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Association of Maternal Depression During Pregnancy and Recent Stress With Brain Age Among Adult Offspring

2023· article· en· W4318485181 on OpenAlexafffund
Klára Marečková, Radek Mareček, Martin Jáni, Lenka Žáčková, Lenka Andrýsková, Milan Brázdil, Yuliya S. Nikolova

Bibliographic record

VenueJAMA Network Open · 2023
Typearticle
Languageen
FieldMedicine
TopicMaternal Mental Health During Pregnancy and Postpartum
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
FundersMasarykova UniverzitaAgentura Pro Zdravotnický Výzkum České RepublikyCentral European Institute of TechnologyMinisterstvo Školství, Mládeže a TělovýchovyEuropean CommissionCentre for Addiction and Mental Health FoundationFP7 People: Marie-Curie ActionsCentre for Addiction and Mental Health
KeywordsOffspringPregnancyCohortDepression (economics)NeuroimagingMedicineLongitudinal studyCohort studyPrenatal stressYoung adultLife course approachIn uteroPsychologyPediatricsPsychiatryFetusInternal medicineDevelopmental psychologyPathology

Abstract

fetched live from OpenAlex

Importance: Maternal mental health problems during pregnancy are associated with altered neurodevelopment in offspring, but the long-term relationship between these prenatal risk factors and offspring brain structure in adulthood remains incompletely understood due to a paucity of longitudinal studies. Objective: To evaluate the association between exposure to maternal depression in utero and offspring brain age in the third decade of life, and to evaluate recent stressful life events as potential moderators of this association. Design, Setting, and Participants: This cohort study examined the 30-year follow-up of a Czech prenatal birth cohort with a within-participant design neuroimaging component in young adulthood conducted from 1991 to 2022. Participants from the European Longitudinal Study of Pregnancy and Childhood prenatal birth cohort were recruited for 2 magnetic resonance imaging (MRI) follow-ups, one between ages 23 and 24 years (early 20s) and another between ages 28 and 30 years (late 20s). Exposures: Maternal depression during pregnancy; stressful life events in the past year experienced by the young adult offspring. Main Outcomes and Measures: Gap between estimated neuroanatomical vs chronological age at MRI scan (brain age gap estimation [BrainAGE]) calculated once in participants' early 20s and once in their late 20s, and pace of aging calculated as the differences between BrainAGE at the 2 MRI sessions in young adulthood. Results: A total of 260 individuals participated in the second neuroimaging follow-up (mean [SD] age, 29.5 [0.6] years; 135 [52%] male); MRI data for both time points and a history of maternal depression were available for 110 participants (mean [SD] age, 29.3 [0.6] years; 56 [51%] male). BrainAGE in participants' early 20s was correlated with BrainAGE in their late 20s (r = 0.7, P < .001), and a previously observed association between maternal depression during pregnancy and BrainAGE in their early 20s persisted in their late 20s (adjusted R2 = 0.04; P = .04). However, no association emerged between maternal depression during pregnancy and the pace of aging between the 2 MRI sessions. The stability of the associations between maternal depression during pregnancy and BrainAGE was also supported by the lack of interactions with recent stress. In contrast, more recent stress was associated with greater pace of aging between the 2 MRI sessions, independent of maternal depression (adjusted R2 = 0.09; P = .01). Conclusions and Relevance: The findings of this cohort study suggest that maternal depression and recent stress may have independent associations with brain age and the pace of aging, respectively, in young adulthood. Prevention and treatment of depression in pregnant mothers may have long-term implications for offspring brain development.

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 score0.053
Threshold uncertainty score0.406

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 head0.015
GPT teacher head0.281
Teacher spread0.266 · 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

Citations24
Published2023
Admission routes2
Has abstractyes

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