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Infertility and Risk of Autism Spectrum Disorder in Children

2023· article· en· W4388824295 on OpenAlexafffundabout
Maria P. Vélez, Natalie Dayan, Jonas Shellenberger, Jessica Pudwell, Dia Kapoor, Simone N. Vigod, Joel G. Ray

Bibliographic record

VenueJAMA Network Open · 2023
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsSt. Michael's HospitalUniversity of TorontoMcGill University Health CentreInstitute for Clinical Evaluative SciencesMcGill UniversityWomen's College HospitalQueen's University
FundersCanadian Institutes of Health Research
KeywordsObstetricsInfertilityMedicineIntracytoplasmic sperm injectionFertilityPregnancyIn vitro fertilisationGynecologyHazard ratioPopulationAssisted reproductive technologyConfidence intervalBiologyInternal medicine

Abstract

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Importance: Previous studies on the risk of childhood autism spectrum disorder (ASD) following fertility treatment did not account for the infertility itself or the mediating effect of obstetrical and neonatal factors. Objective: To assess the association between infertility and its treatments on the risk of ASD and the mediating effect of selected adverse pregnancy outcomes on that association. Design, Setting, and Participants: This was a population-based cohort study in Ontario, Canada. Participants were all singleton and multifetal live births at 24 or more weeks' gestation from 2006 to 2018. Data were analyzed from October 2022 to October 2023. Exposures: The exposure was mode of conception, namely, (1) unassisted conception, (2) infertility without fertility treatment (ie, subfertility), (3) ovulation induction (OI) or intrauterine insemination (IUI), or (4) in vitro fertilization (IVF) or intracytoplasmic sperm injection (ICSI). Main Outcome and Measures: The study outcome was a diagnosis of ASD at age 18 months or older. Cox regression models generated hazard ratios (HR) adjusted for maternal and infant characteristics. Mediation analysis further accounted for the separate effect of (1) preeclampsia, (2) cesarean birth, (3) multifetal pregnancy, (4) preterm birth at less than 37 weeks, and (5) severe neonatal morbidity. Results: A total of 1 370 152 children (703 407 male [51.3%]) were included: 1 185 024 (86.5%) with unassisted conception, 141 180 (10.3%) with parental subfertility, 20 429 (1.5%) following OI or IUI, and 23 519 (1.7%) following IVF or ICSI. Individuals with subfertility or fertility treatment were older and resided in higher-income areas; the mean (SD) age of each group was as follows: 30.1 (5.2) years in the unassisted conception group, 33.3 (4.7) years in the subfertility group, 33.1 (4.4) years in the OI or IUI group, and 35.8 (4.9) years in the IVF or ICSI group. The incidence rate of ASD was 1.93 per 1000 person-years among children in the unassisted conception group. Relative to the latter, the adjusted HR for ASD was 1.20 (95% CI, 1.15-1.25) in the subfertility group, 1.21 (95% CI, 1.09-1.34) following OI or IUI, and 1.16 (95% CI, 1.04-1.28) after IVF or ICSI. Obstetrical and neonatal factors appeared to mediate a sizeable proportion of the aforementioned association between mode of conception and ASD risk. For example, following IVF or ICSI, the proportion mediated by cesarean birth was 29%, multifetal pregnancy was 78%, preterm birth was 50%, and severe neonatal morbidity was 25%. Conclusions and Relevance: In this cohort study, a slightly higher risk of ASD was observed in children born to individuals with infertility, which appears partly mediated by certain obstetrical and neonatal factors. To optimize child neurodevelopment, strategies should further explore these other factors in individuals with infertility, even among those not receiving fertility treatment.

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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 score0.077
Threshold uncertainty score0.607

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.002
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 head0.023
GPT teacher head0.302
Teacher spread0.279 · 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".

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Citations24
Published2023
Admission routes3
Has abstractyes

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