MétaCan
Menu
Back to cohort
Record W4387458237 · doi:10.1016/j.lana.2023.100609

Prenatal sleep health and risk of offspring ADHD symptomatology and associated phenotypes: a prospective analysis of timing and sex differences in the ECHO cohort

2023· article· en· W4387458237 on OpenAlexfundno aff
Claudia Lugo‐Candelas, Tse Hwei, Seonjoo Lee, Maristella Lucchini, Alice Smaniotto Aizza, Linda G. Kahn, Claudia Buß, Thomas G. O’Connor, Akhgar Ghassabian, Amy Padula, Judy L. Aschner, Sean Deoni, Amy Margolis, Glorisa Canino, Catherine Monk, Jonathan Posner, Cristiane S. Duarte

Bibliographic record

VenueThe Lancet Regional Health - Americas · 2023
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsnot available
FundersDuke Clinical Research InstituteBeckman Institute for Advanced Science and Technology, University of Illinois, Urbana-ChampaignNational Institute of Environmental Health SciencesNYU Grossman School of MedicineJohns Hopkins Bloomberg School of Public HealthUniversity of California, San FranciscoUniversity of PittsburghMedical Center, University of PittsburghNational Institutes of HealthOffice of Behavioral and Social Sciences ResearchNational Alliance for Research on Schizophrenia and DepressionYork UniversityRhode Island HospitalUniversity of Illinois SystemUniversity of Illinois at Urbana-ChampaignJohns Hopkins UniversityNational Institute of Mental HealthNorthwestern UniversityBrain and Behavior Research Foundation
KeywordsOffspringProspective cohort studyCohortMedicineCohort studyPhenotypePolygenic risk scorePsychiatryPsychologyClinical psychologyPregnancyInternal medicineGeneticsBiologyGenotypeGeneSingle-nucleotide polymorphism

Abstract

fetched live from OpenAlex

Sleep difficulties are common in pregnancy, yet poor prenatal sleep may be related to negative long-term outcomes for the offspring, including risk for attention-deficit/hyperactivity disorder (ADHD). Existing studies are few and have not examined timing of exposure effects or offspring sex moderation. We thus aimed to test the hypotheses that poor sleep health in pregnancy is associated with increased risk for ADHD symptoms and offspring sleep problems at approximately 4 years of age. Participants were 794 mother-child dyads enrolled in the NIH Environmental Influences on Child Health Outcomes Study (ECHO). Participants self-reported on sleep duration, quality, and disturbances during pregnancy and on children's ADHD symptoms and sleep problems on the Child Behaviour Checklist. Pregnant participants were 32.30 ± 5.50 years and children were 46% female. 44 percent of pregnant participants identified as Hispanic or Latine; 49% identified as White. Second-trimester sleep duration was associated with offspring ADHD symptoms (b = −0.35 [95% CI = −0.57, −0.13], p = 0.026), such that shorter duration was associated with greater symptomatology. Poorer sleep quality in the second trimester was also associated with increased ADHD symptomatology (b = 0.66 [95% CI = 0.18, 1.14], p = 0.037). Greater sleep disturbances in the first trimester were associated with offspring ADHD (b = 1.03 [95% CI = 0.32, 1.03], p = 0.037) and in the second trimester with sleep problems (b = 1.53 [95% CI = 0.42, 2.92], p = 0.026). We did not document substantial offspring sex moderation. Poor prenatal sleep health, particularly quality and duration in the second trimester, may be associated with offspring risk of neurodevelopmental disorders and sleep problems in early childhood. Further research is needed to understand mechanisms, yet our study suggests that prenatal maternal sleep may be a modifiable target for interventions aimed at optimizing early neurodevelopment. NIH grants U2COD023375, U24OD023382, U24OD023319, UH3OD023320, UH3OD023305, UH3OD023349, UH3OD023313, UH3OD023272, UH3OD023328, UH3OD023290, K08MH117452 and NARSAD Young Investigator Award 28545.

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)0.001
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 score0.034
Threshold uncertainty score0.968

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.052
GPT teacher head0.355
Teacher spread0.303 · 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

Citations18
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueThe Lancet Regional Health - AmericasSame topicSleep and related disordersFrench-language works237,207