ArticleFrontiers in medicine2026
Development of a CHD risk prediction model using novel lipid markers based on lipidomics and VAP technology.
Article in Frontiers in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objective: This study aimed to develop a novel risk prediction model for coronary heart disease (CHD) based on lipid subfraction and lipidomics data. Methods: Lipid subfraction analysis was conducted on 624 subjects (312 CHD, 312 controls) using Vertical Auto Profile (VAP). The participants were randomly divided into a training cohort ( Results: A total of 2,335 lipid molecules across eight classes were identified. After FDR correction, the differential lipids remained statistically significant. CHD patients exhibited significant upregulation of RLP-C and sdLDL-C-related lipid components, while HDL2B-related lipids were markedly downregulated. KEGG pathway enrichment analysis revealed that differential lipids were mainly enriched in glycerophospholipid metabolism, choline metabolism in cancer, and fat digestion and absorption. Multivariate logistic regression identified Lp(a) (OR = 1.288), RLP-C (OR = 3.848), sdLDL-C (OR = 5.317), age (OR = 1.053), systolic blood pressure (OR = 1.075), and fasting plasma glucose (OR = 3.903) as independent risk factors for CHD, whereas HDL2B (OR = 1.416 × 10 Conclusion: This study successfully constructed a multivariate logistic regression risk prediction model integrating novel lipid subfractions (sdLDL-C, RLP-C, HDL2B, Lp(a)) and routine clinical variables (age, SBP, FPG). The model exhibits acceptable predictive performance and may provide a complementary tool for CHD risk assessment, pending external validation in larger prospective cohorts.
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