Body mass index versus bioelectrical impedance analysis for classifying physical function impairment in a racially diverse cohort of midlife women: the Study of Women's Health Across the Nation (SWAN).
Body composition
Body mass index
Physical function
Skeletal muscle mass
Journal
Aging clinical and experimental research
ISSN: 1720-8319
Titre abrégé: Aging Clin Exp Res
Pays: Germany
ID NLM: 101132995
Informations de publication
Date de publication:
Sep 2020
Sep 2020
Historique:
received:
18
06
2019
accepted:
13
09
2019
pubmed:
5
10
2019
medline:
1
12
2020
entrez:
5
10
2019
Statut:
ppublish
Résumé
Body composition strongly influences physical function in older adults. Bioelectrical impedance analysis (BIA) differentiates fat mass from skeletal muscle mass, and may be more useful than body mass index (BMI) for classifying women on their likelihood of physical function impairment. This study tested whether BIA-derived estimates of percentage body fat (%BF) and height-normalized skeletal muscle mass (skeletal muscle mass index; SMI) enhance classification of physical function impairment relative to BMI. Black, White, Chinese, and Japanese midlife women (N = 1482) in the Study of Women's Health Across the Nation (SWAN) completed performance-based measures of physical function. BMI (kg/m In the overall sample, a BMI cutpoint of ≥ 30.1 kg/m Some race-specific BMI and %BF cutpoints have moderate utility for identifying impaired physical function among midlife women.
Sections du résumé
BACKGROUND
BACKGROUND
Body composition strongly influences physical function in older adults. Bioelectrical impedance analysis (BIA) differentiates fat mass from skeletal muscle mass, and may be more useful than body mass index (BMI) for classifying women on their likelihood of physical function impairment.
AIMS
OBJECTIVE
This study tested whether BIA-derived estimates of percentage body fat (%BF) and height-normalized skeletal muscle mass (skeletal muscle mass index; SMI) enhance classification of physical function impairment relative to BMI.
METHOD
METHODS
Black, White, Chinese, and Japanese midlife women (N = 1482) in the Study of Women's Health Across the Nation (SWAN) completed performance-based measures of physical function. BMI (kg/m
RESULTS
RESULTS
In the overall sample, a BMI cutpoint of ≥ 30.1 kg/m
CONCLUSION
CONCLUSIONS
Some race-specific BMI and %BF cutpoints have moderate utility for identifying impaired physical function among midlife women.
Identifiants
pubmed: 31584147
doi: 10.1007/s40520-019-01355-8
pii: 10.1007/s40520-019-01355-8
pmc: PMC7125018
mid: NIHMS1541034
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1739-1747Subventions
Organisme : NIH HHS
ID : U01NR004061
Pays : United States
Organisme : NIH HHS
ID : U01AG012539
Pays : United States
Organisme : NIH HHS
ID : U01AG012505
Pays : United States
Organisme : NIH HHS
ID : U01AG012535
Pays : United States
Organisme : NIH HHS
ID : U01AG012495
Pays : United States
Organisme : NIA NIH HHS
ID : U01 AG012505
Pays : United States
Organisme : NIA NIH HHS
ID : U01 AG012531
Pays : United States
Organisme : NIA NIH HHS
ID : U01 AG012554
Pays : United States
Organisme : NIH HHS
ID : U01AG012546
Pays : United States
Organisme : NIH HHS
ID : U01AG012531
Pays : United States
Organisme : NIA NIH HHS
ID : U01 AG012535
Pays : United States
Organisme : NIA NIH HHS
ID : U01 AG012553
Pays : United States
Organisme : NINR NIH HHS
ID : U01 NR004061
Pays : United States
Organisme : NIH HHS
ID : U01AG012554
Pays : United States
Organisme : NIA NIH HHS
ID : U01 AG012539
Pays : United States
Organisme : NIA NIH HHS
ID : U01 AG012546
Pays : United States
Organisme : NIA NIH HHS
ID : U19 AG063720
Pays : United States
Organisme : NIH HHS
ID : U01AG012553
Pays : United States
Organisme : NIA NIH HHS
ID : U01 AG012495
Pays : United States
Références
Metti AL, Best JR, Shaaban CE et al (2018) Longitudinal changes in physical function and physical activity in older adults. Age Ageing 47:558–564. https://doi.org/10.1093/ageing/afy025
doi: 10.1093/ageing/afy025
pubmed: 29546417
pmcid: 6693378
Windham BG, Griswold ME, Wang W et al (2017) The importance of mid-to-late-life body mass index trajectories on late-life gait speed. J Gerontol Ser A Biol Sci Med Sci 72:1130–1136. https://doi.org/10.1093/gerona/glw200
doi: 10.1093/gerona/glw200
Ylitalo KR, Karvonen-Gutierrez CA, Fitzgerald N et al (2013) Relationship of race-ethnicity, body mass index, and economic strain with longitudinal self-report of physical functioning: the Study of Women’s Health Across the Nation. Ann Epidemiol 23:401–408. https://doi.org/10.1016/j.annepidem.2013.04.008
doi: 10.1016/j.annepidem.2013.04.008
pubmed: 23694761
pmcid: 3898343
Bea JW, Going SB, Wertheim BC et al (2018) Body composition and physical function in the women’s health initiative observational study. Prev Med Rep 11:15–22. https://doi.org/10.1016/j.pmedr.2018.05.007
doi: 10.1016/j.pmedr.2018.05.007
pubmed: 30065910
pmcid: 6066466
Kim S, Leng XI, Kritchevsky SB (2017) Body composition and physical function in older adults with various comorbidities. Innov Aging 1:igx008. https://doi.org/10.1093/geroni/igx008
doi: 10.1093/geroni/igx008
pubmed: 30480107
pmcid: 6177091
Reinders I, Murphy RA, Martin KR et al (2015) Body mass index trajectories in relation to change in lean mass and physical function: the health, aging and body composition study. J Am Geriatr Soc 63:1615–1621. https://doi.org/10.1111/jgs.13524
doi: 10.1111/jgs.13524
pubmed: 26289686
pmcid: 4785850
Mongraw-Chaffin M, Golden SH, Allison MA et al (2015) The sex and race specific relationship between anthropometry and body fat composition determined from computed tomography: evidence from the multi-ethnic study of atherosclerosis. PLoS One 10:e0139559. https://doi.org/10.1371/journal.pone.0139559
doi: 10.1371/journal.pone.0139559
pubmed: 26448048
pmcid: 4598154
Yilmaz O, Bahat G (2017) Suggestions for assessment of muscle mass in primary care setting. Aging Male 20:168–169. https://doi.org/10.1080/13685538.2017.1311856
doi: 10.1080/13685538.2017.1311856
pubmed: 28414253
Wheaton FV, Crimmins EM (2016) Female disability disadvantage: a global perspective on sex differences in physical function and disability. Aging Soc 36:1136–1156. https://doi.org/10.1017/s0144686x15000227
doi: 10.1017/s0144686x15000227
Greendale GA, Sternfeld B, Huang M et al (2019) Changes in body composition and weight during the menopause transition. JCI Insight 4:124865. https://doi.org/10.1172/jci.insight.124865
doi: 10.1172/jci.insight.124865
pubmed: 30843880
Sowers MR, Crawford SL, Sternfeld B et al (2000) SWAN: a multi-center, multi-ethnic, community-based cohort study of women and the menopausal transition. In: Lobo RA, Kelsey JL, Marcus R (eds) Menopause: biology and pathobiology. Academic Press, San Diego, pp 175–188
doi: 10.1016/B978-012453790-3/50012-3
Guralnik JM, Simonsick EM, Ferrucci L et al (1994) A short physical performance battery assessing lower extremity function: association with self-reported disability and prediction of mortality and nursing home admission. J Gerontol 49:M85–M94
doi: 10.1093/geronj/49.2.M85
Miller DK, Wolinsky FD, Andresen EM et al (2008) Adverse outcomes and correlates of change in the short physical performance battery over 36 months in the African American health project. J Gerontol Ser A Biol Sci Med Sci 63:487–494
doi: 10.1093/gerona/63.5.487
Chumlea WC, Guo SS, Kuczmarski RJ et al (2002) Body composition estimates from NHANES III bioelectrical impedance data. Int J Obes 26:1596–1609. https://doi.org/10.1038/sj.ijo.0802167
doi: 10.1038/sj.ijo.0802167
Janssen I, Heymsfield SB, Baumgartner RN et al (2000) Estimation of skeletal muscle mass by bioelectrical impedance analysis. J Appl Physiol 89:465–471. https://doi.org/10.1152/jappl.2000.89.2.465
doi: 10.1152/jappl.2000.89.2.465
pubmed: 10926627
Janssen I, Baumgartner RN, Ross R et al (2004) Skeletal muscle cutpoints associated with elevated physical disability risk in older men and women. Am J Epidemiol 159:413–421
doi: 10.1093/aje/kwh058
Perkins NJ, Schisterman EF (2005) The Youden index and the optimal cut-point corrected for measurement error. Biom J 47:428–441
doi: 10.1002/bimj.200410133
Pencina MJ, D’Agostino RB, Pencina KM et al (2012) Interpreting incremental value of markers added to risk prediction models. Am J Epidemiol 176:473–481. https://doi.org/10.1093/aje/kws207
doi: 10.1093/aje/kws207
pubmed: 22875755
pmcid: 3530349
Pencina MJ, D’Agostino RB Sr, D’Agostino RB Jr et al (2008) Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med 27:157–172. https://doi.org/10.1002/sim.2929
doi: 10.1002/sim.2929
pubmed: 17569110
Pencina MJ, D’Agostino RB Sr, Steyerberg EW (2011) Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers. Stat Med 30:11–21. https://doi.org/10.1002/sim.4085
doi: 10.1002/sim.4085
pubmed: 21204120
Pencina MJ, Fine JP, D’Agostino RB (2017) Discrimination slope and integrated discrimination improvement—properties, relationships and impact of calibration. Stat Med 36:4482–4490. https://doi.org/10.1002/sim.7139
doi: 10.1002/sim.7139
pubmed: 27699818
Kerr KF, McClelland RL, Brown ER et al (2011) Evaluating the incremental value of new biomarkers with integrated discrimination improvement. Am J Epidemiol 174:364–374. https://doi.org/10.1093/aje/kwr086
doi: 10.1093/aje/kwr086
pubmed: 21673124
pmcid: 3202159
World Health Organization (2000) Obesity: preventing and managing the global epidemic. Report of a WHO consultation. World Health Organization technical report series 894:1–253
Ward CL, Valentine RJ, Evans EM (2014) Greater effect of adiposity than physical activity or lean mass on physical function in community-dwelling older adults. J Aging Phys Act 22:284–293. https://doi.org/10.1123/japa.2012-0098
doi: 10.1123/japa.2012-0098
pubmed: 23799829
Woo J, Leung J, Kwok T (2007) BMI, body composition, and physical functioning in older adults. Obesity 15:1886–1894. https://doi.org/10.1038/oby.2007.223
doi: 10.1038/oby.2007.223
pubmed: 17636108
Sternfeld B, Ngo L, Satariano WA et al (2002) Associations of body composition with physical performance and self-reported functional limitation in elderly men and women. Am J Epidemiol 156:110–121
doi: 10.1093/aje/kwf023
Kim YH, Kim KI, Paik NJ et al (2016) Muscle strength: a better index of low physical performance than muscle mass in older adults. Geriatr Gerontol Int 16:577–585. https://doi.org/10.1111/ggi.12514
doi: 10.1111/ggi.12514
pubmed: 26017097
Therkelsen KE, Pedley A, Hoffmann U et al (2016) Intramuscular fat and physical performance at the Framingham heart study. Age 38:31. https://doi.org/10.1007/s11357-016-9893-2
doi: 10.1007/s11357-016-9893-2
pubmed: 26899132
pmcid: 5005897
Gonzalez MC, Correia M, Heymsfield SB (2017) A requiem for BMI in the clinical setting. Curr Opin Clin Nutr Metab Care 20:314–321. https://doi.org/10.1097/mco.0000000000000395
doi: 10.1097/mco.0000000000000395
pubmed: 28768291
Muscaritoli M, Anker SD, Argiles J et al (2010) Consensus definition of sarcopenia, cachexia and pre-cachexia: joint document elaborated by special interest groups (SIG) “cachexia-anorexia in chronic wasting diseases” and “nutrition in geriatrics”. Clin Nutr 29:154–159. https://doi.org/10.1016/j.clnu.2009.12.004
doi: 10.1016/j.clnu.2009.12.004
Cruz-Jentoft AJ, Baeyens JP, Bauer JM et al (2010) Sarcopenia: European consensus on definition and diagnosis: report of the European working group on sarcopenia in older people. Age Aging 39:412–423. https://doi.org/10.1093/ageing/afq034
doi: 10.1093/ageing/afq034
Fielding RA, Vellas B, Evans WJ et al (2011) Sarcopenia: an undiagnosed condition in older adults. Current consensus definition: prevalence, etiology, and consequences. International working group on sarcopenia. J Am Med Dir Assoc 12:249–256. https://doi.org/10.1016/j.jamda.2011.01.003
doi: 10.1016/j.jamda.2011.01.003
pubmed: 21527165
Batsis JA, Mackenzie TA, Lopez-Jimenez F et al (2015) Sarcopenia, sarcopenic obesity, and functional impairments in older adults: national health and nutrition examination surveys 1999–2004. Nutr Res 35:1031–1039. https://doi.org/10.1016/j.nutres.2015.09.003
doi: 10.1016/j.nutres.2015.09.003
pubmed: 26472145
pmcid: 4825802
Houston DK, Ding J, Nicklas BJ et al (2005) The association between weight history and physical performance in the health, aging and body composition study. Int J Obes 31:1680–1687. https://doi.org/10.1038/sj.ijo.0803652
doi: 10.1038/sj.ijo.0803652
Santanasto AJ, Glynn NW, Lovato LC et al (2017) Effect of physical activity versus health education on physical function, grip strength and mobility. J Am Geriatr Soc 65:1427–1433. https://doi.org/10.1111/jgs.14804
doi: 10.1111/jgs.14804
pubmed: 28221668
pmcid: 5507738
Sims ST, Kubo J, Desai M et al (2013) Changes in physical activity and body composition in postmenopausal women over time. Med Sci Sports Exerc 45:1486–1492. https://doi.org/10.1249/MSS.0b013e31828af8bd
doi: 10.1249/MSS.0b013e31828af8bd
pubmed: 23439422
pmcid: 3715578