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Record W3213132689 · doi:10.1016/j.tem.2021.10.005

The menace of obesity to depression and anxiety prevalence

2021· review· en· W3213132689 on OpenAlexafffund
Stephanie Fulton, Léa Décarie-Spain, Xavier Fioramonti, Bruno P. Guiard, Shingo Nakajima

Bibliographic record

VenueTrends in Endocrinology and Metabolism · 2021
Typereview
Languageen
FieldNeuroscience
TopicTryptophan and brain disorders
Canadian institutionsCentre Hospitalier de l’Université de Montréal
FundersJapan Society for the Promotion of ScienceFonds de Recherche du Québec - Santé
KeywordsInsulin resistanceAnxietyObesityNeuroinflammationMoodLeptinDepression (economics)Adipose tissueMedicinePsychologyInternal medicineEndocrinologyInflammationPsychiatry

Abstract

fetched live from OpenAlex

The incidence of depression and anxiety is amplified by obesity. Mounting evidence reveals that the psychiatric consequences of obesity stem from poor diet, inactivity, and visceral adipose accumulation. Resulting metabolic and vascular dysfunction, including inflammation, insulin and leptin resistance, and hypertension, have emerged as key risks to depression and anxiety development. Recent research advancements are exposing the important contribution of these different corollaries of obesity and their impact on neuroimmune status and the neural circuits controlling mood and emotional states. Along these lines, this review connects the clinical manifestations of depression and anxiety in obesity to our current understanding of the origins and biology of immunometabolic threats to central nervous system function and behavior.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.042
GPT teacher head0.339
Teacher spread0.297 · 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 designNot applicable
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

Citations426
Published2021
Admission routes2
Has abstractno

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