Do we need race-specific resting metabolic rate prediction equations?

Anthropometric indicators may explain the high incidence of follicular lymphoma in Europeans: Results from a bidirectional two-sample two-step Mendelian randomisation

BACKGROUND

Non-Hodgkin's lymphoma incidence rates vary between European and Asian populations. The reasons remain unclear. This two-sample two-step Mendelian randomisation (MR) study aimed to investigate the causal relationship between anthropometric indicators (AIs) and diffuse large B-cell lymphoma (DLBCL) and follicular lymphoma (FL) and the possible mediating role of basal metabolic rate (BMR) in Europe.

METHODS

We used the following AIs as exposures: body mass index (BMI), whole-body fat mass (WBFM), whole-body fat-free mass (WBFFM), waist circumference(WC), hip circumference(HC), standing height (SH), and weight(Wt). DLBCL and FL represented the outcomes, and BMR was a mediator. A two-sample MR analysis was performed to examine the association between AIs and DLBCL and FL onset. We performed reverse-MR analysis to determine whether DLBCL and FL interfered with the AIs. A two-step MR analysis was performed to determine whether BMR mediated the causality.

FINDINGS

WBFFM and SH had causal relationships with FL. A causal association between AIs and DLBCL was not observed. Reverse-MR analysis indicated the causal relationships were not bidirectional. Two-step MR suggested BMR may mediate the causal effect of WBFFM and SH on FL.

CONCLUSIONS

We observed a causal relationship between WBFFM and SH and the onset of FL in Europeans, Which may explain the high incidence of follicular lymphoma in Europeans.

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.

Development and validation of novel equation for prediction of resting energy expenditure in active Saudi athletes

Being the most stable component of energy expenditure, resting metabolic rate (RMR) is usually used in the calculation of energy requirements for athletes. An adequate energy prescription is essential in supporting athlete development. This work aims to develop and validate an equation for calculating energy requirements for Arabic Saudi athletes. This cross-sectional study included 171 active athletes aged 18 to 45 years. The sample was divided into a development group (n = 127) and a validation group (n = 44). Anthropometry, indirect calorimetry, and body composition analysis via bioelectric impedance analysis were performed on all participants. The novel predictive equations were created by using stepwise linear regression analyses. The accuracy of the novel equations was compared with 10 equations, and Bland and Altman plots were used to estimate the limits of agreement between measured RMR and novel equations. The first novel equation used a set of basic measures, including weight, gender, and age, was [RMR = 1137.094 + (Wt × 14.560)-(Age × 18.162) + (G × 174.917)] (R = 0.753, and R2 = 0.567, wt = weight, G = gender; for male use 1 and female 0). The second equation used fat-free mass, age, and weight [RMR = 952.828 + (fat-free mass × 10.970)-(Age × 18.648) + (Wt × 10.297)] (R = 0.760 and R2 = 0.577). Validation of the second novel equation increased the prediction of measured RMR to 72.7% and reduced the amount of bias to 138.82 ± 133.18 Kcal. Finally, the new set of equations was designed to fit available resources in clubs and showed up to 72.73% accurate prediction and good agreement with measured RMR by Bland and Altman plots.

Comparison of Meal Patterns Across Common Racial Groups in the UK and the USA, Examining Associations with Weight Status and Diet Quality: a Secondary Analysis of NDNS and NHANES Datasets

OBJECTIVE

Understanding diets of population subgroups is essential for monitoring health of diversifying populations, but currently, meal patterns of many population subgroups are not widely known. This paper aimed to identify meal patterns of racial groups in the UK and USA, considering if racial groups exhibit similar patterns of intake irrespective of location and relationships between meal patterns and health parameters.

DESIGN

Data were extracted from the UK (National Diet and Nutrition Survey) and the USA (National Health and Nutrition Examination Survey) national dietary surveys. Temporal and content meal patterns among racial groups in the UK and USA (White, Black, Asian and Other, n = 1780 and n = 4339, respectively) were examined. Kruskal-Wallis tests were applied to understand differences across groups. Logistic regression models identified associations between meal patterns and body mass index and diet quality.

RESULTS

Black groups consumed fewer eating occasions than White and Other groups in both countries, while UK racial groups consumed significantly more snacks than USA groups. Food group contribution to eating occasion consumption was similar across countries where Asian groups in the USA and UK had the lowest meat intake at lunch and dinner. Meal frequency was positively associated with diet quality.

CONCLUSIONS

Overall, meal patterns differ across racial groups within a single country, and some differences were observed within groups of the same race across countries. Learnings from this research highlight the differences in consumption patterns across racial groups and the importance of considering a meal-based approach to dietary guidelines by racial group.

Accuracy of Resting Metabolic Rate Prediction Equations in Athletes: A Systematic Review with Meta-analysis

BACKGROUND

Resting metabolic rate (RMR) prediction equations are often used to calculate RMR in athletes; however, their accuracy and precision can vary greatly.

OBJECTIVE

The aim of this systematic review and meta-analysis was to determine which RMR prediction equations are (i) most accurate (average predicted values closest to measured values) and (ii) most precise (number of individuals within 10% of measured value).

DATA SOURCES

A systematic search of PubMed, CINAHL, SPORTDiscus, Embase, and Web of Science up to November 2021 was conducted.

ELIGIBILITY CRITERIA

Randomised controlled trials, cross-sectional observational studies, case studies or any other study wherein RMR, measured by indirect calorimetry, was compared with RMR predicted via prediction equations in adult athletes were included.

ANALYSIS

A narrative synthesis and random-effects meta-analysis (where possible) was conducted. To explore heterogeneity and factors influencing accuracy, subgroup analysis was conducted based on sex, body composition measurement method, athlete characteristics (athlete status, energy availability, body weight), and RMR measurement characteristics (adherence to best practice guidelines, test preparation and prior physical activity).

RESULTS

Twenty-nine studies (mixed sports/disciplines n = 8, endurance n = 5, recreational exercisers n = 5, rugby n = 3, other n = 8), with a total of 1430 participants (822 F, 608 M) and 100 different RMR prediction equations were included. Eleven equations satisfied criteria for meta-analysis for accuracy. Effect sizes for accuracy ranged from 0.04 to - 1.49. Predicted RMR values did not differ significantly from measured values for five equations (Cunningham (1980), Harris-Benedict (1918), Cunningham (1991), De Lorenzo, Ten-Haaf), whereas all others significantly underestimated or overestimated RMR (p < 0.05) (Mifflin-St. Jeor, Owen, FAO/WHO/UNU, Nelson, Koehler). Of the five equations, large heterogeneity was observed for all (p < 0.05, I2 range: 80-93%) except the Ten-Haaf (p = 0.48, I2 = 0%). Significant differences between subgroups were observed for some but not all equations for sex, athlete status, fasting status prior to RMR testing, and RMR measurement methodology. Nine equations satisfied criteria for meta-analysis for precision. Of the nine equations, the Ten-Haaf was found to be the most precise, predicting 80.2% of participants to be within ± 10% of measured values with all others ranging from 40.7 to 63.7%.

CONCLUSION

Many RMR prediction equations have been used in athletes, which can differ widely in accuracy and precision. While no single equation is guaranteed to be superior, the Ten-Haaf (age, weight, height) equation appears to be the most accurate and precise in most situations. Some equations are documented as consistently underperforming and should be avoided. Choosing a prediction equation based on a population of similar characteristics (physical characteristics, sex, sport, athlete status) is preferable. Caution is warranted when interpreting RMR ratio of measured to predicted values as a proxy of energy availability from a single measurement.

PROSPERO REGISTRATION

CRD42020218212.

Cross-Validation of a New General Population Resting Metabolic Rate Prediction Equation Based on Body Composition

Current prediction equations for resting metabolic rate (RMR) were validated in a relatively small sample with high-individual variance. This study determined the accuracy of five common RMR equations and proposed a novel prediction equation, including body composition. A total of 3001 participants (41 ± 13 years; BMI 28.5 ± 5.5 kg/m²; 48% males) from nutrition clinics in Israel were measured by indirect calorimetry to assess RMR. Dual-energy X-ray absorptiometry were used to evaluate fat mass (FM) and free-fat mass (FFM). Accuracy and mean bias were compared between the measured RMR and the prediction equations. A random training set (75%, n = 2251) and a validation set (25%, n = 750) were used to develop a new prediction model. All the prediction equations underestimated RMR. The Cunningham equation obtained the largest mean deviation [-16.6%; 95% level of agreement (LOA) 1.9, -35.1], followed by the Owen (-15.4%; 95% LOA 4.2, -22.6), Mifflin-St. Jeor (-12.6; 95% LOA 5.8, -26.5), Harris-Benedict (-8.2; 95% LOA 11.1, -27.7), and the WHO/FAO/UAU (-2.1; 95% LOA 22.3, -26.5) equations. Our new proposed model includes sex, age, FM, and FFM and successfully predicted 73.5% of the explained variation, with a bias of 0.7% (95% LOA -18.6, 19.7). This study demonstrates a large discrepancy between the common prediction equations and measured RMR and suggests a new accurate equation that includes both FM and FFM.

Comparison between predicted and measured resting energy expenditures in Korean male collegiate soccer players

[Purpose]

This study aimed to evaluate the differences between predicted resting energy expenditure (REE), using fat-free mass (FFM)-based prediction equations, and measured REE in Korean male collegiate soccer players.

[Methods]

Fifteen male collegiate soccer players (18-21 years) participated in this study. The REE measurements were conducted using the Douglas bag method. Body composition was measured by dual-energy X-ray absorptiometry (DXA). The differences between the measured REE and predicted REE, using the five FFM-based REE equations, were analyzed using the t-test, calculation of errors, regression analysis, and the Bland-Altman method.

[Results]

The Cunningham (1980) and ten Haaf and Weijs (2014) equations showed significantly overestimated REE (1,808 ± 99 kcal/d, p <0.01; 1,838 ± 103 kcal/d, p <0.01; respectively), but the Owen (1988), Taguchi (2011), and Kim (2015) equations’ estimations were not significantly different from the measured REE (1,589 ± 106 kcal/d, 1,640 ± 124 kcal/d, and 1,622 ± 68 kcal/d, respectively). The Taguchi equation gave the best prediction of REE with the lowest constant error (-6 ± 125) and effect size (-0.05), and a non-significant proportional bias (p = 0.95).

[Conclusion]

The Taguchi equation is recommended for predicting REE in Korean collegiate soccer players. The selection process of a REE-prediction equation must take into consideration the target population’s characteristics. Future studies are recommended to evaluate the validity of the different FFM-based REE-prediction equations in various Korean athletes.

Proposal of a Mathematical Model to Monitor Body Mass over Time in Subjects on a Diet

Nowadays, slimming diet methodology works within a reduction of body mass using a decrease of dietary energy intake. However, there is no suitable method for understanding the dynamic process of body mass metabolic transformation over time. In the present paper, we have developed a biomathematic model to explain the temporal trend of body mass and its variations of people who have undergone a change in their diet using the solving equation of the model. Data relating to sex, age, body mass, and BMI were collected, and the compartmental model used to interpret the body mass trends was constructed by assuming that the mass results from the sum of the metabolic processes: catabolic, anabolic, distributive. The validation of the model was carried out by variance analysis both on the total and individual data sets. The results confirm that the trend of body mass and its variations over time depends on metabolic rates. These are specific to each individual and characterize the distribution of nutritional molecules in the various body districts and the processes catabolic, anabolic, distributive. Body mass and its variations are justified by the metabolic transformations of the nutritional quantities. This would explain why energetically equal diets can correspond to people of different body mass and that energy-different diets can correspond to people of body mass at all similar.

A Systematic Review of Literature on the Representation of Racial and Ethnic Minority Groups in Clinical Nutrition Interventions

The racial and ethnic disparities in diet-related chronic diseases are major concerns. This systematic review examines the extent to which diet-induced changes in health outcomes, such as cardiometabolic, inflammation, cancer, bone health, and kidney function outcomes, etc., have been reported and discussed by race or ethnicity in randomized trials with 2 or more diet arms that recruited both minority and non-Hispanic White groups. Databases (i.e., PubMed, Cochrane Library, and Web of Science) were searched up to August 2021. Thirty-four studies that discussed effects of defined dietary interventions on health outcomes by racial or ethnic minority group compared with non-Hispanic Whites were included in the systematic review (PROSPERO registration number: CRD42021229256). Acute trials and those with 1 diet arm that accounted for race or ethnicity in their analyses and studies that focused on a single racial or ethnic group were discussed separately. Most studies were conducted in Black compared with White adults testing effects of energy restriction, macronutrient modification, sodium reduction, or variations of the Dietary Approaches to Stop Hypertension (DASH) diet on cardiometabolic outcomes. There was limited focus on other minority groups. Evidence suggests greater blood pressure reduction for Black adults compared with Whites particularly with DASH (or similar) diets. Overall, there was limited consideration for group-specific eating patterns and diet acceptability. Overall risk of bias was low. With emerging precision nutrition initiatives that aim to optimize metabolic responses in population subgroups through tailored approaches, it is imperative to ensure adequate representation of racial and ethnic subgroups for addressing health disparities. Factors that help explain variability in responses such as socioecological context should be included and adequately powered. Given the racial and ethnic disparities in chronic diseases, studying the adoption, maintenance, and effectiveness of dietary interventions on health outcomes among different groups is critical for developing approaches that can mitigate diet-related health disparities.

RMR Ratio as a Surrogate Marker for Low Energy Availability

PURPOSE OF REVIEW

Low energy availability (EA) poses severe consequences to athlete performance and overall health. Suppressed resting metabolic rate (RMR) has been observed during periods of low EA. Thus, it has been suggested that the ratio of RMR measured via indirect calorimetry to predictive RMR using a standard predictive equation (RMR ratio) may be a useful assessment of EA in athletes. This review evaluated the use of RMR ratio as a surrogate marker for low EA in athletes and compared methodologies for measuring RMR ratio.

RECENT FINDINGS

Decreased RMR ratio in recent studies often correlates with signs of low EA; however, athletes with less severe cases of energy deficiency may not present with a low RMR ratio. Additionally, the methodology for RMR ratio measurements lacks standardization and varies in recent studies. Use of RMR ratio has promise as a complementary EA measurement when used in combination with other assessment tools.

Resting Energy Expenditure and Related Factors in 6- to 9-Year-Old Southern African Children of Diverse Population Groups

Lower resting energy expenditure (REE) may partially explain the disproportionate prevalence of overweight/obesity among black African women. As no previous studies have investigated the REE of Southern African (South. Afr.) children, we aimed to determine, by sex and population group, the REE of 6- to 9-year-old urban school children. In a cross-sectional study with quota sampling, REE was measured with indirect calorimetry (IC). Confounders considered were: body composition (BC) (fat-free mass (FFM), FFM index, fat mass (FM), FM index), assessed using multifrequency bioelectrical impedance analysis, and physical activity (PA) measured with a pedometer. Multivariate regression was used to calculate REE adjusted for phenotypes (BC, z-scores of weight-for-age, height-for-age, body mass index-for-age) and PA. Sex and population differences in REE were determined with two-way ANOVA. Ninety-four healthy children (59.6% girls; 52.1% black) with similar socioeconomic status and PA opportunities participated. Despite BC variations, sex differences in REE were not significant (41 kcal/day; P = 0.375). The REE of black participants was lower than of white (146 kcal/day; P = 0.002). When adjusted for FFM and HFA z-score, the differences in REE declined but remained clinically meaningful at 91 kcal/day (P = 0.039) and 82 kcal/day (P = 0.108), respectively. We recommend the development of population-specific REE prediction equations for South. Afr. children.

Accuracy of the Resting Energy Expenditure Estimation Equations for Healthy Women

Background: There exist several prediction equations for the estimation of resting energy expenditure (REE). However, none of these equations have been validated in the Chilean female population yet. The aims of this study are (1) to determine the accuracy of existing equations for prediction of REE and (2) to develop new equations in a sample of healthy Chilean women. Methods: A cross-sectional descriptive study was carried out on 620 Chilean women. The sample showed an age range between 18 and 73 years, a body mass index average of 28.5 ± 5.2 kg/m², and a prevalence of overweight and obesity of 41% and 33.2%, respectively. REE was measured by indirect calorimetry (REE_IC), which was used as the gold standard to determine the accuracy of twelve available REE prediction equations and to calculate alternative formulas for estimation of REE. Paired t-tests and Bland–Altman plots were used to know the accuracy of the estimation equations with REE_IC. At the same time, multiple linear regressions were performed to propose possible alternative equations. The analyses were carried out by age groups and according to nutritional status. Results: All the equations showed a tendency to overestimate REE, regardless of age or nutritional status. Overall, the Ireton-Jones equation achieved the highest mean percentage difference from REE_IC at 67.1 ± 31%. The alternative new equations, containing variables of body composition, reached a higher percentage of classification within ±10% of REE_IC. Conclusions: The available equations do not adequately estimate REE in this sample of Chilean women. Although they must be validated, the new formulas proposed show better adaptation to this Chilean sample.

Development of a New Equation for the Prediction of Resting Metabolic Rate in Sri Lankan Adults

Resting metabolic rate (RMR) is the key determinant of the energy requirement of an individual. Measurement of RMR by indirect calorimetry is not feasible in field settings and therefore equation-based calculations are used. Since a valid equation is not available for Sri Lankans, it is important to develop a new population-specific equation for field use. The study objective was to develop a new equation for the prediction of RMR in healthy Sri Lankans using a reference method, indirect calorimetry. RMR data were collected from fifty-seven (male 27) adults aged 19 to 60 years. They were randomly assigned to validation (n = 28) and cross-validation (n = 19) groups using the statistical package R (version 3.6.3). Height, weight, and RMR were measured. Multivariable fractional polynomials (MFP) were used to determine explanatory variables and their functional forms for the model. A variable shrinkage method was used to find the best fit predictor coefficients of the equation. The developed equation was cross-validated on an independent group. Weight and sex code (male = 1; female = 0) were identified as reliable independent variables. The new equation developed was RMR (kcal/day) = 284.5 + (13.2 x weight) + (133.0 x sex code). Independent variables of the prediction equation were able to predict 88.5% of the variance. Root mean square error (RMSE) of the prediction equation in validation and cross-validation was 88.11 kcal/day and 79.03 kcal/day, respectively. The equation developed in this study is suitable for predicting RMR in Sri Lankan adults.

Accuracy of resting metabolic rate prediction equations among healthy adults in Trinidad and Tobago

BACKGROUND

Over 50% of adults in Latin America and the Caribbean have a body mass index (BMI) ≥ 25 suggesting excess energy intakes relative to energy expenditure. Accurate estimation of resting metabolic rate (RMR), the largest component of total energy requirements, is crucial to strategies aimed at reducing the prevalence and incidence of overweight and obesity.

AIM

We evaluated the accuracies of established and locally developed RMR prediction equations (RMR_P) among adults.

METHODS

Four hundred adult volunteers ages 20 to 65 years had RMR measured (RMR_M) with a MedGem® indirect calorimeter according to recommended procedures. RMR_P were compared to RMR_M with values ± 10% of RMR_M deemed accurate. Anthropometry was measured using standard procedure. Linear regression with bootstrap analyses was used to develop local RMR_P equations based on anthropometric and demographic variables. The University of the West Indies Ethics Committee approved the study.

RESULTS

Males had higher mean absolute RMR (p < 0.001) but similar mean age-adjusted measured RMR per kg of body (20.9 vs. 21.5 kcals/day; p = 0.1) to females. The top performing established anthropometry-based RMR_P among participants by sex, physical activity (PA) level and BMI status subgroups were Mifflin-St Jeor, Owen, Korth, Harris-Benedict, and Livingston, while Johnstone, Cunningham, Müller (body composition (BC)), Katch and McArdle, Mifflin-St Jeor (BC) were the most accurate BC-based RMR_P. Locally developed RMR_P had accuracies comparable to their top-ranked established RMR_P counterparts.

CONCLUSIONS

Accuracies of established RMR_P depended on habitual PA level, BMI status, BC and sex. Furthermore, locally developed RMR_P provide useful alternatives to established RMR_P.