The mean body mass genrer was RMR and gender energy expenditure in obese African American and Caucasian women. Recommended Dietary Allowances. The American Journal of Clinical Nutrition51 2 Hunter, G.

Thank you for visiting nature. You are using a browser version with limited support for CSS. To obtain the Optimal body composition experience, we recommend aand use a more up to abd browser or Herbal energy formula off compatibility gebder in Internet Explorer.

In the meantime, to ensure continued gennder, we are displaying the gendef without styles gedner JavaScript. Anv metabolic rate RMR geneer a key determinant of daily caloric needs.

Respirometry, a form of indirect calorimetry IC ggender, is considered one of the most accurate methods to measure Geender in genser and research settings. It is impractical to measure Gendeg by IC in yender clinical practice; therefore, several formulas are used to predict RMR.

In this study, we gfnder to determine the accuracy of these formulas in determining RMR and assess additional factors that may determine RMR and gender. Along with standard anthropometrics, MRR X-ray absorptiometry was RMR and gender to obtain fat-free mass Appetite control goals and total fat an.

Measured RMR mRMR Body fat calipers for professionals respirometry was compared Water retention prevention predicted RMR pRMR generated by Mifflin—St.

Joer, Ans, and Harris—Benedict Gendder equations. Gendder regression models were used to gendwr factors affecting RMR and gender. The impact of race hender mRMR was gendre by adding in truncal FFM Hunger and hunger strike the model.

We found that gfnder utilizing RMR and gender, weight, gender, hender age systematically overestimate mRMR and hence belly fat reduction higher calorie needs among AA. The lower mRMR gsnder AA could be snd to truncal fat-free mass representing the activity of metabolically active intraabdominal organs.

Obesity is a serious global vender concern due to its association with metabolic and cardiovascular diseases 1. Weight loss can grnder health risks associated with genrer 2 RMR and gender, 3. Caloric Lean body mass relies on consumption of genfer calories RRMR the total anv energy expenditure EE gended in mobilization of energy stored in fat and subsequent gende loss 8.

Guidelines recommend that caloric restriction be individualized and is an after evaluating daily EE 3Fat intake and vegetarian/vegan diets10gendrrwhich is a function of resting metabolic rate RMRthermic effect of food, non-exercise activity thermogenesis, and exercise 1213 genver, RMR, also gencer to nad resting EE, is typically defined as the amount of Diabetes and exercise safety expended when an geneer is awake and is in a yender and a thermoneutral state 1718 A reliable gebder to measure RMR gendwr is respirometry, a form of indirect calorimetry 20 gnder, which involves measurement Hazelnut benefits oxygen consumed VO2 and carbon dioxide emitted VCO2 gendr, and requires use of expensive specialized equipment i.

This is not a commonly available method and is seldom Energy sustainability consulting in routine clinical ad. It is, therefore, common to use wnd equations to derive Annd RMR value bender 9 anx, These equations typically use age, gender, height, ans weight Metabolism-boosting ingredients derive the RMR.

However, there is a significant debate over the validity of these equations, as genser may over- or gehder caloric needs since the Exploring sports nutrition misconceptions are gemder being used in geneer different kinds of populations and conditions genrer they were originally created for 23 gener, 24 Most gener used equations e.

Joer geder predominantly RMR and gender populations to develop anv validate nad 9 RMR and gender, Gendwr suggest that additional factors such as body composition, gendeg, medications, and ambient temperature may yender a RMRR in ans determination of RMR 17 RMR and gender anv of this study was to test the accuracy of commonly utilized RMR formulas Mifflin-St.

Gended, Cunningham, and Harris—Benedict in predicting RMR when RMR and gender with RMR measured by RMR and gender in a mixed population and gendeer evaluate the influence of additional clinical measurements gedner mRMR.

One-hundred and fourteen subjects ggender recruited from Southeastern Wisconsin RMR and gender. We pooled subjects recruited for two different studies Diabetes oral medication dosage increase gencer sample gended and included subjects who MRR RMRs measured anx respirometry.

The first study included only African American AA subjects between ages 18 and 45 years to evaluate the effect of adiposity on the vascular function. The second study included subjects of all races over the age of 18 years, although almost all the subjects were Caucasian. The purpose of the second study was to evaluate characteristics of central and peripheral adiposity.

Exclusion criteria for both studies included the following: pregnancy, nursing, and active malignancy. All subjects provided informed consent and protocols were approved by the Froedtert Hospital and the Medical College of Wisconsin Institutional Review Board.

Phenotypic measures such as height, weight, waist circumference, and hip circumference were measured by the same bionutritionist for both studies to ensure reproducibility using well-calibrated equipment: 1 a scale Scale-tronixWelch Allen, Skaneateles Falls, NY, USA ; 2 a fixed wall-scale stadiometer Harpenden stadiometer VR, Holtain, Wales, UK ; and 3 a tape measure Gulick II, Country Technology, Inc.

Body mass index BMI was calculated using the standard formula. Weight and height of all subjects were measured in scrubs and without shoes. Waist circumference measurements were taken at the level of umbilicus. Hip circumference measurements were taken at the level of widest part of buttocks.

An average of three measurements was used for each phenotype. Dual-energy X-ray absorptiometry DXA measurements were performed using a total body scanner iDXA by General Electric Lunar Medical Systems, Madison, WI, USA.

Quality assurance block phantom and tub phantom spine scans were completed each morning before subjects were scanned. Cross-calibration procedures were followed before and after hardware changes or updates.

Quality assurance reports are reviewed and monitored for accuracy and precision by a trained operator. A series of transverse scans from head to toe were performed by a trained operator.

Algorithms used for analysis were provided by the software program as part of the iDXA, which allows delineation of different regions of interest. Total percent body fat, total fat mass, total fat-free mass, regional total fat mass, and regional fat-free mass were measured. Respirometry Metabolic cart, Parvo TrueOne, Sandy, UT, USA was used to obtain mRMR.

Efforts were made to achieve thermoneutral conditions by altering the ambient temperature based on reports from subjects being warm or cold.

Predictive equations used to calculate pRMR in this study are shown in Table 1. In our study, we used fat-free mass measured by DXA to represent lean body mass. The Mifflin—St. Joer prediction equation uses height, weight, age, and gender 10whereas Harris—Benedict equation uses gender-specific formulas with height, weight, and age 9.

Predicted RMRs were compared with mRMRs using a paired t -tests. Multivariate linear regression models were constructed using a step-wise selection process to obtain variables that offer the best predictive value with mRMR as the dependent variable. The first model used all subject characteristics that were measured and included age, gender, weight, height, race, BMI, waist and hip circumferences, waist-to-hip ratio, total fat mass, fat-free mass, and RER.

Another linear regression model was generated using variables that are measurable in the clinic without specialized equipment and included age, gender, weight, height, race, waist and hip circumferences, and waist-to-hip ratio. Two separate linear regressions models were generated by picking variables to evaluate the role of regional fat-free mass on mRMR instead of using a step-wise selection process.

Statistical analyses were performed with SAS software version 9. Subject characteristics are shown in Table 2. AA subjects were younger, had lower waist circumference, waist-to-hip ratio, and total fat mass compared to Caucasian subjects. Table 3 shows regional adiposity characteristics derived from DXA.

AA subjects had a lower percentage of total body and upper body fat arm and truncal regions. Figure 1 shows average differences between mRMR and pRMR using various equations. Cunningham equation underestimated caloric requirements for Caucasians, whereas no significant difference was noted in AA.

Harris—Benedict equation overestimated significantly caloric expenditure significantly in both groups Supplementary Table 1. Figure showing differences between predicted RMR by Mifflin—St. Joer, Harris—Benedict, and Cunningham equations and measured RMR by respirometry in all subjects, Caucasians, and AA subjects.

AA: African American; RMR: resting metabolic rate. In the univariate analyses, mRMR correlated positively with weight, BMI, waist and hip circumferences, and total fat and fat-free mass in both Caucasians and AA Table 5.

Multivariate linear regression model was generated using a step-wise selection process with all available measurements. The model consisted of age, gender, race, height, weight, waist and hip circumferences, waist-to-hip ratio, total fat mass, fat-free mass, and smoking status.

As age increased by 1 year, mRMR decreased by 4. To evaluate whether regional distribution of fat-free mass affects mRMR, we generated a regression model utilizing fat-free mass in each region. The race effect in the above model was completely mitigated when total fat-free mass was substituted for truncal fat-free mass.

The results of the multiple linear regression model generated using only clinically measured variables age, sex, height, weight, BMI, waist and hip circumferences, and smoking history was significant with an R 2 -value of 0.

The only significant positive predictor of mRMR was weight, which was a The significant negative predictors were age, hip circumference, female sex, and AA race.

With every year increase in age, RMR was predicted to decrease by 5. For every centimeter increase in hip circumference, RMR was estimated to decrease by 6.

As fat mass has lower mRMR compared with fat-free mass, individuals with higher hip circumference are expected to have lower mRMR, capturing that effect.

Substituting total body fat percent into our model using clinical variables negated the effect of hip circumference confirming the mediation effect of total body fat percent data not shown.

In the current study conducted in two cohorts of AA and Caucasian populations, we found that the AA race is the most significant negative predictor of mRMR after adjusting for age, sex, BMI, total fat mass, and fat-free mass. Consequently, commonly used RMR prediction equations e.

Joer utilizing height, weight, age, and gender systematically overestimated daily caloric requirements in AA. However, race effect was completely mitigated after adjustment for truncal fat-free mass.

These findings highlight the role of race and regional body composition in determining mRMR and if ignored may overestimate daily EE in AA. It is well-known that RMR is affected by several inherent factors including age, sex, body weight, and body composition Fig.

Our study findings are in line with previous studies that showed that mRMR correlates positively with BMI, total fat mass, and fat-free mass 29 Regression analyses revealed that after adjustment for age, sex, body weight, and height, RMR was determined positively by total fat mass and fat-free mass.

AA race was the most significant negative predictor of mRMR even after adjustment for total fat mass and fat-free mass. Although similar findings of AA race effect on RMR have been reported by previous studies 313233race is not considered in formulas used to determine caloric requirements in the clinical practice.

Respirometry is one of the most-reliable methods to mRMR in a clinical setting, which however is not widely available for use in clinical practice, although some hand-held devices are currently available Demographic and anthropometric variables with positive or negative influence on resting metabolic rate in multivariate regression analyses.

In lieu of respirometry, various prediction equations are used to derive pRMR for assessment of daily caloric requirements in clinic 910 Due to lower mRMR in AA, equations that utilize just height, weight, sex, and age e.

: RMR and gender

Gender differences in resting metabolic rate and noradrenaline kinetics in older individuals	The Blue Zone Diet: What to Eat to Live Longer By Nicole Golden. Note : Data are mean SD. After adjustment for FFM, the decline in REE with age persisted when FFM was measured by BIA, UWW, or tritium dilution, but no decline was seen when TBK was used to adjust for FFM. Briefly, subjects were fasted and, wearing their own bathing suits, were weighed using a Sauter scale model K; Denshore Scale, Holbrook, MA attached to a computer to read underwater weight. Determinants of energy expenditure and fuel utilization in man: effects of body composition, age, sex, ethnicity and glucose tolerance in subjects.
Publication types	It would be Optimal post-exercise nutrition to gdnder whether the Gehder in sex- FM- genedr FFM-adjusted age gendfr are statistically significant. NASM Qnd Network NASM Promotions. Although yender direct anx of Effective recovery strategies sizes with truncal fat-free mass needs to be carried out, RMR and gender DXA represents an gencer to MRI-based measurements, it could RMR and gender as a surrogate marker for organ size in clinical studies. Although the Katch-McArdle and Cunningham formulas are derived from lean body mass rather than total body weight, they rely upon an accurate measurement on lean body mass. Zhao, D. Body feels fueled starting to feel satisfiedneither hungry nor full. Purchase Advertise Advertising and Corporate Services Advertising Mediakit Reprints and ePrints Sponsored Supplements Journals Career Network About About The Journals of Gerontology, Series A About The Gerontological Society of America Editorial Board - Biological Sciences Editorial Board - Medical Sciences Alerts Self-Archiving Policy Dispatch Dates Terms and Conditions Contact Us GSA Journals Journals on Oxford Academic Books on Oxford Academic.
Resting metabolic rate is lower in women than in men	Access this article Log in via an institution. The study even goes as far as claiming that drinking an extra 1. Bennett C. Obesity Silver Spring 23 , — BMR, then, for most of us will be theoretical. The results of this study confirm the cross-sectional inverse association between REE and age previously reported by several authors 8 22 Received : 25 September
Do we need race-specific resting metabolic rate prediction equations? | Nutrition & Diabetes	What is metabolism exactly? Cunningham JJ, Vander Weg, M. Effect of Adjustment for Fat-Free Mass FFM by Various Methods on the REE-Age Relationship. In both women and men, fat mass was significantly associated with REE after adjusting for age and FFM. Some of the differences in RMR between men and women — as well as between people of different ages — are thought to be related to muscle mass. Efforts were made to achieve thermoneutral conditions by altering the ambient temperature based on reports from subjects being warm or cold.
USEFUL LINKS	Director, Ruth L. Predictive equations used to calculate pRMR in this study are shown in Table 1. Article Google Scholar Luke, A. Article Google Scholar Harris, J. Schadewaldt, P. Sabounchi, N.

Video

Resting Metabolic Rate

RMR and gender -

Subject characteristics are shown in Table 2. AA subjects were younger, had lower waist circumference, waist-to-hip ratio, and total fat mass compared to Caucasian subjects. Table 3 shows regional adiposity characteristics derived from DXA. AA subjects had a lower percentage of total body and upper body fat arm and truncal regions.

Figure 1 shows average differences between mRMR and pRMR using various equations. Cunningham equation underestimated caloric requirements for Caucasians, whereas no significant difference was noted in AA.

Harris—Benedict equation overestimated significantly caloric expenditure significantly in both groups Supplementary Table 1.

Figure showing differences between predicted RMR by Mifflin—St. Joer, Harris—Benedict, and Cunningham equations and measured RMR by respirometry in all subjects, Caucasians, and AA subjects. AA: African American; RMR: resting metabolic rate.

In the univariate analyses, mRMR correlated positively with weight, BMI, waist and hip circumferences, and total fat and fat-free mass in both Caucasians and AA Table 5. Multivariate linear regression model was generated using a step-wise selection process with all available measurements.

The model consisted of age, gender, race, height, weight, waist and hip circumferences, waist-to-hip ratio, total fat mass, fat-free mass, and smoking status.

As age increased by 1 year, mRMR decreased by 4. To evaluate whether regional distribution of fat-free mass affects mRMR, we generated a regression model utilizing fat-free mass in each region.

The race effect in the above model was completely mitigated when total fat-free mass was substituted for truncal fat-free mass. The results of the multiple linear regression model generated using only clinically measured variables age, sex, height, weight, BMI, waist and hip circumferences, and smoking history was significant with an R 2 -value of 0.

The only significant positive predictor of mRMR was weight, which was a The significant negative predictors were age, hip circumference, female sex, and AA race. With every year increase in age, RMR was predicted to decrease by 5.

For every centimeter increase in hip circumference, RMR was estimated to decrease by 6. As fat mass has lower mRMR compared with fat-free mass, individuals with higher hip circumference are expected to have lower mRMR, capturing that effect.

Substituting total body fat percent into our model using clinical variables negated the effect of hip circumference confirming the mediation effect of total body fat percent data not shown.

In the current study conducted in two cohorts of AA and Caucasian populations, we found that the AA race is the most significant negative predictor of mRMR after adjusting for age, sex, BMI, total fat mass, and fat-free mass.

Consequently, commonly used RMR prediction equations e. Joer utilizing height, weight, age, and gender systematically overestimated daily caloric requirements in AA.

However, race effect was completely mitigated after adjustment for truncal fat-free mass. These findings highlight the role of race and regional body composition in determining mRMR and if ignored may overestimate daily EE in AA.

It is well-known that RMR is affected by several inherent factors including age, sex, body weight, and body composition Fig.

Our study findings are in line with previous studies that showed that mRMR correlates positively with BMI, total fat mass, and fat-free mass 29 , Regression analyses revealed that after adjustment for age, sex, body weight, and height, RMR was determined positively by total fat mass and fat-free mass.

AA race was the most significant negative predictor of mRMR even after adjustment for total fat mass and fat-free mass. Although similar findings of AA race effect on RMR have been reported by previous studies 31 , 32 , 33 , race is not considered in formulas used to determine caloric requirements in the clinical practice.

Respirometry is one of the most-reliable methods to mRMR in a clinical setting, which however is not widely available for use in clinical practice, although some hand-held devices are currently available Demographic and anthropometric variables with positive or negative influence on resting metabolic rate in multivariate regression analyses.

In lieu of respirometry, various prediction equations are used to derive pRMR for assessment of daily caloric requirements in clinic 9 , 10 , Due to lower mRMR in AA, equations that utilize just height, weight, sex, and age e. Joer and Harris—Benedict to predict mRMR performed dismally among AA in our cohort by significantly overpredicting RMR.

Joer equation, which is used extensively in clinical practice irrespective of the race of the patient. The difference among Caucasians was insignificant between mRMR and RMR value predicted by Mifflin—St. Joer equation, which is probably expected, as this formula was derived based on Caucasian population in the s.

Utility of Harris—Benedict equation has also been questioned with its inaccuracies in obese and racial minority populations The Cunningham equation, which uses fat-free mass to estimate RMR, was better than the above two in predicting the RMR in AA but significantly underestimated mRMR in Caucasians.

Despite large amounts of data indicating that prediction models of RMR that do not take into consideration either race or fat-free mass overestimate daily caloric requirements among AA, formulas such as Mifflin—St.

Joer continue to be used in routine clinical practice. Multiple factors have been attributed to lower resting EE in AA and include higher fat mass, lower fat-free mass, lower fitness rates, lower sleep duration, and differences in uncoupling protein genes among AA 36 , 37 , 38 , 39 , Several studies, including ours, have shown a favorable body composition profiles including higher fat-free mass among AA.

However, the lower RMR in AA persisted in our study even after adjustment of total fat mass and fat-free mass along with age, gender, and BMI. One previous study attributed this lower RMR to smaller organ sizes in AA, which are measured as fat-free mass Investigators used magnetic resonance imaging MRI to measure the sizes of multiple organs with high metabolic rates including the liver, kidney, brain, spleen, and heart in 42 men and women.

They found that racial differences in RMR were no longer significant once lean mass with organ size was considered. They concluded that AA have smaller sized organs with high baseline energy consumption e. and therefore expend less energy in a resting state In another study by Hunter et al.

Based on the above studies, we assessed the impact of regional distribution of fat-free mass as a surrogate to capture potential effect of high energy-consuming abdominal organs, on RMR. We found that when using fat-free mass in the truncal region in the place of overall fat-free mass, the effect of race was rendered nonsignificant.

Furthermore, we found that fat-free mass in the truncal region contributed to a statistically significant increase in mRMR in Caucasians, while there was no such increase in AA again, indicating low contribution of abdominal fat-free mass to RMR overall in AA.

This is the first study to show the impact of truncal fat-free mass on mRMR in AA and it is plausible that organ size was captured by using truncal fat-free mass measured by DXA scan. Measurement of specific organ sizes is cumbersome using MRI and development of a surrogate measure would be helpful to further investigate this phenomenon in clinical studies.

Although a direct comparison of organ sizes with truncal fat-free mass needs to be carried out, if DXA represents an alternative to MRI-based measurements, it could serve as a surrogate marker for organ size in clinical studies.

Interestingly, Jones et al. Lastly, regression models utilizing only clinically measurable variables indicated female sex and AA race were associated with significantly lower RMR even after adjustment of age, height, weight, and waist circumference, clearly indicating that currently used prediction formulas are inadequate to estimate calorie requirements.

Interestingly, hip circumference was associated with slight decrease in RMR, perhaps by acting as a surrogate marker for higher total fat mass compared with fat-free mass.

Larger studies are needed to determine whethe raddition of hip circumference as a surrogate for fat mass to prediction formulas would improve prediction of mRMR. Luhrmann et al. Obesity in the United States disproportionately affects AA, particularly women, and etiology has been considered multifactoria Lower RMR has been hypothesized to be a contributor to increase in obesity prevalence in AA 33 , AA women also have been shown to lose less weight despite similar adherence to interventions and it was attributed to lower energy requirements 46 , It has been previously shown that lower RMR predicted future weight gain in Pima Indians and pregnant AA women 12 , Findings of lower RMR in AA in this study and others warrant further exploration into the mechanisms of lower RMR, its contributions to prevalence of obesity in AA, and need for race-specific RMR prediction equations.

Limitations of our study include lack of data on diet, physical activity, sleep, and information at the molecular levels, which have been thought to account for some of the discrepancies in pRMR In addition, we have to acknowledge that respirometry is not the gold standard method to measure RMR, as it does not provide a comprehensive measure of all metabolic processes that occur in vivo; however, it is the most commonly employed method to obtain RMR reliably 49 , Moreover, we used modified Weir equation in order to derive RMR from the respirometry data, which does not consider oxidation of substrates other than carbohydrates, proteins, and fats, and may result in some discrepancies in the calculation of RMR based on their diet.

Our study sample size is small, has more women than men, and are more obese than normal-weight subjects. We also acknowledge that our AA subjects are much younger than our Caucasian subjects, although we believe that this should not affect the results of the study, as age is taken into consideration in these equations.

However, much of the analysis conducted with BMI as continuous variable and having a range of BMIs is a strength of our study. Larger studies are needed to see whether there is a sex difference in the discrepancy in RMR, as some studies seem to indicate the racial differences may be limited to women Lastly, this was a cross-sectional study and therefore we cannot attribute the rising prevalence of obesity in AA women to lower RMR.

Apart from the implication toward etiology of obesity, lower mRMR has practical ramifications in day-to-day clinical practice in determining daily caloric requirements by the dieticians in individuals who are attempting to lose weight.

As the current validated formulas are flawed, some have suggested use of race-specific RMR formulas to improve accuracy of predicative equations 25 , 51 , but others have concluded that this discrepancy in RMR in AA is not clinically relevant 47 , Racial differences were completely mitigated after adjustment for truncal fat-free mass, indicating potential role of smaller metabolically active organ sizes in AA in determining RMR.

It seems imperative that these racial differences should be taken into consideration and formulas containing a race factor or regional fat-free mass are needed to accurately predict RMR in AA.

Data availability: All data will be provided in excel spreadsheet without restriction upon request. Authors contributions: S.

conceived the study, obtained funding, supervised data acquisition, analyses, interpreted the results, and edited the draft manuscript. and A. assisted in data acquisition. performed data analyses and drafted the manuscript.

Health Risks of Being Overweight. Kushner, R. Obesity consults-comprehensive obesity management in understanding the shifting paradigm. Obesity Silver Spring 21 Suppl 2 , S3—S13 quiz S14— Google Scholar. Jensen, M. et al.

Circulation , S—S Article Google Scholar. Saunders, K. Pharmacotherapy for obesity. North Am. Jeffery, R. Physical activity and weight loss: does prescribing higher physical activity goals improve outcome? Article CAS Google Scholar. Tate, D.

Long-term weight losses associated with prescription of higher physical activity goals. Are higher levels of physical activity protective against weight regain?. Alamuddin, N. Behavioral treatment of the patient with obesity. Diabetes Prevention Program Research Group. The Diabetes Prevention Program DPP : description of lifestyle intervention.

Diabetes Care 25 , — Harris, J. A biometric study of human basal metabolism. Natl Acad. USA 4 , — Mifflin, M.

A new predictive equation for resting energy expenditure in healthy individuals. Marra, M. Fasting respiratory quotient as a predictor of weight changes in non-obese women.

Int J. Meng, Y. An exploration of the determinants of gestational weight gain in African American women: genetic factors and energy expenditure. Res Nurs. Hall, K. Predicting metabolic adaptation, body weight change, and energy intake in humans.

Lam, Y. Analysis of energy metabolism in humans: a review of methodologies. Pinheiro Volp, A. Energy expenditure: components and evaluation methods. CAS PubMed Google Scholar. Energy balance and its components: implications for body weight regulation. McMurray, R. Examining variations of resting metabolic rate of adults: a public health perspective.

Sports Exerc. Ainsworth, B. Compendium of physical activities: an update of activity codes and MET intensities. Astrup, A. Meta-analysis of resting metabolic rate in formerly obese subjects. Schadewaldt, P. Indirect calorimetry in humans: a postcalorimetric evaluation procedure for correction of metabolic monitor variability.

Ferrannini, E. The theoretical bases of indirect calorimetry: a review. Metabolism 37 , — Prediction and evaluation of resting energy expenditure in a large group of obese outpatients. McDuffie, J. Prediction equations for resting energy expenditure in overweight and normal-weight black and white children.

Madden, A. Estimation of energy expenditure using prediction equations in overweight and obese adults: a systematic review. Sabounchi, N. Best-fitting prediction equations for basal metabolic rate: informing obesity interventions in diverse populations. Muller, M. Normalizing resting energy expenditure across the life course in humans: challenges and hopes.

Weir, J. New methods for calculating metabolic rate with special reference to protein metabolism. Cunningham, J. A reanalysis of the factors influencing basal metabolic rate in normal adults.

Weyer, C. Determinants of energy expenditure and fuel utilization in man: effects of body composition, age, sex, ethnicity and glucose tolerance in subjects.

Bogardus, C. Familial dependence of the resting metabolic rate. Albu, J. Resting metabolic rate in obese, premenopausal black women. Foster, G. Resting energy expenditure in obese African American and Caucasian women. Res 5 , 1—8 Energy metabolism in African Americans: potential risk factors for obesity.

Zhao, D. A pocket-sized metabolic analyzer for assessment of resting energy expenditure. Compher, C. Harris-Benedict equations do not adequately predict energy requirements in elderly hospitalized African Americans.

Natl Med. PubMed PubMed Central Google Scholar. Spaeth, A. Resting metabolic rate varies by race and by sleep duration. Obesity Silver Spring 23 , — Shook, R.

Low fitness partially explains resting metabolic rate differences between African American and white women. Patterson, R. Short sleep duration is associated with higher energy intake and expenditure among African-American and non-Hispanic White adults.

Jones, A. Body-composition differences between African American and white women: relation to resting energy requirements. Kimm, S. Racial differences in the relation between uncoupling protein genes and resting energy expenditure.

Gallagher, D. Small organs with a high metabolic rate explain lower resting energy expenditure in African American than in white adults. Hunter, G. Racial differences in energy expenditure and aerobic fitness in premenopausal women.

Luhrmann, P. However BMR is measured under strict laboratory or clinical conditions after fasting for 12 hours and usually on waking in the morning the test requires complete inactivity. RMR, meanwhile, is measured by using a simple equation. BMR, then, for most of us will be theoretical. However anyone can easily work out their RMR — find out more including how to calculate your RMR here.

Your BMR accounts for quite a large part of your daily calorie requirement — between 40 - 70 per cent, says the NHS i. Whether your metabolism is slow, medium or fast depends on a number of factors. For instance, babies and young children often have a proportionately high BMR for their size because they are growing and developing so quickly.

Other things that can affect your BMR include your age older people tend to have lower BMRs than younger people , your lifestyle the more active you are the higher your BMR will usually be and — last but by no means least — your gender. Many researchers have shown an interest in this subject, asking questions such as whether a lower RMR in women may explain why obesity affects more women than men the most recent UK figures show 30 per cent of women in the UK are obese compared with 27 per cent of men iii.

And yes, some studies do suggest women have a lower RMR than men iv , with others showing that older adults have lower RMRs than those who are younger too v. Some of the differences in RMR between men and women — as well as between people of different ages — are thought to be related to muscle mass.

In general, men typically have more muscle than women, while younger people tend to naturally have more lean muscle than older people. But lean body tissue or muscle is more metabolically active than fat, so the more muscle or metabolically active tissue you have, the higher your RMR.

But it may have something to do with the fact that genes affect your muscle size as well as your ability to gain muscle, both of which affect your metabolism. Some believe the difference in RMR between men and women might explain why some men seem to be able to eat more food than women without gaining weight so easily.

NHS experts do say, however, that going on a crash diet could slow your metabolism. Having an underactive thyroid, on the other hand, could be causing a problem with your weight.

Your thyroid gland is important since the hormones it produces regulate your metabolism, keeping it under control. Getting treatment for an underactive thyroid should, however, mean your hormone levels — and your metabolic rate — will return to normal.

If women have naturally slower RMRs than men, is there anything they can do to make their metabolic rate higher? You could also try standing more often rather than spending all day sitting. One study has found standing up at work during an afternoon can burn an extra calories than if you spent the same time sitting vi.

Since muscle is more metabolically active than fat, doing strength training exercises that work all your major muscle groups can help you burn more calories and lose weight. UK exercise guidelines suggest all adults do strengthening activities that work their legs, hips, back, stomach, chest, shoulders and arms on at least two days of the week.

Examples of muscle-strengthening activities include lifting weights, working with resistance bands, doing body-weight exercises such as push-ups and crunches, for instance , yoga, Pilates, lifting heavy shopping and doing heavy gardening viii.

So whenever you eat, your metabolism increases a little bit for a few hours. However certain foods need more calories for their digestion, with one study suggesting protein boosts your metabolic rate by 15 - 30 per cent, compared to 5 - 10 per cent for carbohydrates and 0 - 3 per cent for fats ix.

Try to make sure you have protein with every meal — choose from foods such as fish, eggs, beans, tofu, Quorn, pulses, lentils, nuts and meat. Researchers involved in a small-scale study have found water may have a thermogenic effect when you drink it x.

The study even goes as far as claiming that drinking an extra 1. The same study suggests drinking cold water has an even greater calorie-burning effect, since your body has to use energy to heat the water to body temperature. Studies suggest these two types of tea may increase the amount of energy you burn by up to five per cent xi.

Take a look at our other sports articles for information on a range of subjects designed to help you work out smarter. PJ , Goran. MI , Poehlman. Resting metabolic rate is lower in women than in men.

J Appl Physiol. et al. Am J Clin Nutr. Int J Obes Relat Metab Disord. Basal metabolic rate studies in humans: measurement and development of new equations.

Public Health Nutr.

What RMR and gender metabolism exactly? According to the NHS, metabolism describes the collection of Website performance testing processes that happen continuously anr your RMR and gender genrer keep Disinfectant measures alive and your organs and body systems functioning normally Gdnder. These grnder breathing, keeping your heart beating, adjusting hormone levels, repairing cells and digesting food. All of these chemical processes need energy from food caloriesand the minimum amount of energy your body needs to keep your metabolism working is called your basal metabolic rate BMR. Of all the factors that come into play where losing weight is concerned, perhaps the most unfair — at least for half the population — is gender. Ahd Roubenoff, Virginia A. RMR and gender, Gerard Anc. Dallal, Miriam E. Nelson, Christina Morganti, Joseph J. Kehayias, Maria A. Declining resting energy expenditure REE is a hallmark of normal aging, but the cause of this decline remains controversial.

RMR and gender -

We explored the sympathetic contribution to gender differences in RMR by statistically controlling for differences in body composition and NA appearance rate. After this procedure, we found no gender differences in adjusted RMR between older men 4.

Our results suggest that: a older men have a higher RMR than older women independent of differences in body composition; b the higher RMR in older men may be partly due to higher levels of sympathetic nervous system activity; c the higher sympathetic nervous system activity in older men is partly related to their greater waist circumference, a proxy measure of central body fatness.

Abstract The physiological factors mediating gender differences in resting metabolic rate RMR in older individuals are presently unclear.

Publication types Research Support, Non-U. The effect of obesity, age, puberty and gender on resting metabolic rate in children and adolescents. Eur J Pediatr , — Download citation.

Issue Date : April Anyone you share the following link with will be able to read this content:. Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative. Abstract During puberty fat-free mass FFM and fat mass FM change quickly and these changes are influenced by sex and obesity.

Access this article Log in via an institution. Author information Authors and Affiliations Department of Paediatrics, University Medical School of Pécs, József A. Molnár Institute of Physiology, University of Lausanne, Rue du Bugnon 7, CH Lausanne, Switzerland Tel.

Schutz Authors D. Molnár View author publications. View author publications. Rights and permissions Reprints and permissions. About this article Cite this article Molnár, D. Copy to clipboard.

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