A Cross-Sectional Analysis of Late-Life Cardiovascular Factors and Their Relation to Clinically Defined Neurodegenerative Diseases
Bibliographic record
Abstract
Studies have demonstrated associations between cardiovascular factors and Alzheimer disease (AD) with minimal focus on other neurodegenerative diseases. Utilizing cross-sectional data from 17,532 individuals in the National Alzheimer's Coordinating Center, Uniform Data Set, we compared the presence of cardiovascular factors [body mass index (BMI), atrial fibrillation, hypertension, hyperlipidemia, and diabetes] in individuals carrying a diagnosis of Probable AD (ProbAD), Possible AD, vascular dementia, dementia with Lewy bodies (DLB), frontotemporal dementia, Parkinson disease, progressive supranuclear palsy, or corticobasal degeneration, with that of normals. Generalized linear mixed models were fitted with age at visit, gender, and cardiovascular factors as fixed effects and Alzheimer's Disease Centers as random effects. In late life, only BMI of ProbAD and DLB patients was statistically significantly lower than that in normals (P-values <0.001). When accounting for colinearity within cardiovascular factors, a low BMI was a comorbidity of certain dementia etiologies as compared with normals. These data support a concept of disease-specific associations with certain cardiovascular factors.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".