Postpartum Breastfeeding and Cardiovascular Risk Assessment in Women Following Pregnancy Complications
Bibliographic record
Abstract
Background: Breastfeeding is associated with lower cardiovascular (CV) risk over the long-term, however, less is known about its immediate effects among women with a recent complicated pregnancy. The objective of this study is to investigate the short-term effects of breastfeeding on markers of cardiovascular disease risk among women ∼6 months after a pregnancy complicated by a hypertensive disorder, gestational diabetes, intrauterine growth restriction, abruption, or preterm birth. Materials and Methods: Our cross-sectional analysis includes 622 women seen at 6 months postpartum (interquartile range: 5.7–6.7) between November 2011 and December 2017 at a tertiary care center. Self-reported breastfeeding status and measured CV risk factors were assessed at the same visit. CV risk factors were compared between women who did not breastfeed (n = 100, 16%), those who breastfed for less than 6 months (n = 315, 51%), and those who breastfed for 6 months or more (n = 207, 33%) using multivariate logistic and linear regression. Results: Increased breastfeeding duration significantly decreased the likelihood of metabolic syndrome (adjusted odds ratio [95% confidence interval; CI]: 0.89 [0.79–0.99]), abnormal fasting glucose (0.79 [0.64–0.96]), and ratio of total cholesterol to high-density lipoprotein-cholesterol (HDL-C) (0.86 [0.78–0.95]). Furthermore, body mass index (estimated beta coefficients [95% CI] −0.10 [−0.18 to −0.02]), fasting glucose (−0.05 [−0.08 to −0.02]), triglycerides (−0.07 [−0.10 to −0.04]), and ratio of total cholesterol to HDL-C (−0.06 [−0.10 to −0.03]) also decreased with increased breastfeeding duration, while HDL-C increased (0.02 [−0.01 to −0.04]). Conclusions: Our findings suggest that breastfeeding is associated with decreased indicators of CV risk in a cohort of women with recent pregnancy complication.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".