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Record W4408248262 · doi:10.1101/2025.03.03.25322748

The Impact of Age on Gray Matter Volume Reduction in Anorexia Nervosa: A Systematic Review

2025· review· en· W4408248262 on OpenAlexaff
Shuo Chen, Lei Wang, Pei-an Betty Shih

Bibliographic record

VenuemedRxiv · 2025
Typereview
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsMeta-analysisConfoundingNeuroimagingBrain sizeAnorexia nervosaDemographySample size determinationMedicinePsychologyMeta-regressionInternal medicineEating disordersClinical psychologyMagnetic resonance imagingStatisticsPsychiatryMathematics

Abstract

fetched live from OpenAlex

Objective: This study examines the relationship between gray matter (GM) volume reduction and age in individuals with Anorexia Nervosa (AN). Specifically, it investigates whether the magnitude and direction of GM volume differences between AN and healthy controls remain consistent across a range of age groups. Additionally, we reviewed regional GM alterations reported in the literature to characterize unique regional brain profiles observed in AN. By synthesizing neuroimaging studies and mean-age stratified analysis, this work provides insights into the possible impact aging can have on GM reduction in patients with AN. Methods: Systematic review and meta-analysis were conducted using MRI-based neuroimaging studies assessing GM volume in AN patients and controls. A primary meta-analysis was run for all feasible studies combined, followed by a stratified analysis approach examining "younger mean-age" studies and "older mean-age" studies separately. Random effects models were used for the meta-analysis. Meta-regression was used to determine the influence of age on GM volume differences and was controlled for the body mass index to minimize the confounding effect recovery status has on the GM differences between groups. Regional GM alterations were reviewed and discussed. Results: 44 studies, including 1391 individuals with AN and 1566 healthy controls, were included in the primary meta-analysis. No substantial heterogeneity was found across studies. Compared to their respective control groups, the younger-age studies, defined by studies with AN subject of mean age less than 18, exhibited greater significant GM volume loss (-5.39, 95% CI: -7.76 to -3.01, p<0.05) compared to older-age studies (-3.09, 95% CI: -4.16 to -2.03, p<0.05). Meta-regression subgroup results suggest that having older age in AN subjects is linked to less severe GM reduction relative controls. Our review of the regional GM literature reveals that alterations in the hippocampus, amygdala, and precuneus of the medial parietal lobe were more frequently reported than other brain regions in AN. In these regions, we also noticed that younger individuals with AN had more consistent volume reductions across studies, whereas studies with older AN showed greater variability. Conclusion: Grey matter volume loss in AN is more pronounced in younger patients even after controlling for the effect of the recovery status. Having older age appears to contribute to less deficit in brain volume loss in AN, suggesting a protective mechanism underlying GM alteration in older AN patients. These findings reinforce the need for early intervention and prolonged recovery support and emphasize the need to develop lifespan-specific disorder management approaches. Future research should explore long-term GM recovery trajectories and the aging effect on GM alteration for older patients to refine strategies for neuroprotection in AN.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.027
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.009
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.027
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0080.017
Bibliometrics0.0050.005
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.034
GPT teacher head0.388
Teacher spread0.354 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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