The impact of GABA and GABAergic pathway in polycystic ovary syndrome: a systematic review
Bibliographic record
Abstract
Emerging evidence indicates that dysfunction of the gamma-aminobutyric acid (GABA)ergic pathway may contribute to the pathophysiology of polycystic ovary syndrome (PCOS), and GABA demonstrates potential in the management of PCOS symptoms. This systematic review aimed to determine the role of the GABAergic pathway in PCOS and evaluate the impact of GABA on improving the condition. Web of Science, Embase, Scopus, Cochrane, and PubMed databases were systematically searched for experimental studies, clinical trials, animal studies, and cellular investigations. The search was conducted for relevant English-language manuscripts, published up to February 2024, using keywords, such as "polycystic ovary syndrome", PCOS, "gamma-aminobutyric acid" and GABA. Quality assessment of the included studies was performed using the Cochrane Collaboration's tool and the Newcastle-Ottawa scale. The results indicate that GABAergic dysfunction adversely affects gonadotrophin-releasing hormone neuronal activity, leading to hormonal imbalances and reproductive issues. Prenatal androgen exposure and kisspeptin signaling influence GABAergic transmission to GnRH neurons, thereby linking GABA to the pathogenesis of PCOS. Additionally, GABAergic signaling affects peripheral tissues relevant to PCOS, including the immune system, gut-brain axis, and ovaries. GABA supplementation has demonstrated potential benefits in enhancing metabolic and reproductive health, such as reducing insulin resistance and modulating sex hormone levels, as supported by animal models and clinical studies involving females with PCOS. The GABAergic signaling pathway may represent a promising therapeutic target for the management of PCOS. Nevertheless, further studies are required to validate these findings and deepen our understanding of the role of GABA in the pathogenesis and treatment of PCOS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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".