ORIGINAL RESEARCH article
Front. Nutr.
Sec. Nutrition and Metabolism
Volume 12 - 2025 | doi: 10.3389/fnut.2025.1597864
This article is part of the Research TopicNutrient Metabolism and Complications of Type 2 Diabetes MellitusView all 8 articles
Machine Learning with Decision Curve Analysis Evaluates Nutritional Metabolic Biomarkers for Cardiovascular-Kidney-Metabolic Risk: An NHANES Analysis
Provisionally accepted
- 1People's Hospital of Xinjiang Uygur Autonomous Region, Ürümqi, Xinjiang Uyghur Region, China
- 2Graduate School Xinjiang Medical University, XinJiang, China
- 3Shenzhen Institute of Information Technology, Shenzhen, Guangdong Province, China
- 4Jiangsu HengRui Pharmaceuticals, Lianyungang, Jiangsu Province, China
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Background: The American Heart Association recently introduced the concept of Cardiovascular-Kidney-Metabolic Syndrome (CKM), emphasizing the interplay between metabolic disorders, cardiovascular diseases, and kidney diseases. Although insulin resistance (IR) and chronic inflammation are core drivers of CKM, the relationships causing imbalance have not been fully evaluated. Emerging biomarkers (RAR, NPAR, SIRI, Homair) offer multidimensional prediction capabilities by simultaneously assessing nutritional metabolism, cellular inflammation, and insulin resistance in diabetes. Methods: This study included data from 19,884 participants in the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2018. The study developed novel indices (RAR, NPAR, SIRI, Homair) and assessed their CKM predictive value through: Multivariable logistic/Cox regression; Restricted cubic splines; Machine learning (XGBoost, LightGBM); Decision curve analysis. Subgroup analyses were conducted to assess interactive effects on specific populations. Results: After weighted analysis, multi-model logistic regression showed that RAR, SIRI, NPAR, and Homair remained strongly correlated with CKM after adjusting for various factors (p < 0.05), with RAR showing the most pronounced relationship (OR: 2.73, 95% CI: 2.07-3.59, p < 0.001). RCS curves revealed nonlinear relationships between these factors and outcomes (nonlinear p < 0.05). In multi-model Cox regression, RAR, SIRI, and NPAR were associated with all-cause mortality (p < 0.05), and RAR was linked to all-cause, cardiovascular Disease (CVD), and kidney disease mortality (p < 0.05), with the strongest link (OR: 2.38, 95% CI: 1.98-2.88, p < 0.001). Machine learning ranked RAR, SIRI, and Homair as top predictors for CKM diagnosis. The DCA model further validated these three Lasso-selected variables, showing clinical utility. The model combining RAR, diabetes mellitus (DM), and age demonstrated outstanding performance (AUC = 0.907), offering clinical reference value. Conclusion: This study demonstrates significant relationship between RAR, NPAR, SIRI, and Homair with the five stages of CKM, with RAR showing the robust association. DCA-confirmed RAR demonstrates high clinical translatability as a standalone predictor for CKM risk stratification.
Keywords: Cardiovascular-Kidney-Metabolic Syndrome (CKM Syndrome), RAR, Insulin resistance (IR), All-cause mortality, machine learning
Received: 21 Mar 2025; Accepted: 23 Apr 2025.
Copyright: © 2025 Huang, Liu, Feng, Huang and Cheng. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: XinChun Cheng, People's Hospital of Xinjiang Uygur Autonomous Region, Ürümqi, 830001, Xinjiang Uyghur Region, China
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