scholarly journals Resting metabolic rate and its adjustments as  predictors of risk protein-energy wasting in  hemodialysis patients

2021 ◽  
Author(s):  
Jingjing Da ◽  
Yanjun Long ◽  
Qian Li ◽  
Xia Yang ◽  
Jing Yuan ◽  
...  
Keyword(s):  
Body Composition ◽  
Linear Regression ◽  
Metabolic Rate ◽  
Fat Mass ◽  
Linear Regression Analysis ◽  
Resting Metabolic Rate ◽  
Cell Mass ◽  
Hemodialysis Patients ◽  
Protein Energy Wasting ◽  
Measurement Protocol

Background: The purpose of the present study was to explore the association between resting metabolic rate (RMR) and PEW risk in Chinese hemodialysis patients by age and gender subgroup. Methods: RMR and body composition (body cell mass and fat mass) of 774 patients undergoing hemodialysis were estimated by bioelectrical impedance analysis (BIA). Anthropometric data were collected by a standard measurement protocol, and the upper arm muscle circumference (AMC) was calculated. Biochemical nutritional and dialysis parameters were obtained. Linear regression analysis was used to analyze the relationship between RMR, body composition and nutritional factors. Results: The mean age was 54.96±15.78 years. RMR level in patients was 1463.0 (1240.5, 1669.0) kcal/d. In multiple linear regression models, BCM, LCC, fat mass were the determinants association with RMR (P＜0.001). Among the patients in the sample, 133 (17.2%) had diagnosed PEW per ISRNM criteria and 363 (46.9%) were being at risk PEW. The AUC of RMR for predicting risk PEW was greater than RMR/BCM and RMR/BSA. When the cut-off of RMR was 1481 kcal/d had the higher sensitivity and specificity (82% and 42%), and the AUC was 0.68 in elderly MHD patients (P＜0.001). After adjustment for potential confounders, lowest RMR quartile level (<1239) increased the risk of PEW (OR= 4.71, 95% CI 1.33-16.64, P=0.016) in all patients. Conclusions: Older patients with PEW have a lower RMR reduction. RMR and RMR/BCM may play the role in objective screening to detect risk PEW in MHD patients, especially in male.

Resting metabolic rate is lower in women than in men

1993 ◽  
Vol 75 (6) ◽  
pp. 2514-2520 ◽  
Author(s):  
P. J. Arciero ◽  
M. I. Goran ◽  
E. T. Poehlman
Keyword(s):  
Body Composition ◽  
Metabolic Rate ◽  
Fat Mass ◽  
Aerobic Fitness ◽  
Resting Metabolic Rate ◽  
Fat Free Mass ◽  
Peak Oxygen Consumption ◽  
Activity Peak ◽  
Peak Vo2 ◽  
And Gender

This study examined gender differences in resting metabolic rate (RMR) across a broad age spectrum after controlling for differences in body composition and aerobic fitness. Three hundred twenty-eight healthy men (17–80 yr) and 194 women (18–81 yr) volunteers were characterized for RMR, body composition, physical activity, peak oxygen consumption (peak VO2), anthropometrics, and energy intake. Measured RMR was 23% higher (P < 0.01) in men (1,740 +/- 194 kcal/day) than in women (1,348 +/- 125 kcal/day). Multiple regression analysis showed that 84% of individual variation in RMR was explained by fat-free mass, fat mass, peak VO2, and gender. After controlling for differences in fat-free mass, fat mass, and peak VO2, a lower RMR (3%; P < 0.01) persisted in women (1,563 +/- 153 kcal/day) compared with men (1,613 +/- 127 kcal/day). Adjusted RMR in premenopausal (P < 0.01) and postmenopausal (P < 0.05) women was lower than in men of a similar age. Our results support a lower RMR in women than in men that is independent of differences in body composition and aerobic fitness.

scholarly journals Metabolic Adaptations to Weight Loss: Relative Changes in Resting Metabolic Rate and Energy Expenditure for Physical Activity and Association With Weight Loss Maintenance

2021 ◽  
Vol 5 (Supplement_2) ◽  
pp. 526-526
Author(s):  
Rachel Silver ◽  
Sai Das ◽  
Michael Lowe ◽  
Susan Roberts
Keyword(s):  
Physical Activity ◽  
Weight Loss ◽  
Linear Regression ◽  
Energy Expenditure ◽  
Metabolic Rate ◽  
Fat Mass ◽  
Weight Regain ◽  
Resting Metabolic Rate ◽  
Fat Free Mass ◽  
The Mean

Abstract Objectives There is persistent controversy over the extent to which different components of energy expenditure disproportionately decrease after weight loss and contribute to weight regain through decreased energy requirements. We conducted a secondary analysis of the CALERIE I study to test the hypothesis that decreased resting metabolic rate (RMR) and energy expenditure for physical activity (EEPA) after a 6-month calorie restriction intervention would predict weight regain at 12 months, with a greater decrease in RMR than EEPA. Methods Participants (n = 46) received all food and energy-containing beverages for 6 months. Outcome measures included total energy expenditure by doubly labeled water, RMR by indirect calorimetry, and body composition by BOD POD. Predictions for RMR and EEPA were derived from baseline linear regression models including age, sex, fat mass, and fat free mass. Baseline regression coefficients were used to calculate the predicted RMR and EEPA at 6 months. Residuals were calculated as the difference between measured and predicted values and were adjusted for body weight. The presence of metabolic adaptation was evaluated by a paired t-test comparing measured and predicted RMR at 6 months. Differences between 6-month RMR and EEPA residuals were evaluated by the same method. Linear regression was used to assess the association between 6-month residuals and weight loss maintenance (% weight change, 6 to 12 months). Results Mean weight loss was 6.9% at 6 months with 2.1% regain from 6 to 12 months. No adaptation in RMR was observed at 6 months (mean residual: 19 kcal; 95% confidence interval: −9, 48; P = 0.18). However, significant adaptation was observed in EEPA (mean residual: −199 kcal; −126, −272; P &lt; 0.0001). In addition, the mean 6-month RMR residual was significantly greater than the mean 6-month EEPA residual (218 kcal; 133, 304; P &lt; 0.0001). There was no significant association between 6-month RMR or EEPA residuals and weight regain at 12 months (P = 0.56, 0.34). Conclusions There was no measurable decrease in RMR with weight loss after adjusting for changes in fat free mass and fat mass, but there was a decrease in EEPA. Changes in RMR and EEPA with weight loss over 6 months did not predict weight regain at 12 months. Funding Sources Jean Mayer USDA Human Nutrition Research Center on Aging Doctoral Scholarship; USDA agreement #8050–51000-105–01S

scholarly journals 12 days of altitude exposure at 1800 m does not increase resting metabolic rate in elite rowers

2017 ◽  
Vol 42 (6) ◽  
pp. 672-676 ◽  
Author(s):  
Amy L. Woods ◽  
Laura A. Garvican-Lewis ◽  
Anthony Rice ◽  
Kevin G. Thompson
Keyword(s):  
Body Composition ◽  
Metabolic Rate ◽  
Energy Intake ◽  
Fat Mass ◽  
Resting Metabolic Rate ◽  
Altitude Training ◽  
Percent Change ◽  
Altitude Exposure ◽  
Elite Rowers ◽  
And Training

Four elite rowers completed a 12-day altitude training camp living at 1800 m, and training at 1800 m and 915 m, to assess changes in resting metabolic rate (RMR). RMR and body composition were assessed pre- and postcamp. Downward trends in RMR and body composition were observed postaltitude: absolute RMR (percent change: –5.2%), relative RMR (–4.6%), body mass (–1.2%), and fat mass (–4.1%). These variations are likely related to the hypoxic stimulus and an imbalance between training load and energy intake.

Does menarche mark a period of elevated resting metabolic rate?

2004 ◽  
Vol 286 (3) ◽  
pp. E456-E462 ◽  
Author(s):  
Jennifer L. Spadano ◽  
Linda G. Bandini ◽  
Aviva Must ◽  
Gerard E. Dallal ◽  
William H. Dietz
Keyword(s):  
Body Composition ◽  
Metabolic Rate ◽  
Fat Mass ◽  
Resting Metabolic Rate ◽  
Tanner Stage ◽  
Normal Weight ◽  
Fat Free Mass ◽  
Time Points ◽  
Stage 1

Resting metabolic rate (RMR) and body composition were measured in 44 initially nonoverweight girls at three time points relative to menarche: premenarche (Tanner stage 1 or 2), menarche (±6 mo), and 4 yr after menarche. Mean absolute RMR was 1,167, 1,418, and 1,347 kcal/day, respectively. Absolute RMR was statistically significantly higher at menarche than at 4 yr after menarche despite statistically significantly less fat-free mass (FFM) and fat mass (FM), suggesting an elevation in RMR around the time of menarche. The pattern of change in RMR, adjusted for FFM, log transformed FM, age, race, parental overweight, and two interactions (visit by parental overweight, parental overweight by FFM), was also considered. Adjusted RMR did not differ statistically between the visits for girls with two normal-weight parents. For girls with at least one overweight parent, adjusted RMR was statistically significantly lower 4 yr after menarche than at premenarche or menarche. Thus parental overweight may influence changes that occur in RMR during adolescence in girls.

scholarly journals Is resting metabolic rate different between men and women?

2001 ◽  
Vol 86 (6) ◽  
pp. 641-646 ◽  
Author(s):  
Andrea C. Buchholz ◽  
M. Rafii ◽  
P. B. Pencharz
Keyword(s):  
Metabolic Rate ◽  
Body Fat ◽  
Fat Mass ◽  
Resting Metabolic Rate ◽  
Cell Mass ◽  
Open Circuit ◽  
Men And Women ◽  
Adult Men ◽  
Total Body ◽  
Composition Factors

A low resting metabolic rate (RMR) has been proposed as a possible cause for the increased body fat commonly seen in women compared with men. Absolute RMR is higher in men, but whether RMR adjusted for lean body mass (LBM) remains higher is unresolved. The objective of the present study was to determine whether RMR adjusted for various body composition factors differed between healthy adult men and women. Thirty men (28·3±8·0 years, BMI 23·7±2·1 kg/m2) and twenty-eight women (28·7±6·9 years, BMI 22·2±1·9 kg/m2) were included in the analyses. RMR was measured by open-circuit indirect calorimetry for 60 min. Extracellular water (ECW) was measured by corrected Br- space and total body water (TBW) by 2H dilution. LBM was estimated as TBW/0·732. Intracellular water (ICW) was calculated as TBW-ECW, and body cell mass (BCM) as ICW/0·732. Men were heavier and had higher BMI, LBM, BCM and ECW, but less fat mass. Absolute RMR was higher in men than women (7280±844 v. 5485±537 kJ/d, P<0·0001). This difference became non-significant when RMR was adjusted for LBM by ANCOVA (6536±630 v. 6282±641 kJ/d, P=0·2191), but remained significant when adjusted for BCM (6680±744 v. 6128±756 kJ/d, P=0·0249). Fat mass explained a significant amount of variation in RMR in women (r2 0·28, P=0·0038), but not in men (r2 0·03, P=0·3301). The relationships between body fat and the various subcompartments of BCM and RMR require further elucidation.

scholarly journals Correction to: The effect of moderate intermittent versus continuous energy restriction on body composition and resting metabolic rate in resistance-trained females: A randomized controlled trial

2020 ◽  
Vol 17 (1) ◽  
Author(s):  
Madelin R. Siedler ◽  
Eric T. Trexler ◽  
Megan N. Humphries ◽  
Priscila Lamadrid ◽  
Brian Waddell ◽  
...  
Keyword(s):  
Randomized Controlled Trial ◽  
Body Composition ◽  
Metabolic Rate ◽  
Controlled Trial ◽  
Resting Metabolic Rate ◽  
Energy Restriction ◽  
Randomized Controlled ◽  
Continuous Energy

An amendment to this paper has been published and can be accessed via the original article.

The impact of legs fat mass and legs lean fat mass on thigh circumference according cardiovascular risk: a population-based study

2021 ◽  
Vol 28 (Supplement_1) ◽  
Author(s):  
M Chlabicz ◽  
M Paniczko ◽  
J Jamolkowski ◽  
P Sowa ◽  
M Lapinska ◽  
...  
Keyword(s):  
Adipose Tissue ◽  
Regression Analysis ◽  
Linear Regression ◽  
Muscle Tissue ◽  
Fat Mass ◽  
Linear Regression Analysis ◽  
Population Based ◽  
Thigh Circumference ◽  
Tissue Model ◽  
The Mean

Abstract Funding Acknowledgements Type of funding sources: Public Institution(s). Main funding source(s): Medical University of Bialystok, Poland Introduction A low thigh circumference is associated with an increased risk of high blood pressure, diabetes, cardiovascular diseases, and total mortality. Objectives The aim of the study was to investigate the relationship between the thigh circumference and cardiovascular (CV) risk classes, and to assess what type of tissue, adipose tissue or muscle tissue affects the thigh circumference. Methods The longitudinal, population-based, Polish study was conducted in 2017-2020. A total of 931 individuals aged 20-79 were analyzed. Pol-SCORE system was used to assess the 10-year risk of fatal CV based on the following risk factors: age, gender, smoking, systolic blood pressure, and total cholesterol for individuals aged 40-70.  Then, CV risk classes were assessed using the 2019 ESC/EAS guidelines. The measurement of thigh circumference were performed directly below the gluteal fold of the thigh. Both thighs were measured and the mean value was calculated as the final thigh circumference. Body composition was assessed using Dual Energy X-ray Absorptiometry (DEXA). Results The mean age was 49.1 ± 15.5 years and 43.2% male. The mean thigh circumference was 58.2 ± 5.9 cm, the mean legs fat mass was 7.7 ± 2.8kg, and the mean legs lean mass was 16.9 ± 4.0kg. Lower thigh circumference was associated with higher CV risk classes in univariate linear regression analysis (β -0.516, p = 0.002), as well adjusted by age and sex (β -0.839, p = 0.008), adjusted by age, sex, BMI (β -0.886, p &lt;0.001), and age, sex, WHR (β -0.988, p &lt;0.001). In linear regression analysis legs adipose tissue and muscle tissue were related to the thigh circumference independently of CV risk classes (Model 1) and Pol-SCORE value (Model 2). However, fatty tissue (Model 1: β 0.746, p &lt; 0.001; Model 2: β 0.749, p &lt; 0.001) affects the thigh circumference more than the muscle tissue (Model 1: β 0.479, p &lt; 0.001; Model 2: β 0.442, p &lt; 0.001) (Fig. 1). Conclusion Smaller thigh circumference was associated with higher CV risk classes. Thigh circumference was more influenced by adipose tissue than by muscle tissue, regardless of the Pol-SCORE  or CV risk classes. Table 1. Variable Model 1 Model 2 Beta p R2 Beta p R2 Legs fat mass 0.746 &lt;0.001 0.785 0.749 &lt;0.001 0.760 Legs lean mass 0.479 &lt;0.001 0.785 0.442 &lt;0.001 0.760 Model 1: adjusted for Cardiovascular risk classesModel 2: adjusted for Pol-SCOREResults of the linear regression analysis thigh circumference in the general population Abstract Figure 1.

scholarly journals Body composition and resting metabolic rate in obese adolescents

2015 ◽  
Vol 48 (4) ◽  
pp. 359 ◽  
Author(s):  
Bruno A. P. de Oliveira ◽  
Camila B. Gardim ◽  
Carolina N. Ferreira ◽  
Suziane U. Cayres ◽  
Ismael F. Freitas Junior
Keyword(s):  
Body Composition ◽  
Metabolic Rate ◽  
Resting Metabolic Rate ◽  
Obese Adolescents

Objetivo: Analisar a composição corporal e taxa metabólica de repouso (TMR) de adolescentes com obesidade. Metodologia: Foram avaliados 58 adolescentes, entre 12 e 16 anos (13,7±1,2 anos), com obesidade. A composição corporal foi mensurada por meio da Absortiometria de Raios-x de Dupla Energia (DEXA). A taxa metabólica de repouso e a oxidação de glicose e lipídeos foram determinadas pela medida do oxigênio consumido e pela produção de dióxido de carbono. Foi utilizado teste t de Student para variáveis independentes, análise de regressão linear múltipla e correlação de Pearson, com significância de p&lt;5%. Resultados: Foram observadas diferenças na porcentagem de gordura corporal (p=0,01), porcentagem de massa corporal magra (MCM) (p=0,01), porcentagem de gordura do tronco (p=0,01), massa magra membro inferior (p=0,01) e taxa metabólica de repouso (p=0,04) entre os sexos masculino e feminino. No sexo masculino a MCM foi a melhor preditora de TMR (p=0,01). Houve correlação positiva entre TMR e MCM, assim como observada associação entre a oxidação lipídica e MCM. Conclusão: A composição corporal total e por segmento corporal influenciam a taxa metabólica de repouso e a oxida- ção de substratos. Além disso, a MCM pode ser considerada um fator de aumento do gasto energético de repouso de adolescentes com obesidade.

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