Gut microbiota dysbiosis in patients with preeclampsia: A systematic review
Bibliographic record
Abstract
Currently, the etiology of preeclampsia (PE) has not been comprehensively clarified. Accumulating evidence indicated that gut microbiota is associated with the onset of PE. Herein, a systematic review was conducted to explore the dysbiosis of gut microbiota in PE patients compared with healthy controls (HCs). Publications were retrieved from Medline, EMBASE, Web of Science and Scopus. Studies comparing the gut microbiota in PE patients to HCs using culture-independent methods were included. Independent quality assessment and data extraction was performed according to PRISMA statement and Newcastle-Ottawa Scale (NOS). In total, six studies with an overall sample size of 416 PE patients and 704 HCs were included. In terms of alpha- and beta-diversity, consistent results reflecting the alteration of gut microbiota in PE patients. Furthermore, Fusobacterium and Ruminococcus enriched, while Lachnospira, Akkermansia, Faecalibacterium, Bifidobacterium and Alistipes were depleted in PE. This systematic review demonstrates significant dysbiosis of gut microbiota in PE patients and confirms that that the possible correlations between gut microbiota dysbiosis and PE onset. However, heterogeneity in results was also identified, alluding more well-designed studies are warranted. Above all, these evidence demonstrates that the gut microbiota may be a potential treatment and prevention target for PE.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".