Influence of metabolic syndrome on cerebral perfusion and cognition
Bibliographic record
Abstract
Vascular cognitive impairment (VCI) is associated with chronic cerebral hypoperfusion (CCH) and memory deficits, and often occurs concurrently with metabolic syndrome (MetS). Despite their common occurrence, it is unknown whether CCH and MetS act synergistically to exacerbate VCI-associated pathology. Here, using male Sprague-Dawley rats, we examined the effects of a clinically relevant model of adolescent-onset MetS and adult-onset CCH on neuro-vascular outcomes, combining a cafeteria diet with a 2-vessel occlusion (2VO) model. Using longitudinal imaging, histology, and behavioural assessments, we identified several features of MetS and CCH including reduced cerebral blood volume, white matter atrophy, alterations in hippocampal cell density, and memory impairment. Furthermore, we identified a number of significant associations, potentially predictive of MetS and pathophysiological outcomes. White matter volume was positively correlated to HDL cholesterol; hippocampal cell density was negatively correlated to fasted blood glucose; cerebral blood flow and volume was negatively predicted by the combination of 2VO surgery and increased fasted blood glucose. These results emphasize the importance of including comorbid conditions when modeling VCI, and they outline a highly translational preclinical model that could be used to investigate potential interventions to mitigate VCI-associated pathology and cognitive decline.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".