Machine learning with decision curve analysis evaluates nutritional metabolic biomarkers for cardiovascular-kidney-metabolic risk - an NHANES analysis

Q: What data source and study period were used to evaluate CKM risk?

The analysis uses National Health and Nutrition Examination Survey (NHANES) data from 1999 to 2018, covering 19,884 participants.

Q: Which nutritional metabolic biomarkers are evaluated in the study?

The study evaluates novel indices including RAR, NPAR, SIRI, and Homair as nutritional metabolic biomarkers for CKM risk.

Q: How are the predictive models assessed for clinical usefulness?

Predictive value is evaluated using multivariable regression, restricted cubic splines, machine learning ranking (XGBoost/LightGBM), and decision curve analysis (DCA) to confirm clinical utility.

Aug 5, 2026

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Cardiovascular-Kidney-Metabolic Syndrome (CKM) highlights links among metabolic disorders, cardiovascular disease, and kidney disease, yet the causal imbalance remains incompletely assessed. Using NHANES data from 1999–2018 (19,884 participants), the study develops nutritional metabolic indices (RAR, NPAR, SIRI, Homair) and evaluates CKM prediction via multivariable regression, restricted cubic splines, machine learning (XGBoost/LightGBM), decision curve analysis, and subgroup interaction testing.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages17

Reading Time43 min

AccessFree to download

Background

Methods

Statistical and modeling approaches

Results

Conclusion

Introduction

CVD

Diabetes Mellitus (DM)

Cardiovascular-Kidney-Metabolic Syndrome (CKM)

OPEN ACCESS EDITED BY Haoqiang Zhang, University of Science and Technology of China, China REVIEWED BY Han Yan, Zhejiang University, China Akpovi D. Casimir, University of Abomey-Calavi, Benin *CORRESPONDENCE XinChun Cheng [2272871234@qq.com](2272871234@qq.com) †These authors have contributed equally to this work and share first authorship RECEIVED 21 March 2025 ACCEPTED 23 April 2025 PUBLISHED 08 May 2025 CITATION Huang J, Liu Z, Feng W, Huang Y and Cheng X (2025) Machine learning with decision curve analysis evaluates nutritional metabolic biomarkers for cardiovascular-kidney-metabolic risk: an NHANES analysis. Front. Nutr. 12:1597864 . doi: 10.3389/fnut.2025.1597864 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) and the copyright owner(s) 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. TYPE Original Research PUBLISHED 08 May 2025 DOI 10.3389/fnut.2025.1597864 Machine learning with decision curve analysis evaluates nutritional metabolic biomarkers for cardiovascular-kidney-metabolic risk: an NHANES analysis Jun Huang 1,2†, Zhuo Liu 1,2†, WeiPeng Feng3, YuanLing Huang4 and XinChun Cheng 1* 1 People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, China, 2Graduate School, Xinjiang Medical University, Urumqi, China, 3Shenzhen Institute of Information Technology, Shenzhen, China, 4Jiangsu Hengrui Pharmaceuticals Co., Ltd., Lianyungang, China 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

FAQs

What data source and study period were used to evaluate CKM risk?

The analysis uses National Health and Nutrition Examination Survey (NHANES) data from 1999 to 2018, covering 19,884 participants.

Which nutritional metabolic biomarkers are evaluated in the study?

The study evaluates novel indices including RAR, NPAR, SIRI, and Homair as nutritional metabolic biomarkers for CKM risk.

How are the predictive models assessed for clinical usefulness?

Predictive value is evaluated using multivariable regression, restricted cubic splines, machine learning ranking (XGBoost/LightGBM), and decision curve analysis (DCA) to confirm clinical utility.

Aug 5, 2026

Upload by Riley