Evidence map›Paper›PMID 42004235›Full record

ArticleInternational journal of general medicine2026

Laboratory Biomarker Profiles and Phenotypic Discrimination in Coronary Artery Disease with Metabolic and Renal Comorbidities: A Cross-Sectional Study.

Xiaojing Lai, Shiqin Zhong, Chun Lin, Zufu Cheng, Hua Li

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Article in International journal of general 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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5 authors.

Xiaojing LaiDepartment of Cardiology, Guangzhou Liwan Central Hospital, Guangzhou, Guangdong Province, People's Republic of China.
Shiqin ZhongDepartment of Cardiology, Guangzhou Liwan Central Hospital, Guangzhou, Guangdong Province, People's Republic of China.
Chun LinDepartment of Clinical Laboratory, Guangzhou Liwan Central Hospital, Guangzhou, Guangdong Province, People's Republic of China.
Zufu ChengGuangzhou Labway Clinical Laboratory, Guangzhou, Guangdong Province, People's Republic of China.
Hua LiDepartment of Internal Medicine, Guangzhou Liwan Central Hospital, Guangzhou, Guangdong Province, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Coronary artery disease (CAD) frequently coexists with metabolic and renal comorbidities, including type 2 diabetes mellitus (T2DM), hyperuricemia (HUA), and chronic kidney disease (CKD), which may influence laboratory biomarker profiles. This study aimed to characterize haematological, biochemical, and urinary parameters across CAD phenotypes and identify laboratory predictors associated with these comorbidity patterns. Methods: A retrospective cross-sectional study was conducted at Guangzhou Liwan Central Hospital between January 1 and December 31, 2024, including 544 adult patients with CAD. Diagnoses of CAD, T2DM, HUA, and CKD were defined according to established clinical criteria documented in hospital electronic medical records. Patients were stratified into seven phenotypic subgroups based on the presence of T2DM, HUA, and CKD. Demographic characteristics and laboratory parameters-including haematological indices, biochemical markers, and urinary findings-were extracted from electronic records. Between-group comparisons were performed using ANOVA and chi-square tests, and multivariable logistic regression was used to identify laboratory predictors associated with CAD comorbidity phenotypes. Results: Significant differences in demographic and laboratory parameters were observed across CAD phenotypes. Gender distribution differed significantly between groups (p = 0.004). The CAD+HUA group had the highest mean age (84.7 ± 10.1 years), whereas the CAD+T2DM+CKD group had the lowest (76.4 ± 11.0 years; p = 1.77 × 10 Conclusion: Haematological, biochemical, and urinary biomarkers differ across CAD phenotypes with metabolic and renal comorbidities. These laboratory indicators show moderate discriminatory potential for identifying CAD comorbidity patterns, although further validation in larger prospective cohorts is required.

Indexed as

biomarkerscomorbiditiescoronary artery diseaselogistic regressionrisk stratification

Identifiers

PMID42004235
PMCPMC13086037

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