Association of C-reactive protein to albumin ratio with progression of CKD and all-cause mortality in diabetic CKD.

introductionThe C-reactive protein to albumin ratio (CAR), an integrative biomarker of inflammation and malnutrition, has shown prognostic value in various diseases, but its role in diabetic chronic kidney disease (CKD) remains inadequately defined. This study aimed to evaluate the independent association of CAR with progression of chronic kidney disease (CKD) and all-cause mortality. DESIGN AND

methodsWe retrospectively retrieved 231 CKD patients with diabetes who had not received erythropoietin stimulants or iron therapy. The primary outcomes were mortality and progression of CKD (composite), specifically, progression to end-stage renal disease (ESRD) and initiation of renal replacement therapy (RRT), or a doubling of serum creatinine (SCr) levels in patients not receiving RRT. We used Kaplan-Meier survival curves and constructed multivariate Cox proportional hazards models adjusted for potential confounding factors, to estimate the association of CAR with progression of CKD and all-cause mortality; in addition, we employed restricted cubic spline analysis to explore nonlinear relationships. We used the SHAP machine learning algorithm to evaluate the predictive performance of CAR and to analyze the predictive increment of CAR for clinical outcomes. Subgroup analyses were conducted to assess the robustness of the results across different subgroups and modeling choices.

resultsThe analysis cohort included a total of 231 adults. Kaplan-Meier curves showed a progressive and significant increase in cumulative CKD progression and mortality across CAR quartiles. In the fully adjusted model of the Cox multivariate regression analysis, a 1-unit increase in CAR (log-transformed) was associated with a 59% increase in the risk of CKD progression (HR = 1.59, 95% CI 1.20-2.09; P = 0.001) and a 32% increase in the risk of mortality (HR = 1.32, 95% CI 1.03-1.68, P = 0.029); participants in the highest quartile had a significantly higher mortality risk compared to those in the lowest quartile (Q4 vs. Q1, HR = 3.8, 95% CI 1.24-11.67; P = 0.02). Restricted cubic spline analysis revealed a significant linear relationship (nonlinear P > 0.05). Subgroup analysis indicated that CAR was consistently associated with outcomes across different age, sex, and BMI groups, with no significant interactions observed, confirming the robustness of these results. In machine learning models, SHAP analysis identified CAR as a key predictor. Compared with the baseline risk model (UTP, eGFR), adding CAR improved predictive performance for CKD progression and mortality, with enhanced C-statistic, improved discriminatory index (IDI), and improved net reclassification index (NRI).

conclusionsThe CAR serves as a robust, independent predictor of CKD progression and all-cause mortality in patients with diabetic CKD. As a readily accessible biomarker, it holds significant potential to enhance risk stratification and identify candidates warranting intensified clinical management.