TY - JOUR
T1 - Metabolite signatures of metabolic risk factors and their longitudinal changes
AU - Yin, Xiaoyan
AU - Subramanian, Subha
AU - Willinger, Christine M.
AU - Chen, George
AU - Juhasz, Peter
AU - Courchesne, Paul
AU - Chen, Brian H.
AU - Li, Xiaohang
AU - Hwang, Shih Jen
AU - Fox, Caroline S.
AU - O'Donnell, Christopher J.
AU - Muntendam, Pieter
AU - Fuster, Valentin
AU - Bobeldijk-Pastorova, Ivana
AU - Sookoian, Silvia C.
AU - Pirola, Carlos J.
AU - Gordon, Neal
AU - Adourian, Aram
AU - Larson, Martin G.
AU - Levy, Daniel
N1 - Publisher Copyright:
Copyright © 2016 by the Endocrine Society.
PY - 2016/4
Y1 - 2016/4
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85010411567
U2 - 10.1210/jc.2015-2555
DO - 10.1210/jc.2015-2555
M3 - Article
C2 - 26908103
AN - SCOPUS:85010411567
SN - 0021-972X
VL - 101
SP - 1779
EP - 1789
JO - Journal of Clinical Endocrinology and Metabolism
JF - Journal of Clinical Endocrinology and Metabolism
IS - 4
ER -