Original ArticlePredicting resting energy expenditure in young adults
Introduction
Data from the National Health and Nutrition Survey (2011–2012) indicated that the prevalence of overweight and obesity (BMI ≥ 25 kg/m2) in young adults age 20–39 years was 60.3%, with 30.3% classified as obese (BMI ≥ 30 kg/m2) [1]. Thus, young adults represent a large segment of the population in need of weight management. Successful weight management is aided by the establishment of realistic goals for energy intake and energy expenditure based on resting energy expenditure (REE), which represents the largest component of total daily energy expenditure. Although REE can be accurately assessed using indirect calorimetry, the costs of equipment, the need for trained personnel, and the logistics associated with this assessment make its use impractical in the majority of clinical settings.
In lieu of directly assessed REE, weight management clinics typically utilize equations, based on easily assessed parameters such as age, height, and weight, to predict REE. Several REE prediction equations are frequently used in clinical practice. However, information relative to the validity of these equations in young adults across the weight spectrum in limited [2], [3], [4], [5], [6], [7], [8], [9]. Several of these studies have been conducted in only women [2], [3], [5], [8] only normal weight individuals [6], [7] or in specific population sub-groups including Hispanics [2] and Chinese [6], [7]. To date, there is no prediction equation developed specifically for use in young adults across the weight spectrum. Therefore, the purpose of this analysis was to develop and evaluate the accuracy of an equation that uses easily assessed physical characteristics (age, height, weight, sex) to predict REE in a sample that includes normal, overweight and obese young adults, and to compare the accuracy of this equation with other prediction equations frequently used in clinical practice including: Mifflin–St. Jeor [10], Harris–Benedict [11], Owens [12], and Frankenfield [9].
Section snippets
Participants
Baseline data from two recently completed randomized trials conducted at University of Kansas Energy Balance Lab, that used similar inclusion/exclusion criteria (age 18–30 yrs, healthy, non-smoking, planned physical activity <500 kcal/wk) were pooled and served as the basis for this analysis: Trial 1: n = 173, age = 22.2 ± 2.8 yrs, BMI = 25.7 ± 4.6 kg/m2 [13]; trial 2: n = 145, age = 22.8 ± 3.2 yrs, BMI = 30.7 ± 4.6 kg/m2 [14]. The pooled sample was then divided randomly into 2 equal samples; a development (n = 159)
Results
The study sample consisted of overweight and obese (mean BMI ∼ 28 kg/m2, range = 21–41 kg/m2) young adults (mean age ∼22 yrs, range = 18–30 yrs) (Table 2). Approximately 64% of the sample was overweight and obese (BMI ≥ 25 kg/m2) with ∼24% classified as obese (BMI ≥ 30 kg/m2), reflecting the weight distribution of young adults in the general population [1]. There were no significant differences in descriptive characteristics been participants randomly assigned to the development (n = 159, 75 women, 84 men, 39
Discussion
We developed the KU equation specifically for the prediction of REE in young adults across the weight spectrum, and evaluated the accuracy of this equation and 5 other REE prediction equations commonly used in clinical practice. We found that the mean REE predicted using the KU equation was not significantly different from REE measured by indirect calorimetry (i.e., mean bias <2.0%). Similar results were noted for both Frankenfield equations in obese participants, and for the Mifflin–St. Jeor
Disclosure of funding
This study was supported by the National Institutes of Health (DK049181 and DK080832).
Conflict of interest
The authors have disclosed that they have no significant relationships with, or financial interest in, any commercial companies pertaining to this article.
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