Bibliographic record
Abstract
Polycystic ovary syndrome (PCOS) is a highly heritable endocrine disorder in premenopausal females characterized by ovarian dysfunction, hyperandrogenism, and numerous metabolic comorbidities like type 2 diabetes (T2D).Despite the high prevalence of metabolic dysfunction, the genetic etiology between these conditions still remains poorly understood.Using polygenic risk scores (PRS), we showed that PCOS genetic risk drives sex differentiated cardiovascular signatures and is influenced by routinely collected electronic health record (EHR) body mass index (BMI) measurements.Therefore, we aimed to understand the mediating causal effect of BMI between PCOS and cardiometabolic diseases and to determine whether these effects were modified by sex.To do this, we performed a mediation analysis on 72,824 European descent individuals with PCOS PRS as the exposure variable and dichotomized clinical diagnosis as the outcome.When we examined the mediating role of BMI extracted from EHRs, which captures both genetic and environmental variance, we found that BMI was a strong mediator for cardiometabolic outcomes in both sexes (T2D Females =29%, T2D Males =23%, Hypertension Males =17%, P-value=<2e-16; Hypertension Females =41%, P-value=0.002).However, once we partitioned out the genetically regulated BMI variance, our findings revealed that the residual environmental BMI was not mediating the pathway from PCOS PRS to T2D (P-value=0.78) or hypertension (P-value=0.82) in males.Overall, our results implicate genetically regulated BMI as an important risk factor in the early development of cardiovascular diseases in males with high genetic predisposition for PCOS.Therefore, implementation of intervention programs and monitoring procedures are warranted for both sexes with family history of PCOS.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.682 | 0.538 |
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".