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Metabolomic Pattern Predicts Incident Coronary Heart Disease.

TitleMetabolomic Pattern Predicts Incident Coronary Heart Disease.
Publication TypeJournal Article
Year of Publication2019
AuthorsWang Z, Zhu C, Nambi V, Morrison AC, Folsom AR, Ballantyne CM, Boerwinkle E
Secondary AuthorsYu B
JournalArterioscler Thromb Vasc Biol
Volume39
Issue7
Pagination1475-1482
Date Published2019 07
ISSN1524-4636
KeywordsAtherosclerosis, Coronary Disease, Female, Humans, Male, Metabolomics, Middle Aged, Proportional Hazards Models, Prospective Studies, Risk Factors
Abstract

Objective- Alterations in the serum metabolome may be detectable in at-risk individuals before the onset of coronary heart disease (CHD). Identifying metabolomic signatures associated with CHD may provide insight into disease pathophysiology and prevention. Approach and Results- Metabolomic profiling (chromatography-mass spectrometry) was performed in 2232 African Americans and 1366 European Americans from the ARIC study (Atherosclerosis Risk in Communities). We applied Cox regression with least absolute shrinkage and selection operator to select metabolites associated with incident CHD. A metabolite risk score was constructed to evaluate whether the metabolite risk score predicted CHD risk beyond traditional risk factors. After 30 years of follow-up, we observed 633 incident CHD cases. Thirty-two metabolites were selected by least absolute shrinkage and selection operator to be associated with CHD, and 19 of the 32 showed significant individual associations with CHD, including a sugar substitute, erythritol. Theophylline (hazard ratio [95% CI] =1.16 [1.09-1.25]) and gamma-linolenic acid (hazard ratio [95% CI] =0.89 [0.81-0.97]) showed the greatest positive and negative associations with CHD, respectively. A 1 SD greater standardized metabolite risk score was associated with a 1.37-fold higher risk of CHD (hazard ratio [95% CI] =1.37 [1.27-1.47]). Adding the metabolite risk score to the traditional risk factors significantly improved model predictive performance (30-year risk prediction: Δ C statistics [95% CI] =0.016 [0.008-0.024], continuous net reclassification index [95% CI] =0.522 [0.480-0.556], integrated discrimination index [95% CI] =0.038 [0.019-0.065]). Conclusions- We identified 19 metabolites from known and novel metabolic pathways that collectively improved CHD risk prediction. Visual Overview- An online visual overview is available for this article.

DOI10.1161/ATVBAHA.118.312236
Alternate JournalArterioscler Thromb Vasc Biol
PubMed ID31092011
PubMed Central IDPMC6839698
Grant List17SDG33661228 / AHA / American Heart Association-American Stroke Association / United States
U01 HG004402 / HG / NHGRI NIH HHS / United States
HHSN268201700002C / HL / NHLBI NIH HHS / United States
HHSN268201700001I / HL / NHLBI NIH HHS / United States
HHSN268201700004I / HL / NHLBI NIH HHS / United States
HHSN268201700004C / HL / NHLBI NIH HHS / United States
HHSN268201700003I / HL / NHLBI NIH HHS / United States
HHSN268201700005C / HL / NHLBI NIH HHS / United States
HHSN268201700001C / HL / NHLBI NIH HHS / United States
HHSN268201700003C / HL / NHLBI NIH HHS / United States
HHSN268201700002I / HL / NHLBI NIH HHS / United States
HHSN268201700005I / HL / NHLBI NIH HHS / United States