国际医药卫生导报 ›› 2024, Vol. 30 ›› Issue (1): 8-14.DOI: 10.3760/cma.j.issn.1007-1245.2024.01.002

• Meta 分析 • 上一篇    下一篇

身体成分与衰弱发生风险关系的meta分析

赵怡迪1  梁好2  陈木欣3  魏琳4   

  1. 1湖南中医药大学护理学院,长沙 410208;2广州中医药大学第二附属医院神经一科,广州 510120;3广州中医药大学第二临床医学院,广州 510006;4广州中医药大学第二附属医院护理部,广州 510006

  • 收稿日期:2023-09-20 出版日期:2024-01-01 发布日期:2024-02-01
  • 通讯作者: 魏琳,Email:weilin22@gzucm.edu.cn
  • 基金资助:

    国家卫生健康委科学技术研究所课题(2021KYSHX016010201)

Body composition and frailty risk: a meta-analysis

Zhao Yidi1, Liang Hao 2, Chen Muxin 3, Wei Lin 4   

  1. 1 College of Nursing, Hunan University of Traditional Chinese Medicine, Changsha 410208, China; 2 First Department of Neurology, Second Hospital, Guangzhou University of Chinese Medicine, Guangzhou 510120, China; 3 Second Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou 510006, China; 4 Department of Nursing, Second Hospital, Guangzhou University of Chinese Medicine, Guangzhou 510006, China

  • Received:2023-09-20 Online:2024-01-01 Published:2024-02-01
  • Contact: Wei Lin, Email: weilin22@gzucm.edu.cn
  • Supported by:

    Project of Research Program of Science and Technology of National Health Commission (2021KYSHX016010201)

摘要:

目的 对衰弱与身体成分之间关系进行meta分析,明确衰弱的危险因素,得出较为可靠结论,为早期干预提供循证依据。方法 检索中国知网、中国生物医学文献服务系统、维普网、万方数据知识服务平台、读秀、PubMed、Web of Science、Embase数据库中关于衰弱与身体组成的研究,检索时间为建库至2023年1月20日。由两名研究人员独立进行筛选文献、质量评价、数据提取,采用RevMan 5.3软件进行meta分析。结果 共纳入15篇文献:横断面研究14篇,队列研究1篇,总样本量22 774例,包含3 586例衰弱患者,涉及7个危险因素。meta分析结果显示,体质量指数(BMI)<18.5 kg/m2、BMI≥30.0 kg/m2、腰围(女≥88 cm,男≥102 cm)、小腿围、上臂肌围5个因素差异均有统计学意义(均P<0.05),其余因素[25.0 kg/m2≤BMI<30.0 kg/m2、BMI(未分类)、骨骼肌质量、体脂质量、体脂百分比、腰围(未分类)]差异均无统计学意义(均P>0.05)。采用随机效应模型和固定效应模型对纳入的影响因素进行合并比值比和95%置信区间的估计,发现除25.0 kg/m2≤BMI<30.0 kg/m2、BMI(未分类),骨骼肌质量、腰围(未分类)外(I2=50%~98%),其他因素的一致性良好(I2=0%~38%),认为本研究结果可靠。结论 临床医务工作者可以针对这些定量测量的影响因素开发衰弱风险筛查表,对筛查出具有衰弱高风险人群及时实施精准化的预防治疗方案,从而进一步改善老年人的生活质量。

关键词:

衰弱, 身体成分, 危险因素, 横断面研究, 队列研究, meta分析

Abstract:

Objective To conduct a meta-analysis on the relationship between frailty and body composition, identify the risk factors of frailty, draw reliable conclusions, and provide evidence-based basis for early intervention. Methods The databases of China National Knowledge Infrastructure (CNKI), Sinomed, Database for Chinese Technical Periodicals, WanFang Data, Duxiu, PubMed, Web of Science, and Embase were searched for studies on frailty and body composition from their establishment to January 20, 2023. Two researchers independently screened the literatures, evaluated the quality, and extracted the data. The RevMan 5.3 software was used for the meta-analysis. Results A total of 15 studies were included, including 14 cross-sectional studies and 1 cohort study. The total sample size was 22 774, including 3 586 patients with frailty. Seven risk factors were involved. The meta-analysis results showed that there were statistical differences in body mass index (BMI) <18.5 kg/m2, BMI≥30.0 kg/m2, waist circumference (female ≥88 cm and male ≥102 cm), calf circumference, and upper arm muscle circumference (MAMC) (all P<0.05), and no in the rest factors [25.0 kg/m2≤BMI<30.0 kg/m2, BMI (unclassified), skeletal muscle mass, body fat mass, body fat percentage, waist circumference (unclassified)] (all P>0.05). The random effect model and fixed effect model were used to estimate the combined OR and 95% confidence interval of the included factors; it found that the consistency of other factors was good (I2=0-38%) except 25.0 kg/m2≤BMI<30.0 kg/m2, BMI (unclassified), skeletal muscle mass, and waist circumference (unclassified) (I2=50%-98%). The results of this study were reliable. Conclusions Clinical medical workers can develop frailty risk screening tables based on these influencing factors that can be measured quantitatively, and timely implement precise preventive and treatment programs for the screened population with high risk of frailty, so as to further improve the quality of life of the elderly.

Key words:

Frailty, Body composition, Risk factors, Cross-sectional study, Cohort study, Meta-analysis