New predictive equations for estimating resting energy expenditure in subjects with normal weight and overweight

Association of resting energy expenditure with phase angle in hospitalized older patients: a cross-sectional analysis

BACKGROUND/OBJECTIVES

Resting energy expenditure (REE) constitutes the largest component of total energy expenditure and undergoes an age-related decline that is unexplained by decreased fat-free mass. Phase angle (PhA) is a cellular health indicator that is possibly associated with REE. We investigated the association of REE and PhA in hospitalized older adults.

SUBJECTS/METHODS

This single-center, cross-sectional analysis utilized the baseline data from a prospective longitudinal study and included 131 eligible patients aged ≥70 years. The REE was measured using indirect calorimetry, and PhA and body composition were assessed using bioelectrical impedance. The association between REE, PhA, and body composition was examined, and REE was compared using previously reported PhA cutoff values.

RESULTS

In this cohort with a mean (±standard deviation) age of 87.4 (±7.0) years, 34.4% of the participants were men. REE and PhA correlated strongly (r: 0.562, p < 0.001) and significantly after adjusting for age and sex (r: 0.433, p < 0.001). Multivariate analysis showed a significant independent association between REE and PhA and skeletal muscle mass (standardized β [95% CI]; 28.072 [2.188-53.956], p = 0.035) without any significant interaction between PhA and age on REE. The low PhA group had a significantly lower REE (kcal/day; 890 [856-925] vs. 1077 [1033-1122], p < 0.001), and this remained significant after adjusting for age, sex, and skeletal muscle mass index.

CONCLUSIONS

PhA is associated with REE in older adults. Adjusting REE calculation algorithms based on PhA values and correcting predicted REE according to PhA may aid in determining more accurate energy requirements.

Longitudinal estimates of resting energy expenditure using predictive equations in individuals with excess weight after weight loss: A systematic review with meta-analysis

BACKGROUND & AIMS

To determine which resting energy expenditure (REE) predictive equation has the lowest bias in the aggregate level in individuals with excess weight during weight loss interventions.

METHODS

Searches were performed in MEDLINE, Web of Science, Scopus, CENTRAL and gray literature databases. Longitudinal studies on weight loss interventions which evaluated REE by predictive equations compared to that measured by indirect calorimetry in adults with excess weight at different follow-up times were included. Meta-analyses were performed with the differences between biases of predictive equations of the REE at the different follow-up times of weight loss.

RESULTS

Of the total of 2178 occurrences found in the databases, only eight studies were included. The Harris-Benedict (1919) equation showed the smallest differences between bias up to the third month (MD = 103.33 kcal; 95%CI = -39.01; 245.67), in the sixth month (MD = 59.16 kcal; 95%CI = 8.74; 109.57) and at the 12th month (MD = -71.41 kcal; 95%CI = -150.38; 7.55) of weight loss follow-up. Weight loss does not seem to have an effect on bias at different follow-up times.

CONCLUSION

Harris-Benedict (1919) equation seems to be the most adequate to assess the REE of individuals with excess weight during weight loss. However, the finding of large estimated predictive intervals may indicate that predictive equations may not be handy tools for individuals losing and regaining weight due to changes other than body weight.

Prediction of resting energy expenditure in Italian older adults with severe obesity

Background

In the last decade a large number of studies proposed and/or validated equations to estimate the Resting Energy Expenditure (REE) in adults and/or older adults, however, no equation currently available showed good accuracy for older adults with severe obesity. Thus, this study aimed to develop and validate new predictive equations for REE, based on data from the indirect calorimetry, in Italian older adults with severe obesity.

Methods

A retrospective study was as conducted with 764 Caucasian older adults with severe obesity (age range: 60-74 years and BMI ≥ 35 kg/m/²). Four models were used to test the accuracy of anthropometry and body composition variables in multivariable prediction of REE. All models were derived by stepwise multiple regression analysis using a calibration group of 382 subjects [295 females and 87 males] and the equations were cross-validated in the remaining 382 subjects [295 females and 87 males] as validation group. The new prediction equations and the other published equations were tested using the Bland-Altman method. Prediction accuracy was defined as the percentage of subjects whose REE was predicted within ± 10% of measured REE.

Results

All the equations analyzed predicted higher energy requirements for males than females, and most of them underestimated the energy requirement values of our sample. The highest accuracy values were observed in the new equations, with 62% in the anthropometric model and 63% in the body composition model.

Conclusion

Although the accuracy of our equations was slightly higher in comparison with the other taken into consideration, they cannot be considered completely satisfactory for predicting REE in Italians older adults with severe obesity. When predicting equations cannot guarantee precise or acceptable values of REE, the use of indirect calorimetry (if available) should be always recommended, especially in clinical practice.

Dietary Habits Are Related to Phase Angle in Male Patients with Non-Small-Cell Lung Cancer

Introduction: Lung cancer constitutes the most common cause of cancer death. Phase angle (PhA) has been related to lung cancer prognosis, which implies that the identification of dietary or other factors that could predict or modify PhA may have beneficial effects. Νutritional interventions have been linked with positive changes in PhA in certain types of cancer. Aim: The present study aimed to investigate the relationships between dietary habits/nutrition and PhA in NSCLC patients. Methods: The sample consisted of 82 male patients with non-small-cell lung cancer (NSCLC; stage IV) from the 'Theageneio' Cancer Hospital (Thessaloniki, Greece). Several parameters were assessed, such as body mass index (BMI), lean mass, PhA, Mediterranean diet score (MedDietScore), dietary patterns, smoking, resting metabolic rate, resting oxygen consumption (VO₂), ventilation rate, and physical activity. Results: According to our results, a dietary pattern rich in potatoes and animal proteins (meat and poultry) was a significant determinant of PhA (B ± SE, p: 0.165 ± 0.08, p = 0.05) in multiple linear regression models after adjusting for age, smoking, lean tissue, and MedDietScore. Conclusion: In conclusion, dietary patterns may affect PhA, suggesting the crucial role of protein in cancer management and the prevention of sarcopenia.

Evaluation of the Nutritional Status of Gaucher Disease Type I Patients under Enzyme Replacement Treatment

(1) Background: Gaucher disease (GD) is a rare lysosomal storage disease. The few studies analyzing Resting Energy Expenditure (REE) in GD involved mainly untreated patients and supported a hypermetabolic condition possibly due to the associated inflammatory state. Definitive conclusions could not be drawn also because of the heterogeneity and the small size of the samples investigated. In order to expand current knowledge concerning, in particular the condition of patients under Enzyme Replacement Therapy (ERT), we evaluated the nutritional status of a relatively large sample of GD patients followed at Federico II University Hospital in Naples, Italy. (2) Methods: The study, having a cross-sectional design and involving 26 patients on ERT, included routine biochemical analyses, bioelectrical impedance analysis, indirect calorimetry, and administration of food frequency and physical activity questionnaires. The results in GD patients were compared with those from an appropriate control group. (3) Results: GD patients had normal biochemical parameters in 80% of cases, except for HDL-cholesterol, consumed a hyper-lipidic diet, and had a 60% prevalence of overweight/obesity. Body composition did not differ between patients and controls; however, measured REE was significantly lower than predicted and was reduced in comparison with the healthy controls. (4) Conclusions: This study provided novel elements to the present knowledge about REE and the nutritional status of GD patients under ERT. Its results warrant confirmation in even larger GD population samples and a more in-depth investigation of the long-term effects of treatment superimposed on the basic pathophysiological disease condition.

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

Humans acquire energy from the environment for survival. A central question for nutritional sciences is how much energy is required to sustain cellular work while maintaining an adequate body mass. Because human energy balance is not exempt from thermodynamic principles, the energy requirement can be approached from the energy expenditure. Conceptual and technological advances have allowed understanding of the physiological determinants of energy expenditure. Body mass, sex, and age are the main factors determining energy expenditure. These factors constitute the basis for predictive equations for resting (REE) and total (TEE) energy expenditure in healthy adults. These equations yield predictions that differ up to ~400 kcal/d for REE and ~550 kcal/d for TEE. Identifying additional factors accounting for such variability and the most valid equations appears relevant. This review used novel approaches based on mathematical modeling of REE and analyses of the data from which REE predictive equations were generated. As for TEE, R² and SE were considered because only a few predictive equations are available. From these analyses, Oxford's and Plucker's equations appear valid for predicting REE and TEE in adults, respectively.

Resting energy expenditure in elite athletes: development of new predictive equations based on anthropometric variables and bioelectrical impedance analysis derived phase angle

Background

An accurate estimation of athletes’ energy needs is crucial in diet planning to improve sport performance and to maintain an appropriate body composition. This study aimed to develop and validate in elite athletes new equations for estimating resting energy expenditure (REE) based on anthropometric parameters as well as bioimpedance analysis (BIA)-derived raw variables and to validate the accuracy of selected predictive equations.

Methods

Adult elite athletes aged 18–40 yrs were studied. Anthropometry, indirect calorimetry and BIA were performed in all subjects. The new predictive equations were generated using different regression models. The accuracy of the new equations was assessed at the group level (bias) and at the individual level (precision accuracy), and then compared with the one of five equations used in the general population or three athletes-specific formulas.

Results

One-hundred and twenty-six male athletes (age 26.9 ± 9.1 yrs; weight 71.3 ± 10.9 kg; BMI 22.8 ± 2.7 kg/m²) from different sport specialties were randomly assigned to the calibration (n = 75) or validation group (n = 51). REE was directly correlated with individual characteristics, except for age, and raw BIA variables. Most of the equations from the literature were reasonably accurate at the population level (bias within ±5%). The new equations showed a mean bias −0.3% (Eq. A based on anthropometric parameters) and −0.6% (Eq. B based on BIA-derived raw variables). Precision accuracy (individual predicted-measured differences within ±5%) was ~75% in six out of eight of the selected equations and even higher for Eq. A (82.4%) and Eq. B (92.2%).

Conclusion

In elite athletes, BIA-derived phase angle is a significant predictor of REE. The new equations have a very good prediction accuracy at both group and individual levels. The use of phase angle as predictor of REE requires further research with respect to different sport specialties, training programs and training level.