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ORIGINAL RESEARCH article

Front. Nutr.
Sec. Clinical Nutrition
Volume 11 - 2024 | doi: 10.3389/fnut.2024.1492191
This article is part of the Research Topic Systemic Markers of Muscle Loss – Volume II View all 5 articles

Correlation of insulin resistance-related indicators and obesity-related indicators with sarcopenic obesity and development of diagnostic models: NHANES 1999-2006

Provisionally accepted
Chen Zhang Chen Zhang Xintao Huang Xintao Huang Hao Lu Hao Lu Mo Chen Mo Chen Junyu Wang Junyu Wang *Huisheng Den Huisheng Den *
  • Department of General Practice, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China

The final, formatted version of the article will be published soon.

    This study investigates the correlation between insulin resistance and obesity indicators with sarcopenic obesity (SO) and develops diagnostic models. Utilizing the 1999-2006 National Health and Nutrition Examination Survey (NHANES) database, the research included 5,574 adults. Sarcopenic obesity was defined following the 2022 consensus by ESPEN and EASO. The study analyzed indicators such as the triglyceride-glucose index (TyG), homeostasis model assessment of insulin resistance (HOMA-IR), body roundness index (BRI), and lipid accumulation product (LAP). Results indicated a significant positive correlation between these indicators and SO, with the strongest association observed for TyG-WHtR. Predictor variables were identified through logistic and Lasso regression, including age, sex, weight-height ratio (WHtR), and TyG-WCR. The diagnostic model demonstrated good predictive performance with AUC values of 0.897 for internal validation and 0.853 for external validation. The study underscores the importance of early identification of SO patients and provides a theoretical foundation for future prevention and management strategies. Limitations include the cross-sectional study design and the potential limited generalizability of the model based on the American population.

    Keywords: sarcopenic obesity, Diagnostic model, LASSO, NHANES, random forest

    Received: 06 Sep 2024; Accepted: 13 Nov 2024.

    Copyright: © 2024 Zhang, Huang, Lu, Chen, Wang and Den. 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:
    Junyu Wang, Department of General Practice, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
    Huisheng Den, Department of General Practice, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China

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