Association between Polycystic Ovarian Syndrome, Impaired Kidney Function and Hyperuricaemia: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Background: Polycystic ovarian syndrome (PCOS) is a gynaecological problem affecting women within reproductive age, accompanied by several metabolic anomalies, thus leading to alteration in kidney function and hyperuricaemia. Due to the high prevalence of cardiometabolic factors in PCOS, there is a need to anticipate an increased number of kidney impairments amongst these women. Objectives: This review aims to investigate the potential link between PCOS, impaired kidney function, and elevated uric acid levels. By elucidating this association, we hope to provide clinicians with a tool to stratify the risk of kidney disease in women diagnosed with PCOS, based on readily available kidney function parameters. Materials and Methods: The recommendations used for the analysis were outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines. Subsequently, eligible studies were identified using several databases (MEDLINE, ProQuest and EBSCOhost) between 1996 and 2022, with a total of 13 studies included. Serum uric acid, serum creatinine, as well as estimated glomerular filtration rate (eGFR) were evaluated as the outcome of interest. Quality assessment for cohort, case-control and cross-sectional studies was conducted utilising the Newcastle-Ottawa Scale, while Review Manager 5.4 was utilised for meta-analysis. Results: = 0.39, respectively). Interpretation: This review showed that PCOS was significantly associated with elevated uric acid. However, no significant difference was found between eGFR and creatinine levels compared to healthy controls. Routine uric acid assessment in PCOS patients is recommended as a simple tool for risk stratification. Limitations: No body mass index (BMI) subgroup analysis was done due to limited BMI reporting in our included studies. Quantitative analysis of all kidney function parameters was also limited by sparse data on urea and albumin. PROSPERO Registration Number: CRD42023410092 (02 April 2023).
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 | 0.010 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".