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

利用决策曲线分析的机器学习方法评估心血管-肾脏-代谢风险的营养代谢生物标志物:一项NHANES分析

阅读:3

Abstract

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.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。