Abdominal Body Composition Reference Ranges and Association With Chronic Conditions in an Age- and Sex-Stratified Representative Sample of a Geographically Defined American Population.

Alexander D Weston, Brandon R Grossardt, Hillary W Garner, Timothy L Kline, Alanna M Chamberlain, Alina M Allen, Bradley J Erickson, Walter A Rocca, Andrew D Rule, Jennifer L St Sauver
{"title":"Abdominal Body Composition Reference Ranges and Association With Chronic Conditions in an Age- and Sex-Stratified Representative Sample of a Geographically Defined American Population.","authors":"Alexander D Weston, Brandon R Grossardt, Hillary W Garner, Timothy L Kline, Alanna M Chamberlain, Alina M Allen, Bradley J Erickson, Walter A Rocca, Andrew D Rule, Jennifer L St Sauver","doi":"10.1093/gerona/glae055","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Body composition can be accurately quantified from abdominal computed tomography (CT) exams and is a predictor for the development of aging-related conditions and for mortality. However, reference ranges for CT-derived body composition measures of obesity, sarcopenia, and bone loss have yet to be defined in the general population.</p><p><strong>Methods: </strong>We identified a population-representative sample of 4 900 persons aged 20 to 89 years who underwent an abdominal CT exam from 2010 to 2020. The sample was constructed using propensity score matching an age and sex stratified sample of persons residing in the 27-county region of Southern Minnesota and Western Wisconsin. The matching included race, ethnicity, education level, region of residence, and the presence of 20 chronic conditions. We used a validated deep learning based algorithm to calculate subcutaneous adipose tissue area, visceral adipose tissue area, skeletal muscle area, skeletal muscle density, vertebral bone area, and vertebral bone density from a CT abdominal section.</p><p><strong>Results: </strong>We report CT-based body composition reference ranges on 4 649 persons representative of our geographic region. Older age was associated with a decrease in skeletal muscle area and density, and an increase in visceral adiposity. All chronic conditions were associated with a statistically significant difference in at least one body composition biomarker. The presence of a chronic condition was generally associated with greater subcutaneous and visceral adiposity, and lower muscle density and vertebrae bone density.</p><p><strong>Conclusions: </strong>We report reference ranges for CT-based body composition biomarkers in a population-representative cohort of 4 649 persons by age, sex, body mass index, and chronic conditions.</p>","PeriodicalId":94243,"journal":{"name":"The journals of gerontology. Series A, Biological sciences and medical sciences","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10949446/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"The journals of gerontology. Series A, Biological sciences and medical sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/gerona/glae055","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Background: Body composition can be accurately quantified from abdominal computed tomography (CT) exams and is a predictor for the development of aging-related conditions and for mortality. However, reference ranges for CT-derived body composition measures of obesity, sarcopenia, and bone loss have yet to be defined in the general population.

Methods: We identified a population-representative sample of 4 900 persons aged 20 to 89 years who underwent an abdominal CT exam from 2010 to 2020. The sample was constructed using propensity score matching an age and sex stratified sample of persons residing in the 27-county region of Southern Minnesota and Western Wisconsin. The matching included race, ethnicity, education level, region of residence, and the presence of 20 chronic conditions. We used a validated deep learning based algorithm to calculate subcutaneous adipose tissue area, visceral adipose tissue area, skeletal muscle area, skeletal muscle density, vertebral bone area, and vertebral bone density from a CT abdominal section.

Results: We report CT-based body composition reference ranges on 4 649 persons representative of our geographic region. Older age was associated with a decrease in skeletal muscle area and density, and an increase in visceral adiposity. All chronic conditions were associated with a statistically significant difference in at least one body composition biomarker. The presence of a chronic condition was generally associated with greater subcutaneous and visceral adiposity, and lower muscle density and vertebrae bone density.

Conclusions: We report reference ranges for CT-based body composition biomarkers in a population-representative cohort of 4 649 persons by age, sex, body mass index, and chronic conditions.

按年龄和性别划分的具有代表性的美国地域人口样本的腹部身体成分参考范围及其与慢性病的关系。
背景:身体成分可通过腹部 CT 检查准确量化,是预测衰老相关疾病发展和死亡率的指标。然而,CT 得出的肥胖、肌肉疏松症和骨质流失的身体成分测量参考范围尚未在普通人群中确定:我们确定了一个具有人口代表性的样本,其中包括 4,900 名年龄在 20 至 89 岁之间、在 2010 年至 2020 年期间接受过腹部 CT 检查的人。该样本是根据明尼苏达州南部和威斯康星州西部 27 个县地区居民的年龄和性别分层抽样,采用倾向得分匹配法构建的。匹配包括种族、民族、教育水平、居住地区和是否患有 20 种慢性疾病。我们使用一种经过验证的基于深度学习的算法,通过腹部 CT 断面计算皮下脂肪组织面积、内脏脂肪组织面积、骨骼肌面积、骨骼肌密度、椎骨面积和椎骨密度:我们报告了 4,649 名本地区代表性人群的基于 CT 的身体成分参考范围。年龄越大,骨骼肌面积和密度越小,内脏脂肪含量越高。所有慢性疾病都与至少一种身体成分生物标志物的统计学差异有关。存在慢性疾病通常与皮下和内脏脂肪含量增加、肌肉密度和脊椎骨骨密度降低有关:我们根据年龄、性别、体重指数和慢性病状况,报告了具有人口代表性的 4,649 人队列中基于 CT 的身体成分生物标志物的参考范围。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信