estimated Glomerular Filtration Rate (eGFR) of less than 60 mL/min/ 1. 73 m2, calculated using the 2021 CKD-EPI formula. The primary variables analyzed were age, gender, hypertension, Body Mass Index (BMI), diabetes mellitus, and total cholesterol. Binary logistic regression was the main statistical method used to identify independent predictors. Results: The overall prevalence of dipstick-positive proteinuria was 5. 6%, and the overall prevalence of CKD was 2. 6%. Multivariate analysis identified diabetes mellitus as the strongest risk factor (OR=4. 148) for proteinuria. Other significant predictors for proteinuria included CKD (OR=3. 076), hypertension (OR=2. 146), advancing age (OR=1. 010per year), and male gender, which had approximately 1. 5 times higher odds than female gender (female OR=0. 667). A non-linear, U-shaped association was observed for BMI, with both obesity (BMI30 kg/m2, OR=1. 707) and underweight status (BMI<18. 5 kg/ m2, OR=1. 485) associated with significantly higher odds of proteinuria. The presence of proteinuria was the most powerful predictor (OR=2. 940) of CKD. Other significant predictors were advancing age (OR=1. 062per year) and hypertension (OR=1. 283). Male gender was associated with 49% lower odds of having CKD compared to females (OR=0. 510). Conclusion: This study reveals a significant prevalence of dipstickpositive proteinuria and reduced renal function within the asymptomatic, productive population undergoing routine MCU. Proteinuria emerged as the single most powerful independent predictor of reduced renal function, while diabetes mellitus was the most powerful predictor for proteinuria. As the first investigation within a large, productive Indonesian population, these findings emphasize the importance of routine screening for early detection and intervention of CKD. I have no potential conflict of interest to disclose. I did not use generative AI and AI-assisted technologies in the writing process.
Abedini et al. (Wed,) studied this question.