Unmasking covert metabolic syndrome in normal-weight women: regional adiposity and muscle quality outperform BMI in a temporally validated machine learning triage framework.

IF 3.1 3区医学 Q2 ENDOCRINOLOGY & METABOLISM

Acta Diabetologica Pub Date : 2026-10-12 DOI:10.1007/s00592-026-02815-0

Olga S Palamarchuk, Oleh O Moroz, Volodymyr P Feketa

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引用次数: 0

Abstract

Aims: Traditional screening based on body mass index (BMI) fails to identify the metabolically obese normal-weight (MONW) phenotype. We aimed to develop and temporally validate an interpretable machine learning framework utilizing non-invasive surrogate markers of central adiposity (waist-to-height ratio, WHtR) and relative functional muscle capacity (relative handgrip strength, RHGS) for cross-sectional triage of prevalent metabolic syndrome (MetS) in women.

Methods: In 2,418 women (NHANES 2011-2014), we excluded pregnant women and applied independent K-nearest neighbors imputation (k = 5) without data leakage. Using XGBoost, we evaluated baseline M1 (Age + BMI), intermediate M2 (Age + BMI + RHGS), and proposed M3 (Age + WHtR + RHGS). Following TRIPOD Type 3 temporal validation, we trained models on Cycle 2011-2012 (N = 1,167) and tested them on Cycle 2013-2014 (N = 1,251). SHapley Additive exPlanations (SHAP) extracted exploratory, model-dependent decision reference points.

Results: In the unstratified cohort, M3 achieved comparable discrimination to M2 (AUC 0.782 vs. 0.769, DeLong p = 0.117). However, in normal-weight women (n = 371), M3 demonstrated marked superiority over M1 (AUC 0.855 [95% CI 0.800-0.910] vs. 0.757 [95% CI 0.678-0.835], p < 0.001) and M2 (AUC 0.801, p = 0.015), with near-perfect calibration (Brier score = 0.068), high rule-out reliability (NPV = 98.9%), and a negative likelihood ratio (LR-) of 0.12 (95% CI 0.06-0.22). SHAP dependence analysis illustrated non-additive muscle-fat interactions, where lower strength (< 0.891 kg/kg) amplified risk primarily when WHtR exceeded 0.529.

Conclusions: Replacing BMI with indices of visceral adiposity and relative strength markedly improves MetS triage in normal-weight women. This temporally validated framework provides a non-invasive pre-laboratory triage tool to prioritize high-risk MONW individuals for comprehensive clinical evaluation.

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揭示正常体重女性的隐性代谢综合征：在暂时验证的机器学习分类框架中，局部肥胖和肌肉质量优于BMI。

目的：传统的基于身体质量指数（BMI）的筛查无法识别代谢肥胖正常体重（MONW）表型。我们的目标是开发并暂时验证一个可解释的机器学习框架，利用无创的中心肥胖替代标记（腰高比，WHtR）和相对功能肌肉容量（相对握力，RHGS），用于女性普遍代谢综合征（MetS）的横断面分类。方法：在2418名妇女（NHANES 2011-2014）中，我们排除了孕妇，采用独立k -近邻法（k = 5），无数据泄露。使用XGBoost，我们评估了基线M1（年龄+ BMI），中间M2（年龄+ BMI + RHGS）和建议M3（年龄+ WHtR + RHGS）。根据TRIPOD Type 3时间验证，我们在2011-2012周期（N = 1,167）上训练模型，并在2013-2014周期（N = 1,251）上对模型进行测试。SHapley加性解释（SHAP）提取探索性的、依赖于模型的决策参考点。结果：在非分层队列中，M3与M2的鉴别效果相当（AUC 0.782 vs 0.769, DeLong p = 0.117）。然而，在正常体重女性（n = 371）中，M3明显优于M1 (AUC 0.855 [95% CI 0.800-0.910] vs. 0.757 [95% CI 0.678-0.835]， p结论：用内脏脂肪指数和相对力量指数代替BMI可显著改善正常体重女性的MetS分诊。这个暂时验证的框架提供了一种非侵入性的实验室前分诊工具，以优先考虑高风险的MONW个体进行全面的临床评估。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Acta Diabetologica 医学-内分泌学与代谢

CiteScore

7.30

自引率

2.60%

发文量

180

审稿时长

2 months

期刊介绍： Acta Diabetologica is a journal that publishes reports of experimental and clinical research on diabetes mellitus and related metabolic diseases. Original contributions on biochemical, physiological, pathophysiological and clinical aspects of research on diabetes and metabolic diseases are welcome. Reports are published in the form of original articles, short communications and letters to the editor. Invited reviews and editorials are also published. A Methodology forum, which publishes contributions on methodological aspects of diabetes in vivo and in vitro, is also available. The Editor-in-chief will be pleased to consider articles describing new techniques (e.g., new transplantation methods, metabolic models), of innovative importance in the field of diabetes/metabolism. Finally, workshop reports are also welcome in Acta Diabetologica.