scholarly journals Low serum TSH levels are associated with low values of fat-free mass and body cell mass in the elderly

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Till Ittermann ◽  
Marcello R. P. Markus ◽  
Martin Bahls ◽  
Stephan B. Felix ◽  
Antje Steveling ◽  
...  
Keyword(s):  
Body Composition ◽  
Waist Circumference ◽  
Cell Mass ◽  
The Elderly ◽  
Fat Free Mass ◽  
Body Cell Mass ◽  
Body Cell ◽  
Independent Population ◽  
Serum Tsh ◽  
Tsh Levels

AbstractPrevious studies on the association between thyroid function and body composition are conflicting and showed strong differences across age groups. Our aim was to clarify age-specific associations of serum thyroid-stimulating hormone (TSH) levels with markers of body composition including body mass index (BMI), waist circumference, fat mass (FM), fat-free mass (FFM) and body cell mass (BCM). We used data from two independent population-based cohorts within the framework of the Study of Health in Pomerania. The study population included 5656 individuals aged 20 to 90 years. Markers of body composition were measured by bioelectrical impedance analysis. Serum TSH levels were significantly positively associated with BMI (β = 0.16; 95% confidence interval [CI]: 0.06 to 0.27), waist circumference (β = 0.35; 95% CI: 0.08 to 0.62) and FM (β = 0.32; 95% CI: 0.12 to 0.52), but not with FFM and BCM. Interaction analysis revealed positive associations of serum TSH levels with BMI, waist circumference, FM, FFM and BCM in individuals older than 60 years, while no such associations were observed in younger individuals. We demonstrated that lower serum TSH levels were accompanied with lower values of BMI, waist circumference, FM, FFM, and BCM in the elderly, while no such associations were observed in younger individuals.

scholarly journals Age-specific associations of serum TSH levels with markers of body composition in two population-based studies

2020 ◽  
Author(s):  
Till Ittermann ◽  
Marcello Markus ◽  
Martin Bahls ◽  
Stephan Felix ◽  
Antje Steveling ◽  
...  
Keyword(s):  
Body Composition ◽  
Waist Circumference ◽  
Interaction Analysis ◽  
Cell Mass ◽  
Population Based ◽  
Thyroid Stimulating Hormone ◽  
Fat Free Mass ◽  
Independent Population ◽  
Serum Tsh ◽  
Tsh Levels

Abstract Previous studies on the association between thyroid function and body composition are conflicting and showed strong differences across age groups. Our aim was to clarify age-specific associations of serum thyroid-stimulating hormone (TSH) levels with markers of body composition including body mass index (BMI), waist circumference, fat mass (FM), fat-free mass (FFM) and body cell mass (BCM). We used data from two independent population-based cohorts within the framework of the Study of Health in Pomerania (SHIP). The study population included 5,656 individuals aged 20 to 90 years. Markers of body composition were measured by bioelectrical impedance analysis. Serum TSH levels were significantly positively associated with BMI (β=0.16; 95% confidence interval [CI]: 0.06 to 0.27), waist circumference (β=0.35; 95% CI: 0.08 to 0.62) and FM (β=0.32; 95% CI: 0.12 to 0.52), but not with FFM an BCM. Interaction analysis revealed positive associations of serum TSH levels with BMI, waist circumference, FM, FFM and BCM in individuals older than 60 years, while no such associations were observed in younger individuals. We demonstrated that lower serum TSH levels were accompanied with lower values of BMI, waist circumference, FM, FFM, and BCM in the elderly, while no such associations were observed in younger individuals.

scholarly journals COMPARATIVE PHYSIOLOGICAL AND BODY COMPOSITION PARAMETERS IN TEAM SPORTS AND SPRINT TRAINED INDIAN ATHLETES

2021 ◽  
Vol 1 (2021) ◽  
pp. 3-11
Author(s):  
Subhojit Chatterjee ◽  
◽  
Usra Hasan ◽  
Subhra Chatterjee ◽  
◽  
...  
Keyword(s):  
Body Composition ◽  
Muscle Mass ◽  
Fat Mass ◽  
Cell Mass ◽  
Team Sports ◽  
Fat Free Mass ◽  
Training Status ◽  
Body Cell Mass ◽  
Male Athletes ◽  
Body Cell

Introduction: Physiological and body composition variables have important role for assessment of training status and evaluation of health status of athletes. Regular monitoring of these variables during training may provide valuable information to coaches for training and selection of players’ training protocol participating in both team sports and individual events. Purpose and objectives of the study: The aims of this study were to compare physiological and body composition variables between male athletes participating in team sports (football) and individual sprint event and also to correlate training duration (both in years and hours per week) of male athletes participating in both team sports as well as sprint with physiological and body composition variables. Applied Methodology: The study was carried out with thirty-year-old (n=30) and BMI matching male Indian athletes participating in team sports (football, n=16) and individual sprint event (n=14), having minimum 2 years of official training. They were in post competitive phase during the test. Several physiological and body composition variables were assessed such as height, weight, body mass index, training age (years), training time (hours/week), fat mass, fat-free mass, body cell mass, muscle mass, VO2 max, maximal power, training intensity and fatigue index following standard protocol. Achieved major results: The sprinters were found to possess significantly more fat free mass (p < .01), body cell mass (p < .01), muscle mass (p < .01), less fat mass (p < .05) and more average anaerobic power (p < .01) than their peers - football players. However, no significant correlation was found between any of the measured physiological and body composition parameters and the training status of these players. Conclusion: This study would provide useful information for assigning training protocols to the athletes participating in team sports and individual sprint events on the basis of physiological and body composition parameters.

scholarly journals ACE Insertion/Deletion polymorphism (rs4646994) affects body composition in middle-aged premenopausal women with essential hypertension

2019 ◽  
Vol 82 (4) ◽  
pp. 349-355
Author(s):  
Darina Falbová ◽  
Lenka Vorobeľová ◽  
Veronika Candráková Čerňanová ◽  
Radoslav Beňuš ◽  
Daniela Siváková
Keyword(s):  
Body Composition ◽  
Essential Hypertension ◽  
Menopausal Status ◽  
Cell Mass ◽  
Premenopausal Women ◽  
Fat Free Mass ◽  
Body Cell Mass ◽  
Entire Study ◽  
Body Cell ◽  
Polymerase Chain

Abstract This study assesses the association between angiotensin converting enzyme (ACE) I/D (rs4646994) polymorphism and body composition parameters in essential hypertension (HT) and menopausal status in Slovak women. The entire study sample comprised 575 women in two groups: 255 with HT and 320 without. Body composition parameters were measured by bioelectric impedance analyzer and ACE I/D polymorphism genotypes were detected by polymerase chain reaction. Premenopausal HT women with ACE II genotype had significantly lower body cell mass (p=0.004), extra- and intracellular water (p=0.027; p=0.004), fat free mass and muscle mass (p=0.006; P = 0.003), fat free mass index (p=0.006) and body cell mass index (p=0.003) than their ID/DD counterparts. These associations were not determined in normotensive and/or postmenopausal women. This study confirmed that ACE I/D gene polymorphism affects body composition in HT premenopausal women.

scholarly journals Evolution of body composition following successful kidney transplantation is strongly influenced by physical activity: results of the CORPOS study

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Karine Moreau ◽  
Aurélie Desseix ◽  
Christine Germain ◽  
Pierre Merville ◽  
Lionel Couzi ◽  
...  
Keyword(s):  
Physical Activity ◽  
Body Composition ◽  
Kidney Transplantation ◽  
Fat Mass ◽  
Physical Activity Level ◽  
Cell Mass ◽  
Activity Level ◽  
Fat Free Mass ◽  
Body Cell Mass ◽  
Body Cell

Abstract Background Weight gain (mainly gain of fat mass) occurs quickly after successful kidney transplantation and is associated with metabolic complications (alterations of glycaemic control, hyperlipidaemia). Determinants of weight gain are multifactorial and are mainly related to the transplant procedure itself (glucocorticoid use, increased appetite). In the modern era of transplantation, one challenge is to limit these metabolic alterations by promoting gain of muscle mass rather than fat mass. This prospective study was performed to assess determinants of fat mass, fat-free mass and body cell mass changes after kidney transplantation with a focus on physical activity and nutritional behaviour before and after transplantation. Methods Patients were included at the time of listing for deceased donor kidney transplantation. Body composition was determined using dual X-ray absorptiometry and bioimpedance spectroscopy to assess fat mass, fat-free mass and body cell mass (= fat-free mass − extracellular water) at the time of inclusion, 12 months later, and 1, 6, 12 and 24 months after transplantation. Recall dietary data and physical activity level were also collected. Results Eighty patients were included between 2007 and 2010. Sixty-five had a complete 24-month follow-up after kidney transplantation. Fat mass, fat-free mass and body cell mass decreased during the waiting period and early after kidney transplantation. The nadirs of body cell mass and fat-free mass occurred at 1 month and the nadir for fat mass occurred at 6 months. Maximum levels of all parameters of body composition were seen at 12 months, after which body cell mass and fat-free mass decreased, while fat mass remained stable. In multivariate analysis, male recipients, higher physical activity level and lower corticosteroid dose were significantly associated with better body cell mass recovery after kidney transplantation. Conclusions Lifestyle factors, such as physical activity level, together with low dose of corticosteroids seem to influence body composition evolution following kidney transplantation with recovery of body cell mass. Specific strategies to promote physical activity in kidney transplant recipients should be provided before and after kidney transplantation.

scholarly journals Associations among body composition, inflammatory profile and disease extent in ulcerative colitis patients

2018 ◽  
Vol 64 (2) ◽  
pp. 133-139 ◽  
Author(s):  
Ana Paula Signori Urbano ◽  
Ligia Yukie Sassaki ◽  
Mariana de Souza Dorna ◽  
Paula Torres Presti ◽  
Maria Antonieta de Barros Leite Carvalhaes ◽  
...  
Keyword(s):  
Ulcerative Colitis ◽  
Body Composition ◽  
Lean Mass ◽  
Clinical Remission ◽  
Inflammatory Markers ◽  
Cell Mass ◽  
Body Cell Mass ◽  
Disease Extent ◽  
Body Cell ◽  
Inflammatory Profile

Summary Objective: The aim of our study was to assess body composition status and its association with inflammatory profile and extent of intestinal damage in ulcerative colitis patients during clinical remission. Method: This is a cross-sectional study in which body composition data (phase angle [PhA], fat mass [FM], triceps skin fold thickness [TSFt], mid-arm circumference [MAC], mid-arm muscle circumference [MAMC], adductor pollicis muscle thickness [APMt]), inflammatory profile (C-reactive protein [CRP], a1-acid glycoprotein, erythrocyte sedimentation rate [ESR]) and disease extent were recorded. Results: The mean age of the 59 patients was 48.1 years; 53.3% were women. Most patients were in clinical remission (94.9%) and 3.4% was malnourished according to body mass index. PhA was inversely correlated with inflammatory markers such as CRP (R=-0.59; p<0.001) and ESR (R=-0.46; p<0.001) and directly correlated with lean mass: MAMC (R=0.31; p=0.01) and APMt (R=0.47; p<0.001). Lean mass was inversely correlated with non-specific inflammation marker (APMt vs. ESR) and directly correlated with hemoglobin values (MAMC vs. hemoglobin). Logistic regression analysis revealed that body cell mass was associated with disease extent (OR 0.92; 95CI 0.87-0.97; p<0.01). Conclusion: PhA was inversely correlated with inflammatory markers and directly correlated with lean mass. Acute inflammatory markers were correlated with disease extent. Body cell mass was associated with disease extent.

Assessment of body cell mass at bedside in critically ill patients

2012 ◽  
Vol 303 (3) ◽  
pp. E389-E396 ◽  
Author(s):  
Magali Savalle ◽  
Florence Gillaizeau ◽  
Gérard Maruani ◽  
Etienne Puymirat ◽  
Florence Bellenfant ◽  
...  
Keyword(s):  
Body Composition ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Cell Mass ◽  
Stable Model ◽  
Body Cell Mass ◽  
Body Composition Assessment ◽  
Trunk Length ◽  
Body Cell ◽  
Multifrequency Bioimpedance

Critical illness affects body composition profoundly, especially body cell mass (BCM). BCM loss reflects lean tissue wasting and could be a nutritional marker in critically ill patients. However, BCM assessment with usual isotopic or tracer methods is impractical in intensive care units (ICUs). We aimed to modelize the BCM of critically ill patients using variables available at bedside. Fat-free mass (FFM), bone mineral (Mo), and extracellular water (ECW) of 49 critically ill patients were measured prospectively by dual-energy X-ray absorptiometry and multifrequency bioimpedance. BCM was estimated according to the four-compartment cellular level: BCM = FFM − (ECW/0.98) − (0.73 × Mo). Variables that might influence the BCM were assessed, and multivariable analysis using fractional polynomials was conducted to determine the relations between BCM and these data. Bootstrap resampling was then used to estimate the most stable model predicting BCM. BCM was 22.7 ± 5.4 kg. The most frequent model included height (cm), leg circumference (cm), weight shift (Δ) between ICU admission and body composition assessment (kg), and trunk length (cm) as a linear function: BCM (kg) = 0.266 × height + 0.287 × leg circumference + 0.305 × Δweight − 0.406 × trunk length − 13.52. The fraction of variance explained by this model (adjusted r2) was 46%. Including bioelectrical impedance analysis variables in the model did not improve BCM prediction. In summary, our results suggest that BCM can be estimated at bedside, with an error lower than ±20% in 90% subjects, on the basis of static (height, trunk length), less stable (leg circumference), and dynamic biometric variables (Δweight) for critically ill patients.

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