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Record W6889751310 · doi:10.26181/24558184

The long-term effects of childhood adiposity on depression and anxiety in adulthood: A systematic review

2023· article· en· W6889751310 on OpenAlexaboutno aff

Bibliographic record

VenueLa Trobe University · 2023
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsnot available
Fundersnot available
KeywordsAnxietyDepression (economics)Association (psychology)Meta-analysisObesityDepressive symptomsChildhood obesityVulnerability (computing)

Abstract

fetched live from OpenAlex

Objective: This review aimed to evaluate the association between childhood adiposity and depression and anxiety risk in adulthood. Methods: MEDLINE, PsychInfo, Embase, CINAHL, and Scopus were searched on June 6, 2022, to identify studies that investigated the association between childhood weight status (age ≤18 years) and outcomes of depression and/or anxiety in adulthood (age ≥19 years). Study quality was assessed using the Newcastle-Ottawa Scale and results were narratively synthesized. Results: Sixteen studies were eligible for inclusion, with heterogeneity in methods and follow-up durations complicating comparisons. Six out of eight studies found a statistically significant association between childhood adiposity and increased likelihood of depression in adulthood, particularly in females. However, overall evidence was of moderate quality and study limitations prevented causal conclusions. In contrast, limited evidence and mixed findings were reported for the associations between childhood adiposity and depressive symptom severity or anxiety outcomes in adulthood. Conclusions: Evidence suggests that childhood adiposity is associated with greater vulnerability to depression in adulthood, particularly in females. However, further research is warranted to address the limitations discussed. Future research should also explore how changes in weight status from childhood to adulthood might differentially influence the likelihood of depression.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.195
Threshold uncertainty score0.325

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.004
GPT teacher head0.219
Teacher spread0.215 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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
Published2023
Admission routes1
Has abstractyes

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