. 2016 Apr;101(4):1779-89.

doi: 10.1210/jc.2015-2555. Epub 2016 Feb 23.

Metabolite Signatures of Metabolic Risk Factors and their Longitudinal Changes

Xiaoyan Yin¹, Subha Subramanian¹, Christine M Willinger¹, George Chen¹, Peter Juhasz¹, Paul Courchesne¹, Brian H Chen¹, Xiaohang Li¹, Shih-Jen Hwang¹, Caroline S Fox¹, Christopher J O'Donnell¹, Pieter Muntendam¹, Valentin Fuster¹, Ivana Bobeldijk-Pastorova¹, Silvia C Sookoian¹, Carlos J Pirola¹, Neal Gordon¹, Aram Adourian¹, Martin G Larson¹, Daniel Levy¹

Affiliations

Affiliation

¹ Framingham 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.

PMID: 26908103
PMCID: PMC4880163
DOI: 10.1210/jc.2015-2555

Metabolite Signatures of Metabolic Risk Factors and their Longitudinal Changes

Xiaoyan Yin et al. J Clin Endocrinol Metab. 2016 Apr.

. 2016 Apr;101(4):1779-89.

doi: 10.1210/jc.2015-2555. Epub 2016 Feb 23.

Authors

Affiliation

¹ Framingham 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.

PMID: 26908103
PMCID: PMC4880163
DOI: 10.1210/jc.2015-2555

Abstract

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.

PubMed Disclaimer

Cited by

A comparison of associations of body mass index and dual-energy x-ray absorptiometry measured percentage fat and total fat with global serum metabolites in young women.
Dorgan JF, Ryan AS, LeBlanc ES, Van Horn L, Magder LS, Snetselaar LG, Zhang Y, Dallal CM, Jung S, Shepherd JA. Dorgan JF, et al. Obesity (Silver Spring). 2023 Feb;31(2):525-536. doi: 10.1002/oby.23619. Epub 2023 Jan 15. Obesity (Silver Spring). 2023. PMID: 36642094 Free PMC article.
The OMICs Window into Nonalcoholic Fatty Liver Disease (NAFLD).
Carulli L, Zanca G, Schepis F, Villa E. Carulli L, et al. Metabolites. 2019 Feb 1;9(2):25. doi: 10.3390/metabo9020025. Metabolites. 2019. Retraction in: Metabolites. 2019 Apr 11;9(4):E70. doi: 10.3390/metabo9040070. PMID: 30717274 Free PMC article. Retracted. Review.
Metabolomics prospect of obesity and metabolic syndrome; a systematic review.
Payab M, Tayanloo-Beik A, Falahzadeh K, Mousavi M, Salehi S, Djalalinia S, Ebrahimpur M, Rezaei N, Rezaei-Tavirani M, Larijani B, Arjmand B, Gilany K. Payab M, et al. J Diabetes Metab Disord. 2021 Nov 26;21(1):889-917. doi: 10.1007/s40200-021-00917-w. eCollection 2022 Jun. J Diabetes Metab Disord. 2021. PMID: 35673462 Free PMC article. Review.
Nutrients, Physical Activity, and Mitochondrial Dysfunction in the Setting of Metabolic Syndrome.
Lemos GO, Torrinhas RS, Waitzberg DL. Lemos GO, et al. Nutrients. 2023 Feb 28;15(5):1217. doi: 10.3390/nu15051217. Nutrients. 2023. PMID: 36904216 Free PMC article. Review.
Plasma lactate as a marker of metabolic health: Implications of elevated lactate for impairment of aerobic metabolism in the metabolic syndrome.
Jones TE, Pories WJ, Houmard JA, Tanner CJ, Zheng D, Zou K, Coen PM, Goodpaster BH, Kraus WE, Dohm GL. Jones TE, et al. Surgery. 2019 Nov;166(5):861-866. doi: 10.1016/j.surg.2019.04.017. Epub 2019 Jun 25. Surgery. 2019. PMID: 31253418 Free PMC article.

See all "Cited by" articles

References

1. National Heart, Lung, and Blood Institute. Fact Book for Fiscal Year 2002. Bethesda, MD: US Department of Health and Human Services, Public Health Service, National Institutes of Health; 2003.
1. Lopez AD, Mathers CD, Ezzati M, Jamison DT, Murray CJ. Global and regional burden of disease and risk factors, 2001: systematic analysis of population health data. Lancet. 2006;367:1747–1757. - PubMed
1. Khot UN, Khot MB, Bajzer CT, et al. Prevalence of conventional risk factors in patients with coronary heart disease. JAMA. 2003;290:898–904. - PubMed
1. Wilson PW, D'Agostino RB, Levy D, Belanger AM, Silbershatz H, Kannel WB. Prediction of coronary heart disease using risk factor categories. Circulation. 1998;97:1837–1847. - PubMed
1. ATP III. Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) final report. Circulation. 2002;106:3143–3421. - PubMed

Publication types

Actions
Actions

MeSH terms

Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions

Grants and funding

N01 HC025195/HL/NHLBI NIH HHS/United States

LinkOut - more resources

Full Text Sources
Other Literature Sources
- scite Smart Citations
Medical
- MedlinePlus Health Information

Save citation to file

Email citation

Add to Collections

Add to My Bibliography

Your saved search

Create a file for external citation management software

Your RSS Feed

Metabolite Signatures of Metabolic Risk Factors and their Longitudinal Changes

Affiliation

Metabolite Signatures of Metabolic Risk Factors and their Longitudinal Changes

Authors

Affiliation

Abstract

Similar articles

Cited by

References

Publication types

MeSH terms

Grants and funding

LinkOut - more resources

Full Text Sources

Other Literature Sources

Medical