Mathematical modeling of the risk of cardiometabolic comorbidity development: a multi-biomarker approach

This study presents the results of a comprehensive clinical, instrumental, and biochemical investigation aimed at developing logistic regression-based mathematical models to predict the risk of developing cardiometabolic comorbidities in patients with arterial hypertension in combination with obesity (OB) and/or type 2 diabetes mellitus. The research included an in-depth evaluation of clinical parameters, laboratory markers, and echocardiographic indicators to identify independent predictors with significant diagnostic and prognostic value. Among the key predictors were the isovolumic Relaxation Time of the left ventricle, cardiotrophin-1 (CTF-1), cystatin C, neutrophil gelatinase-associated lipocalin, N-terminal pro-B-type natriuretic peptide, 25-hydroxyvitamin D (25(OH)D), the ratio of early to late diastolic filling velocities (VE/VA), body mass index, relative wall thickness of the left ventricle, right atrial area, glycated hemoglobin (HbA1c), catestatin concentration, glomerular filtration rate, and left atrial diameter. Based on these markers, several logistic regression equations were developed for stratifying the risk of developing obesity, type 2 diabetes, or combined comorbidity in patients with hypertension. These models demonstrated high predictive performance, with an Area Under the (ROC) Curve (AUC) exceeding 0.94, indicating excellent diagnostic accuracy. The practical implementation of these models enables clinicians to identify patients at high risk at earlier stages, tailor preventive and therapeutic interventions according to individual biomarker profiles, and improve long-term outcomes through more precise clinical monitoring. The proposed approach supports the broader integration of personalized medicine into internal medicine and cardiology, particularly in the context of managing complex cardiometabolic polymorbidity, and contributes to optimizing decision-making processes in everyday clinical practice. Keywords: arterial hypertension, type 2 diabetes mellitus, obesity, biomarkers, prediction.