Associations of pregnancy complications with paternal cardiovascular risk: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Early cardiovascular disease risk detection opportunities are limited in men, whereas gestational diabetes, gestational hypertension and preeclampsia are risk indicators in women. We hypothesised adverse pregnancy outcomes also signal risk in fathers, due to shared environments and behaviours. METHODS: Our retrospective cohort study included fathers whose female partners had at least two singleton deliveries between April 1990 and December 2012. We examined population-based data up to April 2019 from Quebec province, Canada (health administrative databases, birth, stillbirth and death registries). The primary exposure was cumulative gestational diabetes, gestational hypertension and preeclampsia occurrences across two pregnancies. Outcomes were new diagnoses of diabetes, hypertension and cardiovascular disease in fathers, analysed using Cox proportional hazards models. RESULTS: Among 415 730 fathers, 17 065 developed diabetes, 44 315 developed hypertension and 9695 experienced a cardiovascular disease event over more than a decade. Compared with no gestational diabetes or gestational hypertension/preeclampsia occurrences in partners, the hazards of diabetes in fathers increased by 21% with a single occurrence (HR 1.21, 95% CI 1.16 to 1.26), 40% with two (HR 1.40, 95% CI 1.30 to 1.50) and 84% with three or more (HR 1.84, 95% CI 1.54 to 2.21). Corresponding increases in hypertension hazards were 11% (HR 1.11, 95% CI 1.08 to 1.14), 17% (HR 1.17, 95% CI 1.12 to 1.23) and 39% (HR 1.39, 95% CI 1.22 to 1.58), respectively. Cardiovascular disease hazards increased by 15% with two or more occurrences (HR 1.15, 95% CI 1.04 to 1.27). CONCLUSION: More maternal adverse pregnancy outcomes lead to greater paternal cardiometabolic disease hazards. Partner pregnancy history may help identify at-risk men to support early prevention.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".