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Record W4413284795 · doi:10.1093/ijnp/pyaf052.337

546. REIMAGINING THE DIATHESIS-STRESS MODEL: TRANSDIAGNOSTIC FACTORS TO PRECISION PSYCHIATRY

2025· article· en· W4413284795 on OpenAlexaff
Marco Leyton

Bibliographic record

VenueThe International Journal of Neuropsychopharmacology · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicMental Health and Psychiatry
Canadian institutionsMcGill University
Fundersnot available
KeywordsDiathesisPsychologyPsychiatryClinical psychologyPsychotherapistMedicine

Abstract

fetched live from OpenAlex

Abstract Background We recently reported evidence that commonly comorbid early onset psychiatric disorders are predicted by a biopsychosocial model composed of adolescent externalizing (EXT) behaviors, early life adversity, and midbrain dopamine autoreceptor availability, as measured with positron emission tomography (PET). Aims & Objectives Here, we tested whether two variants of this model could (i) reproduce the transdiagnostic predictions, and (ii) make disorder-specific predictions. Method In Study 1, participants were 1338 IMAGEN database members who had Childhood Trauma Questionnaire (CTQ) data, adolescent EXT scores, and functional magnetic resonance imaging (fMRI) measured responses during the monetary incentive delay (MID) task. Regions of interest were the midbrain dopamine cell bodies, ventral striatum, caudate, putamen, and anterior cingulate cortex. DSM-IV diagnoses were made using the Development and Wellbeing Assessment at ages 14, 16, and 19. Binary logistic regression models tested whether a combination of EXT traits, childhood trauma, and MID fMRI contrasts at either 14 or 19 years of age identified participants with a DSM-IV disorder by age 19. In Study 2 (n = 44), EXT behaviors were measured between ages 10 and 16, and both CTQ data and mesocorticolimbic activations to alcohol and juice cues were collected at age 18. Seven years later, participants completed the Alcohol Use Disorders Identification Test (AUDIT). Binomial logistic regression analyses tested whether these three factors predicted high vs low AUDIT scores at the age 25 follow-up. Results In Study 1, all tested three-factor models predicted who had a DSM-IV disorder by age 19 at a high level of statistical significance (p < 1.0 x 10-21). In every model, EXT and CTQ scores were individual predictors (p < 0.001). At age 14, reward anticipation responses were significant predictors in the ventral striatum, caudate, putamen, and anterior cingulate. At age 19, significant predictors were in the ventral striatum. The relation between trauma and diagnoses was partially mediated by higher EXT (indirect path B = 0.0535, 95% CI = 0.0301-0.0835), and moderated by fMRI responses in the cingulate (p = 0.0038) and putamen (p = 0.014) at age 14. Low striatal reward anticipation responses were associated with higher anhedonia. In Study 2, the combination of high mesocorticolimbic responses to Alcohol vs. Juice cues, high EXT scores, and high CTQ scores predicted future alcohol use problems (p < 0.05). fMRI responses were significant contributors to the model in the ventral striatum, associative striatum, anterior and posterior cingulate cortices, and ventromedial prefrontal cortex (p ≤ 0.05). Discussion & Conclusions These findings tentatively identify constituents of a hierarchical diathesis-stress model that has (i) transdiagnostic predictive value for commonly comorbid early onset disorders, and (ii) narrower predictive value for future alcohol problems. For the latter, high mesocorticolimbic responses to disease-specific cues might imbue the stimuli with aberrant salience leading to distinct symptoms and disorders – these perturbations might be best identified after controlling for early life adversity and behavioral trajectories.

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)0.008
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.025
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.004
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.029
GPT teacher head0.337
Teacher spread0.308 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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