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27 PagesPosted: 12 Nov 2024
See all articles by Ye Kuang
Ye Kuang
Kunming Medical University
Jia Wang
Kunming Medical University
Yang Wang
Kunming Medical University
Chuanmei Peng
Kunming Medical University
Pei He
Kunming Medical University
Yong Ji
Kunming Medical University
Jinrong Tian
Kunming Medical University
Yong Yuan
Kunming Medical University
Lei Feng
Kunming Medical University
More...
Abstract
Background: Diabetes mellitus with hypertension (DM+HTN) is a common diabetic comorbidity. Individuals with DM+HTN experience notably increased rates of cardiovascular disease-related morbidity and mortality. However, the risk factors associated with DM+HTN and the predictive models for it have not yet been clearly elucidated.
Methods: Through detailed exclusion rules, we ultimately included 777 participants from the National Health and Nutrition Examination Survey (NHANES), 1085 participants from the China Health and Retirement Longitudinal Survey (CHARLS) and 3348 participants from the Sixth Affiliated Hospital of Kunming Medical University (China). We constructed a risk prediction model for DM+HTN via both univariate and multivariate weighted logistic regression analyses. The model was evaluated through receiver operating characteristic (ROC) curves, calibration curves and decision curve analysis. Restricted cubic spline and smooth fitting curve were used to detect the nonlinear relationships between variables. Finally, we further validated the performance of the established model via 10 machine learning methods and explained the model via the SHapley additive explanation (SHAP).
Findings: This study revealed that the serum concentration of cystatin c (CysC) was closely associated with the risk of DM+HTN as an independent indicator. A risk prediction model was subsequently constructed using the CysC concentration and adjusted for age, sex, race, education, BMI, smoking status and drinking status. When the concentration of CysC exceeded 0.94 mg/L, as the CysC concentration increased, the risk of DM+HTN increased, and a trend effect was present. Finally, we confirmed that the model had good predictive performance and net clinical benefit via 10 machine learning.
Interpretation: The findings of this study reveal that CysC is a key predictor for the risk of DM+HTN and that a model based on CysC can be helpful for clinicians to identify individuals at high risk for DM+HTN at an early stage.
Funding: The study was supported by the National Natural Science Foundation of China (82160402); the National Natural Science Foundation of China (82360030); Central guidance for local scientific and technological development special funds (202407AD110004); Key joint special projects for applied basic research in science and technology office of Yunnan province and Kunming Medical University (202301AY070001-024); High-level Talent Cultivation and Attraction Support Plan for Yunnan Province (YNQR-QNRC-2020-091).
Declaration of Interest: We declare no competing interests related to this study.
Ethical Approval: This study qualified for ethics committee of the Sixth Affiliated Hospital of Kunming Medical University (trial registration number: 2022-kmykdx6f-90).
Keywords: Cystatin C, Diabetes mellitus with hypertension, NHANES, CHARLS, Prediction model
Suggested Citation:Suggested Citation
Kuang, Ye and Wang, Jia and Wang, Yang and Peng, Chuanmei and He, Pei and Ji, Yong and Tian, Jinrong and Yuan, Yong and Feng, Lei, The Association between Cystatin C and the Risk of Diabetes Complicated with Hypertension in the Adult Population of China and the USA: National Retrospective Cross-Sectional Study. Available at SSRN: https://ssrn.com/abstract=5016426 or http://dx.doi.org/10.2139/ssrn.5016426
