Metabolic Signatures and Diagnostic Prediction Models for Coronary Artery Disease and Type 2 Diabetes Mellitus: Insights From an Exploratory Study
Year: 2025
Type: article
Study design: Observational Study
Abstract: ABSTRACT Coronary artery disease (CAD) and type 2 diabetes mellitus (T2DM) often coexist and mutually exacerbate each other by shared mechanisms (including deregulated glucolipid metabolism and insulin resistance), yet their global metabolic signature remain poorly characterized. In this exploratory study, we collected serum samples from 21 CAD patients, 20 T2DM subjects, and 20 healthy individuals (HH). Metabolomics and lipidomics were performed, and bioinformatics analysis identified disease‐related pathways and biomarkers. A machine learning‐based diagnostic prediction model was built using these metabolic biomarkers. Our analysis unveiled significant metabolic resemblance between CAD and T2DM. Apart from higher covariates such as age and weight compared to the healthy population, both conditions demonstrated elevated levels of triglycerides (TGs), diglycerides (DGs), phosphatidylcholines (PCs), and phosphatidylethanolamines (PEs). Concurrently, lipid metabolism‐related signaling pathways were notably activated. Machine learning identified seven lipid metabolites (e.g., TG 16:0/16:0/18:3, PC 31:1, and PE 34:1) that exhibited an auROC ≥ 0.9 for both CAD and T2DM, suggesting their potential as shared biomarkers. A prediction model based on 7‐metabolites achieved predictive accuracies exceeding 95% and 92% for CAD and T2DM, respectively. Furthermore, the model also demonstrated predictive accuracies surpassing 98% and 95% in an external validation cohort, highlighting its robust predictive performance. Through this exploratory study, we have preliminarily delineated the metabolic signatures of CAD and T2DM, and further strengthened the intrinsic relationship between the two diseases from a metabolic perspective.
Source: iNew Medicine
DOI: 10.1002/inm3.70007
Authors: Zhihua Wang, Meiming Su, Ying Li, Yapei Li, Shuo Chen, Ting Peng, Hans Strijdom, Jiangang Wang, Jianping Weng, Suowen Xu
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: hybrid
OpenAlex ID: W7107964014