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. 2022 Jun 30;14(13):2727.
doi: 10.3390/nu14132727.

Clinical Correlates of Measured and Predicted Resting Energy Expenditure in Patients with Anorexia Nervosa: A Retrospective Cohort Study

Affiliations

Clinical Correlates of Measured and Predicted Resting Energy Expenditure in Patients with Anorexia Nervosa: A Retrospective Cohort Study

Rami Bou Khalil et al. Nutrients. .

Abstract

Resting energy expenditure (REE; i.e., the calorie amount required for 24 h during a non-active period) is an important parameter in nutritional rehabilitation of patients with anorexia nervosa (AN). This study determined whether age, body mass index, AN duration/subtype/specific symptoms/clinical severity, cognitive function alterations, and psychiatric comorbidities influenced REE or the difference between the calculated and estimated REE. Patients with AN who were followed at a daycare treatment facility between May 2017 and January 2020 (n = 138) underwent a complete assessment that included the MINI, Eating Disorder Examination Questionnaire, d2 test of attention, body fat composition by bioelectrical impedance analysis (BIA) and REE measurement by indirect calorimetry (REEIC). AN subtype (N = 66 for restrictive subtype and N = 69 for non-restrictive subtype; p = 0.005), free-fat mass (<0.001), and fat mass (<0.001) were associated with REEIC. Age (p < 0.001), height (p = 0.003), and AN duration (N = 46 for <3 years and N = 82 for ≥3 years; p = 0.012) were associated with the difference between estimated REE (using the Schebendach equation) and measured REEIC. Therefore, the Schebendach equation was adjusted differently in the two patients’ subgroups (AN duration ≤ or >3 years). Overall, REE was higher in patients with restrictive than non-restrictive AN. In the absence of BIA measures, REE-estimating equations should take into account AN duration.

Keywords: AN duration; AN subtype; anorexia nervosa; resting energy expenditure.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The Bland–Altman plots show that the REE estimated with the Muller equation that requires BIA measurements (Muller 2) is in agreement with REEIC the most, followed by the REE estimated with the Schebendach equation.
Figure 2
Figure 2
Raincloud plot of repeated measures ANOVA showing the distribution of the estimated REE and REEIC values.

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