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.

Estimates of resting energy expenditure and total energy expenditure using predictive equations in adults with overweight and obesity: a systematic review with meta-analysis.

CONTEXT: Energy expenditure predictive equations can generate inaccurate estimates for overweight or obese individuals. OBJECTIVE: The objective of this review was to determine which predictive equations for resting energy expenditure (REE) and total energy expenditure (TEE) have the lowest bias and the highest precision in adults with overweight and obesity. DATA SOURCES: Searches were performed in January 2022 in MEDLINE, Web of Science, Scopus, CENTRAL, and the gray literature databases. DATA EXTRACTION: Meta-analyses were performed with equations included in more than 1 study. The DerSimonian and Laird random-effects model and the I2 statistic were used to quantify heterogeneity in the quantitative analyses. The Egger test was performed to assess potential publication biases, and metaregressions were conducted to explore the heterogeneity. Findings were presented separated by participants' body mass index classification (overweight and obesity). DATA ANALYSIS: Sixty-one studies were included. The FAO/WHO/UNU (1985) equation, which uses only body weight in its formula, showed the lowest bias in estimating REE (mean difference [MD] = 8.97 kcal; 95% CI = -26.99; 44.94). In the subgroup analysis for individuals with obesity, the Lazzer (2007) equation showed the lowest bias (MD = 4.70 kcal; 95% CI = -95.45; 104.86). The Harris-Benedict equation (1919) showed the highest precision values for individuals with overweight (60.65%) and for individuals with obesity (62.54%). Equations with body composition data showed the highest biases. The equation proposed by the Institute of Medicine (2005) showed the lowest bias (MD = -2.52 kcal; 95% CI = -125.94; 120.90) in estimating the TEE. Most analyses showed high heterogeneity (I2 > 90%). There was no evidence of publication bias. CONCLUSION: For individuals with overweight, the FAO/WHO/UNU (1985) and the Harris-Benedict equations (1919) showed the lowest bias and the highest precision in predicting the REE, respectively. For individuals with obesity, the Harris-Benedict equation (1919) showed the highest precision and the Lazzer equation (2007) showed the lowest bias. More studies are needed on predictive equations to estimate the TEE. SYSTEMATIC REVIEW REGISTRATION: PROSPERO registration no. CRD42021262969.

Association between sitting/lying down, standing, walking time and number of steps per day with the hormonal profile and resting energy expenditure of women with obesity living in a low-income region.

Reducing sedentary behaviour (SB) and increasing physical activity (PA) by sitting less and standing/walking more is advised to prevent chronic diseases. However, the mechanisms underlying this recommendation are not well established, especially in individuals with obesity living in low-income regions. The present study evaluated whether there are associations between PA indicators (PAI - standing time, walking time and the number of steps/d) and SB indicators (SBI - sitting/lying down time) with the hormonal profile and resting energy expenditure (REE) of adult women living in a low-income region. This is a cross-sectional study. We collected data on hormones (insulin resistance, leptin and thyroid axis), body composition (tetrapolar bioimpedance), REE (indirect calorimetry), and PAI and SBI (triaxial accelerometers, ActivPAL). Multivariable linear models adjusting for age and fat-free mass were performed. Fifty-eight women (mean age of 31 years and BMI of 33 kg/m2) were included. The mean sitting/lying down time and standing time were 16·08 and 5·52 h/d, respectively. Sitting/lying down time showed a direct association with free thyroxine (FT4) (ß = 0·56 ng/dl; 95 % CI = -1·10, -0·02). Standing time showed a direct association with FT4 (ß = 0·75 ng/dl; 95 % CI = 0·01; 1·48) and inverse association with free triiodothyronine (ß = -2·83 pg/ml 95 % CI = -5·56, -0·10). There were no associations between PAI and SBI with the REE, insulin resistance, leptin and thyroid-stimulating hormone. Thus, decreased SB is associated with thyroid hormones levels but not with REE, insulin resistance or leptin in women with obesity living in low-income regions.