Serum Iron as a Key Predictor of Diabetes Risk: Findings from 13 Cities in Saudi Arabia

Diabetes mellitus represents a major public health challenge in Saudi Arabia. This study investigated the associations of gender, age, body mass index (BMI), and serum iron levels with diabetes risk across 13 Saudi cities and developed a machine-learning model for diabetes prediction. Data from 49,259 individuals were obtained from Al Borg Laboratories for the period 2015–2023. Descriptive statistics, independent-samples t-tests, chi-squared tests, and correlation analyses were performed. Logistic Regression, Random Forest, and Gradient Boosting models were evaluated using gender, age, BMI, and serum iron as predictors. The best-performing model was optimized through grid search with five-fold cross-validation. The cohort had a mean age of 45.92 years, mean BMI of 27.33 kg/m ² , and mean serum iron concentration of 87.11 µg/dL. The prevalence of diabetes was 10.31% and was higher among males than females (11.42% vs. 9.24%). Individuals with diabetes had significantly lower serum iron concentrations than those without diabetes (79.76 vs. 88.26 µg/dL; P < 0.001), and gender was significantly associated with diabetes status ( P < 0.001). Random Forest demonstrated the best predictive performance, with an accuracy of 0.92, precision of 0.90, recall of 0.94, and area under the receiver operating characteristic curve of 0.96. Following hyperparameter tuning, the optimized model achieved a test-set accuracy of 0.90. Overall, gender, age, BMI, and serum iron levels were significantly associated with diabetes in the Saudi population, with lower serum iron levels linked to a higher prevalence of the disease. The optimized Random Forest model may provide a useful tool for diabetes risk prediction, supporting the potential application of machine learning in early detection and prevention. These findings also highlight the importance of monitoring iron status and developing tailored strategies for individuals with diabetes.