255-OR: Postpartum Weight Retention and the Early Evolution of Cardiovascular Risk over the First 5 Years after Delivery
Bibliographic record
Abstract
The cumulative effect of postpartum weight retention from each pregnancy in a woman’s life may contribute to her risk of ultimately developing cardiovascular (CV) disease and type 2 diabetes. However, there is limited direct evidence supporting this hypothesis. Thus, we sought to characterize the impact of postpartum weight retention on the trajectories of cardiometabolic risk factors over the first 5 years after pregnancy. Three-hundred-and-thirty women underwent cardiometabolic characterization in pregnancy and at 3-months, 1-yr, 3-yr, and 5-yrs postpartum. They were stratified into 3 groups based on the magnitude of weight change between pre-pregnancy and 5-yrs postpartum as follows: weight gain <0% (n=100), weight gain 0-6% (n=110), and weight gain >6% (n=120). CV risk factors did not differ between these groups at 1-yr postpartum but showed a stepwise worsening across the groups at 3- and 5-yrs (Fig). Specifically, adverse lipids, insulin resistance, and CRP progressively worsened from the weight gain <0% group to weight gain 0-6% to weight gain >6% (all p<0.05). On logistic regression analyses of prediabetes/diabetes at 5-years postpartum, weight gain >6% was shown to be significant (adjusted OR 3.40 [1.63 to 7.09]). In conclusion, postpartum weight retention predicts trajectories of enhanced CV risk and rising glycemia over the first 5-yrs after delivery. Disclosure C.K. Kramer: Research Support; Boehringer Ingelheim Inc. C. Ye: None. A. Hanley: None. P.W. Connelly: None. B. Zinman: Consultant; Abbott Diabetes, Eli Lilly and Company, Novo Nordisk A/S, Novo Nordisk Canada Inc., Boehringer Ingelheim Inc., Merck & Co., Inc. R. Retnakaran: Research Support; Boehringer Ingelheim/Mount Sinai Hospital, Novo Nordisk. Other Relationship; Sanofi, Eli Lilly and Company.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".