IMPACT OF PREVENTIVE SCREENING PROGRAMS ON EARLY DETECTION OF CARDIOMETABOLIC DISEASES

Abstract

Cardiometabolic diseases such as type 2 diabetes, high blood pressure, and obesity are an increasing health burden in the world, with a significant percentage of the disease remaining undiagnosed until late stages. Population screening is important, but the best multiparametric models using both traditional and new biomarkers have not been developed, especially in younger adults below the age of fifty years. This cross-sectional research study involved the participation of 1,086 adults aged between twenty and ninety years in urban primary healthcare facilities. The participants were evaluated with detailed measurements of anthropometry, blood pressure, fasting blood biochemistry of glucose, insulin, and lipid profile, salivary biomarkers of glucose, uric acid, and cortisol, and bioelectrical impedance evaluation of phase angle and area of visceral fats. To predict undiagnosed metabolic syndrome based on harmonized Joint Interim Statement criteria, nine logistic regression models were developed, and model performance was assessed based on area under the receiver operating characteristic curve, calibration, net reclassification improvement, and decision curve. Metabolic syndrome was 28.6 percent prevalent and previously undiagnosed. The elastic net regularized model had the best discriminative performance with an area under the curve of 0.914 and a 95 percent confidence interval between 0.894 and 0.934 with a sensitivity of 88.3 percent and specificity of 88.9 percent. The strongest association was observed with salivary glucose with an odds ratio of 3.436 and a p less than 0.001, then HOMA-IR with an odds ratio of 2.403, and lastly the triglyceride-HDL ratio with an odds ratio of 1.264. Phase angle exhibited a strong protective effect whose odds ratio is 0.708. The model was very robust in terms of age, sex, and body mass index subgroups, with the best calibration as evident by a Hosmer-Lemeshow p-value of 0.621 and the greatest net benefit at all risk levels. A combined screening model that includes salivary glucose, bioimpedance-derived phase angle, and conventional cardiometabolic risk factors offers great discrimination of adults less than fifty years of age with undiagnosed metabolic syndrome. This is a noninvasive, multiparametric method that provides a feasible and economically viable solution to population-based proactive screening initiatives to help prevent the development of cardiometabolic diseases.