DEVELOPMENT OF A MULTI-MARKER PREDICTION MODEL INTEGRATING ANKLE-BRACHIAL INDEX, LIPID PROFILE, AND INFLAMMATORY BIOMARKERS FOR PERIPHERAL ARTERY DISEASE RISK STRATIFICATION IN INDIVIDUALS WITH METABOLIC SYNDROME - Ima Rahmawati; Regan Arethusa Prabowo; Irmawati Ningsih (Departement Research and Community Engagement Bina Sehat PPNI Institute of Health Science, Indonesia, 2026)
Background: Peripheral artery disease (PAD) is a common atherosclerotic complication among individuals with metabolic syndrome and is associated with increased cardiovascular morbidity and mortality. Although the Ankle-Brachial Index (ABI) is widely used as a screening tool, its diagnostic performance is limited when applied as a standalone measure. Therefore, an integrated approach combining vascular, metabolic, and inflammatory indicators is needed to improve the accuracy of early PAD detection. This study aimed to examine the effects of ABI, lipid profile, and inflammatory biomarkers on PAD occurrence and to develop a multi-marker prediction model for individuals with metabolic syndrome. Methods: The quantitative study employed a predictive analytical approach with a cross-sectional design. A total of 106 individuals with metabolic syndrome were recruited through purposive sampling from the Dlanggu Community Health Center catchment area, Mojokerto Regency, Indonesia. Data were collected using structured questionnaires, ABI measurements, lipid profile assessments, and inflammatory biomarkers, including the Monocyte-to-HDL Ratio (MHR), Neutrophil-to-HDL Ratio (NHR), C-Reactive Protein (CRP), and the Systemic Inflammatory Response Index (SIRI). Data were analyzed using descriptive statistics, multivariable logistic regression, and Receiver Operating Characteristic (ROC) curve analysis with IBM SPSS Statistics version 29. Results: ABI was significantly associated with PAD occurrence (β = −3.524, p < 0.001). Lipid profile components, including low-density lipoprotein (LDL), high-density lipoprotein (HDL), and triglycerides, as well as the inflammatory biomarkers MHR, NHR, CRP, and SIRI, were also significantly associated with PAD (p < 0.05). The proposed multi-marker prediction model demonstrated excellent predictive performance, with a Nagelkerke R² of 0.631 and an area under the ROC curve (AUC) of 0.891. Conclusions: A multi-marker prediction model integrating ABI, lipid profile parameters, and inflammatory biomarkers effectively predicts PAD among individuals with metabolic syndrome. These findings support a multidimensional approach to vascular risk stratification and suggest that this model has the potential to enhance early screening, facilitate timely clinical decision-making, and optimize preventive strategies in high-risk populations.