Research Paper Volume 12, Issue 11 pp 10317—10336
Nomogram for the prediction of diabetic nephropathy risk among patients with type 2 diabetes mellitus based on a questionnaire and biochemical indicators: a retrospective study
- 1 School of Public Health, Shanghai University of Traditional Chinese Medicine, Shanghai, China
Received: December 27, 2019 Accepted: May 1, 2020 Published: June 2, 2020
https://doi.org/10.18632/aging.103259How to Cite
Copyright © 2020 Hu et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
Purpose: Develop a diabetic nephropathy incidence risk nomogram in a Chinese population with type 2 diabetes mellitus.
Results: Predictors included systolic blood pressure, diastolic blood pressure, fasting blood glucose, glycosylated hemoglobin A1c, total triglycerides, serum creatinine, blood urea nitrogen and body mass index. The model displayed medium predictive power with a C-index of 0.744 and an area under curve of 0.744. Internal verification of C-index reached 0.737. The decision curve analysis showed the risk threshold was 20%. The value of net reclassification improvement and integrated discrimination improvement were 0.131, 0.05, and that the nomogram could be applied in clinical practice.
Conclusion: Diabetic nephropathy incidence risk nomogram incorporating 8 features is useful to predict diabetic nephropathy incidence risk in type 2 diabetes mellitus patients.
Methods: Questionnaires, physical examinations and biochemical tests were performed on 3489 T2DM patients in six communities in Shanghai. LASSO regression was used to optimize feature selection by running cyclic coordinate descent. Logistic regression analysis was applied to build a prediction model incorporating the selected features. The C-index, calibration plot, curve analysis, forest plot, net reclassification improvement, integrated discrimination improvement and internal validation were used to validate the discrimination, calibration and clinical usefulness of the model.
Abbreviations
T2DM: Type 2 diabetes mellitus; DN: Diabetic nephropathy; NDN: Nondiabetic nephropathy; ESRD: End-stage renal disease; CKD: Chronic kidney disease; UMA: Urinary Microalbumin; SBP: Systolic Blood Pressure; DBP: Diastolic Blood Pressure; BMI: Body Mass Index; WHR: Waist-to-Hip Ratio; FBG: Fasting Blood Glucose; PBG: Postprandial Blood Glucose; HbA1c: Glycosylated Hemoglobin A1c; LDL: Low-density Lipoprotein; HDL: High-density Lipoprotein; TGs: Total Triglycerides; TC: Total Cholesterol; SCR: Serum Creatinine; BUN: Blood Urea Nitrogen; UCR: Uric Creatinine; UA: Uric Acid; NAU: Nonalbuminuria; MAU: Microalbuminuria; CAU: Clinical albuminuria; GFR: Glomerular Filtration Rate; RAS: The renin-angiotensin system; LASSO: Least absolute shrinkage and selection operator; NRI: Net Reclassification Improvement; IDI: Integrated Discrimination Improvement; UAER: Urinary Albumin Excretion Rate.