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Introduction <p>The European Working Group on Sarcopenia in Older People (EWGSOP2) defines sarcopenia as a muscle disease (muscle failure) rooted in adverse muscle changes that accrue across a lifetime; sarcopenia is common among adults of older age. New findings on the hormonal and metabolic characteristics of patients with sarcopenia have aided in developing more targeted therapeutic strategies. However, treating older patients with sarcopenia still poses a number of challenges. Despite numerous studies on sarcopenia, no comprehensive phenotyping of older sarcopenic patients has yet to be offered. Cluster analysis has been successfully used to study various diseases. It may be extremely advantageous for collecting data on specific sarcopenia progressions based on a simultaneous assessment of a whole range of factors.</p> Aim <p>To identify disease progression specific to older patients based on cluster analysis of blood biomarkers and lifestyle.</p> Methods <p>&#xa0;This study included 1709 participants aged 90 and older. The median age was 92. Seventy-one&#xa0;percent of participants were female. Participants underwent a comprehensive geriatric assessment and had their metabolic, hormonal, and&#xa0;inflammatory blood biomarkers measured. The data were analyzed and clustered using&#xa0;the R&#xa0;programming language.</p> Results <p>&#xa0;Seven sarcopenia clusters were identified. The most significant variables, in&#xa0;descending order, were malnutrition, physical activity, body mass index, handgrip strength, testosterone, albumin, sex, adiponectin, total protein, vitamin D, hemoglobin, estradiol, C-reactive protein, glucose, monocytes, and insulin. Handgrip strength measurements and free T3 levels increased linearly between the cluster with the lowest measurements and the cluster with&#xa0;the&#xa0;highest measurements.</p> Conclusion <p>The findings of this study may greatly aid in understanding the relationship between blood biomarkers, lifestyle and sarcopenia progression in older adults, and may help in&#xa0;developing better prevention and diagnostic strategies as well as more personalized therapeutic interventions.</p>

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Cluster analysis of sarcopenia in older adults: significant factors contributing to disease severity

  • 期刊论文

  • 发表日期：

    2025年1月14日

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  • L. V. Machekhina,
  • O. N. Tkacheva,
  • E. N. Dudinskaya,
  • E. M. Shelley,
  • A. A. Mamchur,
  • V. V. Daniel,
  • M. V. Ivanov,
  • D. A. Kashtanova,
  • A. M. Rumyantseva,
  • L. R. Matkava,
  • V. S. Yudin,
  • V. V. Makarov,
  • A. A. Keskinov,
  • S. A. Kraevoy,
  • S. M. Yudin,
  • I. D. Strazhesko

摘要

Introduction

The European Working Group on Sarcopenia in Older People (EWGSOP2) defines sarcopenia as a muscle disease (muscle failure) rooted in adverse muscle changes that accrue across a lifetime; sarcopenia is common among adults of older age. New findings on the hormonal and metabolic characteristics of patients with sarcopenia have aided in developing more targeted therapeutic strategies. However, treating older patients with sarcopenia still poses a number of challenges. Despite numerous studies on sarcopenia, no comprehensive phenotyping of older sarcopenic patients has yet to be offered. Cluster analysis has been successfully used to study various diseases. It may be extremely advantageous for collecting data on specific sarcopenia progressions based on a simultaneous assessment of a whole range of factors.

Aim

To identify disease progression specific to older patients based on cluster analysis of blood biomarkers and lifestyle.

Methods

 This study included 1709 participants aged 90 and older. The median age was 92. Seventy-one percent of participants were female. Participants underwent a comprehensive geriatric assessment and had their metabolic, hormonal, and inflammatory blood biomarkers measured. The data were analyzed and clustered using the R programming language.

Results

 Seven sarcopenia clusters were identified. The most significant variables, in descending order, were malnutrition, physical activity, body mass index, handgrip strength, testosterone, albumin, sex, adiponectin, total protein, vitamin D, hemoglobin, estradiol, C-reactive protein, glucose, monocytes, and insulin. Handgrip strength measurements and free T3 levels increased linearly between the cluster with the lowest measurements and the cluster with the highest measurements.

Conclusion

The findings of this study may greatly aid in understanding the relationship between blood biomarkers, lifestyle and sarcopenia progression in older adults, and may help in developing better prevention and diagnostic strategies as well as more personalized therapeutic interventions.