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.

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

OBJECTIVE: The aim of this study was to develop and validate new predictive equations for estimating resting energy expenditure (REE) in subjects with normal weight and overweight, considering anthropometric parameters as well as raw variables from bioimpedance analysis (BIA). METHODS: Adult participants with normal weight and overweight were recruited and randomly split into calibration and validation groups. Indirect calorimetry (IC) and BIA were performed in all subjects. New predictive equations were developed using the following models: model 1 with age, weight, stature, and body mass index (BMI) as predictors; and model 2: model 1 + raw BIA variables (bioimpedance index and phase angle). The accuracy of the new equations at both the group (bias) and individual (within ±10%) levels was tested in the validation group. Three published predictive equations were also compared, with the REE values measured by IC. RESULTS: A total of 2483 adults were included for developing and validating the new equations. All selected formulas, including the new ones, showed a bias of <5% in estimating REE at the group level. Accuracy at the individual level was slightly higher for the new equations, especially for the equation based on raw BIA variables (men = 70.3%; women = 72.3%). CONCLUSIONS: Compared to the equations in the literature, the new equations showed good accuracy at both the group and individual levels, with a slight improvement in individual accuracy for the formula including raw BIA variables. However, future research is required to verify the role of the raw BIA variables in predicting REE in subjects with normal weight and overweight.

Long-Term Outcomes from a 10-Year Follow-Up of Women Living with a Restrictive Eating Disorder: A Brief Report.

BACKGROUND: This study aimed to evaluate several socio-demographic and long-term clinical outcomes in a cohort of women living with a restrictive eating disorder. METHODS: Patients were asked to fill in a general data collection form aiming to investigate their current conditions and to attend the outpatient unit for a 10-year follow-up clinical and laboratory evaluation. RESULTS: Forty-four patients completed the follow-up general data collection form and 20 agreed to attend the outpatient unit for the 10 year-follow-up evaluation. In total, 52% of patients were single, 55% had achieved a university degree, and 55% had steady employment. After 10 years, there was a clear improvement in biochemical markers, but cholesterol levels were still slightly high. The prevalence of osteopenia in the whole sample was 70% when measured on the lumbar column and 20% on the total body, while osteoporosis was found in 10% of patients and only on the lumbar column. CONCLUSION: According to the collected data, women with a history of restrictive eating disorders appear to re-adapt well to social life by obtaining the level of their unaffected peers in terms of education and employment.

New Predictive Equations for Estimating Resting Energy Expenditure in Adults With Crohn's Disease.

BACKGROUND: Increased resting energy expenditure (REE) has been hypothesized to be a potential cause of weight loss in individuals with Crohn's disease (CD). This study aimed to develop and validate new predictive equations for estimating REE in adults with CD. METHODS: Adults, ages 18-65 years, with CD were recruited. Anthropometry, indirect calorimetry, and bioimpedance analysis were performed in all patients. Disease activity was assessed by Crohn's Disease Activity Index. The new predictive equations were generated using different regression models. Prediction accuracy of the new equations was assessed and compared with the most commonly used equations. RESULTS: A total of 270 CD patients (159 males, 111 females) were included and randomly assigned to the calibration (n = 180) and validation groups (n = 90). REE was directly correlated with weight and bioimpedance index, whereas the relation with both age and disease activity was inverse. The new equations were suitable for estimating REE at population level (bias: -0.2 and -0.3, respectively). Individual accuracy was good in both models (≥80%, respectively), especially in females; and similar results were shown by some of the selected equations. But, when accuracy was set within ±5%, the new equations gave the highest prediction. CONCLUSION: The new, disease-specific, equations for predicting REE in individuals with CD give a good prediction accuracy as far as those proposed in the literature for the general population. However, the new ones performed better at the individual level. Further studies are needed to verify the reliability and usefulness of these new equations.