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
{"title":"Cluster analysis of sarcopenia in older adults: significant factors contributing to disease severity.","authors":"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","doi":"10.1007/s41999-024-01153-0","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>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><p><strong>Aim: </strong>To identify disease progression specific to older patients based on cluster analysis of blood biomarkers and lifestyle.</p><p><strong>Methods: </strong> 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.</p><p><strong>Results: </strong> 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.</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":49287,"journal":{"name":"European Geriatric Medicine","volume":" ","pages":""},"PeriodicalIF":3.5000,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"European Geriatric Medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s41999-024-01153-0","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"GERIATRICS & GERONTOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
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.
期刊介绍：
European Geriatric Medicine is the official journal of the European Geriatric Medicine Society (EUGMS). Launched in 2010, this journal aims to publish the highest quality material, both scientific and clinical, on all aspects of Geriatric Medicine.
The EUGMS is interested in the promotion of Geriatric Medicine in any setting (acute or subacute care, rehabilitation, nursing homes, primary care, fall clinics, ambulatory assessment, dementia clinics..), and also in functionality in old age, comprehensive geriatric assessment, geriatric syndromes, geriatric education, old age psychiatry, models of geriatric care in health services, and quality assurance.
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