Overlap Between Apolipoprotein Eε4 Allele and Slowing Gait Results in Cognitive Impairment
Bibliographic record
Abstract
Background: Although apolipoprotein E polymorphism ε4 allele (ApoE4) and slow gait are well-known risk factors for cognitive impairment, examination of their combined effect on cognitive function is lacking. Our objective was to elucidate whether a combination of ApoE4 phenotyping and slow gait resulted in greater cognitive impairment. Methods: Overall, 1,085 community-dwelling older adults, either ApoE4 carriers (n = 167, 15.4%) or non-ApoE4 carriers, were included from the “Takashimadaira study.” Gait speed was assessed with an electronic walkway and slow gait was defined as <1 m/s. Cognitive performance was also assessed using the Mini-Mental State Exam (MMSE) and the Trail Making Test (TMT) -A and -B. A two-way analysis of covariance (ANCOVA) (ApoE and gait velocity factors) adjusted for covariates was performed for each analysis. Results: Gait and cognitive performances were similar for ApoE4 and non-ApoE4 carriers. A two-way ANCOVA of the MMSE showed a significant interaction between the two factors. ApoE4 carriers with slow gait had lower MMSE scores than ApoE4 carriers without slow gait and non-ApoE4 carriers with slow gait. Also, a significant main effect of gait velocity on TMT-A was observed, indicating that slow gait is associated with lower scores irrespective of presence of ApoE4. There was no main effect or interaction observed on the TMT-B. Conclusions: Our results suggest that concurrent presence of at least one copy of ApoE4 and slow gait can define a subgroup with the lowest cognition. Elucidating the mechanisms underlying these associations may point out modifiable factors in populations at risk of dementia.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".