Diabetes, Brain Infarcts, Cognition, and Small Vessels in the Canadian Alliance for Healthy Hearts and Minds Study
Bibliographic record
Abstract
BACKGROUND: Diabetes is a risk factor for cerebrovascular disease and cognitive impairment. The anatomical basis for this is uncertain. METHODS: The Canadian Alliance for Healthy Hearts and Minds collected brain and carotid magnetic resonance imaging (MRI) and 2 cognitive tests (the Digit Symbol Substitution Test and the Montreal Cognitive Assessment test) in a cross-sectional sample of men and women. Brain MRIs identified brain infarcts (BI), lacunar BI, high white matter hyperintensity (WMH), vascular brain injury (VBI; BI or high WMH), and small vessel VBI (lacunar BI or high WMH). Carotid MRIs estimated carotid wall volume, a measure of subclinical atherosclerosis. Cognitive scores were standardized to each site's mean score, and cognitive impairment was identified by 1 or both test scores ≤1 standard deviation below the site's mean score on that test. RESULTS: The 7733 participants included 495 participants (6.4%) with diabetes, of whom 388 were taking diabetes drugs. After age and sex adjustment, diabetes was independently associated with BI (odds ratio [OR] 1.53, 95% confidence interval [CI] 1.05, 2.24), VBI (OR 1.64, 95% CI 1.26, 2.13), small vessel VBI (OR 1.67, 95% CI 1.28, 2.19), and cognitive impairment (OR 1.47, 95% CI 1.20, 1.80). The association between diabetes and small vessel VBI persisted after adjustment for cerebrovascular disease risk factors and nonlacunar infarcts (OR 1.52, 95% CI 1.15, 2.01), and the association with cognitive impairment persisted after adjustment for small vessel VBI (OR 1.27, 95% CI 1.03, 1.56). CONCLUSION: Small vessel disease characterizes much of the relationship between diabetes and VBI. However, additional factors are required to disentangle the relationship between diabetes and cognitive impairment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 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".