超重或肥胖老年人的肌肉骨骼特征:INVEST in Bone Health试验基线分析

S Delanie Lynch, Marjorie Howard, Daniel P Beavers, Leon Lenchik, Ryan Barnard, Joshua R Stapleton, Erica Lawrence, Peggy M Cawthon, Fang-Chi Hsu, Kristen M Beavers, Ashley A Weaver
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引用次数: 0

摘要

目的:本研究的目的是检查计算机断层扫描(CT)衍生的肌肉骨骼测量与人口统计学和传统肌肉骨骼特征的关系。方法:结合营养、背心、教育和力量训练(INVEST)的骨骼健康试验(NCT04076618)在150名超重或肥胖的老年人中获得了一系列肌肉骨骼测量。基线时,CT(即体积骨密度、皮质厚度、肌肉放射组学和肌肉/肌间脂肪组织[IMAT]面积和密度)、双能x线吸收仪(DXA;收集了实际骨密度、总体脂量、阑尾瘦质量和瘦体重)、力量评估(即握力和膝关节伸肌力量)以及人口统计学和临床特征。分析采用线性回归和混合效应模型以及因子分析对放射组学数据进行降维。结果:参与者年龄较大(平均[SD]年龄:66岁),大多数为女性(75%),生活超重或肥胖(平均[SD] BMI: 33.6 [3.3] kg/m2)。年龄与大多数ct衍生的骨骼、IMAT或肌肉测量没有显著相关性。BMI与DXA和CT衍生的肌肉和IMAT测量值显著相关,男性高于女性(所有p结论:机器学习衍生的CT指标与DXA和肌肉力量密切相关,BMI越高,IMAT越高,肌肉质量越差。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Musculoskeletal characteristics in older adults with overweight or obesity: INVEST in Bone Health trial baseline analysis.

Objective: The objective of this study was to examine associations of computed tomography (CT)-derived musculoskeletal measures with demographics and traditional musculoskeletal characteristics.

Methods: The Incorporating Nutrition, Vests, Education, and Strength Training (INVEST) in Bone Health trial (NCT04076618) acquired a battery of musculoskeletal measures in 150 older-aged adults living with overweight or obesity. At baseline, CT (i.e., volumetric bone mineral density, cortical thickness, muscle radiomics, and muscle/intermuscular adipose tissue [IMAT] area and density), dual-energy x-ray absorptiometry (DXA; i.e., areal bone mineral density, total body fat mass, appendicular lean mass, and lean body mass), and strength assessments (i.e., grip and knee extensor strength) were collected, along with demographic and clinical characteristics. Analyses employed linear regression and mixed-effects models along with factor analysis for dimensionality reduction of the radiomics data.

Results: Participants were older-aged (mean [SD] age: 66 [5] years), mostly female (75%), and were living with overweight or obesity (mean [SD] BMI: 33.6 [3.3] kg/m2). Age was not significantly associated with most CT-derived bone, IMAT, or muscle measures. BMI was significantly associated with DXA and CT-derived muscle and IMAT measures, which were higher in male than female individuals (all p < 0.01). For the midthigh, muscle size was significantly related to grip and knee extensor strength (both p < 0.01).

Conclusions: Machine learning-derived CT metrics correlated strongly with DXA and muscle strength, with higher BMI linked to greater IMAT and poorer muscle quality.

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