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ORIGINAL RESEARCH article

Front. Nutr.
Sec. Clinical Nutrition
Volume 11 - 2024 | doi: 10.3389/fnut.2024.1470669
This article is part of the Research Topic Systemic Markers of Muscle Loss – Volume II View all 6 articles

Handgrip strength and body mass index exhibit good predictive value for sarcopenia in patients on peritoneal dialysis

Provisionally accepted
Hongyan Li Hongyan Li 1,2Yuanhua Zheng Yuanhua Zheng 3*Yuanyuan Zhang Yuanyuan Zhang 1,4Xiaotian Zhang Xiaotian Zhang 2Wei Luo Wei Luo 5Weiyi Zhu Weiyi Zhu 6*Yaqing Zhang Yaqing Zhang 1*
  • 1 School of Nursing, Shanghai Jiao Tong University School of Medicine, Shanghai, China
  • 2 School of Nursing, Jiangxi Medical College,Nanchang University, Nanchang, Jiangxi Province, China
  • 3 Peritoneal dialysis center, the first affiliated hospital, Nanchang University,, Nanchang, China
  • 4 Urinary Surgery Center,Shanghai Jiao Tong University School of Medicine Affiliated General Hospital, Shanghai, China
  • 5 Department of Nursing,Jiangxi Provincial People's Hospital, Nanchang, China
  • 6 Department of Nursing,Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China

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

    The diagnosis of sarcopenia in patients on peritoneal dialysis (PD) in clinics is limited owing to its relatively complicated process and the need for expensive assessment equipment. This study aimed to develop and validate sex-specific nomogram models based on body mass index (BMI), handgrip strength, and other routine follow-up examination indicators to predict sarcopenia in patients on PD.Methods: From March 2023 to February 2024, 699 eligible patients were recruited from the PD centres of two tertiary hospitals in southeastern China. Routine follow-up examination indicators such as age, BMI, biochemical indicators, dialysis adequacy, handgrip strength, and five-repetition sit-to-stand test, were used as potential predictive variables. Multivariate logistic regression analyses were used to separately determine the predictive factors for men and women. Nomogram models were constructed based on the results of the multivariate analyses, which were internally validated using a bootstrap re-sampling method (n = 2000). Predictive performance was validated using a receiver operating characteristic (ROC) curve .The prevalence of sarcopenia in Chinese patients on PD was 13.92%. The nomogram models based on multivariate analyses revealed both handgrip strength and BMI as independent predictors of sarcopenia in men and women on PD. The bootstrap-corrected area under the ROC curves of the models was 0.924 (95% CI: 0.888-0.959) and 0.936 (95% CI: 0.906-0.966) for men and women, respectively. The calibration curves of both models demonstrated high consistency between the observed and anticipated values.The two nomogram models based on BMI and handgrip strength demonstrated good predictive ability for sarcopenia in male and female patients on PD. Subsequently, these may be used as convenient and inexpensive methods for the early detection and timely management of sarcopenia in patients on PD.

    Keywords: Body Mass Index, handgrip strength, nomogram, Peritoneal Dialysis, Risk factors, Sarcopenia

    Received: 25 Jul 2024; Accepted: 02 Dec 2024.

    Copyright: © 2024 Li, Zheng, Zhang, Zhang, Luo, Zhu and Zhang. 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:
    Yuanhua Zheng, Peritoneal dialysis center, the first affiliated hospital, Nanchang University,, Nanchang, China
    Weiyi Zhu, Department of Nursing,Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
    Yaqing Zhang, School of Nursing, Shanghai Jiao Tong University School of Medicine, Shanghai, China

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