Bibliographic record
Abstract
Perinatal mental illnesses affect approximately 1 out of 5 women in Canada, with postpartum depression (PPD) the most common complication of pregnancy, affecting 1 out of 10 women in Canada. Women who experience PPD often show symptoms during pregnancy, and best practice clinical guidelines increasingly call for screening of maternal mental health symptoms in the antenatal period. To do so effectively we must first identify relevant risk factors that associate with, and predict, risk of perinatal mental illness. Epidemiological and genomic studies carried out to date suggest contributions of a number of environmental and genetic risk factors to perinatal mental illness. Prediction models that consider both environmental and biological risk factors may be able to better identify women at risk of perinatal mental illness. In this thesis, I examined the relationship between environmental risk factors for perinatal mental illness in a large prospective cohort from the United Kingdom: the Avon Longitudinal Study of Parents and Children (ALSPAC, N=15242). Women provided detailed assessments of maternal anxiety and depression twice during pregnancy (at 18 and 32 weeks of gestation) and at multiple timepoints in the postpartum where I focus on the 8 week and 8 months postpartum assessments. Paired genetic data and epigenetic data (DNA methylation), were available on 9299 and 924 women, respectively. Linear regression models identified an ‘environmental’ risk model for maternal depressive/anxiety symptoms in the ALSPAC cohort including prenatal social support that cumulatively explained 17.4-32.5% of the variance in maternal symptoms in perinatal period. Next, leveraging recent advances in population genetics and large genome-wide association studies of psychiatric disorders, I integrated polygenic risk scores (PRS: a summary measure of genetic risk for a given phenotype) within the ‘environmental’ risk model. The PRS for depressive symptoms explained approximately 1% of the variance in perinatal depressive and anxiety symptoms. Lastly, I explored known epigenetic biomarkers of PPD to predict PPD in the ALSPAC mothers, and found evidence of an association between epigenetic variation and PPD. Based on the findings of the current study, perinatal mental health can be influenced by various environmental and biological factors, however environmental factors including history of sexual abuse and prenatal social support account for the largest proportion of variance in maternal mood. Together these findings highlight the importance of social support in pregnancy as well as the necessity to prevent sexual abuse in order to promote maternal perinatal mental health
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".