Laboratory biomarkers, including leukocyte esterase positivity (OR 2.41) and β2-microglobulin (OR 1.52), were identified as significant predictors of coronary artery disease comorbidity phenotypes.
Cross-Sectional (n=544)
No
Haematological, biochemical, and urinary biomarkers differ across CAD phenotypes with metabolic and renal comorbidities, showing moderate discriminatory potential for identifying comorbidity patterns.
Effect estimate: OR 2.41 (95% CI 1.38-4.19)
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 − 7 ). Multivariable logistic regression identified leukocyte esterase positivity (OR 2.41, 95% CI 1.38– 4.19), β 2-microglobulin (OR 1.52, 95% CI 1.16– 2.01), potassium (OR 1.37, 95% CI 1.08– 1.74), glucosuria (OR 0.58, 95% CI 0.35– 0.96), nitrite positivity (OR 1.89, 95% CI 1.07– 3.34), and serum calcium (OR 0.73, 95% CI 0.55– 0.96) as significant predictors of CAD comorbidity phenotypes. 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. Keywords: coronary artery disease, comorbidities, biomarkers, logistic regression, risk stratification
Lai et al. (2026) conducted a cross-sectional in Coronary artery disease with metabolic and renal comorbidities (n=544). Laboratory biomarkers, including leukocyte esterase positivity (OR 2.41) and β2-microglobulin (OR 1.52), were identified as significant predictors of coronary artery disease comorbidity phenotypes.