Association of Gensini score with inflammatory-metabolic markers in women with CAD: a multidimensional analysis

AimsThe diagnosis and assessment of coronary artery disease (CAD) in women remain challenging, as conventional diagnostic tools often show limited sensitivity and risk underdiagnosis. This study aimed to examine the associations between routinely available clinical and laboratory parameters and the severity of coronary artery lesions, quantified by the Gensini score, in female patients. In addition, we sought to develop and validate a predictive model to identify women at high risk of severe atherosclerosis. MethodsThis retrospective, cross-sectional study was conducted at the Second Hospital of Shanxi Medical University and included 91 patients with established CAD. Laboratory assessments comprised complete blood counts, high-sensitivity C-reactive protein (hs-CRP), interleukin-6 (IL-6), lipid profiles, and uric acid (UA). Sleep quality was measured using the Athens Insomnia Scale (AIS). Statistical analyses included Spearman correlation, multiple linear regression, and logistic regression to evaluate the relationships between these variables and the Gensini score, and to assess their predictive value for high-risk disease. ResultsIn univariate analysis, the Gensini score was significantly correlated with inflammatory markers (IL-6: r = 0.61, P < 0.001; hs-CRP: r = 0.43, P = 0.012) and diabetes (r = 0.36, P = 0.037). Multiple linear regression identified insomnia, hypertension, diabetes, IL-6, and hs-CRP as independent predictors of the Gensini score, yielding a high goodness of fit (R{superscript 2} = 0.823, adjusted R{superscript 2} = 0.783, F = 20.19, P < 0.001). A logistic regression model for predicting high-risk disease (Gensini score >20) demonstrated excellent discrimination, with an area under the curve (AUC) of 0.964 in the training set and 0.833 in the test set. ConclusionIn women with CAD, the severity of coronary atherosclerosis is strongly associated with inflammation, metabolic dysregulation, and insomnia, with risk profiles distinct from those observed in men. A significant interaction between hs-CRP and diabetes was identified as a key modulator of CAD severity. These findings highlight the value of integrated statistical models that account for interacting risk factors, offering improved risk stratification and supporting proactive, personalized interventions in female patients with CAD.