Accuracy of the Common Predictive Equations for Estimating Resting Energy Expenditure among Normal and Overweight Girl University Students

Prediction the changes of anthropometric indices following a weight-loss diet in overweight and obese women by mathematical models

Estimating the change rates in body size following the weight loss programs is very important in the compliance of those programs. Although, there is enough evidence on the significant association of body weight change with the other anthropometric indices and/ or body composition, there is so limited studies that have depicted this relationship as mathematical formulas. Therefore, the present research designed to use a mathematical model to predict changes of anthropometric indices following a weight-loss diet in the overweight and obese women. In this longitudinal study, 212 overweight/obese women who received an individualized low-calorie diet (LCD) were selected and followed-up for five months. Anthropometric measurements such as weight, waist circumference (WC), hip circumference (HC), and body composition (lean mass and fat mass) were performed. Then, body mass index, waist to hip ratio (WHR), waist to height ratio (WHtR), a body shape index (ABSI), abdominal volume index (AVI), and body adiposity index (BAI) were calculated using the related formula. Following the LCD led to the substantial and consistent changes in various anthropometric indices over time. All of these anthropometric variations were significantly related with the percent change (PC) of body weight except than WHR. Moreover, according to the mathematical formulas, weight loss was closely related to the decrease of WC (PC-WC =  - 0.120 + 0.703 × PC-WT), HC (PC-HC =  - 0.350 + 0.510 × PC-WT), body fat percentage (PC-Body Fat =  - 0.019 + 0.915 × PC-WT), WHtR (PC-WHtR =  - 0.113 + 0.702 × PC-WT), and improvements in ABSI (PC-ABSI =  - 0.112 + 0.034 × PC-WT) and AVI (PC-AVI =  - 0.324 + 1.320 × PC-WT). The decreasing rates of WC, HC, body fat percentage, WHtR, ABSI, and AVI in relation to the weight loss were clinically and statistically significant. This means that a healthy weight lowering diet would be accompanied by decreasing the body fat, body size and also the risk of morbidities.

Metabolic age correlates better than chronological age with waist-to-height ratio, a cardiovascular risk index

OBJECTIVES

Chronological age confers an increased risk for cardiovascular disease; however, chronological age does not reflect the subject's current health status. Therefore, we assessed whether Metabolic age (Met-age), based on free fat mass, is a predictor of cardiovascular risk (CVR).

METHODS

Subjects attending either IMSS UMF-2 or CUSC-1 were asked to participate. CVR was assessed using the waist-to-height ratio (WHtR), whereas Met-age was determined using the TANITA bio-analyser (model: BC-545F Fitscan). The strengthen of association was determined by calculating Pearson's r and predictability was determined by the area-under-a-receiver-operating characteristic curve (AUC).

RESULTS

284 subjects participated in this study, of which 61.6% had increased CVR. As expected, the chronological age was significantly higher in the CVR(+) group than the CVR(-) group (47.3±14.4 v. 35.2±12.7, respectively, p<.001) as well as Met-age (59.3±15.5 v. 34.3±14.3, respectively, p<.001). There was a strong association between WHtR and Met-age (r=.720, p<.001) and a moderate association for chronological age (r=.407 p<.001); however, the correlation between WHtR and Met-age was significantly better than chronological age (Z=-5.91, p<.01). Met-age was a good predictor of CVR (AUC=.88, 95%CI: .83-.92, p<.001), whereas chronological age was a fair predictor (AUC=.72, 95%CI: .66-.78, p<.001). However, Met-age showed a higher discriminatory capacity for CVR than chronological age (z=-4.597, p<.001).

CONCLUSIONS

Here, we determined that Met-age correlated with a CVR index, WHtR, and was able to predict subjects with increased CVR better than chronological age.

Accuracy of the Resting Energy Expenditure Estimation Equations for Healthy Women

Background: There exist several prediction equations for the estimation of resting energy expenditure (REE). However, none of these equations have been validated in the Chilean female population yet. The aims of this study are (1) to determine the accuracy of existing equations for prediction of REE and (2) to develop new equations in a sample of healthy Chilean women. Methods: A cross-sectional descriptive study was carried out on 620 Chilean women. The sample showed an age range between 18 and 73 years, a body mass index average of 28.5 ± 5.2 kg/m², and a prevalence of overweight and obesity of 41% and 33.2%, respectively. REE was measured by indirect calorimetry (REE_IC), which was used as the gold standard to determine the accuracy of twelve available REE prediction equations and to calculate alternative formulas for estimation of REE. Paired t-tests and Bland–Altman plots were used to know the accuracy of the estimation equations with REE_IC. At the same time, multiple linear regressions were performed to propose possible alternative equations. The analyses were carried out by age groups and according to nutritional status. Results: All the equations showed a tendency to overestimate REE, regardless of age or nutritional status. Overall, the Ireton-Jones equation achieved the highest mean percentage difference from REE_IC at 67.1 ± 31%. The alternative new equations, containing variables of body composition, reached a higher percentage of classification within ±10% of REE_IC. Conclusions: The available equations do not adequately estimate REE in this sample of Chilean women. Although they must be validated, the new formulas proposed show better adaptation to this Chilean sample.

Accuracy of resting metabolic rate prediction equations among healthy adults in Trinidad and Tobago

BACKGROUND

Over 50% of adults in Latin America and the Caribbean have a body mass index (BMI) ≥ 25 suggesting excess energy intakes relative to energy expenditure. Accurate estimation of resting metabolic rate (RMR), the largest component of total energy requirements, is crucial to strategies aimed at reducing the prevalence and incidence of overweight and obesity.

AIM

We evaluated the accuracies of established and locally developed RMR prediction equations (RMR_P) among adults.

METHODS

Four hundred adult volunteers ages 20 to 65 years had RMR measured (RMR_M) with a MedGem® indirect calorimeter according to recommended procedures. RMR_P were compared to RMR_M with values ± 10% of RMR_M deemed accurate. Anthropometry was measured using standard procedure. Linear regression with bootstrap analyses was used to develop local RMR_P equations based on anthropometric and demographic variables. The University of the West Indies Ethics Committee approved the study.

RESULTS

Males had higher mean absolute RMR (p < 0.001) but similar mean age-adjusted measured RMR per kg of body (20.9 vs. 21.5 kcals/day; p = 0.1) to females. The top performing established anthropometry-based RMR_P among participants by sex, physical activity (PA) level and BMI status subgroups were Mifflin-St Jeor, Owen, Korth, Harris-Benedict, and Livingston, while Johnstone, Cunningham, Müller (body composition (BC)), Katch and McArdle, Mifflin-St Jeor (BC) were the most accurate BC-based RMR_P. Locally developed RMR_P had accuracies comparable to their top-ranked established RMR_P counterparts.

CONCLUSIONS

Accuracies of established RMR_P depended on habitual PA level, BMI status, BC and sex. Furthermore, locally developed RMR_P provide useful alternatives to established RMR_P.

Throwing darts in ICU: how close are we in estimating energy requirements?

Background

Indirect calorimetry (IC) is the gold standard for determining energy requirement. Due to lack of availability in many institutions, predictive equations are used to estimate energy requirements. The purpose of this study is to determine the accuracy of predictive equations (ie, Harris-Benedict equation (HBE), Mifflin-St Jeor equation (MSJ), and Penn State University equation (PSU)) used to determine energy needs for critically ill, ventilated patients compared with measured resting energy expenditure (mREE).

Methods

The researchers examined data routinely collected as part of clinical care for patients within intensive care units (ICUs). The final sample consisted of 68 patients. All studies were recorded during a single inpatient stay within an ICU.

Results

Patients, on average, had an mREE of 33.9 kcal/kg using IC. The estimated energy requirement when using predictive equations was 24.8 kcal/kg (HBE×1.25), 24.0 kcal/kg (MSJ×1.25), and 26.8 kcal/kg (PSU).

Discussion

This study identified significant differences between mREE and commonly used predictive equations in the ICU.

Level of evidence

III.

Estimating the agreement between the metabolic rate calculated from prediction equations and from a portable indirect calorimetry device: an effort to develop a new equation for predicting resting metabolic rate

BACKGROUND

Many studies have been performed over time in order to determine the reliability of metabolic rate prediction equations.

PURPOSE

To evaluate the agreement, in terms of bias, absolute bias and accuracy between metabolic rate prediction equations and measured metabolic rate using indirect calorimetry system (IC), investigating also the factors affecting this agreement.

METHODS

The anthropometric features of 383 Caucasian participants of all Body Mass Index (BMI) classes were recorded and Resting Metabolic Rate (RMR) was measured by using the IC Fitmate portable device. The resulting values were compared with the predictive values of Harris & Benedict, Schofield, Owen, FAO-WHO-UNU, Mifflin and Harrington equations.

RESULTS

A closer approximation in agreement was obtained using the Harrington equation (based on BMI, age and gender). The equations using variables, such as weight, height, age and gender demonstrated higher agreement than the equations using merely weight and gender. Higher educational level was associated with normal weight, while higher calorific ratio was found in the class of normal-weighted individuals. An inverse relationship between ΒΜΙ and RMR was also observed and a logarithmic equation for calculating RMR was created, which was differentiated in relation to BMI classes, using the weight and gender variables.

CONCLUSION

A better measurement agreement between RMR prediction equations and IC may be achieved due to BMI consideration. The present findings contributed to a better understanding of the measured parameters, confirming the inverse relationship between BMI and RMR. Age group and gender variables may also exert significant role on the bias response of some RMR equations.

Predicting resting energy expenditure in young adults

PURPOSE

To develop and validate a REE prediction equation for young adults.

METHODS

Baseline data from two studies were pooled (N=318; women=52%) and randomly divided into development (n=159) and validation samples (n=159). REE was measured by indirect calorimetry. Stepwise regression was used to develop an equation to predict REE (University of Kansas (KU) equation). The KU equation and 5 additional REE prediction equations used in clinical practice (Mifflin-St. Jeor, Harris-Benedict, Owens, Frankenfield (2 equations)) were evaluated in the validation sample.

RESULTS

There were no significant differences between predicted and measured REE using the KU equation for either men or women. The Mifflin-St. Jeor equation showed a non-significant mean bias in men; however, mean bias was statistically significant in women. The Harris-Benedict equation significantly over-predicted REE in both men and women. The Owens equation showed a significant mean bias in both men and women. Frankenfield equations #1 and #2 both significantly over-predicted REE in non-obese men and women. We found no significant differences between measured REE and REE predicted by the Frankenfield #2 equations in obese men and women.

CONCLUSION

The KU equation, which uses easily assessed characteristics (age, sex, weight) may offer better estimates of REE in young adults compared with the 5 other equations. The KU equation demonstrated adequate prediction accuracy, with approximately equal rates of over and under-prediction. However, enthusiasm for recommending any REE prediction equations evaluated for use in clinical weight management is damped by the highly variable individual prediction error evident with all these equations.