The Impact of Idiopathic Intracranial Hypertension on Cardiovascular Disease Risk Among UK Women: An Obesity-Adjusted Analysis
Bibliographic record
Abstract
Introduction: Idiopathic intracranial hypertension (IIH) is known to elevate cardiovascular disease (CVD) risk, but the extent to which obesity and IIH-specific factors contribute to this risk is not well understood. WE aim to separate the effects of obesity from IIH-specific factors on the risk of stroke and CVD, building on previous findings that indicate a two-fold increase in cardiovascular events in women with IIH compared to BMI-matched controls. Methods: An obesity-adjusted risk analysis was conducted using Indirect Standardization based on data from a cohort study by Adderley et al., which included 2,760 women with IIH and 27,125 matched healthy controls from The Health Improvement Network (THIN). Advanced statistical models were employed to adjust for confounding effects of obesity and determine the risk contributions of IIH to ischemic stroke and CVD, independent of obesity. Four distinct models explored the interactions between IIH, obesity, and CVD risk. Results: The analysis showed that IIH independently contributes to increased cardiovascular risk beyond obesity alone. Risk ratios for cardiovascular outcomes were significantly higher in IIH patients compared to controls within similar obesity categories. Notably, a synergistic effect was observed in obese IIH patients, with a composite CVD risk ratio of 6.19 (95% CI: 4.58-8.36, p<0.001) compared to non-obese controls. Conclusions: This study underscores a significant, independent cardiovascular risk from IIH beyond obesity. The findings advocate for a shift in managing IIH to include comprehensive cardiovascular risk assessment and mitigation. Further research is required to understand the mechanisms and develop specific interventions for this group.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".