Preeclampsia and postpartum mental health: mechanisms and clinical implications
Bibliographic record
Abstract
Preeclampsia is one of the leading causes of maternal morbidity and mortality worldwide, with the short and long-term implications for maternal health being increasingly recognized. Yet the effects of preeclampsia on mental health are often overlooked, effects which can be evident both immediately postpartum and decades later. In particular, preeclampsia has been associated with increased risk and severity of cognitive impairment, psychosocial distress, and psychiatric disorders including depression, anxiety, and post-traumatic stress disorder. While these outcomes are reported, few have proposed how the pathophysiology of preeclampsia may contribute to changes in postpartum mental health. Studies have suggested that anti-angiogenic factors and pro-inflammatory cytokines released from the preeclamptic placenta may damage the blood-brain barrier endothelium, leading to long-term structural and functional cerebral changes. These changes may contribute to subsequent impairments in mental health. In addition, the pro-inflammatory profile and patterns of cerebral damage observed in preeclampsia are similar to that of psychiatric disorders and cognitive impairment, suggesting they may share common mechanisms. Yet, there is limited evidence on how these mechanisms may interact. The purpose of this review is to summarize the evidence for these pathophysiological mechanisms and propose how they may work synergistically to affect brain structure, cognition, and postpartum mental health in preeclampsia. The role of psychosocial factors, disease severity, and psychological treatment in the mental health of preeclampsia patients will also be discussed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".