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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".