Integrated Clinical–Genetic Predictive Model for Chronic Kidney Disease Risk in Indonesian Adults with Type 2 Diabetes Mellitus: A Cross-Sectional Study

Background: Chronic kidney disease (CKD) is a major microvascular complication of type 2 diabetes mellitus (T2DM), with substantial clinical and economic burdens, particularly in low-resource settings. Conventional clinical markers often fail to fully capture interindividual variability in CKD susceptibility, prompting interest in genetic determinants. This study aimed to develop an integrated clinical genetic model to predict moderate to very high CKD risk among Indonesian adults with T2DM. Methods: A cross-sectional study was conducted involving 70 adults with T2DM recruited from an outpatient clinic in Indonesia. Participants were categorized into low-risk or moderate-to-very-high-risk CKD groups based on KDIGO 2024 criteria. Clinical characteristics, biochemical parameters, and genotypes of three polymorphisms TGF-β rs1800470, ELMO1 rs1882071, and ACE1 rs4343 were assessed. Statistical analyses included Hardy-Weinberg equilibrium testing, logistic regression, multivariate modelling, and evaluation of predictive performance using receiver operating characteristic (ROC) curves. Results: Patients with moderate to very high CKD risk exhibited significantly higher serum creatinine, urine creatinine, albuminuria, and albumin creatinine ratio, as well as lower eGFR (all p < 0.001). Genotype analyses showed that the TT genotype of TGF-β rs1800470, AG genotype of ACE1 rs4343, and GG genotype of ELMO1 rs1882071 were significantly associated with increased CKD risk. Multivariate analysis identified uncontrolled blood pressure and these three polymorphisms as independent predictors of CKD risk severity. The integrated model demonstrated strong discriminative ability in ROC analysis. Conclusion: Incorporating genetic polymorphisms of TGF-β, ELMO1, and ACE1 with clinical parameters substantially improves prediction of moderate-to-very-high CKD risk in Indonesian adults with T2DM. The findings support the potential application of genotype informed risk stratification in resource limited settings and highlight the need for larger longitudinal studies to validate these results.