Arterial Stiffness Due to Carotid Calcification Disrupts Cerebral Blood Flow Regulation and Leads to Cognitive Deficits
Bibliographic record
Abstract
Background Arterial stiffness is associated with cognitive decline and dementia; however, the precise mechanisms by which it affects the brain remain unclear. Methods and Results Using a mouse model based on carotid calcification this study characterized mechanisms that could contribute to brain degeneration due to arterial stiffness. At 2 weeks postcalcification, carotid stiffness attenuated resting cerebral blood flow in several brain regions including the perirhinal/entorhinal cortex, hippocampus, and thalamus, determined by autoradiography ( P<0.05). Carotid calcification impaired cerebral autoregulation and diminished cerebral blood flow responses to neuronal activity and to acetylcholine, examined by laser Doppler flowmetry ( P<0.05, P<0.01). Carotid stiffness significantly affected spatial memory at 3 weeks ( P<0.05), but not at 2 weeks, suggesting that cerebrovascular impairments precede cognitive dysfunction. In line with the endothelial deficits, carotid stiffness led to increased blood-brain barrier permeability in the hippocampus ( P<0.01). This region also exhibited reductions in vessel number containing collagen IV ( P<0.01), as did the somatosensory cortex ( P<0.05). No evidence of cerebral microhemorrhages was present. Carotid stiffness did not affect the production of mouse amyloid-β (Aβ) or tau phosphorylation, although it led to a modest increase in the Aβ40/Aβ42 ratio in frontal cortex ( P<0.01). Conclusions These findings suggest that carotid stiffness alters brain microcirculation and increases blood-brain barrier permeability associated with cognitive impairments. Therefore, arterial stiffness should be considered a relevant target to protect the brain and prevent cognitive dysfunctions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".