EXPLORING THE ASSOCIATION BETWEEN INSULIN RESISTANCE AND METABOLIC, HORMONAL, AND REPRODUCTIVE OUTCOMES IN WOMEN WITH POLYCYSTIC OVARY SYNDROME: A SYSTEMATIC REVIEW
Bibliographic record
Abstract
Background: Insulin resistance (IR) is a pivotal pathophysiological feature of polycystic ovary syndrome (PCOS), yet its comprehensive impact on the spectrum of metabolic, hormonal, and reproductive outcomes within this population requires systematic elucidation. Objective: This systematic review aims to evaluate the association between insulin resistance and metabolic, hormonal, and reproductive health outcomes in women diagnosed with PCOS. Methods: A systematic search was conducted across PubMed, Scopus, Web of Science, and the Cochrane Library for observational studies published between 2014 and 2024. Inclusion criteria encompassed studies comparing PCOS women with and without IR, defined by clamp techniques or validated indices like HOMA-IR. Data extraction and risk of bias assessment, using the Newcastle-Ottawa Scale, were performed by two independent reviewers. A qualitative synthesis of the evidence was conducted. Results: Eight studies (n=1,843 participants) were included. The presence of IR was consistently associated with a significantly worse metabolic profile, including adverse lipid parameters (elevated triglycerides, lower HDL-C), and a more severe hyperandrogenic phenotype (higher testosterone, lower SHBG). Evidence also suggested IR is linked to poorer reproductive outcomes, including reduced ovulation rates and an increased risk of gestational diabetes. Conclusion: Insulin resistance identifies a distinct PCOS subgroup with a more severe metabolic and hormonal burden and potentially worse reproductive prognosis. These findings underscore the critical need for routine IR assessment to guide risk stratification and personalized management strategies. Further longitudinal research is warranted to establish causality and explore interventions targeting IR.
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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 | 0.008 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".