Abstract P377: Insulin, Muscle Mass and Exercise Performance in Overweight Women

Circulation ◽  
2012 ◽  
Vol 125 (suppl_10) ◽  
Author(s):  
Benjamin Leon ◽  
Andrea Carnie ◽  
Shannon Jenkins ◽  
Kevin Smith ◽  
Gloria Zalos ◽  
...  
Keyword(s):  
Skeletal Muscle ◽  
Upper Extremity ◽  
Muscle Mass ◽  
Fat Mass ◽  
Lean Mass ◽  
Exercise Performance ◽  
Plasma Insulin ◽  
Fasting Insulin ◽  
Load Bearing ◽  
Skeletal Mass

Introduction Obesity is associated with many negative health impacts, including hyperinsulinemia and reduced exercise performance, despite being associated with greater lean skeletal mass which works as the insulin-targeting and exercising organ. Purpose of Study We delineated the associations amongst cardiorespiratory capacity, fat mass, skeletal mass distributions, and fasting plasma insulin in overweight, non-diabetic women. Methods One hundred and seventy-two sedentary women, age 22 to 68 years (range), body mass index (BMI) (34.2 ± 6.3 [mean ± SD]; range 25.3 to 57.6 kg/m 2 ), underwent dual energy x-ray absorptiometry for body composition, fasting insulin, and graded treadmill exercise test using the Bruce protocol with measurement of oxygen consumption (peak VO 2 ). Results After adjustment for age, fasting insulin (9.8 ± 8.1; range 1.9 to 47.6 mcU/ml) was positively associated with BMI (r = 0.43, p<0.001), fat mass (r = 0.41, p< 0.001), load-bearing skeletal muscle mass (lower extremity lean mass; r = 0.29, p< 0.001), and non-load-bearing skeletal muscle mass (upper extremity lean mass; Figure, Panel A). By multiple regression analysis with age, fat mass and lower and upper extremity lean masses as covariates, fat mass, age and upper extremity lean mass (Figure, Panel B) were independent negative predictors of peak VO 2 (all p< 0.01). Lower extremity, however, trended to be positively predictive of peak VO 2 (p = 0.067). Conclusions In non-load-bearing muscle, increased lean mass associated with elevated plasma insulin is predictive of reduced oxygen consumption during exercise, suggesting additional load that may diminish cardiorespiratory exercise performance or intrinsic impairment in skeletal muscle function. In load-bearing muscle, compensatory hypertrophy due to increased fat and lean mass loads may preserve exercise performance.

Appendicular skeletal muscle mass to truncal fat mass ratio as a new index for osteosarcopenic obesity syndrome in older adults: emphasizing the role of central obesity

2020 ◽  
Author(s):  
Lazuardhi Dwipa ◽  
Rini Widiastuti ◽  
Alif Bagus Rakhimullah ◽  
Marcellinus Maharsidi ◽  
Yuni Susanti Pratiwi ◽  
...  
Keyword(s):  
Older Adults ◽  
Skeletal Muscle ◽  
Body Composition ◽  
Muscle Mass ◽  
Fat Mass ◽  
Central Obesity ◽  
Skeletal Muscle Mass ◽  
Positive Correlation ◽  
Appendicular Skeletal Muscle ◽  
Appendicular Skeletal Muscle Mass

Abstract Background The relationship between obesity and low bone mineral density (BMD) in older adults is still unclear. Most of the previous study did not account the factor of sarcopenia which is the progressive loss of skeletal muscle mass due to aging, and distribution of fat in obesity. Thus, this study was aimed to explore the correlation between appendicular skeletal muscle mass (ASMM), total fat mass (FM), and truncal fat mass (TrFM) as well as indexes (ASMM/FM and ASMM/TrFM ratio) with BMD in older adults.Methods This was an analytic cross-sectional study. Dual x-ray absorptiometry (DXA) and bioelectric impedance analysis (BIA) were used to assess BMD and body composition, respectively. Appendicular Skeletal Muscle Mass (ASMM) were used in the analysis to reflect sarcopenia, Fat Mass (FM) and Trunkal Fat Mass (TrFM) were used to reflect general and central obesity, respectively. All data were obtained from medical records of Geriatric Clinic of Hasan Sadikin General Hospital Bandung Indonesia from January 2014 to December 2018. The correlation between body compositions variable with BMD were analyzed using Spearman’s test. We also conducted a comparison analysis of body composition variables between low and normal BMD using Mann-Whitney test. Results A total of 112 subjects were enrolled in the study. ASMM and TrFM were positive (rs=0.517, p<0.001) and negative (rS=-0.22, p=0.02) correlated with BMD, respectively. FM were not correlated with BMD, rS=-0.113 (p=0.234). As indexes, ASMM/FM and ASMM/TrFM had positive correlation with BMD, rS=0.277 (p<0.001), and rS=0.391 (p<0.001), respectively. The ASMM, TrFM, and ASMM/TrFM ratio between normal and low BMD also significantly different (p<0.001), meanwhile FM were not (p=0.204).Conclusion ASMM and TrFM have a positive and negative correlation with BMD, respectively. ASMM/TrFM ratio as new sarcopenia-central obesity index has a positive correlation with BMD.

P1569ADIPONECTIN IS ASSOCIATED WITH DECLINE IS SKELETAL MUSCLE MASS AND BONE MINERAL CONTENT IN PATIENTS ON HEMODIALYSIS

2020 ◽  
Vol 35 (Supplement_3) ◽  
Author(s):  
Hiroshi Ogawa ◽  
Toshimitsu Koga ◽  
Daisuke Fuwa ◽  
Hirofumi Tamaki ◽  
Takayuki Nanbu ◽  
...  
Keyword(s):  
Skeletal Muscle ◽  
Bone Mineral Content ◽  
Muscle Mass ◽  
Body Fat ◽  
Bone Mineral ◽  
Fat Mass ◽  
Skeletal Muscle Mass ◽  
Mineral Content ◽  
Body Fat Mass ◽  
Total Plasma

Abstract Background and Aims Patients on hemodialysis are prone to undernutrition, malnutrition-inflammation-atherosclerosis (MIA) syndrome, and protein-energy wasting (PEW). One of the major adipocytokines adiponectin (ADPN) is involved in anti-arteriosclerotic and anti-inflammatory processes. However, ADPN is implicated in muscle weakness and loss of muscle mass in the elderly in addition to sarcopenia. At the 2019 ERA-EDTA Congress, we announced that total plasma ADPN levels in patients on hemodialysis (HD) showed a significant inverse correlation with BMI, body fat in percentage, mass and estimated skeletal muscle mass, and ADPN may be involved in sarcopenia in patients on HD. Herein, we investigated the association of ADPN level with sarcopenia in patients on HD using a method different from the one used in our previous study. We examined the relationship between total plasma ADPN level and the rate of change in estimated skeletal muscle mass, bone mineral content, and body fat mass over 5 years after the plasma ADPN measurement. Furthermore, we analyzed whether an elevated ADPN level was predictive of a subsequent decline in these parameters. Method Total plasma ADPN levels were measured using ELISA (Bio Vendor-Laboratorni Medicina a.s., Czech Republic) in 42 male patients on HD (age: 51.1 ± 9.0 years, dialysis vintage: 144.8 ± 99.2 months, BMI: 21.8 ± 3.2, dry BW: 62.0 ± 10.9 kg, dialysis time: 15.6 ± 3.1 hours/week). The estimates of skeletal muscle mass, bone mineral content, and body fat mass were made using multi-frequency bioelectrical impedance analysis (MFBIA) within the same year when total plasma ADPN level were first measured in 2011 as well as in 2016. We then calculated the rates of change in the estimated skeletal muscle mass, bone mineral content, and body fat mass over the 5 years and correlated these parameters with the total plasma ADPN measurements. Results Conclusion Total plasma ADPN levels inversely correlate with larger rates of decrease in estimated skeletal muscle mass and bone mineral content in patients on HD. This suggests that ADPN may play a role in the decline in skeletal muscle mass and bone mineral content over time in patients on HD.

scholarly journals The Appendicular Lean Mass Index Is a Suitable Surrogate for Muscle Mass in Children with Cerebral Palsy

2019 ◽  
Vol 149 (10) ◽  
pp. 1863-1868
Author(s):  
Ibrahim Duran ◽  
Kyriakos Martakis ◽  
Mirko Rehberg ◽  
Christina Stark ◽  
Anne Koy ◽  
...  
Keyword(s):  
Skeletal Muscle ◽  
Cerebral Palsy ◽  
Muscle Mass ◽  
Lean Mass ◽  
Skeletal Muscle Mass ◽  
Rehabilitation Program ◽  
Appendicular Lean Mass ◽  
Significant Difference ◽  
Children With Cerebral Palsy ◽  
Level I

ABSTRACT Background Densitometrically measured lean body mass (LBM) is often used to quantify skeletal muscle mass in children with cerebral palsy (CP). Since LBM depends on the individual's height, the evaluation of $\frac{{{\rm{LBM}}}}{{heigh{t^2}}}\ $ (lean BMI) is often recommended. However, LBM includes not only skeletal muscle mass but also the mass of skin, internal organs, tendons, and other components. This limitation applies to a far lesser extent to the appendicular lean mass index (LMIapp). Objectives The aim of the study was to evaluate skeletal muscle mass in children with CP using total lean BMI (LMItot) and LMIapp. Methods The present study was a monocentric retrospective analysis of prospectively collected data among children and adolescents with CP participating in a rehabilitation program. In total, 329 children with CP [148 females; Gross Motor Function Classification Scale (GMFCS) I, 32 children; GMFCS II, 73 children; GMFCS III, 133 children; GMFCS IV, 78 children; and GMFCS V, 13 children] were eligible for analysis. The mean age was 12.3 ± 2.75 y. Pediatric reference centiles for age-adjusted LMIapp were generated using data from NHANES 1999–2004. Low skeletal muscle mass was defined as a z score for DXA determined LMItot and LMIapp less than or equal to −2.0. Results The z scores for LMIapp were significantly lower than LMItot in children with CP, GMFCS levels II–V (P < 0.001), with the exception of GMFCS level I (P = 0.121), where no significant difference was found. The prevalence of low LMItot (16.1%; 95% CI: 16.1, 20.1%) was significantly lower (P < 0.001) than the prevalence of LMIapp (42.2%; 95% CI: 36.9, 47.9%) in the study population. Conclusions The prevalence of low skeletal muscle mass in children with CP might be underestimated by LMItot. LMIapp is more suitable for the evaluation of skeletal muscle mass in children with CP.

scholarly journals Effects of PM2.5 on Skeletal Muscle Mass and Body Fat Mass of the Elderly in Taipei, Taiwan

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Chi-Hsien Chen ◽  
Li-Ying Huang ◽  
Kang-Yun Lee ◽  
Chih-Da Wu ◽  
Hung-Che Chiang ◽  
...  
Keyword(s):  
Skeletal Muscle ◽  
Muscle Mass ◽  
Body Fat ◽  
Fat Mass ◽  
Skeletal Muscle Mass ◽  
The Elderly ◽  
Body Fat Mass

FRI0323 The association of fat mass and skeletal muscle mass with knee OA: The neo study

2013 ◽  
Vol 71 (Suppl 3) ◽  
pp. 423.1-423
Author(s):  
W. Visser ◽  
M. den Heijer ◽  
M. Reijnierse ◽  
R. de Mutsert ◽  
F. Rosendaal ◽  
...  
Keyword(s):  
Skeletal Muscle ◽  
Muscle Mass ◽  
Fat Mass ◽  
Skeletal Muscle Mass ◽  
Knee Oa

Fat mass and skeletal muscle mass in hip-fracture women: A cross-sectional study

Maturitas ◽  
2007 ◽  
Vol 56 (4) ◽  
pp. 404-410 ◽  
Author(s):  
Marco Di Monaco ◽  
Fulvia Vallero ◽  
Roberto Di Monaco ◽  
Rosa Tappero ◽  
Alberto Cavanna
Keyword(s):  
Skeletal Muscle ◽  
Hip Fracture ◽  
Muscle Mass ◽  
Fat Mass ◽  
Skeletal Muscle Mass ◽  
Cross Sectional Study ◽  
Sectional Study ◽  
Cross Sectional

Protein Intake for Skeletal Muscle Hypertrophy with Resistance Training in Seniors

2006 ◽  
Vol 16 (4) ◽  
pp. 362-372 ◽  
Author(s):  
Ryan D. Andrews ◽  
David A. MacLean ◽  
Steven E. Riechman
Keyword(s):  
Skeletal Muscle ◽  
Muscle Mass ◽  
Lean Mass ◽  
Protein Intake ◽  
Muscle Hypertrophy ◽  
Protein Supplementation ◽  
Protein Supplement ◽  
Resistance Exercise Training ◽  
Post Exercise ◽  
Skeletal Muscle Hypertrophy

Variability in protein consumption may influence muscle mass changes induced by resistance exercise training (RET). We sought to administer a post-exercise protein supplement and determine if daily protein intake variability affected variability in muscle mass gains. Men (N = 22) and women (N = 30) ranging in age from 60 to 69 y participated in a 12-wk RET program. At each RET session, participants consumed a post-exercise drink (0.4 g/kg lean mass protein). RET resulted in significant increases in lean mass (1.1 ±1.5 kg), similar between sexes (P > 0.05). Variability in mean daily protein intake was not associated with change in lean mass (r < 0.10, P > 0.05). The group with the highest protein intake (1.35 g · kg−1 · d−1, n = 8) had similar (P > 0.05) changes in lean mass as the group with the lowest daily protein intake (0.72 g · kg−1 · d−1, n = 9). These data suggest that variability in total daily protein intake does not affect variability in lean mass gains with RET in the context of post-exercise protein supplementation.

scholarly journals A Machine Learning Approach to Predict Body Composition in Advanced Cancer Patients.

2021 ◽  
Author(s):  
Pablo Cresta Morgado ◽  
Alfredo Navigante ◽  
Adriana Pérez
Keyword(s):  
Machine Learning ◽  
Skeletal Muscle ◽  
Body Composition ◽  
Variable Selection ◽  
Cancer Patients ◽  
Muscle Mass ◽  
Fat Mass ◽  
Skeletal Muscle Mass ◽  
Daily Practice ◽  
Smoothing Splines

Abstract BACKGROUND:Body composition and its changes affect cancer patient outcomes. Its determination requires specific and expensive devices. We designed a study to evaluate machine learning approaches to predict fat and skeletal muscle mass using daily practice clinical variables.METHODS:We designed a cross-sectional study in advanced gastrointestinal cancer patients. Response variables were skeletal muscle mass and body fat mass, measured by bioimpedance analysis. Predictors were laboratory and anthropometric variables. Imputation methods were applied. Six approaches were analyzed: (1) multicollinearity analysis, best subset selection (BSS) and multiple linear regression; (2) multicollinearity, BSS and generalized additive models (GAM); (3) multicollinearity, lasso to perform variable selection and GAM; (4) ridge regression; (5) lasso regression; (6) random forest. Model selection was performed evaluating the Mean Squared Error calculated by leave-one-out cross-validation.RESULTS:We included 101 patients under chemotherapy treatment. For skeletal muscle mass, the best approach was the combination of multicollinearity analysis followed by BSS and GAM using smoothing splines with 6 variables (albumin, Hb, height, weight, sex, lymphocytes). The adjusted R2 was 0.895. The best approach for fat mass was multicollinearity analysis, variable selection by lasso, and GAM using smoothing splines with 3 variables (waist-hip ratio, weight, sex). The adjusted R2 was 0.917.CONCLUSION:We developed the first accurate predictive models for body composition in cancer patients applying daily practice clinical variables. This study shows that machine learning is a useful tool to apply in body composition. This is a starting point to evaluate these approaches in research and clinical practice.

scholarly journals Skeletal muscle mass by bioelectrical impedance analysis and calf circumference for sarcopenia diagnosis

2020 ◽  
Vol 11 (1) ◽  
pp. 57-61
Author(s):  
C. H. González-Correa ◽  
M. C. Pineda-Zuluaga ◽  
F. Marulanda-Mejía
Keyword(s):  
Skeletal Muscle ◽  
Muscle Mass ◽  
Bioelectrical Impedance Analysis ◽  
Skeletal Muscle Mass ◽  
Bioelectrical Impedance ◽  
Impedance Analysis ◽  
Calf Circumference ◽  
Middle Income ◽  
Middle Income Countries ◽  
Skeletal Mass

AbstractSkeletal muscle mass (SMM) plays an important role in health and physical performance. Its estimation is critical for the early detection of sarcopenia, a disease with high prevalence and high health costs. While multiple methods exist for estimating this body component, anthropometry and bioelectrical impedance analysis (BIA) are the most widely available in low- to middle-income countries. This study aimed to determine the correlation between muscle mass, estimated by anthropometry through measurement of calf circumference (CC) and skeletal mass index (SMI) by BIA. This was a cross-sectional and observational study that included 213 functional adults over 65 years of age living in the community. Measurements of height, weight, CC, and SMM estimated by BIA were made after the informed consent was signed. 124 women mean age 69.6 ± 3.1 years and 86 men mean age 69.5 ± 2.9 years had the complete data and were included in the analysis. A significant positive moderate correlation among CC and SMI measured by BIA was found (Pearson r= 0.57 and 0.60 for women and men respectively (p=0.0001)). A moderate significant correlation was found between the estimation of SMM by CC and by BIA. This suggests that CC could be used as a marker of sarcopenia for older adults in settings in lower-middle-income countries where no other methods of diagnosing muscle mass are available. Although the CC is not the unique parameter to the diagnosis of sarcopenia, it could be a useful procedure in the clinic to identify patients at risk of sarcopenia.

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