Phenotypic differences between people varying in muscularity

Skeletal muscle (SM), which is found mainly within the appendicular lean soft tissue (ALST) compartment, is a biological important body compartment. Simple and accurate methods to estimate both SM and ALST remain difficult to obtain. We aimed to develop and to cross-validate anthropometric models for ALST in athletes, using dual-energy x-ray absorptiometry (DXA) as the reference method. ALST equations were developed in 176 athletic boys (15.0 +/- 2.8 yr; 64.5 +/- 15.8 kg; 1.72 +/- 0.15 m) and 92 athletic girls (14.6 +/- 2.8 yr; 53.0 +/- 13.1 kg; 1.61 +/- 0.13 m). Skinfolds were measured at the triceps, the thigh, and medial calf, and circumferences were measured at the midupper arm, the midthigh, and the midcalf. ALST was assessed using DXA (QDR-4500; Hologic, Walthman, MA; fan-beam mode). Two models were developed: a body weight model (WHt model) and a corrected muscle girth model (CMG model, which included the parameters height x CAG, height x CTG, and height x CCG, where CAG is corrected arm girth, CTG is corrected thigh girth, and CCG is the corrected calf). Simple regression analysis was used to identify the best model fit. The equations were internally cross-validated using the predicted residual sum of squares method, and performance of new equations was analyzed by regression analysis and agreement between methods. The new WHt model generated the following equation: ALST = -20.338 + 0.199(W) + 3.294(gender) + 14.230(height) + 0.192(age), where gender = 1 for male and 0 for female. The CMG model produced the following equation: ALST = 3.260 + 0.002(height x CTG) + 0.007(height x CAG) + 0.003(height x CCG). WHt equation had an R = 0.91 and an SEE = 2.00 kg, whereas CMG equation presented an R = 0.93 and an SEE = 1.80 kg. In both equations, slopes and intercepts did not differ from the line of identity; no mean differences between predicted and measured values and no trend line were observed (P > 0.05). Both models accurately predict ALST in young athletes, affording a practical means to quantify this compartment.

Lean body mass (LBM) and muscle mass remain difficult to quantify in large epidemiological studies due to the unavailability of inexpensive methods. We therefore developed anthropometric prediction equations to estimate the LBM and appendicular lean soft tissue (ALST) using dual-energy X-ray absorptiometry (DXA) as a reference method. Healthy volunteers (n = 2,220; 36% women; age 18-79 yr), representing a wide range of body mass index (14–44 kg/m2), participated in this study. Their LBM, including ALST, was assessed by DXA along with anthropometric measurements. The sample was divided into prediction (60%) and validation (40%) sets. In the prediction set, a number of prediction models were constructed using DXA-measured LBM and ALST estimates as dependent variables and a combination of anthropometric indices as independent variables. These equations were cross-validated in the validation set. Simple equations using age, height, and weight explained >90% variation in the LBM and ALST in both men and women. Additional variables (hip and limb circumferences and sum of skinfold thicknesses) increased the explained variation by 5–8% in the fully adjusted models predicting LBM and ALST. More complex equations using all of the above anthropometric variables could predict the DXA-measured LBM and ALST accurately, as indicated by low standard error of the estimate (LBM: 1.47 kg and 1.63 kg for men and women, respectively), as well as good agreement by Bland-Altman analyses (Bland JM, Altman D.Lancet 1: 307–310, 1986). These equations could be a valuable tool in large epidemiological studies assessing these body compartments in Indians and other population groups with similar body composition.

Assessment of Skeletal Muscle Mass in Older People: Comparison Between 2 Anthropometry-Based Methods and Dual-Energy X-ray Absorptiometry

0506 B-mode ultrasound has been widely employed to index skeletal muscle (SM) mass in vivo. However ultrasound-derived prediction equations for total body SM mass based on direct measurement, such as magnetic resonance imaging (MRI), have not been developed. PURPOSE: To develop regression-based prediction equations for SM mass by ultrasound, and to investigate the validity of these equations in Japanese adults. METHODS: Healthy Japanese men (n = 35) and women (n = 29) aged 18–61 yrs were randomly separated into 2 groups, a model development group (n = 48) and a validation group (n = 16). Total and regional SM mass was measured using magnetic resonance imaging (MRI) 1.5 T-scanners with spin-echo sequence. Contiguous transverse images (about 150 slices) with 1-cm slice thickness were obtained from the first cervical vertebrae to ankle joints. The volume of SM was calculated from the summation of digitized cross-sectional area. SM volume was converted into mass by an assumed SM density of 1.04 kg/l. Muscle thickness (MTH) was measured by B-mode ultrasound (5 MHz scanning head) at 9 sites on anatomical SM belly (lateral forearm, anterior and posterior upper arm, abdomen, subscapula, anterior and posterior thigh, anterior and posterior lower leg). RESULTS: Significant correlations were observed between MTH multiplied height and site-matched SM mass (total, arm, trunk body, thigh, and lower leg) by MRI measurement in the model development group (n = 48, r = 0.90–0.98, p<0.01, SEE = 0.1–1.2kg). When the SM mass prediction equations were applied to the validation group, significant correlations were also observed between the MRImeasured and predicted SM mass (n = 16, r = 0.94–0.98, p<0.01, SEE = 0.2–1.8kg). The predicted total SM mass for the validation group was 20.8 ± 6.9 kg, and was similar to the MRI-measured SM mass of 20.9 ± 7.0 kg. Bland-Altman analysis failed to disclose a bias in prediction of total SM mass for the validation group (n = 16, r = 0.03, NS). CONCLUSIONS: These results indicated that ultrasound-derived prediction equations are a valid method to predict SM mass and an alternative to MRI measurement in Japanese adults.

Background: We examined the effects of exercise training on resting metabolic rate (RMR), and whether changes in body composition are associated with changes in RMR in adolescents with overweight and obesity. Methods: One hundred forty adolescents (12-18 years, BMI ≥85th percentile) participated in randomized exercise trials (3-6 months) at UPMC Children's Hospital of Pittsburgh (18 control, 51 aerobic, 50 resistance, and 21 combined aerobic and resistance exercise). All participants had RMR assessments by indirect calorimetry after a 10-12 hour overnight fast, and body composition by magnetic resonance imaging and dual-energy X-ray absorptiometry. Results: There were no significant changes in RMR (kcal/day) between exercise groups vs. controls (p > 0.05). All exercise groups decreased visceral fat (-0.2 ± 0.02 kg; p < 0.05) compared to control. Increases in fat-free mass (FFM) were only seen in the combined group (2.3 ± 0.4 kg; p < 0.05), whereas increases in skeletal muscle mass were observed in both resistance (1.2 ± 0.2 kg; p < 0.05) and combined (1.5 ± 0.3 kg; p < 0.05) groups vs. control. Change in FFM, but not fat mass (FM), visceral fat, or skeletal muscle mass (p > 0.05), was a significant determinant of changes in RMR, independent of exercise modality (p = 0.04). Conclusion: Although exercise modality was not associated with changes in RMR, change in FFM, but not skeletal muscle or FM, was a significant correlate of changes in RMR in adolescents with overweight and obesity. Clinicaltrials.gov registration numbers: NCT00739180, NCT01323088, NCT01938950.

This study aimed to calculate the cut-off values for severe decline in muscle strength and skeletal muscle mass in post-acute patients with sarcopenia and to determine their effect on activities of daily living. This retrospective cohort study included patients with sarcopenia consecutively admitted to a Japanese rehabilitation hospital between 2014 and 2016. Sarcopenia was diagnosed based on handgrip strength and skeletal muscle mass index. The outcome was the functional independence measure of motor function at discharge. Cut-off values for predicting severe decline in handgrip strength and skeletal muscle mass index were calculated separately by sex. Values below the cut-off were used to define severely low handgrip strength and severely low skeletal muscle mass index. Overall, 451 patients (median age, 83 years; 61.4% women) were evaluated. The median functional independence measure of motor function score at discharge was 57. The optimal cut-off values for severely low handgrip strength were 9.0 kg for women and 14.6 kg for men, and those for severely low skeletal muscle mass index were 4.6 kg/m2 for women and 5.6 kg/m2 for men. Severely low handgrip strength and skeletal muscle mass index were independently associated with the functional independence measure of motor function score at discharge (β = -0.178, P = 0.005). In post-acute patients with sarcopenia, a severe decline in muscle strength and skeletal muscle mass is further negatively associated with an improvement in activities of daily living. The cut-off values herein can serve as indicators to assess sarcopenia severity. Geriatr Gerontol Int 2023; 23: 493-499.

Small organs with a high metabolic rate explain lower resting energy expenditure in African American than in white adults

There is a high prevalence of diabetes mellitus associated with insulin resistance in Indian adults, probably due to an inappropriately high accumulation of body fat at lower body mass indices (BMIs, kg/m(2)), as well as to a lower skeletal muscle mass. Although skeletal muscle is an important site of glucose disposal, the strength of its association with insulin sensitivity (IS) is unknown in the Indian population. This was a cross-sectional study performed to assess the association of body fat, muscle and muscle function with IS in 51 young, healthy, nonobese Indian men with BMI ranging from 15 to 25 kg/m(2), using hyperinsulinemic euglycemic clamp method. The median IS was 5.32 (mg/(kg min))/(microU/ml), lower, upper quartile, 4.03, 6.56); (SI units: 4.25, lower, upper quartile, 3.22, 5.24 (micromol/(kg min))/(pmol/l)). A multiple linear regression of the natural log transformed value of IS on BMI and body muscle mass measured as the appendicular lean soft tissue (expressed as a percentage of body weight) explained 49% of the variance in IS. Independently, body fat percent and muscle (handgrip) strength showed significant bivariate correlations with IS (rho=-0.61, P<0.001; rho=0.32, P=0.027, respectively), although these variables did not enter into the multiple regression. BMI and body fat have been shown to be inversely related to IS at higher BMIs. This study indicates that a relative increase in BMI and reduced muscle mass and possibly function are also associated with reduced IS in lean Indian men.

To the Editor : The authors of the Point:Counterpoint debate ([1][1], [2][2]) collectively thank our colleagues for their thoughtful responses to the topic “Satellite cell addition is/is not obligatory for skeletal muscle hypertrophy.” While the goal of our initial position piece and rebuttal

Recent recommendations for increases in desirable body weights are based upon studies which did not consider the potential confounding effect of cigarette consumption on body weight. We investigated the relation between tobacco use and several anthropometric measurements in 12,103 men and women 19-74 years of age in the United States examined between 1976 and 1980 during the Second National Health and Nutrition Examination Survey (NHANES II). Cigarette smokers weighed less (mean +/- standard error = 69.8 +/- 0.2 kg) and were leaner (body mass index (weight (kg)/height (m)2) = 24.6 +/- 0.1) than nonsmokers (72.5 +/- 0.2 kg and 25.7 +/- 0.1, respectively), controlling for age and sex. Body leanness increased with the duration (but not intensity) of smoking. Ex-smokers were not heavier or fatter than nonsmokers, and these groups experienced similar weight gain after age 25 (approximately 6 kg in men, 9 kg in women), while current smokers gained substantially less weight (3.5 kg in men, 5.4 kg in women). Compared to nonsmokers, former and current smokers were also slightly taller. Most of these associations were evident in both sexes and all ages evaluated, and were not explained by differences in caloric intake, physical activity, illness, or socioeconomic status. Our findings suggest that the increased mortality observed among lean individuals in previous studies may have been due to smoking rather than leanness per se, and that as a result, currently accepted desirable body weights may be overestimated.

Resting energy expenditure (REE) constitutes the largest component of total energy expenditure and undergoes an age-related decline that is unexplained by decreased fat-free mass. Phase angle (PhA) is a cellular health indicator that is possibly associated with REE. We investigated the association of REE and PhA in hospitalized older adults. This single-center, cross-sectional analysis utilized the baseline data from a prospective longitudinal study and included 131 eligible patients aged ≥70 years. The REE was measured using indirect calorimetry, and PhA and body composition were assessed using bioelectrical impedance. The association between REE, PhA, and body composition was examined, and REE was compared using previously reported PhA cutoff values. In this cohort with a mean (±standard deviation) age of 87.4 (±7.0) years, 34.4% of the participants were men. REE and PhA correlated strongly (r: 0.562, p < 0.001) and significantly after adjusting for age and sex (r: 0.433, p < 0.001). Multivariate analysis showed a significant independent association between REE and PhA and skeletal muscle mass (standardized β [95% CI]; 28.072 [2.188-53.956], p = 0.035) without any significant interaction between PhA and age on REE. The low PhA group had a significantly lower REE (kcal/day; 890 [856-925] vs. 1077 [1033-1122], p < 0.001), and this remained significant after adjusting for age, sex, and skeletal muscle mass index. PhA is associated with REE in older adults. Adjusting REE calculation algorithms based on PhA values and correcting predicted REE according to PhA may aid in determining more accurate energy requirements.

Sarcopenia is the age-related reduction of skeletal muscle mass in older individuals. Respiratory muscle strength may be related to skeletal muscle mass and, thus, the present study attempted to estimate the risk of sarcopenia relative to decreased pulmonary function. The present findings demonstrated that low pulmonary function was associated with low muscle mass in community-dwelling older adults. Lean body mass is related to pulmonary function in patients with chronic obstructive pulmonary disease (COPD). However, the relationship between muscle mass and pulmonary function in healthy older adults has yet to be clarified. Thus, the present study investigated the association of pulmonary function with muscle mass in an older community-dwelling Korean population. This study included 463 disease-free subjects over 65 years of age who underwent anthropometric measurements, laboratory tests, spirometry, and the estimation of appendicular skeletal muscle (ASM) mass in the 2008-2011 Korea National Health and Nutrition Examination Survey (KNHANES). Low muscle mass was defined as the value of ASM divided by height squared (ASM/height(2)) that was less than two standard deviations (SD) below the sex-specific mean of the young reference group. Forced expiratory volume in 1 s (FEV1[L]) and forced vital capacity (FVC[L]) were positively correlated with ASM/height(2) in males (p < 0.001 and p = 0.001, respectively) but not in females (p = 0.360 and p = 0.779, respectively). A univariate logistic regression analysis revealed that males with low FEV1 or FVC were more likely to have low muscle mass (odds ratio [OR] = 3.11, 95% confidence interval [CI] 1.62-5.99 for FEV1; OR = 1.99, 95% CI 1.13-3.53 for FVC); similar results were found for females, but the significance was lower (OR = 11.37, 95% CI 0.97-132.91 for FEV1; OR = 7.31, 95% CI 1.25-42.74 for FVC). After adjusting for age, smoking, and moderate physical activity, a low FEV1 value was associated with low muscle mass in both males (OR = 2.90, 95% CI 1.50-5.63) and females (OR = 9.15, 95% CI 1.53-54.77). Using nationally representative data from the 2008-2011 KNHANES, low pulmonary function was found to be associated with low muscle mass in community-dwelling older Korean adults.

Objective: To investigate the correlation between body mass and functional movements with skeletal muscle mass and skeletal muscle distribution in women, to determine the association between body mass and functional movements with skeletal muscle mass and distribution in women, and to provide theoretical support for improving the performance of body mass and functional movements in women through dietary or exercise improvement, thus providing a basis for maintaining women's health and preventing the adverse effects of low skeletal muscle mass on health and quality of life after menopause or old age. The study was conducted in a randomized sample of Zunyi Medical College. Methods: A number of female Zunyi medical students were randomly selected, and 78 of them (age, 20.99±2.10 years, BMI, 20.55±1.58 kg/m2) who met the criteria were screened for body composition and functional movement and tested for body mass. Correlation analysis was performed. Results: 1) Total physical test score, spirometry score, sprint 50m score, and endurance run score were significantly correlated (P&lt;0.05), where the correlation between total physical test score and skeletal muscle mass was moderate (r=0.45) and the other correlations were weak; 2) skeletal muscles distributed in the trunk were significantly correlated with total physical test score, spirometry score, seated forward bend score, sprint 50m score standing long jump, and endurance run score (P&lt;0.05). (2) Skeletal muscles distributed in the trunk were significantly correlated with total physical test scores, lung capacity scores, sitting forward bend scores, sprint 50m score standing long jump, and endurance run scores (P&lt;0.05), with the physical test scores moderately correlated with the distribution of skeletal muscles in the trunk (r=0.483) and weakly correlated with all other parts. (3) Skeletal muscle mass distributed in the right upper limb, left upper limb, trunk, right lower limb, and left lower limb were not significantly correlated with the total FMS score and each of the FMS scores. Conclusions: 1) skeletal muscle mass was correlated with female body mass, including physiological function, burst and endurance running; 2) skeletal muscles distributed in the trunk were correlated with female body mass, including physiological function, burst and endurance; 3) female skeletal muscle mass and the distribution of skeletal muscles in the trunk could be improved through diet and exercise to improve female body mass.

Risk factors for loss of skeletal muscle mass in patients with cirrhosis.

Background/Objective: Resting energy expenditure (REE) is the amount of energy needed to maintain energy balance. Prediction equations exist to estimate REE for adults. Recommended dietary allowances (RDA) can be used to estimate energy needs for children but may not be suitable for children with spinal cord injury (SCI). Limited information is available on energy expenditure of children with SCI. Research is needed to determine a method to estimate energy needs for children with SCI. This research project was designed to determine REE in children with SCI who attended an SCI clinic at a children’s hospital, determine variables that are correlated to REE in children with SCI, and determine a regression equation to estimate REE in children with SCI.Methods: A sample of convenience was obtained from children attending the SCI clinic at Shriners Hospitals for Children–Chicago. The REE was measured after a 4-hour fast usinga portable hand–held MedGem device to perform indirect calorimetry. Data including height, weight, age, months since injury,level of injury, gender, race, American Spinal Injury Association (ASIA) Motor Score, ASIA Impairment Score, and body mass index were obtained as covariates.Results: Regression equations to predict REE were developed for boys and girls. The REE varied significantly from results obtained using both RDA and the Harris–Benedict equation. Height and level of injury were the only variables that correlated with REE in this sample.Conclusions: Measured REE was lower than the results of prediction equations. A regression equation was developed to estimate REE in children with SCI based on height and level of injury. Further validation studies are needed.