Racial differences in the effect of <i>APOE‐ε4</i> genotypes on trail making test B in Alzheimer's disease: A longitudinal study
Bibliographic record
Abstract
OBJECTIVES: The trail making test part B (TMT-B) evaluates executive functions, memory, and sensorimotor functions. No previous study was found to examine the longitudinal effect of APOE-ε4 genotypes on the TMT-B scores in Alzheimer's disease (AD) across racial groups. METHODS: This study used the data from Alzheimer's Disease Neuroimaging Initiative (ADNI): 382 participants with AD, 503 with cognitive normal (CN), 1293 with mild cognitive impairment (MCI) at baseline and follow-up of four years. The multivariable linear mixed model was used to investigate the effect of APOE-ε4 genotypes on changes in TMT-B scores. RESULTS: Compared with Whites, African Americans (AA) and Hispanics had higher TMT-B scores (poor cognitive function). Furthermore, Whites subjects with 1 or 2 APOE-ε4 alleles had significantly higher TMT-B scores compared with individuals without APOE-ε4 allele at baseline and four follow-up visits; however, no differences in TMT-B were found between APOE-ε4 alleles in the Hispanic and AA groups. No APOE-ε4 by visit interactions was found for 3 racial groups. Stratified by AD diagnosis, the APOE-ε4 allele was associated with TMT-B scores only in the MCI group, while there were significant interactions for visit by education, APOE-ε4 allele, and the Mini Mental State Examination (MMSE) score in the MCI group. In addition, TMT-B was significantly correlated with the MMSE, AD Assessment Scale-cognitive subscale 13 (ADAS13), tTau, pTau, Aβ42, and hippocampus. CONCLUSIONS: APOE-ɛ4 allele is associated with TMT-B scores in Whites subjects, but not in the Hispanic and AA groups. APOE-ε4 showed interaction with visit in the MCI group.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".