Early Life Risk Factors for Incident Atrial Fibrillation in the Helsinki Birth Cohort Study
Bibliographic record
Abstract
Background Early life risk factors are associated with cardiometabolic disease, but have not been fully studied in atrial fibrillation ( AF ). There are discordant results from existing studies of birth weight and AF , and the impact of maternal body size, gestational age, placental size, and birth length is unknown. Methods and Results The Helsinki Birth Cohort Study includes 13 345 people born as singletons in Helsinki in the years 1934–1944. Follow‐up was through national registries, and ended on December 31, 2013, with 907 incident cases. Cox regression analyses stratified on year of birth were constructed for perinatal variables and incident AF , adjusting for offspring sex, gestational age, and socioeconomic status at birth. There was a significant U‐shaped association between birth weight and AF ( P for quadratic term=0.01). The lowest risk of AF was found among those with a birth weight of 3.4 kg (3.8 kg for women [85th percentile] and 3.0 kg for men [17th percentile]). High maternal body mass index (≥30 kg/m 2 ) predicted offspring AF ; hazard ratio 1.36 (95% CI 1.07–1.74, P =0.01) compared with normal body mass index (<25 kg/m 2 ). Maternal height was associated with early‐onset AF (<65.3 years), hazard ratio 1.47 (95% CI 1.24–1.74, P <0.0001), but not with later onset AF . Results were independent of incident coronary artery disease, hypertension, or diabetes mellitus. Conclusions High maternal body mass index during pregnancy and maternal height are previously undescribed predictors of offspring AF . Efforts to prevent maternal obesity might reduce later AF in offspring. Birth weight has a U‐shaped relation to incident AF independent of other perinatal variables.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".