An early prediction model for chronic kidney disease

Jing Zhao,Kai Zhang,Shuzhi Feng,Jiali Qiu,Chen Chen,Wei-Dong Li,Xiaodan Zhang,Yuan Zhang,Jiayi Feng,Fengjiang Wei

doi:10.1038/s41598-022-06665-y

Abstract

Based on the high incidence of chronic kidney disease (CKD) in recent years, a better early prediction model for identifying high-risk individuals before end-stage renal failure (ESRD) occurs is needed. We conducted a nested case–control study in 348 subjects (116 cases and 232 controls) from the “Tianjin Medical University Chronic Diseases Cohort”. All subjects did not have CKD at baseline, and they were followed up for 5 years until August 2018. Using multivariate Cox regression analysis, we found five nongenetic risk factors associated with CKD risks. Logistic regression was performed to select single nucleotide polymorphisms (SNPs) from which we obtained from GWAS analysis of the UK Biobank and other databases. We used a logistic regression model and natural logarithm OR value weighting to establish CKD genetic/nongenetic risk prediction models. In addition, the final comprehensive prediction model is the arithmetic sum of the two optimal models. The AUC of the prediction model reached 0.894, while the sensitivity was 0.827, and the specificity was 0.801. We found that age, diabetes, and normal high values of urea nitrogen, TGF-β, and ADMA were independent risk factors for CKD. A comprehensive prediction model was also established, which may help identify individuals who are most likely to develop CKD early.

Highlights

Based on the high incidence of chronic kidney disease (CKD) in recent years, a better early prediction model for identifying high-risk individuals before end-stage renal failure (ESRD) occurs is needed
It has been speculated that cystatin C (CysC) could be used together with serum creatinine as a new biomarker or as a substitute for serum creatinine to better identify the occurrence of kidney disease in the general population[13,14]
A Cox proportional risk regression model showed that age, diabetes mellitus, a normal high value of urea, a normal high value of transforming growth factor-β (TGF-β), and asymmetric dimethylarginine (ADMA) were independent risk factors for CKD (Table 2; Supplementary Table S3)

Summary

Introduction

Based on the high incidence of chronic kidney disease (CKD) in recent years, a better early prediction model for identifying high-risk individuals before end-stage renal failure (ESRD) occurs is needed. A comprehensive prediction model was established, which may help identify individuals who are most likely to develop CKD early. Previous studies have reported that more than 50 single nucleotide polymorphisms (SNPs) are associated with renal function indexes or CKD w orldwide[19]. Many prediction models reached high prediction power in a relatively large population, early prediction [at least when eGFR > 60 mL/(min·1.73 m2)] is essential for CKD treatment and prevention. We developed genetic, nongenetic (including biomarkers), and comprehensive risk score prediction models for CKD in a nested case–control study

Methods

Results

Conclusion