Abstract
BACKGROUND AND AIMS: Obesity is widely recognized as a significant contributor to the onset of type 2 diabetes mellitus (T2DM). This study aimed to identify the most accurate anthropometric cut-off points for predicting T2DM in adults aged 35-70 years through a cross-sectional analysis of the Bandare-Kong cohort in southern Iran. METHODS: This study analyzed data from the Bandare-Kong Non-Communicable Diseases (BKNCD) cohort. Anthropometric indices were analyzed using statistical tests, including t-tests and χ (2) tests for continuous and categorical variables, respectively. Receiver operating characteristic (ROC) curves and the area under the curve (AUC) were employed to evaluate the diagnostic performance of anthropometric indices in predicting T2DM. A p-value of < 0.05 was considered statistically significant. RESULTS: A total of 2749 participants were included, evenly distributed between men and women. T2DM was identified in 176 men (6.4%) and 242 women (8.8%). In women, abdominal obesity indicators such as waist-to-hip ratio (WHR) (cut-off: 0.98, AUC = 0.72) and waist-to-height ratio (WHtR) (cut-off: 0.59, AUC = 0.64) demonstrated the strongest associations with T2DM. In men, WHR (cut-off: 0.94, AUC = 0.65) and WHtR (cut-off: 0.52, AUC = 0.59) also showed significant associations. When analyzed by age, WHR (cut-offs: 0.96 for < 50 years and 0.98 for ≥ 50 years) and WHtR (cut-offs: 0.55 for < 50 years and 0.58 for ≥ 50 years) remained consistently associated with T2DM across age groups. Conversely, body mass index (BMI) showed weaker associations in both genders and age groups. CONCLUSION: WHR and WHtR, key indicators of abdominal obesity, demonstrated a strong association with T2DM in both men and women, with WHR showing a particularly stronger relationship in women. These findings highlight the importance of focusing on abdominal obesity measures, especially in women, for better diabetes risk assessment. Future multicenter longitudinal studies are essential to confirm these findings and enhance risk-stratification approaches.