Divergent Trajectories of Cardiovascular Risk factors in the Years before Pregnancy in Women with and without Gestational Diabetes: A Population-based Study
Bibliographic record
Abstract
BACKGROUND: Women who develop GDM have an elevated lifetime risk of cardiovascular disease, which has been attributed to an adverse cardiovascular risk factor profile that is apparent even within the first year postpartum. Given its presence in the early postpartum, we hypothesized that this adverse cardiovascular risk factor profile may develop over time in the years prior to pregnancy. METHODS: With population-based administrative databases, we identified all nulliparous women in Ontario, Canada, who had singleton pregnancies between January/2011 and December/2016 and ≥2 measurements of the following analytes between 2007 and the start of pregnancy: A1c, fasting glucose, random glucose, lipids, and transaminases. This population consisted of 8,047 women who developed GDM and 93,114 women who did not. RESULTS: The two most recent pregravid tests were performed at median 0.61 years and 1.86 years before pregnancy, respectively. Women who went on to develop GDM had higher pregravid A1c, fasting glucose, random glucose, LDL-cholesterol, triglycerides, and ALT, and lower HDL-cholesterol, than their peers (all p<0.0001). Notably, in the years before pregnancy, women who went on to develop GDM had higher annual increases than their peers in A1c (1.9-fold higher) (difference 0.0089%/year, 95%CI 0.0043 to 0.0135) and random glucose (4.3-fold); greater annual decrease in HDL-cholesterol (5.5-fold); and lesser annual decline in LDL-cholesterol (0.4-fold)(all p≤0.0002). During this time, fasting glucose and triglycerides increased in women who developed GDM but decreased in their peers (both p<0.0001). CONCLUSION: The adverse cardiovascular risk factor profile of women with GDM evolves over time in the years before pregnancy.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".