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Am J Clin Nutr. 2017 Nov;106(5):1206-1212. doi: 10.3945/ajcn.117.153718. Epub 2017 Sep 6.

Investigating predictors of eating: is resting metabolic rate really the strongest proxy of energy intake?

Author information

1
Cancer Epidemiology and Prevention Research, Alberta Health Services, Calgary, Alberta, Canada.
2
Department of Mathematics and Statistics and.
3
Healthy Active Living and Obesity Research Group, Children's Hospital of Eastern Ontario Research Institute, Ottawa, Ontario, Canada.
4
Behavioral and Metabolic Research Unit, School of Human Kinetics, Faculty of Health Sciences, University of Ottawa, Ottawa, Ontario, Canada.
5
Institute of Nutrition and Functional Food, Laval University, Quebec, Quebec, Canada; and.
6
Institut de recherche de l'Hôpital Montfort, Ottawa, Ontario, Canada.
7
Behavioral and Metabolic Research Unit, School of Human Kinetics, Faculty of Health Sciences, University of Ottawa, Ottawa, Ontario, Canada; edoucet@uottawa.ca.

Abstract

Background: Evidence suggests that fat-free mass and resting metabolic rate (RMR), but not fat mass, are strong predictors of energy intake (EI). However, body composition and RMR do not explain the entire variance in EI, suggesting that other factors may contribute to this variance.Objective: We aimed to investigate the associations between body mass index (in kg/m2), fat mass, fat-free mass, and RMR with acute (1 meal) and daily (24-h) EI and between fasting appetite ratings and certain eating behavior traits with daily EI. We also evaluated whether RMR is a predictor of the error variance in acute and daily EI.Design: Data collected during the control condition of 7 studies conducted in Ottawa, Ontario, Canada, were included in these analyses (n = 191 and 55 for acute and daily EI, respectively). These data include RMR (indirect calorimetry), body composition (dual-energy X-ray absorptiometry), fasting appetite ratings (visual analog scales), eating behavior traits (Three-Factor Eating Questionnaire), and EI (food buffet or menu).Results: Fat-free mass was the best predictor of acute EI (R2 = 0.46; P < 0.0001). The combination of fasting prospective food consumption ratings and RMR was the best predictor of daily EI (R2 = 0.44; P < 0.0001). RMR was a statistically significant positive predictor of the error variance for acute (R2 = 0.20; P < 0.0001) and daily (R2 = 0.23; P < 0.0001) EI. RMR did, however, remain a statistically significant predictor of acute (R2 = 0.32; P < 0.0001) and daily (R2 = 0.30; P < 0.0001) EI after controlling for this error variance.Conclusions: Our findings suggest that combined measurements of appetite ratings and RMR could be used to estimate EI in weight-stable individuals. However, greater error variance in acute and daily EI with increasing RMR values was observed. Future studies are needed to identify whether greater fluctuations in daily EI over time occur with increasing RMR values. This trial was registered at clinicaltrials.gov as NCT02653378.

KEYWORDS:

appetite; eating behavior traits; energy intake; error variance; resting metabolic rate

PMID:
28877891
DOI:
10.3945/ajcn.117.153718
[Indexed for MEDLINE]

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