Sex Differences in APOE Variation and Neurocognitive Health in Older Black Americans
Bibliographic record
Abstract
BACKGROUND: Older Black Americans are at higher risks for neurocognitive decline and dementia due to a combination of genetic, environmental, and social determinants of health. Most research focuses on comparing different racial and ethnic groups, so there has been less attention given to investigating within-group differences that shape neurocognitive outcomes. Understanding within-group differences is essential to uncovering the specific risk factors that increase or protect against the risk of deleterious cognitive and brain aging for specific populations. This study aims to better understand the within-group variations that influences brain health in older Black Americans. METHOD: = 76.05, SD = 7.37; 118 females) were drawn from the University of Kentucky Alzheimer's Disease Research Center (UK-ADRC) cohort. The participants completed a standardized data protocol, including a review of health history, physical and neurological exams, genetic testing, and neuropsychological testing. We performed descriptive and correlation analyses using SPSS version 29.0. RESULT: Controlling for age, education, and marital status, our results showed that in Black American males, but not females, APOE e4 allele was significantly associated with abnormal cognitive status (r = .363, p = .01), longer processing speed (r = .336, p = .03), poorer performance on working memory task (r = .415, p = .01), and worse Mini-Mental State Examination scores (r = -.369, p = .01). However, APOE e4 allele was significantly associated with lower scores on the Montreal Cognitive Assessment (r = -.308, p < .01) in females but not in males. CONCLUSION: Studying within-group differences in Black Americans will allow researchers and clinicians to better tailor interventions, improve diagnostics, and gain deeper understanding of the complexities of cognitive abilities in this population. Here we report sex differences in how the APOE gene is linked to cognitive performance. Recognizing and exploring within-groups is crucial for identifying specific factors that influence brain health and developing personalized prevention and intervention strategies. Factors such as age, education, and lifestyle choices can lead to diverse cognitive outcomes even among people who appear to be similar.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".