Patterns of Cardiovascular Risk Factors in the Years Before Pregnancy in Nulliparous Women With and Without Preterm Birth and Small‐for‐Gestational‐Age Delivery
Bibliographic record
Abstract
Background Women with either preterm or small-for-gestational-age (SGA) delivery have an elevated lifetime risk of cardiovascular disease that has been attributed to the accrual of vascular risk factors over time. We sought to determine whether an adverse cardiovascular risk factor profile develops in the years before pregnancies complicated by preterm delivery or SGA. Methods and Results Using administrative databases, we identified all 156 278 nulliparous women in Ontario, Canada, who had singleton pregnancies between January 2011 and December 2018 and ≥2 measurements of the following analytes between January 2008 and the start of pregnancy: glycosylated hemoglobin, glucose, lipids, and alanine aminotransferase. There were 11 078 women with preterm delivery and 19 367 with SGA. The 2 most recent pregravid tests were performed at median 0.6 (interquartile range, 0.3-1.4) and 1.9 (interquartile range, 1.1-3.3) years before pregnancy, respectively. Women with preterm delivery had higher pregravid glycosylated hemoglobin, glucose, low-density lipoprotein cholesterol, triglycerides, and alanine aminotransferase, and lower high-density lipoprotein cholesterol, than those without preterm delivery. In contrast, women with SGA had lower pregravid fasting glucose, random glucose, and triglycerides than those without SGA. In the years before pregnancy, women with preterm delivery had higher annual increases than their peers in glycosylated hemoglobin (0.7-times higher), triglycerides (7.9-times higher), and alanine aminotransferase (2.2-times higher). During this time, fasting glucose increased in women who developed preterm delivery but decreased in their peers. Conclusions An adverse cardiovascular risk factor profile evolves over time in the years before pregnancy complicated by preterm delivery, but does not necessarily precede SGA.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.001 | 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".