Development and Validation of a Multidimensional Risk Spectrum Screening Tool for the Early Detection of Metabolic Syndrome.

Background Metabolic syndrome (MetS) is a cluster of risk factors, including dyslipidemia, hypertension, hyperglycemia, and central obesity, underpinned by insulin resistance (IR). Individuals with MetS have a markedly increased risk of developing type 2 diabetes mellitus and cardiovascular disease. Early detection before significant metabolic deterioration is essential for prevention. Objective The objective of this study is to develop and validate a practical screening tool for identifying individuals at varying risk levels for MetS by integrating anthropometric, biochemical, lifestyle, and demographic factors. Methods A literature review informed the selection of 23 modifiable and non-modifiable risk factors, including waist circumference (WC), BMI, fasting blood glucose (FBG), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), blood pressure (BP), lifestyle habits, and female-specific factors. Each factor was assigned a weighted risk score. The tool was pilot-tested and refined before being applied to 148 adults (47 (31.8%) men and 101 (68.2%) women; mean age, 42.4 ± 11.7 years) attending a clinical setting. Data were collected through structured interviews, anthropometric assessments, and biochemical tests. The triglyceride-glucose (TyG) index was calculated as a surrogate for IR. Differences in clinical parameters across risk categories were evaluated using one-way ANOVA. Results Participants were classified as low risk (31 (21.0%)), moderate risk (93 (63.0%)), or high risk (24 (16.2%)). MetS risk scores increased significantly with systolic BP, diastolic BP, WC, FBG, TG, and the TyG index, while HDL-C showed a significant negative association (all p < 0.01). The mean TyG index increased progressively from low (8.3 ± 0.4) to moderate (8.7 ± 0.4) to high risk (9.4 ± 0.3). Conclusions The MetS spectrum assessment tool demonstrated strong construct validity (through associations with established MetS diagnostic criteria) and criterion validity (via correlation with the TyG index), allowing early risk detection even in individuals who do not meet full diagnostic thresholds. Integration into clinical practice may facilitate timely intervention to prevent progression to MetS and related complications.