Association between metabolic phenotypes and incident pre-sarcopenia: 3 years follow-up of Tehran Lipid and Glucose Study

代谢表型与新发肌少症前期之间的关联:德黑兰脂质和葡萄糖研究的3年随访

阅读:2

Abstract

OBJECTIVES: In the context of musculoskeletal health, the emergence of pre-sarcopenia as a precursor to sarcopenia has garnered attention for its potential insights into early muscle loss. We explored the association between different metabolic phenotypes of obesity, and the incidence of pre-sarcopenia over a 3-year follow-up in a cohort from the Tehran Lipid and Glucose Study (TLGS). METHODS: In this 3-year longitudinal study, 2257 participants were categorized into four groups based on their BMI and metabolic status: metabolically healthy normal weight (MHNW), metabolically healthy overweight/obese (MHO), metabolically unhealthy normal weight (MUNW), and metabolically unhealthy overweight/obese (MUO). The participants were assessed for various anthropometric and body composition indices including muscle mass determined by bioelectrical impedance analysis (BIA). Blood samples were collected for metabolic indices, and participants underwent measurements for blood pressure. Pre-sarcopenia was defined based on low muscle mass. Statistical analyses included logistic regression and chi-squared tests. RESULTS: The MUNW group exhibited the highest prevalence of pre-sarcopenia (33.5%), while the MHO group had the lowest (2.8%). Adjusted models revealed that the odds ratio for pre-sarcopenia was higher in the MUNW group (OR = 2.23, P < 0.001), whereas the MHO and MUO groups showed lower odds (OR = 0.11 and 0.13, both P < 0.001). Notably, the association was gender-dependent, with MUNW females having a higher risk even after adjustments (OR = 2.37, P = 0.042). CONCLUSION: Our findings suggest that metabolic health may play a pivotal role in pre-sarcopenia, emphasizing the need for targeted interventions based on both metabolic and obesity phenotypes.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。