Assessing Sex Differences in Metabolic Disease on Vasculopathy Using the Vascular Health Index
Bibliographic record
Abstract
INTRODUCTION: Investigation into vascular health and disease across elevated risk conditions has been intensively studied for many years. However, the ability to understand integrated vascular health status has been challenging, as most previous work has focused on specific outcomes, interventions, or potential mechanistic links. While these efforts have revealed many factors contributing to vasculopathy, challenges remain for comparing results across research groups, models, and conditions to understand vascular health status. In the present study, our objective was to quantify sex-dependent differences in peripheral and cerebral vascular health across metabolic disease. METHODS: Utilizing the vascular health index (VHI), a validated metric allowing for simultaneous assessment of vascular reactivity/endothelial function, vascular wall mechanics, and microvessel density within cerebral and skeletal muscle networks, we focus on the impact of elevated metabolic disease risk between male and female obese Zucker rats (OZR). In addition, we study VHI in female OZR following ovariectomy (OVX), with all outcomes compared to results from "healthy" lean Zucker rats (LZRs). RESULTS: Across all ages, male and female LZR demonstrated comparable VHI, although increased metabolic disease risk reduced both skeletal muscle and cerebral VHI in male OZR more rapidly, and to a greater extent, as compared to female OZR. Protection for VHI for female OZR with elevated disease risk was dependent on intact sex hormone cycling, as OVX in female OZR removed protection in VHI compared to normal female OZR. CONCLUSION: These results indicate that sex-based protections in peripheral and cerebral vascular health with metabolic disease in female OZR (versus males) are present at multiple levels of resolution and are dependent on normal female sex hormone cycling.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".