Gender disparities in random blood glucose levels among Pakistani adults with type 2 diabetes: a cross-sectional analysis

This study investigated gender disparities in random blood glucose (RBS) levels among Pakistani adults with Type 2 Diabetes (T2D), examining biological and sociocultural determinants. A cross-sectional analysis of 300 age-matched adults with T2D (150 men, 150 women; age 35–60 years) from four tertiary hospitals in Peshawar, Pakistan (February–July 2023). RBS was measured via the Microlab-300 system (Beer–Lambert Law). Multivariate regression and machine learning models (Ridge Regression, Random Forest, Support Vector Regression (SVR), Neural Network, Polynomial Regression) with nested cross-validation were used to analyze associations between demographic factors and RBS. Women had significantly higher mean RBS than men (243. 6 vs. 210. 8 mg/dL, p < 0. 001) and a higher prevalence of severe hyperglycemia (260 mg/dL: 38. 7% vs. 12. 0%). Gender alone explained 16. 5% of RBS variance in simple linear regression. Age showed a moderate positive correlation with RBS (r = 0. 587, p < 0. 001). In multivariate analysis, female gender (= 24. 76, p < 0. 001), age (= 3. 01 per year, p < 0. 001), and BMI (= 0. 88, p = 0. 034) were significant predictors, while family history showed a protective effect (= –13. 36, p < 0. 001). Machine learning models using only demographic variables achieved moderate predictive performance (R² = 0. 421–0. 470), with Ridge Regression performing best (R² = 0. 470, MAE = 23. 68 mg/dL). Feature importance analysis identified age (70. 9%), gender (17. 8%), and BMI (8. 9%) as the dominant predictors. Significant gender disparities exist in random blood glucose among Pakistani adults with T2D, with women exhibiting higher mean values and greater prevalence of severe hyperglycemia. Age, gender, BMI, and family history are important demographic determinants, but demographic factors alone explain less than half of RBS variance. These findings highlight the need for gender-sensitive diabetes management strategies in South Asia and emphasize the necessity of incorporating direct biomarkers in future prediction efforts.