Birth Weight, Stress, and Symptoms of Depression in Adolescence: Evidence of Fetal Programming in a National Canadian Cohort
Bibliographic record
Abstract
OBJECTIVE: To investigate evidence of fetal programming in humans by studying whether adolescents born at high or low birth weights (LBW) are more likely to experience symptoms of depression and anxiety after experiencing stress. METHOD: The sample included 3732 members of a prospective Canadian cohort study assessed for symptoms of depression and anxiety at age 12 to 15 years (2006/2007), and had birth weight and gestational age (GA) data recorded in 1994/1995. Major stressful life events and chronic stressors were also reported throughout childhood. RESULTS: After adjusting for acute and chronic stress, being born small for GA (SGA) (OR 1.50; 95% CI 1.08 to 2.08) or large (OR 1.31; 95% CI 0.99 to 1.72) for GA was associated with an increased risk of depression and anxiety in adolescence, compared with adolescents who were born at a weight appropriate for their GA. Most interactions between birth weight and stress were not significant; however, the relation between chronic stress and adolescent depression and anxiety was more pronounced in males who were born SGA (interaction P < 0.05). CONCLUSIONS: The link between birth weight and depression is complex and evidence of fetal programming is inconsistent; however, people born at LBW may be at an increased risk of depression in the face of chronic stress.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".