7 years of follow-up for risk factor changes.
Setting:
Observational studies.
Participants:
Cross-sectional samples with or without obesity, dysglycemia, and dyslipidemia, excluding prevalent cardiovascular disease and diabetes or dyslipidemia treatment. Age- and sex-matched by group.
Interventions:
None.
Main Outcome Measure(S):
Gas chromatography-mass spectrometry detected 119 plasma metabolites. Cross-sectional associations with obesity, dyslipidemia, and dysglycemia were tested in discovery, with external replication of 37 metabolites. Single- and multi-metabolite markers were tested for association with longitudinal changes in risk factors.
Results:
Cross-sectional metabolite associations were identified with obesity (n = 26), dyslipidemia (n = 21), and dysglycemia (n = 11) in discovery. Glutamic acid, lactic acid, and sitosterol associated with all three risk factors in meta-analysis (P < 4.5 × 10−4). Metabolites associated with longitudinal risk factor changes were enriched for bioactive lipids. Multi-metabolite panels explained 2.5–15.3% of longitudinal changes in metabolic traits.
Conclusions:
Cross-sectional results implicated dysregulated glutamate cycling and amino acid metabolism in metabolic risk. Certain bioactive lipids were associated with risk factors cross-sectionally and over time, suggesting their upstream role in risk factor progression. Functional studies are needed to validate findings and facilitate translation into treatments or preventive measures.
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