Validity of predictive equations to estimate RMR in females with varying BMI

Expert-Reviewed Nutritional Guidance for Adults with Spinal Cord Injury: A Delphi Study

Background/Objectives: Nutritional needs for people with chronic spinal cord injury (SCI) are inadequately addressed due to the lack of comprehensive evidence and scattered research. We established a consensus-based framework for addressing the nutritional needs of community-dwelling adults with chronic SCI who can ingest food orally. Methods: A web-based Delphi design was employed to ascertain an expert consensus. The Delphi panel consisted of physicians, registered dietitians (RDs), and researchers knowledgeable in SCI and nutrition. Informed by a literature review, 18 nutrition statements were rated by 15 panelists. The survey included statements about SCI-specific dietary energy assessments and macro- and micronutrients. Results: The response rate for the panel (N = 15) was 100%. Consensus levels, scores, stability levels, and response numbers were documented for each statement. The statements received consensus scores ranging from 4.14 to 8.13 on a 9-point Likert scale. Alternative expert comments and suggestions were also provided for each statement. Conclusion: Engaging a diverse panel of experts, the real-time Delphi process yielded expert-reviewed nutrition statements based on an extensive literature review and expert opinions. The rated statements contribute to the ongoing dialogue in SCI-specific nutrition, providing a practical resource for healthcare professionals working with adults with chronic SCI.

Modelling metabolic performance in paediatric obstructive sleep disordered breathing: A case-control study

Paediatric obstructive sleep disordered breathing (OSDB) has a considerable impact on cardiovascular physiology, but the consequences on children's basal metabolism and response to exercise are far from being known. The objective was to propose model estimations for paediatric OSDB metabolism at rest and during exercise. A retrospective case-control analysis of data from children submitted to otorhinolaryngology surgery was performed. The heart rate (HR) was measured, while oxygen consumption (VO2) and energy expenditure (EE) at rest and during exercise were obtained using predictive equations. The results for the patients with OSDB were compared with controls. A total of 1256 children were included. A total of 449 (35.7%) had OSDB. The patients with OSDB showed a significantly higher resting heart rate (94.55 ± 15.061 bpm in OSDB vs. 92.41 ± 15.332 bpm in no-OSDB, p = 0.041). The children with OSDB showed a higher VO2 at rest (13.49 ± 6.02 mL min-1kg-1 in OSDB vs. 11.55 ± 6.83 mL min-1kg-1 in no-OSDB, p = 0.004) and a higher EE at rest (67.5 ± 30.10 cal min-1kg-1 in OSDB vs. 57.8 + 34.15 cal min-1kg-1 in no-OSDB, p = 0.004). At maximal exercise, patients with OSDB showed a lower VO2max (33.25 ± 5.82 mL min-1kg-1 in OSDB vs. 34.28 ± 6.71 in no-OSDB, p = 0.008) and a lower EE (166.3 ± 29.11 cal min-1kg-1 in OSDB vs. 171.4 ± 33.53 cal min-1kg-1 in no-OSDB, p = 0.008). The VO2/EE increment with exercise (Δ VO2 and Δ EE) was lower in OSDB for all exercise intensities (p = 0.009). This model unveils the effect of paediatric OSDB on resting and exercise metabolism. Our findings support the higher basal metabolic rates, poorer fitness performance, and cardiovascular impairment found in children with OSDB.

Development of New Predictive Equations for the Resting Metabolic Rate (RMR) of Women with Lipedema

This study aimed to develop a novel predictive equation for calculating resting metabolic rate (RMR) in women with lipedema. We recruited 119 women diagnosed with lipedema from the Angiology Outpatient Clinic at Wroclaw Medical University, Poland. RMR was assessed using indirect calorimetry, while body composition and anthropometric measurements were conducted using standardized protocols. Due to multicollinearity among predictors, classical multiple regression was deemed inadequate for developing the new equation. Therefore, we employed machine learning techniques, utilizing principal component analysis (PCA) for dimensionality reduction and predictor selection. Regression models, including support vector regression (SVR), random forest regression (RFR), and k-nearest neighbor (kNN) were evaluated in Python's scikit-learn framework, with hyperparameter tuning via GridSearchCV. Model performance was assessed through mean absolute percentage error (MAPE) and cross-validation, complemented by Bland-Altman plots for method comparison. A novel equation incorporating body composition parameters was developed, addressing a gap in accurate RMR prediction methods. By incorporating measurements of body circumference and body composition parameters alongside traditional predictors, the model's accuracy was improved. The segmented regression model outperformed others, achieving an MAPE of 10.78%. The proposed predictive equation for RMR offers a practical tool for personalized treatment planning in patients with lipedema.

The accuracy of ten common resting metabolic rate prediction equations in men and women collegiate athletes

Predictive resting metabolic rate (RMR) equations are widely used to determine total daily energy expenditure (TDEE). However, it remains unclear whether these predictive RMR equations accurately predict TDEE in the athletic populations. The purpose of this study was to examine the accuracy of 10 commonly used RMR prediction equations (Cunningham, De Lorenzo, Freire, Harris-Benedict, Mifflin St. Jeor, Nelson, Owen, Tinsley, Watson, Schofield) in collegiate men and women athletes. One-hundred eighty-seven National Collegiate Athletic Association Division III men (n = 97) and women (n = 90) athletes were recruited to participate in one day of metabolic testing. RMR was measured using indirect calorimetry and body composition was analyzed using air displacement plethysmography. A repeated measures ANOVA with Bonferroni post hoc analyses was selected to determine mean differences between measured and predicted RMR. Linear regression analysis was used to assess the accuracy of each RMR prediction method (p<0.05). All prediction equations significantly underestimated RMR (p<0.001), although there was no difference between the De Lorenzo and Watson equations and measured RMR (p = 1.00) for women, only. In men, the Tinsley and Freire equations were the most agreeable formulas with the lowest root-mean-square prediction error value of 404 and 412 kcals, respectively. In women, the De Lorenzo and Watson equations were the most agreeable equations with the lowest root-mean-squared error value of 171 and 211 kcals, respectively. The results demonstrate that such RMR equations may underestimate actual energy requirements of athletes and thus, practitioners should interpret such values with caution.Highlights All prediction equations significantly underestimated RMR in men athletes.All prediction equations, except for the De Lorenzo and Watson equations, significantly underestimated RMR in women athletes.Although a significant underestimation of RMR in men athletes, the Freire and Tinsley equations were the most agreeable prediction equations.In women athletes, the De Lorenzo and Watson equations were the most agreeable prediction equations.

Position statement on nutrition therapy for overweight and obesity: nutrition department of the Brazilian association for the study of obesity and metabolic syndrome (ABESO-2022)

Obesity is a chronic disease resulting from multifactorial causes mainly related to lifestyle (sedentary lifestyle, inadequate eating habits) and to other conditions such as genetic, hereditary, psychological, cultural, and ethnic factors. The weight loss process is slow and complex, and involves lifestyle changes with an emphasis on nutritional therapy, physical activity practice, psychological interventions, and pharmacological or surgical treatment. Because the management of obesity is a long-term process, it is essential that the nutritional treatment contributes to the maintenance of the individual's global health. The main diet-related causes associated with excess weight are the high consumption of ultraprocessed foods, which are high in fats, sugars, and have high energy density; increased portion sizes; and low intake of fruits, vegetables, and grains. In addition, some situations negatively interfere with the weight loss process, such as fad diets that involve the belief in superfoods, the use of teas and phytotherapics, or even the avoidance of certain food groups, as has currently been the case for foods that are sources of carbohydrates. Individuals with obesity are often exposed to fad diets and, on a recurring basis, adhere to proposals with promises of quick solutions, which are not supported by the scientific literature. The adoption of a dietary pattern combining foods such as grains, lean meats, low-fat dairy, fruits, and vegetables, associated with an energy deficit, is the nutritional treatment recommended by the main international guidelines. Moreover, an emphasis on behavioral aspects including motivational interviewing and the encouragement for the individual to develop skills will contribute to achieve and maintain a healthy weight. Therefore, this Position Statement was prepared based on the analysis of the main randomized controlled studies and meta-analyses that tested different nutrition interventions for weight loss. Topics in the frontier of knowledge such as gut microbiota, inflammation, and nutritional genomics, as well as the processes involved in weight regain, were included in this document. This Position Statement was prepared by the Nutrition Department of the Brazilian Association for the Study of Obesity and Metabolic Syndrome (ABESO), with the collaboration of dietitians from research and clinical fields with an emphasis on strategies for weight loss.

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.

Body composition affects the accuracy of predictive equations to estimate resting energy expenditure in older adults: An exploratory study

BACKGROUND

To investigate the accuracy of ten different predictive equations to estimate resting energy expenditure (REE) in a sample of Brazilian older adults and develop a predictive equation for estimating REE based on body composition data.

METHODS

A cross-sectional study with thirty-eight Brazilian older adults aged 60-84 years, who had their REE measured by indirect calorimetry (IC) and BC assessed by dual-energy x-ray absorptiometry (DXA). REE was compared to the estimation of ten predictive equations, and the differences between BC and anthropometric-based equations were investigated using Bland-Altman plots and Lin's concordance correlation. Accuracy was evaluated considering ±10% of the ratio between estimated and measured REE.

RESULTS

The sample was composed of 57.9% men, with a mean age of 68.1 (5.8) years, and a mean REE by IC of 1528 (451) kcal. The highest accuracy was 47.4% obtained by Luhrmann and Fredrix equations, and the lowest accuracy was 13.2% reached by Weigle equation. In general, the proportion of underestimation was higher than overestimation. All anthropometric-based equations presented a good agreement with REE from IC. For those equations derived from BC, however, three of them reached only a moderate agreement. In terms of accuracy, all equations presented lower than 50% of accurate prediction of REE.

CONCLUSIONS

In this sample of older adults, previous predictive equations to estimate REE did not show good accuracy, and those based on BC presented even worse results, showing that changes in BC related to aging could impact the accuracy of these equations.

Revised Harris-Benedict Equation: New Human Resting Metabolic Rate Equation

This paper contains a revision of the Harris-Benedict equations through the development and validation of new equations for the estimation of resting metabolic rate (RMR) in normal, overweight, and obese adult subjects, taking into account the same anthropometric parameters. A total of 722 adult Caucasian subjects were enrolled in this analysis. After taking a detailed medical history, the study enrolled non-hospitalized subjects with medically and nutritionally controlled diseases such as diabetes mellitus, cardiovascular disease, and thyroid disease, excluding subjects with active infections and pregnant or lactating women. Measurement of somatometric characteristics and indirect calorimetry were performed. The values obtained from RMR measurement were compared with the values of the new equations and the Harris-Benedict, Mifflin-St Jeor, FAO/WHO/UNU, and Owen equations. New predictive RMR equations were developed using age, body weight, height, and sex parameters. RMR males: (9.65 × weight in kg) + (573 × height in m) - (5.08 × age in years) + 260; RMR females: (7.38 × weight in kg) + (607 × height in m) - (2.31 × age in years) + 43; RMR males: (4.38 × weight in pounds) + (14.55 × height in inches) - (5.08 × age in years) + 260; RMR females: (3.35 × weight in pounds) + (15.42 × height in inches) - (2.31 × age in years) + 43. The accuracy of the new equations was tested in the test group in both groups, in accordance with the resting metabolic rate measurements. The new equations showed more accurate results than the other equations, with the equation for men (R-squared: 0.95) showing better prediction than the equation for women (R-squared: 0.86). The new equations showed good accuracy at both group and individual levels, and better reliability compared to other equations using the same anthropometric variables as predictors of RMR. The new equations were created under modern obesogenic conditions, and do not exclude individuals with regulated (dietary or pharmacological) Westernized diseases (e.g., cardiovascular disease, diabetes, and thyroid disease).

Predictive Equations Overestimate Resting Metabolic Rate in Young Chilean Women with Excess Body Fat

Underestimating/overestimating resting metabolic rate (RMR) affects energy prescription. The objective was to compare RMR by indirect calorimetry (RMR IC) and RMR estimated by predictive equations in women with excess body fat. This was an analytical cross-sectional study with 41 women aged 18-28 with overnutrition according to body composition. The RMR IC was measured and RMR estimated using the FAO/WHO/UNU (1985), FAO/WHO/UNU (2004), Harris-Benedict, and Mifflin-St Jeor equations. The percentage of adequacy (90-110%), overestimation (>110%), and underestimation (<90%) were evaluated for RMR IC. Data were described by percentiles because of non-normal distribution according to the Shapiro-Wilk test. The Kruskal-Wallis test and Bland-Altman analysis were applied at a significance level of α < 0.05. The RMR IC was 1192 and 1183 calories/day (p = 0.429) in women with obesity and overweight, respectively. The FAO/WHO/UNU (1985), FAO/WHO/UNU (2004), Harris-Benedict, and Mifflin-St Jeor equations overestimated the RMR IC by 283.2, 311.2, 292.7, and 203.0 calories/day and by 296.7, 413.8, 280.0, and 176.6 calories/day for women with overweight and obesity (p < 0.001), respectively. The Harris-Benedict adjusted weight (0.5) equation underestimated RMR IC by 254.7 calories/day. The predictive equations overestimated RMR IC in women with excess body fat. The Mifflin-St Jeor equation showed less overestimation and better adequacy, but was not exempt from inaccuracy.

Erratum: Validity of predictive equations to estimate RMR in females with varying BMI - CORRIGENDUM

[This corrects the article DOI: 10.1017/jns.2020.11.].