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An early prediction model for chronic kidney disease
Jing Zhao1, Yuan Zhang1,2, Jiali Qiu1
1Department of Genetics, College of Basic Medical Sciences, Tianjin Medical University, Tianjin, 300070, China.
Scientific Reports
|February 18, 2022
Summary
A new prediction model effectively identifies individuals at high risk for chronic kidney disease (CKD) using genetic and nongenetic factors. This tool aids in early detection, potentially preventing end-stage renal disease (ESRD).
Area of Science:
- Nephrology
- Genetics
- Epidemiology
Background:
- High incidence of chronic kidney disease (CKD) necessitates improved early prediction models.
- Current models lack sufficient accuracy in identifying individuals at risk for end-stage renal disease (ESRD).
Purpose of the Study:
- To develop and validate a comprehensive prediction model for early CKD detection.
- To identify key genetic and nongenetic risk factors for CKD.
Main Methods:
- Nested case-control study involving 348 participants followed for 5 years.
- Multivariate Cox regression and logistic regression analyses were employed.
- Combined genetic data from GWAS with nongenetic risk factors.
Main Results:
- Identified five nongenetic risk factors: age, diabetes, urea nitrogen, TGF-β, and ADMA.
- Developed a comprehensive prediction model with an AUC of 0.894.
- Achieved high sensitivity (82.7%) and specificity (80.1%) in CKD prediction.
Conclusions:
- The comprehensive model accurately predicts CKD risk, aiding early intervention.
- Age, diabetes, urea nitrogen, TGF-β, and ADMA are significant independent risk factors for CKD.
- Early identification of high-risk individuals can potentially prevent progression to ESRD.
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