Estimated 24-h urinary sodium excretion and risk of end-stage kidney disease.

The association between sodium intake and long-term kidney disease endpoints is debated and yet to be proven. We aimed to investigate the associations of estimated 24-h urinary sodium excretion, reflecting daily sodium intake, with the incidence of end-stage kidney disease (ESKD). In this prospective cohort study including 444,375 UK Biobank participant, 865 (0.2%) ESKD events occurred after median follow-up of 12.7 years. For every 1 g increment in estimated 24-h urinary sodium excretion, multivariable-adjusted hazard ratio for incident ESKD was 1.09 (95% confidence interval 0.94-1.26). Nonlinear associations were not detected with restricted cubic splines. The null findings were confirmed by a series of sensitivity analyses, which attenuated potential bias from measurement errors of the exposure, regression dilution, reverse causality, and competing risks. In conclusion, there is insufficient evidence that estimated 24-h urinary sodium excretion is associated with the incidence of ESKD.

Development and validation of a cardiovascular diseases risk prediction model for Chinese males (CVDMCM).

Background: Death due to cardiovascular diseases (CVD) increased significantly in China. One possible way to reduce CVD is to identify people at risk and provide targeted intervention. We aim to develop and validate a CVD risk prediction model for Chinese males (CVDMCM) to help clinicians identify those males at risk of CVD and provide targeted intervention. Methods: We conducted a retrospective cohort study of 2,331 Chinese males without CVD at baseline to develop and internally validate the CVDMCM. These participants had a baseline physical examination record (2008-2016) and at least one revisit record by September 2019. With the full cohort, we conducted three models: A model with Framingham CVD risk model predictors; a model with predictors selected by univariate cox proportional hazard model adjusted for age; and a model with predictors selected by LASSO algorithm. Among them, the optimal model, CVDMCM, was obtained based on the Akaike information criterion, the Brier's score, and Harrell's C statistic. Then, CVDMCM, the Framingham CVD risk model, and the Wu's simplified model were all validated and compared. All the validation was carried out by bootstrap resampling strategy (TRIPOD statement type 1b) with the full cohort with 1,000 repetitions. Results: CVDMCM's Harrell's C statistic was 0.769 (95% CI: 0.738-0.799), and D statistic was 4.738 (95% CI: 3.270-6.864). The results of Harrell's C statistic, D statistic and calibration plot demonstrated that CVDMCM outperformed the Framingham CVD model and Wu's simplified model for 4-year CVD risk prediction. Conclusions: We developed and internally validated CVDMCM, which predicted 4-year CVD risk for Chinese males with a better performance than Framingham CVD model and Wu's simplified model. In addition, we developed a web calculator-calCVDrisk for physicians to conveniently generate CVD risk scores and identify those males with a higher risk of CVD.