Abstract

Background

Incidence rates of cardiovascular disease (CVD) are increasing, partly driven by the diabetes epidemic. Novel prediction tools and modifiable treatment targets are needed to enhance risk assessment and management. Plasma metabolite associations with subclinical atherosclerosis were investigated in the Diabetes Heart Study (DHS), a cohort enriched for type 2 diabetes (T2D).

Methods

The analysis included 700 DHS participants, 438 African Americans (AAs), and 262 European Americans (EAs), in whom coronary artery calcium (CAC) was assessed using ECG-gated computed tomography. Plasma metabolomics using liquid chromatography-mass spectrometry identified 853 known metabolites. An ancestry-specific marginal model incorporating generalized estimating equations examined associations between metabolites and CAC (log-transformed (CAC + 1) as outcome measure). Models were adjusted for age, sex, BMI, diabetes duration, date of plasma collection, time between plasma collection and CT exam, low-density lipoprotein cholesterol (LDL-C), and statin use.

Results

At an FDR-corrected p-value < 0.05, 33 metabolites were associated with CAC in AAs and 36 in EAs. The androgenic steroids, fatty acid, phosphatidylcholine, and bile acid metabolism subpathways were associated with CAC in AAs, whereas fatty acid, lysoplasmalogen, and branched-chain amino acid (BCAA) subpathways were associated with CAC in EAs.

Conclusions

Strikingly different metabolic signatures were associated with subclinical coronary atherosclerosis in AA and EA DHS participants.

Details

Title
Plasma metabolomic profiling in subclinical atherosclerosis: the Diabetes Heart Study
Author
Parag Anilkumar Chevli; Freedman, Barry I; Fang-Chi, Hsu; Xu, Jianzhao; Rudock, Megan E; Ma, Lijun; Parks, John S; Palmer, Nicholette D; Shapiro, Michael D  VIAFID ORCID Logo 
Pages
1-12
Section
Original investigation
Publication year
2021
Publication date
2021
Publisher
BioMed Central
e-ISSN
14752840
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2611232631
Copyright
© 2021. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.