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. Some call into question the established formulas used in many RMR calculations, for example Owen et al.

However, this conclusion is based on only 44 women aged and it is unclear how representative a sample they are for the general population.

More recent and much larger studies like Wright et al. However, their statistical analysis, sadly, gives no indication of the direction of the bias. A much larger study by Marra et al. They, however, also note that using obese-specific REE formulas do not improve prediction rates compared to equations based on normal-weight subjects.

According, again, to Marra et al. including body fat percentage or fat free mass percentage does not improve predictions versus those based on weight, height, age, and gender alone.

The RMR graph is for a male and female of different age but same mass and stature. The effect of gender is also clearly visible with women having lower average resting energy expenditure. The figure can be replicated for any dataset of interest, as long as it can be broken down by men and women.

IC is a non-invasive procedure, generally considered the gold standard in clinical practice, however the cost and time, as well as the availability of a clinic with the necessary equipment should be considered.

Our resting energy expenditure calculator remains an inexpensive and decently accurate alternative for less demanding cases. I "Essentials of Exercise Physiology", fourth edition. If you'd like to cite this online calculator resource and information as provided on the page, you can use the following citation: Georgiev G.

php URL [Accessed Date: 14 Feb, ]. Read my next blog to learn how to use this feature to determine weight gain, weight loss and weight maintenance. Brooks GA, Butte NF, Rand WM, Flatt JP, Caballero B. Chronicle of the Institute of Medicine physical activity recommendation: how a physical activity recommendation came to be among dietary recommendations.

Am J Clin Nutr. Cunningham JJ. Body composition as a determinant of energy expenditure: a synthetic review and a proposed general prediction equation.

Dietary Reference Intakes for Energy, Carbohydrate, Fiber, Fat, Fatty Acids, Cholesterol, Protein, and Amino Acids Macronutrients. Panel on Macronutrients, Panel on the Definition of Dietary Fiber, Subcommittee on Upper Reference Levels of Nutrients, Subcommittee on Interpretation and Uses of Dietary Reference Intakes, and the Standing Committee on the Scientific Evaluation of Dietary Reference Intakes, Food and Nutrition Board.

The National Academies Press: Washington, D. Harris JA, Benedict FG. A biometric study of human basal metabolism. Proc Natl Acad Sci U.