Advertisement

Estimation of Appendicular Skeletal Muscle Mass for Women Aged 60-70 Years Using a Machine Learning Approach

Published:October 07, 2022DOI:https://doi.org/10.1016/j.jamda.2022.09.002

      Abstract

      Objectives

      This article aimed to develop and validate an anthropometric equation based on the least absolute shrinkage and selection operator (LASSO) regression, a machine learning approach, to predict appendicular skeletal muscle mass (ASM) in 60-70-year-old women.

      Design

      A cross-sectional study.

      Setting and participants

      Community-dwelling women aged 60-70 years.

      Methods

      A total of 1296 community-dwelling women aged 60-70 years were randomly divided into the development or the validation group (1:1 ratio). ASM was evaluated by bioelectrical impedance analysis (BIA) as the reference. Variables including weight, height, body mass index (BMI), sitting height, waist-to-hip ratio (WHR), calf circumference (CC), and 5 summary measures of limb length were incorporated as candidate predictors. LASSO regression was used to select predictors with 10-fold cross-validation, and multiple linear regression was applied to develop the BIA-measured ASM prediction equation. Paired t test and Bland-Altman analysis were used to validate agreement.

      Results

      Weight, WHR, CC, and sitting height were selected by LASSO regression as independent variables and the equation is ASM = 0.2308 × weight (kg) – 27.5652 × WHR + 8.0179 × CC (m) + 2.3772 × Sitting height (m) + 22.2405 (adjusted R2 = 0.848, standard error of the estimate = 0.661 kg, P < .001). Bland-Altman analysis showed a high agreement between BIA-measured ASM and predicted ASM that the mean difference between the 2 methods was −0.041 kg, with the 95% limits of agreement of −1.441 to 1.359 kg.

      Conclusions and Implications

      The equation for 60-70-year-old women could provide an available measurement of ASM for communities that cannot equip with BIA, which promotes the early screening of sarcopenia at the community level. Additionally, sitting height could predict ASM effectively, suggesting that maybe it can be used in further studies of muscle mass.

      Keywords

      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribe:

      Subscribe to Journal of the American Medical Directors Association
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

        • Song P.
        • Han P.
        • Zhao Y.
        • et al.
        Muscle mass rather than muscle strength or physical performance is associated with metabolic syndrome in community-dwelling older Chinese adults.
        BMC Geriatr. 2021; 21: 191
        • Chen L.K.
        • Woo J.
        • Assantachai P.
        • et al.
        Asian Working Group for Sarcopenia: 2019 consensus update on sarcopenia diagnosis and treatment.
        J Am Med Dir Assoc. 2020; 21: 300-307.e2
        • Cruz-Jentoft A.J.
        • Baeyens J.P.
        • Bauer J.M.
        • et al.
        Sarcopenia: European consensus on definition and diagnosis: Report of the European Working Group on Sarcopenia in Older People.
        Age Ageing. 2010; 39: 412-423
        • Kitamura A.
        • Seino S.
        • Abe T.
        • et al.
        Sarcopenia: prevalence, associated factors, and the risk of mortality and disability in Japanese older adults.
        J Cachexia Sarcopenia Muscle. 2021; 12: 30-38
        • Papadopoulou S.K.
        Sarcopenia: a contemporary health problem among older adult populations.
        Nutrients. 2020; 12: 1293
        • Tournadre A.
        • Vial G.
        • Capel F.
        • et al.
        Sarcopenia.
        Joint Bone Spine. 2019; 86: 309-314
        • Xin C.
        • Sun X.
        • Lu L.
        • Shan L.
        Prevalence of sarcopenia in older Chinese adults: a systematic review and meta-analysis.
        BMJ Open. 2021; 11: e041879
        • Han P.
        • Kang L.
        • Guo Q.
        • et al.
        Prevalence and factors associated with sarcopenia in suburb-dwelling older Chinese using the Asian Working Group for Sarcopenia definition.
        J Gerontol A Biol Sci Med Sci. 2016; 71: 529-535
        • Shou J.
        • Chen P.J.
        • Xiao W.H.
        Mechanism of increased risk of insulin resistance in aging skeletal muscle.
        Diabetol Metab Syndr. 2020; 12: 14
        • Cruz-Jentoft A.J.
        • Bahat G.
        • Bauer J.
        • et al.
        Sarcopenia: revised European consensus on definition and diagnosis.
        Age Ageing. 2019; 48: 16-31
        • Costanzo L.
        • De Vincentis A.
        • Di Iorio A.
        • et al.
        Impact of low muscle mass and low muscle strength according to EWGSOP2 and EWGSOP1 in community-dwelling older people.
        J Gerontol A Biol Sci Med Sci. 2020; 75: 1324-1330
        • Chiba I.
        • Lee S.
        • Bae S.
        • et al.
        Difference in sarcopenia characteristics associated with physical activity and disability incidences in older adults.
        J Cachexia Sarcopenia Muscle. 2021; 12: 1983-1994
        • Cruz-Jentoft A.J.
        • Sayer A.A.
        Sarcopenia.
        Lancet. 2019; 393: 2636-2646
        • Chien K.Y.
        • Chen C.N.
        • Chen S.C.
        • et al.
        A community-based approach to lean body mass and appendicular skeletal muscle mass prediction using body circumferences in community-dwelling elderly in Taiwan.
        Asia Pac J Clin Nutr. 2020; 29: 94-100
        • Hsiao M.Y.
        • Chang K.V.
        • Wu W.T.
        • et al.
        Grip strength and demographic variables estimate appendicular muscle mass better than bioelectrical impedance in Taiwanese older persons.
        J Am Med Dir Assoc. 2021; 22: 760-765
        • Furushima T.
        • Miyachi M.
        • Iemitsu M.
        • et al.
        Development of prediction equations for estimating appendicular skeletal muscle mass in Japanese men and women.
        J Physiol Anthropol. 2017; 36: 34
        • Kulkarni B.
        • Kuper H.
        • Taylor A.
        • et al.
        Development and validation of anthropometric prediction equations for estimation of lean body mass and appendicular lean soft tissue in Indian men and women.
        J Appl Physiol (1985). 2013; 115: 1156-1162
        • Steyerberg E.W.
        • Eijkemans M.J.
        • Habbema J.D.
        Stepwise selection in small data sets: a simulation study of bias in logistic regression analysis.
        J Clin Epidemiol. 1999; 52: 935-942
        • Meng Z.
        • Wang M.
        • Guo S.
        • et al.
        Development and validation of a LASSO prediction model for better identification of ischemic stroke: a case-control study in China.
        Front Aging Neurosci. 2021; 13: 630437
        • Che W.
        • Wang Y.
        • Wang X.
        • Lyu J.
        Midlife brain metastases in the United States: is male at risk?.
        Cancer Med. 2022; 11: 1202-1216
        • Sun B.
        • Deng R.
        • Ren B.
        • et al.
        Identification method of market power abuse of generators based on lasso-logit model in spot market.
        Energy. 2022; 238: 121634
        • Kohannim O.
        • Hibar D.P.
        • Stein J.L.
        • et al.
        Discovery and replication of gene influences on brain structure using LASSO regression.
        Front Neurosci. 2012; 6: 115
        • Demjaha A.
        • Lappin J.M.
        • Stahl D.
        • et al.
        Antipsychotic treatment resistance in first-episode psychosis: prevalence, subtypes and predictors.
        Psychol Med. 2017; 47: 1981-1989
        • Chen K.
        • He Q.
        • Pan Y.
        • et al.
        Short video viewing, and not sedentary time, is associated with overweightness/obesity among Chinese women.
        Nutrients. 2022; 14: 1309
        • Li T.
        • Pan Y.
        • He Q.
        • et al.
        Associations between sedentary behaviour, physical activity and frailty in older Chinese women: a cross-sectional study.
        J Clin Nurs. 2022; https://doi.org/10.1111/jocn.16373
        • Chen X.
        • Kong C.
        • Yu H.
        • et al.
        Association between osteosarcopenic obesity and hypertension among four minority populations in China: a cross-sectional study.
        BMJ Open. 2019; 9: e026818
        • Wen X.
        • Wang M.
        • Jiang C.M.
        • Zhang Y.M.
        Anthropometric equation for estimation of appendicular skeletal muscle mass in Chinese adults.
        Asia Pac J Clin Nutr. 2011; 20: 551-556
        • do Nascimento R.A.
        • Vieira M.C.A.
        • Dos Santos Aguiar Gonçalves R.S.
        • et al.
        Cutoff points of adiposity anthropometric indices for low muscle mass screening in middle-aged and older healthy women.
        BMC Musculoskelet Disord. 2021; 22: 713
        • Burton R.F.
        • Burton F.L.
        When is sitting height a better measure of adult body size than total height, and why? The contrasting examples of body mass, waist circumference, and lung volume.
        Am J Hum Biol. 2021; 33: e23433
        • Qazi S.L.
        • Rikkonen T.
        • Kröger H.
        • et al.
        Relationship of body anthropometric measures with skeletal muscle mass and strength in a reference cohort of young Finnish women.
        J Musculoskelet Neuronal Interact. 2017; 17: 192-196
        • Hawkes C.P.
        • Mostoufi-Moab S.
        • McCormack S.E.
        • et al.
        Sitting height to standing height ratio reference charts for children in the United States.
        J Pediatr. 2020; 226: 221-227.e15
        • McNeish D.M.
        Using lasso for predictor selection and to assuage overfitting: a method long overlooked in behavioral sciences.
        Multivariate Behav Res. 2015; 50: 471-484
        • Friedman J.
        • Hastie T.
        • Tibshirani R.
        Regularization paths for generalized linear models via coordinate descent.
        J Stat Softw. 2010; 33: 1-22
        • Yarkoni T.
        • Westfall J.
        Choosing prediction over explanation in psychology: lessons from machine learning.
        Perspect Psychol Sci. 2017; 12: 1100-1122
        • Xu H.Q.
        • Liu J.M.
        • Zhang X.
        • et al.
        Estimation of skeletal muscle mass by bioimpedance and differences among skeletal muscle mass indices for assessing sarcopenia.
        Clin Nutr. 2021; 40: 2308-2318
        • Yamada Y.
        • Nishizawa M.
        • Uchiyama T.
        • et al.
        Developing and validating an age-independent equation using multi-frequency bioelectrical impedance analysis for estimation of appendicular skeletal muscle mass and establishing a cutoff for sarcopenia.
        Int J Environ Res Public Health. 2017; 14: 809
        • Kawakami R.
        • Miyachi M.
        • Tanisawa K.
        • et al.
        Development and validation of a simple anthropometric equation to predict appendicular skeletal muscle mass.
        Clin Nutr. 2021; 40: 5523-5530
        • Ma H.
        • Wu X.
        • Guo X.
        • et al.
        Optimal body mass index cut-off points for prediction of incident diabetes in a Chinese population.
        J Diabetes. 2018; 10: 926-933
        • Jiang Y.
        • Zhang X.
        • Xu T.
        • et al.
        Secular difference in body mass index from 2014 to 2020 in Chinese older adults: a time-series cross-sectional study.
        Front Nutr. 2022; 9: 923539