Carotid and Vertebral Flow Velocities: Relationships With Cognitive Function in Wisconsin Native American Population
Bibliographic record
Abstract
Introduction: Native American individuals are more frequently affected by cerebro-cardiovascular disease and its comorbidities, hypertension, hypercholesterolemia, diabetes, obesity, vascular brain injury, and dementias. Prevalence of Alzheimer disease and other dementias is increasing, with age being the primary risk factor. It is hypothesized that age-related changes in cardiovascular structure contribute to cognitive decline, and one proposed mechanism is reduced cerebral perfusion. We hypothesized that blood flow velocities in the common carotid artery (CCA), internal carotid artery (ICA), and vertebral arteries could be used as surrogates for cerebral perfusion and are associated with cognitive performance in our Wisconsin Native American population. Methods: 119 Native American individuals from the Oneida Nation tribe in Wisconsin enrolled in the “Stroke Prevention in the Wisconsin Native American Population” study and underwent a targeted health history, as well as blood work, clinical carotid ultrasound with B-mode, color Doppler and pulse wave Doppler, and cognitive testing using the Montreal Cognitive Assessment–First Nations (MoCA-FN). Results: Higher end diastolic velocities measured in the CCA and ICA were positively associated with higher scores on the MoCA-FN (r = 0.233, P = .012 and r = 0.198, P = .042, respectively). Distal CCA and ICA peak systolic velocities and ICA:CCA ratios were not found to correlate with cognitive performance nor did systolic or diastolic blood pressure (all P -values > .05). Associations between carotid end diastolic velocities and MoCA-FN were not statistically significant after adjustment for traditional stroke risk factors (age, gender, body mass index [BMI], systolic blood pressure, diastolic blood pressure, current smoking, physical activity, total cholesterol, low-density lipoprotein (LDL)-C, high-density lipoprotein (HDL)-C, and hemoglobin A1c), ( P > .05). The presence of plaque was also not associated with the MoCA-FN score ( P > .05). Conclusion: Future longitudinal studies are needed for this population that evaluate the composite of all risk factors and treatments targeting multiple risk factors at the same time.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".