Effect of Lactation on Maternal Hypertension: A Systematic Review
Bibliographic record
Abstract
INTRODUCTION: Hypertension is relatively common in pregnancy, and pregnancy may unmask hypertension among women who are predisposed to it. Lactation may be a means through which to mitigate pregnancy-related vascular risk. The impact of lactation on maternal blood pressure, and the duration of any effect, remains unclear. This study aimed at systematically reviewing the literature evaluating the impact of lactation on the development of hypertension. MATERIALS AND METHODS: We searched PubMed, including EMBASE and MEDLINE, for studies that reported on the association between breastfeeding and maternal risk of hypertension that were published in a peer-reviewed source. The quality of the studies included was assessed by using the Newcastle-Ottawa Scale. RESULTS: Nineteen studies met all inclusion criteria for this review. Of the four studies with short-term follow-up, 50% showed a protective association. The fifteen studies with longer-term follow-up were stratified by outcome assessed. Sixty-seven percent of the studies that evaluated for elevated blood pressure and 100% of the studies evaluating for an outcome of hypertension showed a protective association. The minimum duration of lactation associated with a benefit was 1 month. This association was demonstrated in follow-up periods as long as two to three decades. Studies that showed a protective association had overall higher quality ratings. DISCUSSION: Lactation is associated with a beneficial effect on maternal blood pressure that persists for decades. These results add to the growing body of literature demonstrating the protective association of lactation on maternal cardiovascular risk. Providers may incorporate the decreased risk of hypertension into their counseling on the maternal benefits of lactation.
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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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".