The Energy Balance Study: The Design and Baseline Results for a Longitudinal Study of Energy Balance (original) (raw)
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Issues in Measuring and Interpreting Energy Balance and Its Contribution to Obesity
Current Obesity Reports, 2019
Purpose of Review Obesogenic environment challenges individuals' ability to preserve energy homeostasis, leading to weight gain. To understand how this energy imbalance proceeds, several methods and analytical procedures to determine energy intake and expenditure are currently available. However, these methods and procedures are not exempt from issues that may lead to equivocal conclusions. Our purpose herein is to discuss major issues involved in energy balance assessment. Recent Findings Measurement of energy intake mostly relies on self-report methods that provide inaccurate data. In contrast, determination of energy expenditure is more accurate as long as methodological and analytical issues are correctly addressed. Summary Accurate measurements of energy expenditure can be obtained with the current methods once issues in measuring and interpreting data are correctly addressed. However, development of new technologies to measure energy intake is imperative to further understand the small and chronic energy imbalance leading to obesity.
Energy requirements in nonobese men and women: results from CALERIE
American Journal of Clinical Nutrition, 2014
Background: The energy intake necessary to maintain weight and body composition is called the energy requirement for weight maintenance and can be determined by using the doubly labeled water (DLW) method. Objective: The objective was to determine the energy requirements of nonobese men and women in the Comprehensive Assessment of Long-Term Effects of Reducing Intake of Energy 2 study. Design: Energy requirements were determined for 217 healthy, weight-stable men and women [aged .21 to ,50 y; 70% female, 77% white; body mass index (BMI; in kg/m 2 ) 22 to ,28; 52% overweight] over 28 d with 2 consecutive 14-d DLW assessments in addition to serial measures of body weight and fat-free mass and fat mass by dual-energy X-ray absorptiometry. Energy intake and physical activity were also estimated by self-report over $6 consecutive d in each DLW period. Results: Total daily energy expenditure (TDEE) was consistent between the 2 DLW studies (TDEE1: 2422 6 404 kcal/d; TDEE2: 2465 6 408 kcal/d; intraclass correlation coefficient = 0.90) with a mean TDEE of 2443 6 397 kcal/d that was, on average, 20% (580 kcal/d) higher in men than in women (P , 0.0001). The regression equation relating mean TDEE to demographics and weight was as follows: TDEE (kcal/d) = 1279 + 18.3 (weight, kg) + 2.3 (age, y) 2 338 (sex: 1 = female, 0 = male); R 2 = 0.57. When body composition was included, TDEE (kcal/d) = 454 + 38.7 (fat-free mass, kg) 2 5.4 (fat mass, kg) + 4.7 (age in y) + 103 (sex: 1 = female, 0 = male); R 2 = 0.65. Individuals significantly underreported energy intake (350 kcal/d; 15%), and underreporting by overweight individuals (w400 kcal/d; 16%) was greater (P , 0.001) than that of normal-weight individuals (w270 kcal/d; 12%). Estimates of TDEE from a 7-d physical activity recall and measured resting metabolic rate also suggested that individuals significantly underreported physical activity (w400 kcal/d; 17%; P , 0.0001). Conclusion: These new equations derived over 1 mo during weight stability can be used to estimate the free-living caloric requirements of nonobese adults. This trial was registered at clinicaltrials.gov as NCT00427193.
British Journal of Nutrition, 2005
The objective of the present study was to investigate the contribution of intra-individual variance of resting energy expenditure (REE) to interindividual variance in REE. REE was measured longitudinally in a sample of twenty-three healthy men using indirect calorimetry. Over a period of 2 months, two consecutive measurements were done in the whole group. In subgroups of seventeen and eleven subjects, three and four consecutive measurements were performed over a period of 6 months. Data analysis followed a standard protocol considering the last 15 min of each measurement period and alternatively an optimised protocol with strict inclusion criteria. Intra-individual variance in REE and body composition measurements (CV intra ) as well as interindividual variance (CV inter ) were calculated and compared with each other as well as with REE prediction from a population-specific formula. Mean CV intra for measured REE and fat-free mass (FFM) ranged from 5·0 to 5·6 % and from 1·3 to 1·6 %, respectively. CV intra did not change with the number of repeated measurements or the type of protocol (standard v. optimised protocol). CV inter for REE and REE adjusted for FFM (REE adj ) ranged from 12·1 to 16·1 % and from 10·4 to 13·6 %, respectively. We calculated total error to be 8 %. Variance in body composition (CV intra FFM) explains 19 % of the variability in REE adj , whereas the remaining 81 % is explained by the variability of the metabolic rate (CV intra REE). We conclude that CV intra of REE measurements was neither influenced by type of protocol for data analysis nor by the number of repeated measurements. About 20 % of the variance in REE adj is explained by variance in body composition.
2020
This study aimed to determine whether the measured resting energy expenditure (REE) in overweight and obese patients living in a temperate climate is lower than the predicted REE; and to ascertain which equation should be used in patients living in a temperate climate. REE (indirect calorimetry) and body composition (DXA) were measured in 174 patients (88 men and 86 women; 20–68 years old) with overweight or obesity (BMI 27–45 kg m−2). All volunteers were residents in Gran Canaria (monthly temperatures: 18–24 °C). REE was lower than predicted by most equations in our population. Age and BMI were similar in both sexes. In the whole population, the equations of Mifflin, Henry and Rees, Livingston and Owen, had similar levels of accuracy (non-significant bias of 0.7%, 1.1%, 0.6%, and −2.2%, respectively). The best equation to predict resting energy expenditure in overweight and moderately obese men and women living in a temperate climate all year round is the Mifflin equation. In men, ...
Examining Variations of Resting Metabolic Rate of Adults
Medicine & Science in Sports & Exercise, 2014
Purpose: There has not been a recent comprehensive effort to examine existing studies on the resting metabolic rate (RMR) of adults to identify the effect of common population demographic and anthropometric characteristics. Thus, we reviewed the literature on RMR (kcalIkg j1 Ih j1 ) to determine the relationship of age, sex, and obesity status to RMR as compared with the commonly accepted value for the metabolic equivalent (MET; e.g., 1.0 kcalIkg j1 Ih j1 ). Methods: Using several databases, scientific articles published from 1980 to 2011 were identified that measured RMR, and from those, others dating back to 1920 were identified. One hundred and ninety-seven studies were identified, resulting in 397 publication estimates of RMR that could represent a population subgroup. Inverse variance weighting technique was applied to compute means and 95% confidence intervals (CI). Results: The mean value for RMR was 0.863 kcalIkg j1 Ih j1 (95% CI = 0.852-0.874), higher for men than women, decreasing with increasing age, and less in overweight than normal weight adults. Regardless of sex, adults with BMI Q 30 kgIm j2 had the lowest RMR (G0.741 kcalIkg j1 Ih j1 ). Conclusions: No single value for RMR is appropriate for all adults. Adhering to the nearly universally accepted MET convention may lead to the overestimation of the RMR of approximately 10% for men and almost 15% for women and be as high as 20%-30% for some demographic and anthropometric combinations. These large errors raise questions about the longstanding adherence to the conventional MET value for RMR. Failure to recognize this discrepancy may result in important miscalculations of energy expended from interventions using physical activity for diabetes and other chronic disease prevention efforts.
A new predictive equation for resting energy expenditure in healthy individuals�3
2000
A predictive equation for resting energy ex- penditure (REE) was derived from data from 498 healthy sub- jects, including females (n = 247) and males (n = 25 1), aged 19-78 y (45 ± 14 y, I ± SD). Normal-weight (n = 264) and obese (n = 234) individuals were studied and REE was mea- sured by indirect calorimetry. Multiple-regression