Microbiome alterations in women with gestational diabetes mellitus and their offspring: A systematic review
Bibliographic record
Abstract
Aims: Gestational diabetes mellitus (GDM) is a metabolic disorder that might predispose pregnant women to develop type 2 Diabetes Mellitus or lead to severe adverse outcomes in their offspring. One of the factors that have been thought to be involved in the pathology behind this disorder is the microbiome. In this systematic review, we comprehensively review the documents regarding the microbiota alterations in different tracts of pregnant women with GDM and their offspring. Methods: A comprehensive search was conducted in major databases including MEDLINE (PubMed), Scopus, and Web of sciences up to August 2021. Data on the demographics, methodology, and microbiome alterations were extracted and classified according to the type of microbiome in pregnant women with GDM and their offspring. The quality of studies was assessed using the Newcastle-Ottawa Scale (NOS). Results: In 49 articles which were retrieved, the findings were variable on the level of changes in alpha and beta diversity, enrichment or depletion in phyla, genera, species and OTUs, in each microbiome type. Although there were some inconsistencies among the results, a pattern of significant alterations was seen in the gut, oral, vaginal microbiome of women with GDM and gut, oral, and placental microbiome of their offspring. Conclusion: Even though the alteration of the microbiome of the different tracts was seen in the cases of GDM, the inconsistency among the studies prevents us from identifying unique pattern. However, the results seem promising and further studies that overcome the confounding factors related to the demographics and methodology are needed.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.007 | 0.008 |
| 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) | 0.003 | 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".