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Skeletal muscle mass adjusted by height correlated better with muscular functions than that adjusted by body weight in defining sarcopenia.

ABSTRACT: Sarcopenia, characterized by low muscle mass and function, results in frailty, comorbidities and mortality. However, its prevalence varies according to the different criteria used in its diagnosis. This cross-sectional study investigated the difference in the number of sarcopenia cases recorded by two different measurement methods of low muscle mass to determine which measurement was better. We recruited 878 (54.2% female) individuals aged over 65 years and obtained their body composition and functional parameters. Low muscle mass was defined as two standard deviations below either the mean height-adjusted (hSMI) or weight-adjusted (wSMI) muscle mass of a young reference group. The prevalence of sarcopenia was 6.7% vs. 0.4% (male/female) by hSMI, and 4.0% vs. 10.7% (male/female) by wSMI. The κ coefficients for these two criteria were 0.39 vs. 0.03 (male/female), and 0.17 in all subjects. Serum myostatin levels correlated positively with gait speed (r = 0.142, p = 0.007) after adjustment for gender. hSMI correlated with grip strength, cardiopulmonary endurance, leg endurance, gait speed, and flexibility. wSMI correlated with grip strength, leg endurance, gait speed, and flexibility. Since hSMI correlated more closely with grip strength and more muscular functions, we recommend hSMI in the diagnosis of low muscle mass.

SUBMITTER: Han DS

PROVIDER: S-EPMC4726295 | biostudies-other | 2016 Jan

REPOSITORIES: biostudies-other

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Project description:BackgroundThis study aimed to adjust and cross-validate skeletal muscle mass measurements between bioimpedance analysis (BIA) and dual-energy X-ray absorptiometry (DXA) for the screening of sarcopenia in the community and to estimate the prevalence of sarcopenia in Hong Kong.MethodsScreening of sarcopenia was provided to community-dwelling older adults. Appendicular skeletal muscle mass (ASM) was evaluated by BIA (InBody 120 or 720) and/or DXA. Handgrip strength and/or gait speed were assessed. Diagnosis of sarcopenia was based on the 2019 revised Asian Working Group for Sarcopenia cut-offs. Agreement analysis was performed to cross-validate ASM measurements by BIA and DXA. Multiple regression was used to explore contribution of measured parameters in predicting DXA ASM from BIA.ResultsA total of 1587 participants (age = 72 ± 12 years) were recruited; 1065 participants were screened by BIA (InBody 120) with 18 followed up by DXA, while the remaining 522 participants were assessed by the BIA (InBody 720) and DXA. The appendicular skeletal muscle mass index (ASMI) evaluated by BIA showed a mean difference of 2.89 ± 0.38 kg/m2 (InBody 120) and 2.97 ± 0.45 kg/m2 (InBody 720) against DXA gold standard. A significant overestimation of muscle mass was measured by BIA compared with DXA (P < 0.005). BIA data were adjusted using prediction equation and mean difference reduced to -0.02 ± 0.31 kg/m2 in cross-validation. Prevalence of sarcopenia in older adults ≥65 ranged from 39.4% (based on ASMI by DXA) to 40.8% (based on predicted DXA ASMI from BIA). Low ASMI by DXA was found in 68.5% of the older adults screened. The percentage of older adults exhibited low handgrip strength ranged from 31.3% to 56%, while 49% showed low gait speed.ConclusionsBioimpedance analysis was found to overestimate skeletal muscle mass compared with DXA. With adjustment equations, BIA can be used as a quick and reliable tool for screening sarcopenia in community and clinical settings with limited access to better options.

Project description:The relationship between ewe body condition score (BCS) and liveweight (LW) has been exploited previously to predict the former from LW, LW-change, and previous BCS records. It was hypothesized that if fleece weight and conceptus-free liveweight and LW-change, and in addition, height at withers were used, the accuracy of current approaches to predicting BCS would be enhanced. Ewes born in 2017 (n = 429) were followed from 8 mo to approximately 42 mo of age in New Zealand. Individual ewe data were collected on LW and BCS at different stages of the annual production cycle (i.e., prebreeding, at pregnancy diagnosis, prelambing, and weaning). Additionally, individual lambing dates, ewe fleece weight, and height at withers data were collected. Linear regression models were fitted to predict current BCS at each ewe age and stage of the annual production cycle using two LW-based models, namely, unadjusted for conceptus weight and fleece weight (LW alone1) and adjusted (LW alone2) models. Furthermore, another two models based on a combination of LW, LW-change, previous BCS, and height at withers (combined models), namely, unadjusted (combined1) and adjusted for conceptus and fleece weight (combined2), were fitted. Combined models gave more accurate (with lower root mean square error: RMSE) BCS predictions than models based on LW records alone. However, applying adjusted models did not improve BCS prediction accuracy (or reduce RMSE) or improve model goodness of fit (R 2) (P > 0.05). Furthermore, in all models, both LW-alone and combined models, a great proportion of variability in BCS, could not be accounted for (0.25 ≥ R 2 ≥ 0.83) and there was substantial prediction error (0.33 BCS ≥ RMSE ≥ 0.49 BCS) across age groups and stages of the annual production cycle and over time (years). Therefore, using additional ewe data which allowed for the correction of LW for fleece and conceptus weight and using height at withers as an additional predictor did not improve model accuracy. In fact, the findings suggest that adjusting LW data for conceptus and fleece weight offer no additional value to the BCS prediction models based on LW. Therefore, additional research to identify alternative methodologies to account for individual animal variability is still needed.

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Skeletal muscle mass adjusted by height correlated better with muscular functions than that adjusted by body weight in defining sarcopenia.

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