Sex-specific equations to estimate body composition: Derivation and validation of diagnostic prediction models using UK Biobank

被引:5
作者
Lu, Yueqi [1 ]
Shan, Ying [2 ]
Dai, Liang [2 ]
Jiang, Xiaosen [1 ]
Song, Congying [2 ]
Chen, Bangwei [1 ,3 ]
Zhang, Jingwen [4 ]
Li, Jing [2 ,4 ]
Zhang, Yue [4 ]
Xu, Junjie [2 ]
Li, Tao [1 ]
Xiong, Zuying [4 ]
Bai, Yong [1 ,5 ]
Huang, Xiaoyan [2 ,4 ,6 ]
机构
[1] BGI Shenzhen, Shenzhen, Peoples R China
[2] Peking Univ, Shenzhen Hosp, Clin Res Acad, Shenzhen, Peoples R China
[3] South China Univ Technol, Sch Biol & Biol Engn, Guangzhou, Peoples R China
[4] Peking Univ, Shenzhen Hosp, Renal Div, Shenzhen, Peoples R China
[5] BGI Shenzhen, Bldg 11, Beishan Ind Zone, Shenzhen 518083, Guangdong, Peoples R China
[6] Peking Univ, Shenzhen Hosp, Renal Div, Lianhua Rd 1120, Shenzhen 518036, Guangdong, Peoples R China
基金
中国国家自然科学基金;
关键词
Visceral adipose tissue; Abdominal subcutaneous adipose tissue; Total thigh fat-free muscle; Volumetric magnetic resonance imaging; Prediction model; Machine learning; SUBCUTANEOUS ADIPOSE-TISSUE; MASS INDEX; CARDIOVASCULAR-DISEASE; SCIENTIFIC STATEMENT; ASSOCIATION; OBESITY; HEALTH; MUSCLE; RISK; REGRESSION;
D O I
10.1016/j.clnu.2023.02.005
中图分类号
R15 [营养卫生、食品卫生]; TS201 [基础科学];
学科分类号
100403 ;
摘要
Background & aims: Body mass index and waist circumference are simple measures of obesity. However, they do not distinguish between visceral and subcutaneous fat, or muscle, potentially leading to biased relationships between individual body composition parameters and adverse health outcomes. The purpose of this study was to develop and validate prediction models for volumetric adipose and muscle. Methods: Based on cross-sectional data of 18,457, 18,260, and 17,052 White adults from the UK Biobank, we developed sex-specific equations to estimate visceral adipose tissue (VAT), abdominal subcutaneous adipose tissue (ASAT), and total thigh fat-free muscle (FFM) volumes, respectively. Volumetric magnetic resonance imaging served as the reference. We used the least absolute shrinkage and selection operator and the extreme gradient boosting methods separately to fit three sequential models, the inputs of which included demographics and anthropometrics and, in some, bioelectrical impedance analysis parameters. We applied comprehensive metrics to assess model performance in the temporal validation set. Results: The equations that included more predictors generally performed better. Accuracy of the equations was moderate for VAT (percentage of estimates that differed <30% from the measured values, 70 to 78 in males, 64 to 69 in females) and good for ASAT (85 to 91 in males, 90 to 95 in females) and FFM (99 to 100 in both sexes). All the equations appeared precise (interquartile range of the difference, 0.89 to 1.76 L for VAT, 1.16 to 1.61 L for ASAT, 0.81 to 1.39 L for FFM). Bias of all the equations was negligible (-0.17 to 0.05 L for VAT,-0.10 to 0.12 L for ASAT,-0.07 to 0.09 L for FFM). The equations achieved superior cardiometabolic correlations compared with body mass index and waist circumference. Conclusions: The developed equations to estimate VAT, ASAT, and FFM volumes achieved moderate to good performance. They may be cost-effective tools to revisit the implications of diverse body components. (c) 2023 The Authors. Published by Elsevier Ltd.
引用
收藏
页码:511 / 518
页数:8
相关论文
共 47 条
[1]   Changes in abdominal subcutaneous adipose tissue phenotype following menopause is associated with increased visceral fat mass [J].
Abildgaard, Julie ;
Ploug, Thorkil ;
Al-Saoudi, Elaf ;
Wagner, Thomas ;
Thomsen, Carsten ;
Ewertsen, Caroline ;
Bzorek, Michael ;
Pedersen, Bente Klarlund ;
Pedersen, Anette Tonnes ;
Lindegaard, Birgitte .
SCIENTIFIC REPORTS, 2021, 11 (01)
[2]   Association Between Visceral and Subcutaneous Adipose Depots and Incident Cardiovascular Disease Risk Factors [J].
Abraham, Tobin M. ;
Pedley, Alison ;
Massaro, Joseph M. ;
Hoffmann, Udo ;
Fox, Caroline S. .
CIRCULATION, 2015, 132 (17) :1639-1647
[3]   Health Effects of Overweight and Obesity in 195 Countries over 25 Years [J].
Afshin, Ashkan ;
Forouzanfar, Mohammad H. ;
Reitsma, Marissa B. ;
Sur, Patrick ;
Estep, Kara ;
Lee, Alex ;
Marczak, Laurie ;
Mokdad, Ali H. ;
Moradi-Lakeh, Maziar ;
Naghavi, Mohsen ;
Salama, Joseph S. ;
Vos, Theo ;
Abate, Kalkidan H. ;
Abbafati, Cristiana ;
Ahmed, Muktar B. ;
Al-Aly, Ziyad ;
Alkerwi, Ala'a ;
Al-Raddadi, Rajaa ;
Amare, Azmeraw T. ;
Amberbir, Alemayehu ;
Amegah, Adeladza K. ;
Amini, Erfan ;
Amrock, Stephen M. ;
Anjana, Ranjit M. ;
Arnlov, Johan ;
Asayesh, Hamid ;
Banerjee, Amitava ;
Barac, Aleksandra ;
Baye, Estifanos ;
Bennett, Derrick A. ;
Beyene, Addisu S. ;
Biadgilign, Sibhatu ;
Biryukov, Stan ;
Bjertness, Espen ;
Boneya, Dube J. ;
Campos-Nonato, Ismael ;
Carrero, Juan J. ;
Cecilio, Pedro ;
Cercy, Kelly ;
Ciobanu, Liliana G. ;
Cornaby, Leslie ;
Damtew, Solomon A. ;
Dandona, Lalit ;
Dandona, Rakhi ;
Dharmaratne, Samath D. ;
Duncan, Bruce B. ;
Eshrati, Babak ;
Esteghamati, Alireza ;
Feigin, Valery L. ;
Fernandes, Joao C. .
NEW ENGLAND JOURNAL OF MEDICINE, 2017, 377 (01) :13-27
[4]   Prognosis and prognostic research: validating a prognostic model [J].
Altman, Douglas G. ;
Vergouwe, Yvonne ;
Royston, Patrick ;
Moons, Karel G. M. .
BMJ-BRITISH MEDICAL JOURNAL, 2009, 338 :1432-1435
[5]   Association between body composition parameters and risk of mild cognitive impairment in older Japanese adults [J].
Bae, Seongryu ;
Shimada, Hiroyuki ;
Park, Hyuntae ;
Lee, Sangyoon ;
Makizako, Hyuma ;
Doi, Takehiko ;
Yoshida, Daisuke ;
Tsutsumimoto, Kota ;
Anan, Yuya ;
Suzuki, Takao .
GERIATRICS & GERONTOLOGY INTERNATIONAL, 2017, 17 (11) :2053-2059
[6]   Regression to the mean: what it is and how to deal with it [J].
Barnett, AG ;
van der Pols, JC ;
Dobson, AJ .
INTERNATIONAL JOURNAL OF EPIDEMIOLOGY, 2005, 34 (01) :215-220
[7]   Advanced body composition assessment: from body mass index to body composition profiling [J].
Borga, Magnus ;
West, Janne ;
Bell, Jimmy D. ;
Harvey, Nicholas C. ;
Romu, Thobias ;
Heymsfield, Steven B. ;
Leinhard, Olof Dahlqvist .
JOURNAL OF INVESTIGATIVE MEDICINE, 2018, 66 (05) :887-895
[8]   Validation of a fast method for quantification of intra-abdominal and subcutaneous adipose tissue for large-scale human studies [J].
Borga, Magnus ;
Thomas, E. Louise ;
Romu, Thobias ;
Rosander, Johannes ;
Fitzpatrick, Julie ;
Leinhard, Olof Dahlqvist ;
Bell, Jimmy D. .
NMR IN BIOMEDICINE, 2015, 28 (12) :1747-1753
[9]   Abdominal subcutaneous adipose tissue: a favorable adipose depot for diabetes? [J].
Chen, Peizhu ;
Hou, Xuhong ;
Hu, Gang ;
Wei, Li ;
Jiao, Lei ;
Wang, Hongmei ;
Chen, Siyu ;
Wu, Jingzhu ;
Bao, Yuqian ;
Jia, Weiping .
CARDIOVASCULAR DIABETOLOGY, 2018, 17
[10]   Frailty and the endocrine system [J].
Clegg, Andrew ;
Hassan-Smith, Zaki .
LANCET DIABETES & ENDOCRINOLOGY, 2018, 6 (09) :743-752