Apolipoprotein E gene, environmental risk factors, and their interactions in dementia among seniors
Bibliographic record
Abstract
OBJECTIVES: Little research has been conducted to explore the joint effect of apolipoprotein E (ApoE) genotypes and environmental risk factors on dementia. In this study, we examined the roles of ApoE alleles and genotypes in dementia and cognitively impaired not demented (CIND), assessed the risk of co-existing or prior health conditions (i.e. depression), family history of diseases, and lifestyle factors on dementia, and explored the interactions between genetic and environmental risk factors and their joint effects on dementia and cognitive impairment. METHODS: This is a genetic association study. A total of 1185 seniors (391 dementia, 389 CIND, and 405 cognitively intact, matched for age and gender) were selected from the Canadian Study of Health and Aging clinical assessment datasets. Multivariate logistic regression was used to explore the association between ApoE, environment risk factors, and outcomes. RESULTS: Participants with ApoE ε4 alleles or ε3/ε4 genotypes were at risk of dementia. More education reduced the risk of dementia or CIND. Previous health conditions (e.g. stroke) increased the risk of dementia or CIND. Regular exercise decreased the risk of CIND. ApoE ε3/ε4 genotype and baseline depression had a 7.97-fold greater risk of incident dementia after adjusting for other significant risk factors. No interactions were found in any dementia and CIND models. CONCLUSIONS: More attention should be paid to assess and treat depressed older people, especially for those with ApoE ε3/ε4 genotypes. Further replication studies in different populations are warranted.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".