Risk factor analysis and predictive model construction and evaluation of fractional uric acid excretion in Chinese inpatients with type 2 diabetes

Abstract Background and Aims: Fractional excretion of uric acid (FEUA) is currently acknowledged as a new risk factor of nephropathy. Precise measurement of FEUA requires withdraw medication during the washout time and 24h urine sample collecting, not always convenient, fostering construction of predictive models. No existing model could portrait FEUA. Our aim is to develop and evaluate a model which could predict FEUA based on several easily available factors. Methods : 2301 patients hospitalized in the Department of Endocrinology, Peking University People’s Hospital were chose. 1510 cases enrolled during 2018–2024 were used to construct predictive models using linear regression and to perform internal validation. 791 cases enrolled in 2017 and 2025 were used as an external validation. Results : Sex, SUA, SCr, and FPG closely related with FEUA in internal dataset. The predictive formula was: Ln (FEUA) = 1.9408 + 0.1462*gender (male=1female = 2) − 0.0031*SUA(µmol/L) + 0.0112*SCr(µmol/L) + 0.0068*FPG (mmol/L) Predicted FEUA = Exp(Ln(FEUA))*1.0265; 1.0265 was the Duan’s smearing factor. Root mean squared errors (RMSE) between the observed and predicted FEUA was 1.690%. Spearman correlation coefficient was 0.7870 ( P < 0.001). For predicting elevated FEUA (≥ 11%), the overall accuracy was 94.70%; ROC was 0.9552(95%CI: 0.9413 ~ 0.9691). Youden index (0.7814) at the cut-point (8.0257%) of FEUA was identified with a high sensitivity (0.9541) and specificity (0.8273). RMSE for 1-fold, 5-fold, 10-fold and leave-one-out cross-validation were 1.6898, 1.7087 ± 0.1716, 1.6964 ± 0.2761, and 1.7081, respectively. ROC values in subgroups of internal dataset were all higher than 0.949. In external dataset validation, Spearman correlation coefficient between observed and predicted FEUA was 0.7451( P < 0.001), ROC for elevated FEUA was 0.8946 (95% CI: 0.8545 ~ 0.9347). A stability of all indices was identified for subgroups of external dataset. Conclusion : Our study identified an effective and practical FEUA predictive model in T2DM inpatients using linear regression with four common indexes: sex, SUA, SCr, FPG.