Does flavor-nutrient learning promote or protect against diet-induced obesity? Individual differences in conditionability predict resistance to weight gain in rats

preprint OA: closed CC-BY-NC-4.0
📄 Open PDF Full text JSON View at publisher

Abstract

Flavor-nutrient learning (FNL) refers to learning associations between a food’s flavor and the rewarding appetition signals that arise from post-oral nutrient sensing during or after a meal. In rodent models FNL reliably produces strong flavor preferences and increased intake of nutrient-paired flavors, implicating FNL as a presumptive obesogenic influence in the modern environment. However, evidence that FNL plays a causal role in diet-induced obesity is ambiguous. We have previously shown that degree of weight gain on a high-fat/sugar diet is associated with stronger FNL responses, but direction of causation was unclear. This paper reports three experiments investigating whether individual differences in FNL ‘conditionability’ are linked to obesity proneness prior to obesity onset. Two experiments comparing selectively-bred obesity-prone vs resistant strains found no strain differences in FNL. A third study in lean, outbred rats evaluated whether baseline individual differences in FNL prospectively predict weight gain on a cafeteria diet. Unexpectedly, rats who showed the strongest learned increase in intake of a nutrient-paired flavor subsequently gained the least weight when switched to cafeteria diet, suggesting FNL protects against weight gain. In fact, individual differences in FNL explained a portion of variance in cafeteria weight gain over and above measured kcal intake, implying a function for FNL in adaptively modulating metabolic responses to energy intake. Collectively, several studies have now shown individual differences in obesity proneness to be either positively correlated, uncorrelated, or negatively correlated with FNL, calling for a more nuanced view of how appetition influences intake and energy balance.
Full text 1,843 characters · extracted from oa-doi-fallback · click to expand
Abstract Flavor-nutrient learning (FNL) refers to learning associations between a food’s flavor and the rewarding appetition signals that arise from post-oral nutrient sensing during or after a meal. In rodent models FNL reliably produces strong flavor preferences and increased intake of nutrient-paired flavors, implicating FNL as a presumptive obesogenic influence in the modern environment. However, evidence that FNL plays a causal role in diet-induced obesity is ambiguous. We have previously shown that degree of weight gain on a high-fat/sugar diet is associated with stronger FNL responses, but direction of causation was unclear. This paper reports three experiments investigating whether individual differences in FNL ‘conditionability’ are linked to obesity proneness prior to obesity onset. Two experiments comparing selectively-bred obesity-prone vs resistant strains found no strain differences in FNL. A third study in lean, outbred rats evaluated whether baseline individual differences in FNL prospectively predict weight gain on a cafeteria diet. Unexpectedly, rats who showed the strongest learned increase in intake of a nutrient-paired flavor subsequently gained the least weight when switched to cafeteria diet, suggesting FNL protects against weight gain. In fact, individual differences in FNL explained a portion of variance in cafeteria weight gain over and above measured kcal intake, implying a function for FNL in adaptively modulating metabolic responses to energy intake. Collectively, several studies have now shown individual differences in obesity proneness to be either positively correlated, uncorrelated, or negatively correlated with FNL, calling for a more nuanced view of how appetition influences intake and energy balance. Competing Interest Statement The authors have declared no competing interest.

Text is read by the "Ask this paper" AI Q&A widget below. Extraction quality varies by source — PMC NXML preserves structure cleanly, OA-HTML may include some navigation residue, and OA-PDF can have broken hyphenation. The publisher copy (via DOI) is the canonical version.

My notes (saved in your browser only)

Ask this paper AI returns verbatim quotes from the full text · source: oa-doi-fallback

Answers must be backed by verbatim quotes from this paper's full text. Hallucinated quotes are dropped automatically; if no verbatim passage answers the question, we say so. How this works

Citation neighborhood (no data yet)

We don't have any in-corpus citations linked to this paper yet. This is a recent paper (2026) — citers typically take a year or two to land, and the OpenAlex reference graph may still be filling in.

Source provenance

europepmc
last seen: 2026-05-20T01:45:00.602351+00:00
unpaywall
last seen: 2026-05-24T02:00:01.246996+00:00
License: CC-BY-NC-4.0