No Change of Body Mass, Fat Mass, and Skeletal Muscle Mass in Ultraendurance Swimmers After 12 Hours of Swimming

Background There is debate whether body mass index is a good predictor of health outcomes because different tissues, namely skeletal muscle mass (SMM) and fat mass (FM), may be differentially associated with risk. We investigated the association of appendicular SMM (aSMM) and FM with fatal and nonfatal cardiovascular disease (CVD) and all‐cause mortality. We compared their prognostic value to that of body mass index. Methods and Results We studied 356 590 UK Biobank participants aged 40 to 69 years with bioimpedance analysis data for whole‐body FM and predicted limb muscle mass (to calculate aSMM). Associations between aSMM and FM with CVD and all‐cause mortality were examined using multivariable Cox proportional hazards models. Over 3 749 501 person‐years of follow‐up, there were 27 784 CVD events and 15 844 all‐cause deaths. In men, aSMM was positively associated with CVD incidence (hazard ratio [HR] per 1 SD 1.07; 95% CI, 1.06–1.09) and there was a curvilinear association in women. There were stronger positive associations between FM and CVD with HRs per SD of 1.20 (95% CI, 1.19–1.22) and 1.25 (95% CI, 1.23–1.27) in men and women respectively. Within FM tertiles, the associations between aSMM and CVD risk largely persisted. There were J‐shaped associations between aSMM and FM with all‐cause mortality in both sexes. Body mass index was modestly better at discriminating CVD risk. Conclusions FM showed a strong positive association with CVD risk. The relationship of aSMM with CVD risk differed between sexes, and potential mechanisms need further investigation. Body fat and SMM bioimpedance measurements were not superior to body mass index in predicting population‐level CVD incidence or all‐cause mortality.

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EFFECTS OF A 6-WEEK CONTROLED EXERCISE PROGRAM AND SEMI-CONTROLED DIET ON BODY FAT AND SKELETAL MUSCLE MASS IN ADULTS

Human Sport Medicine ◽

10.14529/hsm190101 ◽

2019 ◽

Vol 19 (1) ◽

pp. 7-14 ◽

Cited By ~ 2

Author(s):

F Kukić ◽

N Todorović ◽

N Cvijanović

Keyword(s):

Skeletal Muscle ◽

Muscle Mass ◽

Body Mass ◽

Body Fat ◽

Fat Mass ◽

Skeletal Muscle Mass ◽

Body Height ◽

Exercise Program ◽

Body Fat Mass ◽

Dietary Regimen

Aim. To investigate the effects of a 6-week of controlled exercise program followed by a semi-controlled dietary regimen on indicators of body fat mass (BF) and skeletal muscle mass (SMM) of adults. Materials and methods. The sample consisted of 28 particpants with the main characteristics of the sample being: age = 29.70 ± 8.35 years, body height (BH) = 177.35 ± 9.36 cm, and body mass (BM) = 105.20 ± 27.06 kg. Body composition parameters, BM, body fat mass (BF), trunk fat (TF), skeletal muscle mass (SMM), percent of body fat (PBF), percent of skeletal muscle mass (PSMM), body mass index (BMI), and index of hypokinesia (IH) were collected before and after six weeks of exercise program and semi-controlled diet regimen. A Paired sample T-test and effect size (ES) were used to determine the effects and their magnitude of the treatment applied. Results. A 6-week treatment significantly affected investigated variables, wherein BF (–6.75 kg, p < 0.001), TF (–3.28 kg, p < 0.001), and SMM (–0.91 kg, p = 0.003) tissue decreased in a different degree, leading to a small but highly significant increase in PSMM (2.60 %, p < 0.001). A decrease in BF and SMM resulted in a significant reduction in BMI, while IH decreased in a smaller degree than BMI because PBF and PSMM changed inversely. Conclusion. Six weeks of a controlled exercise program 3 times/week and semi-controlled diet is an effective approach to the reduction of BM, BF, and TF and to increasing the movement potential by changing the proportions of PBF and PSMM.

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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

10.21203/rs.2.22583/v1 ◽

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.

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

Nephrology Dialysis Transplantation ◽

10.1093/ndt/gfaa142.p1569 ◽

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.

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

Scientific Reports ◽

10.1038/s41598-019-47576-9 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 3

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

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

Annals of the Rheumatic Diseases ◽

10.1136/annrheumdis-2012-eular.2780 ◽

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

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

Maturitas ◽

10.1016/j.maturitas.2006.11.003 ◽

2007 ◽

Vol 56 (4) ◽

pp. 404-410 ◽

Cited By ~ 20

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

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

10.21203/rs.3.rs-200977/v1 ◽

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.

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Low skeletal muscle mass is associated with the risk of all-cause mortality in patients with type 2 diabetes mellitus

Therapeutic Advances in Endocrinology and Metabolism ◽

10.1177/2042018819842971 ◽

2019 ◽

Vol 10 ◽

pp. 204201881984297

Author(s):

Hitomi Miyake ◽

Ippei Kanazawa ◽

Ken-ichiro Tanaka ◽

Toshitsugu Sugimoto

Keyword(s):

Diabetes Mellitus ◽

Type 2 Diabetes ◽

Skeletal Muscle ◽

Type 2 Diabetes Mellitus ◽

Muscle Mass ◽

Body Mass ◽

Skeletal Muscle Mass ◽

Historical Cohort ◽

All Cause Mortality

Background: Patients with type 2 diabetes mellitus (T2DM) have an increased risk of muscle mass reduction. However, the association between muscle mass and mortality in T2DM remains unknown. Methods: This was a historical cohort study with the endpoint of all-cause mortality. This study included 163 Japanese men and 141 postmenopausal women with T2DM whose body compositions were evaluated using dual-energy X-ray absorptiometry. Low muscle mass was defined as a skeletal muscle mass index (SMI) of <7.0 kg/m2 for men and <5.4 kg/m2 for women. Results: During the 6-year follow-up period, 32 men and 14 women died. In a Cox regression analysis adjusted for age, T2DM duration, glycated hemoglobin, serum creatinine, fasting C-peptide, body mass index, and lean body mass were associated with the risk of mortality in men [hazard ratio (HR) = 1.81, 95% confidence interval (CI) = 1.00–3.28 per standard deviation (SD) decrease, p = 0.049] and women (HR = 4.53, 95% CI = 1.14–17.96 per SD decrease, p = 0.032). Neither fat mass nor bone mineral content was associated with mortality. Low SMI was associated with increased mortality in women (HR = 5.97, 95% CI = 1.04–34.37, p = 0.045), while the association between low SMI and mortality was marginal in men (HR = 2.38, 95% CI = 0.92–6.14, p = 0.074). Conclusions: Low muscle mass was independently associated with all-cause mortality in patients with T2DM. The preservation of skeletal muscle mass is important to protect patients with T2DM from increased mortality risk.

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