Machekhina L V, Tkacheva O N, Dudinskaya E N, Shelley E M, Mamchur A A, Daniel V V, Ivanov M V, Kashtanova D A, Rumyantseva A M, Matkava L R, Yudin V S, Makarov V V, Keskinov A A, Kraevoy S A, Yudin S M, Strazhesko I D
Federal State Autonomous Educational Institution of Higher Education "Russian National Research Medical University named after N.I. Pirogov" of the Ministry of Health of the Russian Federation, Separate structural unit "Russian Gerontology Research and Clinical Centre", 16 1st Leonova Street, Moscow, Russia, 129226.
Federal State Budgetary Institution "Center for Strategic Planning and Management of Medical and Biological Health Risks" of the Federal Medical and Biological Agency, 10, Building 1, Pogodinskaya Street, Moscow, Russia, 119121.
Eur Geriatr Med. 2025 Feb;16(1):45-54. doi: 10.1007/s41999-024-01153-0. Epub 2025 Jan 14.
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.
To identify disease progression specific to older patients based on cluster analysis of blood biomarkers and lifestyle.
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.
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.
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.
欧洲老年人肌少症工作组(EWGSOP2)将肌少症定义为一种源于一生中累积的不良肌肉变化的肌肉疾病(肌肉功能衰竭);肌少症在老年人中很常见。关于肌少症患者激素和代谢特征的新发现有助于制定更具针对性的治疗策略。然而,治疗老年肌少症患者仍然面临许多挑战。尽管对肌少症进行了大量研究,但尚未对老年肌少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。基于对一系列因素的同时评估,它对于收集特定肌少症进展的数据可能极为有利。
基于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。
本研究纳入了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医学评估,并测量了他们的代谢、激素和炎症血液生物标志物。使用R编程语言对数据进行分析和聚类。
识别出七个肌少症聚类。按重要性降序排列,最显著的变量依次为营养不良、身体活动、体重指数、握力、睾酮、白蛋白、性别、脂联素、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。握力测量值和游离T3水平在测量值最低的聚类与测量值最高的聚类之间呈线性增加。
本研究结果可能极大地有助于理解老年人血液生物标志物、生活方式与肌少症进展之间的关系,并可能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。