A Nomogram for Predicting the Risk of CKD Based on Cardiometabolic Risk Factors

Abstract

Background

In China, the spectrum of causes for CKD has been changing in recent years, and the proportion of CKD caused by cardiometabolic diseases, such as diabetes and hypertension continues to increase. Thus, predicting CKD based on cardiometabolic risk factors can to a large extent help identify those at increased risk and facilitate the prevention of CKD. In this study, we aimed to develop a nomogram for predicting CKD risk based on cardiometabolic risk factors.

Methods

We developed a nomogram for predicting CKD risk by using a subcohort population of the 4C study, which was located in central China. The prediction model was designed by using a logistic regression model, and a backwards procedure based on the Akaike information criterion was applied for variable selection. The performance of the model was evaluated by the concordance index (C-index), and Hosmer‒Lemeshow goodness-of-fit test. The bootstrapping method was applied for internal validation.

Results

During the 3-years follow-up, 167 cases of CKD developed. By using univariate and multivariate logistic regression models, the following factors were identified as predictors in the nomogram: age, sex, HbA1c, baseline eGFR, low HDL-C levels, high TC levels and SBP. The bootstrap-corrected C-index for the model was 0.84, which indicated good discrimination ability. The Hosmer‒Lemeshow goodness-of-fit tests yielded chi-square of 13.61 (P=0.192), and the calibration curves demonstrated good consistency between the predicted and observed probabilities, which indicated satisfactory calibration ability.

Conclusion

We developed a convenient and practicable nomogram for the 3‑year risk of incident CKD among a population in central China, which may help to identify high-risk individuals for CKD and contribute to the prevention of CKD.

Keywords:

• CKD
• cardiometabolic risk factors
• nomogram

Abbreviations

CKD, chronic kidney disease; eGFR, estimated glomerular filtration rate; WC, waist circumference; WHtR, waist-to-height ratio; TC, total cholesterol; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; TG, triglyceride; AST, aspartate aminotransferase; ALT, alanine aminotransferase; SD, standard deviation; IQI, interquartile interval; CVD, cardiovascular disease; RCS, restricted cubic spline; ROC, receiver operating characteristic curve; AUC, area under curve.

Data Sharing Statement

The datasets analyzed in the current study are not publicly available due to the limits on the data-sharing agreement of the China Cardiometabolic Disease and Cancer Cohort study group but are available upon reasonable request and approval.

Ethics Approval and Consent to Participate

Informed consent was obtained from all subjects involved in the study and our study complies with the Declaration of Helsinki.

Acknowledgments

We thank all members in China Cardiometabolic Disease and Cancer Cohort Study Group for their great efforts in the survey.

Disclosure

The authors declare no competing interests in this work.

Additional information

Funding

This research was funded by grants from the National Natural Science Foundation of China (82270880).