Reconsidering the Energy Homeostasis Hypothesis. the Proposed Role of Fat-Free Mass (FFM) and Resting Metabolic Rate (RMR) Driving Increased Energy Intake After Weight Loss (OR09-04-19). (13th June 2019)

• Record Type: Journal Article
• Authors: Pankey, Chris; Flack, Kyle; Ufholz, Kelsey; Johnson, LuAnn; Roemmich, James
• Is Part Of: Current developments in nutrition. Volume 3(2019)Supplement 1
• Publication Date: 2019-06-13
• Subjects: Nutrition -- Periodicals; Nutritional Physiological Phenomena; Nutrition; Periodicals; Periodicals; Fulltext; Internet Resources; Periodicals; 612.3
• DOI: 10.1093/cdn/nzz041.OR09-04-19
• Languages: English
• ISSNs: 2475-2991

Abstract

Abstract: Objectives: Hypotheses of appetite control and food reinforcement are based on gut and adipose peptide signaling to central appetite centers. Contemporary models propose that RMR and FFM changes modify food reinforcement and this may be best observed after weight loss when body mass is purported to be regained until pre-weight loss FFM is restored. Here we assess the associations of change (∆, post-training value minus pre-training value) in food reinforcement with ∆ fat mass (FM), ∆FFM, and ∆RMR after exercise-induced weight loss. Methods: Subjects ( n = 29, BMI = 25–35 kg/m 2 ) engaged in a 6-wk aerobic exercise protocol expending either 300 or 600 kcal, 5 d/wk. Relative reinforcement value of food (RRVfood ) was measured via a computer-based operant responding task, in which subjects could "earn" access to food or sedentary reinforcers. Schedules of reinforcement for each alternative started at 4 and doubled after every 5 points. Completed schedules were recorded for each alternative (PMaxfood, PMaxsed ) and the ratio ((PMaxfood /(PMaxfood + PMaxsed )) determined RRVfood . RMR was determined by indirect calorimetry. FFM was determined by DEXA. Spearman correlation analysis determined correlations between variables at pre and post, and between ∆ scores. A generalized linear mixed model tested the main and interactive effects of ∆FFM and ∆RMR on ∆PMaxfood . Results: At baseline, there were no correlations between outcome measures. At post-training, FFM correlated to PMaxfood ( P < 0.01, r = 0.52). ∆RMR negatively correlated with ∆PMaxfood ( P < 0.01, r = −0.48) and with ∆RRVfood ( P < 0.06, r = −0.36). ∆PMaxfood did not associate with ∆FFM ( P = 0.71, r = 0.07). ∆RMR predicted ( P < 0.05) ∆PMaxfood when controlling for ∆FFM and ∆PMaxfood * ∆FFM. Conclusions: FFM correlated with PMaxfood post-training; however, ∆PMaxfood did not correlate to ∆FFM, so, ∆FFM may be necessary, but insufficient to increase PMaxfood after weight loss. ∆RMR inversely predicted ∆PMaxfood when controlling for FFM, suggesting that reductions in RMR with weight loss increases ∆PMaxfood perhaps as a means of restoring pre-weight loss FFM and RMR. This model would predict that limiting reductions in RMR during weight loss could benefit weight loss maintenance by limiting increases in food reinforcement. Funding Sources: United States Department of Agriculture, Agricultural Research Service project 3062-51000-51-00D.