Underestimation of resting metabolic rate using equations compared to indirect calorimetry in normal-weight subjects: Consideration of resting metabolic rate as a function of body composition

Nösslinger, Hannes; Mair, Ewald; Toplak, Hermann; Hörmann-Wallner, Marlies

doi:10.1016/j.nutos.2021.01.003

Cited by 6 publications

(6 citation statements)

References 47 publications

(40 reference statements)

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“…In general, a higher FM is associated with a smaller difference between measured and predicted RMR. This suggests that some equations may have been developed using populations with higher values of FM than our sample, as mentioned by Nösslinger et al (2021). At the same time, this may also reflect the effect of exercise on our participants (leaner population).…”

Section: Discussionmentioning

confidence: 80%

“…This may not be surprising given FFM is a better predictor of RMR than BM. In contrast, others have not observed superior performance of a variety of FFM equations (Balci et al, 2021; De Lorenzo et al, 2001; Flack et al, 2016; Jagim et al, 2018, 2019; Nösslinger et al, 2021). Nevertheless, the notable accuracy of Pontzer FFM equation may be related to the sample size and better representation of individuals with characteristics similar to those in the present study.…”

Section: Discussionmentioning

confidence: 97%

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Validation of predictive equations to estimate resting metabolic rate of females and males across different activity levels

Prado‐Nóvoa,

Howard,

Laskaridou

et al. 2023

American J Hum Biol

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ObjectivesUsing equations to predict resting metabolic rate (RMR) has yielded different degrees of validity, particularly when sex and different physical activity levels were considered. Therefore, the purpose of the present study was to determine the validity of several different predictive equations to estimate RMR in female and male adults with varying physical activity levels.MethodWe measured the RMR of 50 adults (26 females and 24 males) evenly distributed through activity levels varying from sedentary to ultra‐endurance. Body composition was measured by dual X–ray absorptiometry and physical activity was monitored by accelerometry. Ten equations to predict RMR were applied (using Body Mass [BM]: Harris & Benedict, 1919; Mifflin et al., 1990 [MifflinBM]; Pontzer et al., 2021 [PontzerBM]; Schofield, 1985; FAO/WHO/UNU, 2004; and using Fat‐Free Mass (FFM): Cunningham, 1991; Johnstone et al., 2006; Mifflin et al., 1990 [MifflinFFM]; Nelson et al. 1992; Pontzer et al., 2021 [PontzerFFM]). The accuracy of these equations was analyzed, and the effect of sex and physical activity was evaluated using different accuracy metrics.ResultsEquations using BM were less accurate for females, and their accuracy was influenced by physical activity and body composition. FFM equations were slightly less accurate for males but there was no obvious effect of physical activity or other sample parameters. PontzerFFM provides higher accuracy than other models independent of the magnitude of RMR, sex, activity levels, and sample characteristics.ConclusionEquations using FFM were more accurate than BM equations in our sample. Future studies are needed to test the accuracy of RMR prediction equations in diverse samples.

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Section: Discussionmentioning

confidence: 80%

Section: Discussionmentioning

confidence: 97%

Validation of predictive equations to estimate resting metabolic rate of females and males across different activity levels

Prado‐Nóvoa,

Howard,

Laskaridou

et al. 2023

American J Hum Biol

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“…Males had higher resting metabolism, muscle mass, height and weight than females in this study. However, muscle mass, had a considerable impact on resting metabolic rate (Nosslinger et al, 2021). Several studies have found that the expression of obesity-related genes may predetermine the fingerprint pattern in obese people in utero (Cummins and Midlo, 1943).…”

Section: Discussionmentioning

confidence: 99%

Fingerprints white line counts (fWLC): An unfolding panacea of body composition estimation among students of selected tertiary institution in Kano State Nigeria

Nasir¹,

Salisu²,

Rayyan³

et al. 2022

dujopas

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The term "fingerprint white lines (fWL)" refers to a region of ridge hypoplasia that appears as white lines in a captured fingerprint, dubbed "white lines". The aim of this work was to see if there is any sexual dimorphism in fingerprint white line count (fWLC) and body composition parameters (BCP), as well as look into the relationship between the two. The study was a cross sectional design with 300 participants. The body composition parameters were measured using a bioelectric impedance machine. The fingerprint was captured using a live scanner to determine the fWLC. Females had considerably higher metabolic age, body fat, and visceral fat. In all ten digits, statistically significant differences in fWLC were found. Females were shown to have a higher fWLC, metabolic age, body fat, and visceral fat than males. Except for percent body fat, the fWLC revealed a significant negative correlation with all body composition indices. The fWLC of the left thumb correlates with percent visceral fat while the left and right middle fWLC showed a significant correlation with metabolic age. All ten digits fWLC had a strong link with percent body fat, percent muscle mass, and resting metabolism, with the left ring and thumb fWLC having a higher correlation. Thumb, index, middle, and little fWLC of the left digit, and middle and ring fWLC of the right digit are used to calculate BMI. The greatest predictors for BMI and percent body fat were found to be the left middle and right little digits fWLC. Left ring and right middle digit fWLC were the best predictors of percentage muscle mass and metabolic ages, respectively. Both resting metabolism and percent visceral fat were best predicted by the left thumb fWLC. Finally, sexual dimorphism in relation to BCP and fWLC was discovered.

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“…Resting metabolic rate (RMR) was measured using an indirect calorimeter (Fitmate Pro, Cosmed, Rome, Italy) and according to the recent guidelines [ 26 ]. Total daily energy expenditure was determined from RMR × daily physical activity level beside training + estimated energy expenditure during exercise.…”

Section: Methodsmentioning

confidence: 99%

Effects of pre-sleep protein supplementation on plasma markers of muscle damage and inflammatory cytokines resulting from sprint interval training in trained swimmers

Deng

Gao

2023

Journal of the International Society of Sports Nutrition

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Background Pre-sleep protein has been shown to improve muscle recovery overnight following exercise-induced muscle damage. Whether such an approach affects recovery from sprint interval training (SIT) has yet to be elucidated. This study examined the effects of protein supplementation every night before sleep on early (45 min post-SIT) and late (24 and 48 h after SIT) responses of creatine kinase (CK) and inflammatory cytokines, including interleukin-6 and 10 (IL-6 and IL-10) and tumor necrosis factor-alpha (TNFα). Methods Twenty trained swimmers underwent a 2-week in-water swimming SIT (two sets of 12 × 50-m all-out swims, interspersed by 1:1 recovery between each sprint and 3 min of rest between sets) and were randomized to two intervention groups receiving either 0.5 g kg −1 day −1 protein beverage (PRO) or the same amount of carbohydrate (CHO) preceding going to bed every night. For initial and final training sessions, CK and cytokine responses were analyzed at different time points, including resting, immediately after completion, 45 min post-SIT, and 24 and 48 h after SIT. Results CK concentrations elevated from resting point to 24 and 48 h post-SIT for both PRO and CHO groups ( p < 0.05). In both training groups, the peak levels of IL-6 and 10 were observed 45 min post-SIT on both occasions. TNFα levels significantly elevated from rest to immediately after SIT ( p < 0.001) and returned to values equivalent to the baseline afterward in both groups and on both occasions. In both groups, swimming SIT also switched the cytokine response 48 hours after exercise to an anti-inflammatory status by decreasing the ratio of IL-6 to IL-10 ( p < 0.04) in the last training session. Conclusions Pre-sleep protein ingestion failed to ameliorate blood markers of muscle damage. The late anti-inflammatory profile of cytokines and exercise-induced muscle damage improved after two weeks of swimming SIT with either protein or carbohydrate ingestion before sleep.

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Underestimation of resting metabolic rate using equations compared to indirect calorimetry in normal-weight subjects: Consideration of resting metabolic rate as a function of body composition

Cited by 6 publications

References 47 publications

Validation of predictive equations to estimate resting metabolic rate of females and males across different activity levels

Validation of predictive equations to estimate resting metabolic rate of females and males across different activity levels

Fingerprints white line counts (fWLC): An unfolding panacea of body composition estimation among students of selected tertiary institution in Kano State Nigeria

Effects of pre-sleep protein supplementation on plasma markers of muscle damage and inflammatory cytokines resulting from sprint interval training in trained swimmers

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