Predictive equations for energy expenditure in adult humans: From resting to free-living conditions

Sum of Skinfold-Corrected Girths Correlates with Resting Energy Expenditure: Development of the NRGCO Equation

Our study aimed to validate existing equations and develop the new NRGCO equation to estimate resting energy expenditure (REE) in the Colombian population with moderate-to-high physical activity levels. Upon satisfying the inclusion criteria, a total of 86 (43F, 43M) healthy adults (mean [SD]: 27.5 [7.7] years; 67.0 [13.8] kg) were evaluated for anthropometric variables and REE by indirect calorimetry using wearable gas analyzers (COSMED K4 and K5). Significant positive correlations with REE were found for body mass (r = 0.65), body mass-to-waist (r = 0.58), arm flexed and tensed girth (r = 0.66), corrected thigh girth (r = 0.56), corrected calf girth (r = 0.61), and sum of breadths (∑3D, r = 0.59). As a novelty, this is the first time a significant correlation between REE and the sum of corrected girths (∑3CG, r = 0.63) is reported. Although existing equations such as Harris-Benedict (r = 0.63), Mifflin-St. Jeor (r = 0.67), and WHO (r = 0.64) showed moderate-to-high correlations with REE, the Bland-Altman analysis revealed significant bias (p < 0.05), indicating that these equations may not be valid for the Colombian population. Thus, participants were randomly distributed into either the equation development group (EDG, n = 71) or the validation group (VG, n = 15). A new model was created using body mass, sum of skinfolds (∑8S), corrected thigh, corrected calf, and age as predictors (r = 0.755, R2 = 0.570, RMSE = 268.41 kcal). The new NRGCO equation to estimate REE (kcal) is: 386.256 + (24.309 × BM) - (2.402 × ∑8S) - (21.346 × Corrected Thigh) + (38.629 × Corrected Calf) - (7.417 × Age). Additionally, a simpler model was identified through Bayesian analysis, including only body mass and ∑8S (r = 0.724, R2 = 0.525, RMSE = 282.16 kcal). Although external validation is needed, our validation resulted in a moderate correlation and concordance (bias = 91.5 kcal) between measured and estimated REE values using the new NRGCO equation.

Energy Expenditure in Humans: Principles, Methods, and Changes Throughout the Life Course

Humans require energy to sustain their daily activities throughout their lives. This narrative review aims to (a) summarize principles and methods for studying human energy expenditure, (b) discuss the main determinants of energy expenditure, and (c) discuss the changes in energy expenditure throughout the human life course. Total daily energy expenditure is mainly composed of resting energy expenditure, physical activity energy expenditure, and the thermic effect of food. Total daily energy expenditure and its components are estimated using variations of the indirect calorimetry method. The relative contributions of organs and tissues determine the energy expenditure under different physiological conditions. Evidence shows that energy expenditure varies along the human life course, at least in part due to changes in body composition, the mass and specific metabolic rates of organs and tissues, and levels of physical activity. This information is crucial to estimate human energy requirements for maintaining health throughout the life course.

Validity of predictive equations for total energy expenditure against doubly labeled water

Variations in physical activity energy expenditure can make accurate prediction of total energy expenditure (TEE) challenging. The purpose of the present study was to determine the accuracy of available equations to predict TEE in individuals varying in physical activity (PA) levels. TEE was measured by DLW in 56 adults varying in PA levels which were monitored by accelerometry. Ten different models were used to predict TEE and their accuracy and precision were evaluated, considering the effect of sex and PA. The models generally underestimated the TEE in this population. An equation published by Plucker was the most accurate in predicting the TEE in our entire sample. The Pontzer and Vinken models were the most accurate for those with lower PA levels. Despite the levels of accuracy of some equations, there were sizable errors (low precision) at an individual level. Future studies are needed to develop and validate these equations.

Impact of Equation Choice on Resting Metabolic Rate Ratio in High-Level Men and Women Athletes

OBJECTIVE

To examine the impact of the RMR ratio cutoff point selected on the categorization of prevalence/absence of low energy availability among predictive equations in high-level athletes (n = 241 [99 women]; 52% competed at the World Championship and Olympic Games), and whether this categorization is influenced by sex and the predictive equation used.

METHODS

We assessed RMR using indirect calorimetry, predicted the RMR using the equations proposed by Harris-Benedict, FAO/WHO/UNU, de Lorenzo, ten Haaf and Wejis, Wong, Jagim, Cunningham, and Freire, and computed the RMR ratio for each equation.

RESULTS

We observed that the cumulative percentage of RMR ratio values increased at a faster rate using Jagim, ten Haaf and Wejis, and Cunningham equations compared to the other equations. At the 0.90 value (the most used cutoff point in literature), the Jagim equation categorized ≥ 50% of the athletes into "low energy availability". No Sex × Equation × Sport interaction effect was observed (F = 0.10, p = 1.0). There was a significant main effect to Sex (F = 11.7, p < 0.001, ES = 0.05), Sport (F = 16.4, p < 0.001, ES = 0.01), and Equation (F = 64.1, p < 0.001, ES = 0.19). Wong and FAO/WHO/UNU equations yielded the largest errors (assessed vs. predicted RMR) in men and women, respectively.

CONCLUSION

The selected RMR ratio cutoff point influences the prevalence/absence of low energy availability characterization in high-level athletes and suggests that certain equations could bias its assessment.

Multiple factor assessment for determining resting metabolic rate in young adults

Existing formulas cannot fully explain the variation of resting metabolic rate (RMR). This study aims to examine potential influencing factors beyond anthropometric measurements and develop more accurate equations using accessible parameters. 324 healthy adults (230 females; 18-32 years old) participated in the study. Height, fat-free mass (FFM), fat mass (FM) and RMR were measured. Menstrual cycle, stress levels, living habits, and frequency of consuming caffeinated foods were collected. Measured RMR were compared with predictive values of the new equations and previous 11 equations. Mean RMR for men and women was 1825.2 ± 248.8 and 1345.1 ± 178.7 kcal/day, respectively. RMR adjusted for FFM0.66FM0.066 was positively correlated with BMI. The multiple regression model showed that RMR can be predicted in this population with model 1 (with FFM, FM, age, sex and daily sun exposure duration) or model 2 (with weight and height replacing FFM and FM). The accuracy was 75.31% in the population for predictive model 1 and 70.68% for predictive model 2. The new equations had overall improved performance when compared with existing equations. The predictive formula that consider daily sun exposure duration improve RMR prediction in young adults. Additional investigation is required among individuals in the middle-aged and elderly demographic.

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

OBJECTIVES

Using 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.

METHOD

We 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.

RESULTS

Equations 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.

CONCLUSION

Equations 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.

Mitochondrial metabolism in blood more reliably predicts whole-animal energy needs compared to other tissues

Understanding energy metabolism in free-ranging animals is crucial for ecological studies. In birds, red blood cells (RBCs) offer a minimally invasive method to estimate metabolic rate (MR). In this study with European starlings Sturnus vulgaris, we examined how RBC oxygen consumption relates to oxygen use in key tissues (brain, liver, heart, and pectoral muscle) and versus the whole organism measured at basal levels. The pectoral muscle accounted for 34%-42% of organismal MR, while the heart and liver, despite their high mass-specific metabolic rate, each contributed 2.5%-3.0% to organismal MR. Despite its low contribution to organismal MR (0.03%-0.04%), RBC MR best predicted organismal MR (r = 0.70). Oxygen consumption of the brain and pectoralis was also associated with whole-organism MR, unlike that of heart and liver. Overall, our findings demonstrate that the metabolism of a systemic tissue like blood is a superior proxy for organismal energy metabolism than that of other tissues.

Time reallocation of physical behaviors induced by endurance exercise in physically active individuals

ABSTRACT

Increasing moderate-vigorous physical activity (MVPA) through exercise requires reallocating time from other physical behavior(s). We aimed to determine the reallocations induced by endurance exercise in physically active individuals. We also searched for behavioral compensatory responses, and explored the effect of exercise on daily energy expenditure. Fourteen participants (8 women; median age 37.8 [IQR 29.9–48.5] yr) exercised on Monday, Wednesday, and Friday mornings (cycling MVPA, 65 min/session; "exercise days"), and avoided exercising on Tuesday and Thursday ("rest days"). Time spent on sleep, sedentary behavior, light-intensity physical activity, and MVPA was determined each day by accelerometers and logs. An energy expenditure index was computed considering minutes spent on each behavior and fixed metabolic equivalents. We found that all participants had lower sleep and higher total (including exercise) MVPA on exercise days compared to rest days. Thus, on exercise vs. rest days, sleep was lower (490 [453–553] vs. 553 [497–599] min/day, respectively, P<0.001), and total MVPA was higher (86 [80–101] vs. 23 [15–45] min/day, respectively; P<0.001). No differences in other physical behaviors were detected. Notably, exercise not only induced reallocations (i.e. less time in other behaviors) but also behavioral compensatory responses in some participants (e.g. increased sedentary behavior). This rearrangement of physical behaviors manifested in exercise-induced increases in energy expenditure from 96 to 232 MET×min/day. In conclusion, active individuals reallocated time from sleep to accommodate morning exercise. Yet exercise induced variable rearrangements of behaviors, with some individuals manifesting compensatory responses. Understanding individual rearrangements may help improve exercise interventions.