The Protein Partitioning Model of Body Composition

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Authors: Douglas Roy, Jody Roy
Cited by: 2

Abstract

Two leading paradigms dominate perspectives on obesity: (1) the Energy Balance Model (EBM), which views obesity as a phenomenon of essentially excess calories consumed over calories expended; and (2) the Carbohydrate-Insulin Model (CIM), which emphasizes carbohydrate-driven metabolic changes that promote fat storage. We introduce the Protein Partitioning Model (PPM) as a muscle-centric perspective on body composition. On this view, dietary protein and muscle activity can critically influence fat gain and loss – even under constant caloric intake – in ways that subsume the CIM-based mechanisms of fat-insulin dynamics. Building on comparisons with CIM, we extend PPM to a Rational Addiction framework to explore how protein and sugar consumption change over time in interaction with their biological stocks, muscle and fat, respectively, allowing total caloric intake to vary. When combined with regular resistance exercise, protein intake builds muscle, which, in turn, increases the marginal utility of protein consumption, contributing to a virtuous feedback loop of “protein addiction”, where consumption is bounded and converges on stable steady states characterised by modest changes in body composition. By contrast, sugar addiction is volatile and binge-prone, driven by insulin-mediated energy partitioning. These differences suggest that PPM offers an important angle in addition to simple EBM-based views because of the differing implications of whether caloric surpluses occur through protein or non-protein sources of energy and the motivations driving consumption behaviour. Put simply, factors promoting addiction to protein may protect against obesity arising from excessive eating in general and from high levels of carbohydrate consumption in particular.

Fields of Study & Subjects

Muscle metabolism and nutrition Diet and metabolism studies Regulation of Appetite and Obesity

Publication details

Journal / source
Preprints.org
Publisher
MDPI AG
Publication date
6 February 2026
Document type
Preprint