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

The Influence of Intermittent Fasting on Selected Human Anthropometric Parameters

Background: Intermittent fasting may be an effective tool for weight loss, but it is still unclear from previous studies to date whether it is as effective as a continuous energy restriction in terms of reducing adipose tissue and whether it leads to unwanted muscle loss. Objectives: The aim of this study was to compare the effect of intermittent fasting (IF) with continuous energy restriction (CER) on the body weight and body composition and to assess the effect of intermittent fasting also in isolation from the energy restriction. Methods: After completion of a three-week dietary intervention, differences in the weight loss and differences in the body composition were compared between three groups. The first group consumed 75% of their calculated energy intake requirements in a six-hour time window. The second group consumed 75% of their calculated energy intake requirements without a time window and the third group consumed 100% of their calculated energy intake requirements in a six-hour time window. The changes in the weight and body composition were assessed by BIA. Results: Of the 95 randomized participants, 75 completed the intervention phase of the study. The highest mean weight loss was achieved by the IF with ER (energy restriction) group (2.3 ± 1.4 kg), followed by the CER group (2.2 ± 1.1 kg); the difference between the groups did not reach statistical significance. The lowest mean weight loss was observed in the IF without ER group (1.1 ± 1.2 kg), the difference reaching statistical significance compared to the IF with ER (p=0.003) and CER (p=0.012) groups. The highest mean adipose tissue loss was observed in the CER group (1.5 ± 1.2 kg) followed by the IF with ER group (1.3 ± 1.1 kg), with no statistically significant differences between the groups. A mean adipose tissue loss was found in the IF without ER group (0.9 ± 1.1 kg) with no statistically significant differences compared to the IF with ER and CER groups. The highest mean fat-free mass loss was found in the IF with ER group (1.1 ± 1.0 kg), followed by the CER group (0.65 ± 0.91 kg) with no statistically significant differences. The IF without ER group showed the lowest mean fat-free mass loss (0.2 ± 1.3 kg), which reached statistical significance compared to the IF with ER group (p=0.027). Conclusion: The results showed a comparable effect in the weight loss and body fat reduction regardless of the timing of the food intake. The diet quality, together with the energy intake, appeared to be one of the most important factors influencing the body composition.

Development and validation of age-specific predictive equations for total energy expenditure and physical activity levels for older adults

BACKGROUND

Predicting energy requirements for older adults is compromised by the underpinning data being extrapolated from younger adults.

OBJECTIVES

To generate and validate new total energy expenditure (TEE) predictive equations specifically for older adults using readily available measures (age, weight, height) and to generate and test new physical activity level (PAL) values derived from 1) reference method of indirect calorimetry and 2) predictive equations in adults aged ≥65 y.

METHODS

TEE derived from "gold standard" methods from n = 1657 (n = 1019 females, age range 65-90 y), was used to generate PAL values. PAL ranged 1.28-2.05 for males and 1.26-2.06 for females. Physical activity (PA) coefficients were also estimated and categorized (inactive to very active) from population means. Nonlinear regression was used to develop prediction equations for estimating TEE. Double cross-validation in a randomized, sex-stratified, age-matched 50:50 split, and leave one out cross-validation were performed. Comparisons were made with existing equations.

RESULTS

Equations predicting TEE using the Institute of Medicine method are as follows: For males, TEE = -5680.17 - 17.50 × age (years) + PA coefficient × (6.96 × weight [kilograms] + 44.21 × height [centimeters]) + 1.13 × resting metabolic rate (RMR) (kilojoule/day). For females, TEE = -5290.72 - 8.38 × age (years) + PA coefficient × (9.77 × weight [kilograms] + 41.51 × height [centimeters]) + 1.05 × RMR (kilojoule/day), where PA coefficient values range from 1 (inactive) to 1.51 (highly active) in males and 1 to 1.44 in females respectively. Predictive performance for TEE from anthropometric variables and population mean PA was moderate with limits of agreement approximately ±30%. This improved to ±20% if PA was adjusted for activity category (inactive, low active, active, and very active). Where RMR was included as a predictor variable, the performance improved further to ±10% with a median absolute prediction error of approximately 4%.

CONCLUSIONS

These new TEE prediction equations require only simple anthropometric data and are accurate and reproducible at a group level while performing better than existing equations. Substantial individual variability in PAL in older adults is the major source of variation when applied at an individual level.

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.

Estimates of resting energy expenditure using predictive equations in adults with severe burns: A systematic review and meta-analysis

BACKGROUND

Many equations to estimate the resting energy expenditure (REE) of patients with burns are currently available, but which of them provides the best guide to optimize nutrition support is controversial. This review examined the bias and precision of commonly used equations in patients with severe burns.

METHODS

A systematic search of the PubMed, Web of Science, Embase, and Cochrane Library databases was undertaken on June 1, 2023, to identify studies comparing predicted REE (using equations) with measured REE (by indirect calorimetry [IC]) in adults with severe burns. Meta-analyses of bias and calculations of precisions were performed in each predictive equation, respectively.

RESULTS

Nine eligible studies and 12 eligible equations were included. Among the equations, the Toronto equation had the lowest bias (26.1 kcal/day; 95% CI, -417.0 to 469.2), followed by the Harris-Benedict equation × 1.5 (1.5HB) and the Milner equation. The Ireton-Jones equation (303.4 kcal/day; 95% CI, 224.5-382.3) acceptably overestimated the REE. The accuracy of all of the equations was <50%. The Ireton-Jones equation had the relatively highest precision (41.2%), followed by the 1.5HB equation (37.0%) and the Toronto equation (34.7%).

CONCLUSION

For adult patients with severe burns, all of the commonly used equations for the prediction of REE are inaccurate. It is recommended to use IC for accurate REE measurements and to use the Toronto equation, 1.5HB equation, or Ireton-Jones equation as a reference when IC is not available. Further studies are needed to propose more accurate REE predictive models.

Analysis of Morphological Parameters and Body Composition in Adolescents with and without Intellectual Disability

Compared to the tremendous volume of studies focusing on children and teenagers without disabilities, research regarding weight and body composition among young populations with an intellectual disability is relatively rare. Their number further decreases when we refer to specific age groups with intellectual deficits, such as children and adolescents younger than 18. In addition, studies are even scarcer when we wish to compare groups of subjects with different degrees of intellectual disability by gender. This study has a constative nature. The research sample comprises 212 subjects-girls and boys with an average age of 17.7 ± 0.2, divided into six groups by gender and type of intellectual disability. The parameters considered within the study include anthropometrical data and body composition determined using a professional device (Tanita MC 580 S). The findings of this study highlight the impact of intellectual disability on body composition in this age category. We hope it will help develop efficient strategies, recommendations, and intervention plans to ensure active participation in physical activities and categorisation within the optimal parameters of body composition indicators.