Predictive equations are inaccurate to assess caloric needs in non-white adults from Chile

Predictive Equations Overestimate Resting Metabolic Rate in Young Chilean Women with Excess Body Fat

Underestimating/overestimating resting metabolic rate (RMR) affects energy prescription. The objective was to compare RMR by indirect calorimetry (RMR IC) and RMR estimated by predictive equations in women with excess body fat. This was an analytical cross-sectional study with 41 women aged 18-28 with overnutrition according to body composition. The RMR IC was measured and RMR estimated using the FAO/WHO/UNU (1985), FAO/WHO/UNU (2004), Harris-Benedict, and Mifflin-St Jeor equations. The percentage of adequacy (90-110%), overestimation (>110%), and underestimation (<90%) were evaluated for RMR IC. Data were described by percentiles because of non-normal distribution according to the Shapiro-Wilk test. The Kruskal-Wallis test and Bland-Altman analysis were applied at a significance level of α < 0.05. The RMR IC was 1192 and 1183 calories/day (p = 0.429) in women with obesity and overweight, respectively. The FAO/WHO/UNU (1985), FAO/WHO/UNU (2004), Harris-Benedict, and Mifflin-St Jeor equations overestimated the RMR IC by 283.2, 311.2, 292.7, and 203.0 calories/day and by 296.7, 413.8, 280.0, and 176.6 calories/day for women with overweight and obesity (p < 0.001), respectively. The Harris-Benedict adjusted weight (0.5) equation underestimated RMR IC by 254.7 calories/day. The predictive equations overestimated RMR IC in women with excess body fat. The Mifflin-St Jeor equation showed less overestimation and better adequacy, but was not exempt from inaccuracy.

Comparison of Actual and Predicted Resting Metabolic Rate in Women with Lipedema

Background: An adequate dietary energy supply is particularly important in patients with lipedema as it promotes weight and fat loss. Accurate estimation of resting metabolic rate (RMR) allows implementing a proper calorie restriction diet in patients with lipedema. Our study aimed to compare actual resting metabolic rate (aRMR) with predicted resting metabolic rate (pRMR) in women with lipedema and to determine the association between individual body composition parameters, body mass index, and aRMR. Methods and Results: A total of 108 women diagnosed with lipedema were enrolled in the study. aRMR was obtained by indirect calorimetry (IC) using FitMate WM metabolic system (Cosmed, Rome, Italy). pRMR was estimated with predictive equations and bioelectric impedance analysis (BIA). All body composition parameters were based on BIA. The mean aRMR in the study group was 1705.2 ± 320.7 kcal/day. This study found the agreement of predictive equations compared to IC is low (<60%). Most methods of predicted RMR measurement used in our study significantly underpredicted aRMR in patients with lipedema. Therefore, the most applied equations remain useless in clinical practice in this specific population due to large individual differences among the studied women. Conclusions: IC is the best tool to evaluate RMR in evaluated patients with lipedema. It is necessary to propose a new equation to RMR determination in clinical practice.

The centenary of the Harris-Benedict equations: How to assess energy requirements best? Recommendations from the ESPEN expert group

BACKGROUND & AIMS

The year 2019 marked the centenary of the publication of the Harris and Benedict equations for estimation of energy expenditure. In October 2019 a Scientific Symposium was organized by the European Society for Clinical Nutrition and Metabolism (ESPEN) in Vienna, Austria, to celebrate this historical landmark, looking at what is currently known about the estimation and measurement of energy expenditure.

METHODS

Current evidence was discussed during the symposium, including the scientific basis and clinical knowledge, and is summarized here to assist with the estimation and measurement of energy requirements that later translate into energy prescription.

RESULTS

In most clinical settings, the majority of predictive equations have low to moderate performance, with the best generally reaching an accuracy of no more than 70%, and often lead to large errors in estimating the true needs of patients. Generally speaking, the addition of body composition measurements did not add to the accuracy of predictive equations. Indirect calorimetry is the most reliable method to measure energy expenditure and guide energy prescription, but carries inherent limitations, greatly restricting its use in real life clinical practice.

CONCLUSIONS

While the limitations of predictive equations are clear, their use is still the mainstay in clinical practice. It is imperative to recognize specific patient populations for whom a specific equation should be preferred. When available, the use of indirect calorimetry is advised in a variety of clinical settings, aiming to avoid under-as well as overfeeding.