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
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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":"{\"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. 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引用次数: 0
摘要
导言：欧洲老年人肌肉疏松症工作组（EWGSOP2）将 "肌肉疏松症 "定义为一种肌肉疾病（肌肉衰竭），其根源在于一生中累积的肌肉不良变化；肌肉疏松症在老年人中很常见。有关肌肉疏松症患者荷尔蒙和新陈代谢特征的新发现有助于制定更有针对性的治疗策略。然而，治疗老年肌肉疏松症患者仍面临诸多挑战。尽管对肌肉疏松症进行了大量研究，但尚未对老年肌肉疏松症患者进行全面的表型分析。聚类分析已成功用于研究各种疾病。目的：根据对血液生物标志物和生活方式的聚类分析，确定老年患者特有的疾病进展情况： 这项研究包括 1709 名 90 岁及以上的参与者。中位年龄为 92 岁。71%的参与者为女性。参与者接受了全面的老年病评估，并测量了他们的代谢、荷尔蒙和炎症血液生物标志物。我们使用 R 编程语言对数据进行了分析和聚类： 结果：确定了七个肌肉疏松症群组。最重要的变量从高到低依次为营养不良、体力活动、体重指数、手握力、睾酮、白蛋白、性别、脂肪连接蛋白、总蛋白、维生素 D、血红蛋白、雌二醇、C 反应蛋白、葡萄糖、单核细胞和胰岛素。手握力量测量值和游离 T3 水平在测量值最低的组群和测量值最高的组群之间呈线性增长：本研究的结果将大大有助于了解血液生物标志物、生活方式和老年人肌肉疏松症进展之间的关系，并有助于制定更好的预防和诊断策略以及更个性化的治疗干预措施。
Cluster analysis of sarcopenia in older adults: significant factors contributing to disease severity.
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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