Abstract

Obesity and non-communicable diseases (NCDs) continue to rise among Indonesian adults, particularly in occupational groups with predominantly sedentary work patterns such as private school teachers. Obesity-related metabolic alterations—including dyslipidemia, hypertension, impaired glucose regulation, and early renal dysfunction—collectively elevate cardiometabolic risk. However, predictive mathematical models that integrate these indicators to estimate early NCD risk among teachers have not yet been developed. Establishing the metabolic profile of this population is therefore essential to inform population-specific modelling efforts. This study aimed to (1) describe the prevalence of obesity and metabolic abnormalities among private school teachers in Yogyakarta, (2) identify clinical markers associated with NCD risk, and (3) assess the relevance of these indicators as potential inputs for a future predictive mathematical model. A cross-sectional study was conducted among 131 private elementary school teachers. Anthropometric measurements, blood pressure, fasting glucose, lipid profile, liver enzymes, and renal function markers were obtained through standardized clinical and laboratory assessments. Lifestyle behaviors and herbal supplement use were recorded using validated questionnaires. Descriptive and bivariate analyses were performed to examine the distribution and interrelationships of metabolic indicators. A substantial proportion of teachers were overweight or obese, with elevated cholesterol and increased blood pressure commonly observed. Mild creatinine elevation, although less frequent, correlated strongly with other cardiometabolic indicators. More than one-third of participants exhibited clustering of at least two metabolic risk factors. These findings indicate that BMI, lipid profile, and creatinine are the most informative predictors for preliminary NCD risk modeling. In conclusion, private school teachers in Yogyakarta exhibit a considerable metabolic risk related to obesity. The observed clustering of metabolic abnormalities underscores the need for early detection strategies and supports the development of a mathematical prediction model tailored to this occupational group.