Evidence map›Paper›PMID 42466153›Full record

ReviewFrontiers in nutrition2026

A conceptual framework integrating mechanisms linking nut consumption and energy balance.

Tim D Cassettari, Carlene S Starck, Emma L Beckett, Flávia Fayet-Moore

Abstract readReview
In one paragraph

Review in Frontiers in nutrition, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Tim D CassettariFOODiQ Global Australian Catholic University, Sydney, NSW, Australia.
Carlene S StarckFOODiQ Global Australian Catholic University, Sydney, NSW, Australia.
Emma L BeckettFOODiQ Global Australian Catholic University, Sydney, NSW, Australia.
Flávia Fayet-MooreFOODiQ Global Australian Catholic University, Sydney, NSW, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Although nuts are calorie-dense foods, meta-analyses of randomized trials show that nut consumption generally does not promote weight gain over the short term across commonly consumed nut types. In contrast, prospective cohort studies indicate that higher nut intake is associated with modest reductions in long-term risk of weight gain, although these findings do not establish causality. These findings are not easily explained by simplified interpretations of energy balance that primarily emphasize calorie content. This review examines the key mechanisms that may contribute to the relationship between nut consumption and body weight and proposes a conceptual framework that integrates these mechanisms within established energy balance models. Reduced metabolizable energy and increased energy compensation represent the most consistently supported mechanisms underlying the weight-neutrality of nut intake, with limited evidence suggesting potential modest increases in energy expenditure. Emerging evidence also points to potential metabolic adaptations, including modulation of gut microbiota and short-chain fatty acid production, improved insulin sensitivity, and lower inflammation, which may contribute to longer-term regulation of energy balance. Together, the framework illustrates how nuts may influence energy balance through pathways beyond their direct calorie contribution while remaining consistent with thermodynamic principles, emphasizing the importance of their broader nutritional composition and physical structure.

Indexed as

body weightconceptual frameworkenergy balancegut microbiotainsulin sensitivitymetabolizable energynutssatiety

Identifiers

PMID42466153
PMCPMC13373753

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.