An Early Prediction Model for Chronic Kidney Disease

Abstract Background: Identifying individuals predisposed to chronic kidney disease (CKD) is crucial for intervention and treatment, however, risk equations were relatively rare among the middle-aged and elderly in China.Methods: Eventually, 116 CKD patients and 232 their healthy counterparts from “Tianjin Medical University Chronic Disease Cohort (N=21750)” were included in this study. One nested case-control set (N = 348; followed up for at least five years) was used to develop and internally validate the predictive model. The clinical, demographic and laboratory features were collected and subjected to logistic regression analyses. Using natural logarithm odds ratio value weighting methods, several risk factors (genetic and non-genetic) were selected to construct predictive models. The final comprehensive prediction model is the arithmetic sum of the two optimal models.Results: We found that transforming growth factor-β (TGF-β), and asymmetric dimethylarginine (ADMA) were effective biomarkers for CKD, the area under the curve (AUC), specificity and sensitivity for the non-genetic equation were 0.889, 0.851 and 0.770, respectively, in the genetic equation, 0.643, 0.794 and 0.838, respectively, and 0.894, 0.827, 0.801, respectively, in the comprehensive prediction model. After internal verification of Bootstrap, its AUC value was 0.820, indicating it showed a favorable predictive performance.Conclusions: A comprehensive prediction model was established, which may help early identify individuals who are most likely to develop CKD. Its feasibility in the clinic warrants further investigation.