scholarly journals 1244Associations of endothelial function, arterial elasticity, and arterial stiffness with adiponectin and skeletal muscle mass

2021 ◽  
Vol 50 (Supplement_1) ◽  
Author(s):  
Andrew O. Agbaje ◽  
Alan R. Barker ◽  
Tomi-Pekka Tuomainen
Keyword(s):  
Skeletal Muscle ◽  
Arterial Stiffness ◽  
Endothelial Function ◽  
Muscle Mass ◽  
Fat Mass ◽  
Brachial Artery ◽  
Skeletal Muscle Mass ◽  
Percentage Change ◽  
Arterial Elasticity ◽  
Total Fat

Abstract Background A temporal association where better arterial function and structure predicts adiponectin level and skeletal muscle mass during childhood remains uninvestigated. Methods We studied 5566 children and adolescents (51% girls) aged 9-11 years from the Avon Longitudinal Study of Parents and Children (ALSPAC) birth cohort, Bristol, UK. Brachial artery endothelial function was determined using flow-mediated dilation (FMD), expressed as the percentage change in diameter from baseline after reactive hyperemia; arterial elasticity as distensibility coefficient (DC) expressed in mean percentage change in cross-sectional area relative to blood pressure (BP); and arterial stiffness by carotid to radial pulse wave velocity (crPWV). Skeletal muscle mass and total fat mass were assessed by dual-energy Xray absorptiometry. We conducted multivariable linear regressions with Sidak correction and adjusted for age, sex, total fat mass, cardiorespiratory fitness, pubertal status, brachial artery diameter, systolic BP, low-density lipoprotein cholesterol, mother’s social-economic class, and time (years) between the measurement of predictors and outcomes. Results FMD (β [95% CI]) = (0.027 [0.007 to 0.047]; P = 0.009) and DC (0.229 [0.088 to 0.369]; P = 0.001) were directly associated with skeletal muscle mass. FMD had a borderline inverse association with adiponectin (-0.004 [-0.008 to < 0.0001]; P = 0.056). crPWV was unrelated to adiponectin and skeletal muscle mass, while DC was not associated with adiponectin. Conclusions Better endothelial function and arterial elasticity were associated with higher skeletal muscle mass while arterial stiffness was unrelated to adiponectin and lean mass. Key message Healthy arterial function and structure may enhance muscle growth in children.

scholarly journals Selecting Appropriate Sarcopenia Screening Methods for Asian Populations

2020 ◽  
Vol 9 (8) ◽  
pp. 2333
Author(s):  
Yu-Ching Lin ◽  
Yi-Chien Lu ◽  
Fang-Ping Chen ◽  
Ying Chin Lin ◽  
Yun-Chung Cheung ◽  
...  
Keyword(s):  
Skeletal Muscle ◽  
Muscle Mass ◽  
Fat Mass ◽  
Skeletal Muscle Mass ◽  
Healthy Individuals ◽  
Community Based ◽  
Skeletal Muscle Index ◽  
Asian Populations ◽  
Age Dependent ◽  
Total Fat

We aimed to determine the most appropriate sarcopenia screening method for Asian populations. We retrospectively studied the physiological differences between the sexes in healthy individuals and prospectively compared using skeletal muscle mass versus handgrip strength (HS) to screen for sarcopenia in a community-based population. Skeletal muscle mass was determined using dual-energy X-ray absorptiometry. Of 5881 healthy individuals recruited, 101 were from urban populations and 349 from a community-based population. The sexes were comparable in total lean muscle mass declines after peaking around 20 years of age. An age-dependent decline in total fat mass was found only among men;a persistent increase in total fat mass was observed only among women. The prevalence of low skeletal muscle mass significantly increased with age in both sexes only when applying the weight-adjusted skeletal muscle index (wSMI); it was significant only among men when applying the height-adjusted skeletal muscle index (hSMI). Using HS resulted in a much higher prevalence of sarcopenia in both sexes. A significant age-dependent increase in fat mass in women showed that the most appropriate adjustment method is wSMI for women and hSMI for men. Nevertheless, a primary HS survey is recommended for both sexes in Asian populations.

scholarly journals Augmentation Index Is Inversely Associated with Skeletal Muscle Mass, Muscle Strength, and Anaerobic Power in Young Male Adults: A Preliminary Study

2021 ◽  
Vol 11 (7) ◽  
pp. 3146
Author(s):  
Dongmin Lee ◽  
Kyengho Byun ◽  
Moon-Hyon Hwang ◽  
Sewon Lee
Keyword(s):  
Skeletal Muscle ◽  
Muscle Strength ◽  
Arterial Stiffness ◽  
Muscle Mass ◽  
Muscular Strength ◽  
Skeletal Muscle Mass ◽  
Augmentation Index ◽  
Anaerobic Power ◽  
Negatively Associated ◽  
Increased Risk

Arterial stiffness is associated with an increased risk of cardiovascular disease. Previous studies have shown that there is a negative correlation between arterial stiffness and variables such as skeletal muscle mass, muscular strength, and anaerobic power in older individuals. However, little research has been undertaken on relationships in healthy young adults. This study presents a preliminary research that investigates the association between arterial stiffness and muscular factors in healthy male college students. Twenty-three healthy young males (23.9 ± 0.5 years) participated in the study. The participants visited the laboratory, and variables including body composition, blood pressure, arterial stiffness, blood parameters, grip strength, and anaerobic power were measured. Measurements of augmentation index (AIx) and brachial-ankle pulse wave velocity (baPWV) were performed to determine arterial stiffness. There were significant positive correlations among skeletal muscle mass, muscle strength, and anaerobic power in healthy young adult males. AIx was negatively associated with a skeletal muscle mass (r = −0.785, p < 0.01), muscular strength (r = −0.500, p < 0.05), and anaerobic power (r = −0.469, p < 0.05), respectively. Likewise, AIx@75 corrected with a heart rate of 75 was negatively associated with skeletal muscle mass (r = −0.738, p < 0.01), muscular strength (r = −0.461, p < 0.05), and anaerobic power (r = −0.420, p < 0.05) respectively. However, the baPWV showed no correlation with all muscular factors. Our findings suggest that maintaining high levels of skeletal muscle mass, muscular strength, and anaerobic power from relatively young age may lower AIx.

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 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

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.

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