African-American Race Predicts 1-Year Cognitive Decline Among Adults Without Moderate Dementia
Bibliographic record
Abstract
Abstract Previous literature shows conflicting conclusions about the association between race and cognitive decline, particularly in early impairment. In this study, we aimed to test whether race predicted 1-year change in Montreal Cognitive Assessment (MoCA) score among older adults without moderate-severe dementia. We secondarily explored whether multimorbidity, polypharmacy, depressed mood, antidepressant use, body composition, or frailty changed the association. We analyzed data (n=122) from predominantly African American (AfA, 78.7%) community-dwelling older adults from the south side of Chicago. Participants underwent baseline and 1-year MoCA testing. Age, gender, race, education, monthly income, co-morbidities (Charlson Comorbidity Index), medication use (<5 vs ≥5), depression (PHQ-2), proportion lean mass (DEXA), and the frailty phenotype (range 0-5) were collected at baseline. In a multivariate linear model, we regressed 1-year MoCA score on baseline MoCA score, race, and demographics and then evaluated the impact of each covariate added separately to the model on the race-cognition relationship. The mean MoCA score at baseline was 25.2+/-0.2 (range 18-30) and 41.0% of participants experienced ≥1 point MoCA decline at 1 year. After adjusting for demographics, AfAs experienced a greater 1-year MoCA decline (β= -1.3, p=0.04) compared to other races. The effect size was unchanged after adjusting for multimorbidity and polypharmacy (β= -1.3, p=0.04), attenuated slightly after adjusting for frailty (β= -1.2, p=0.06), depressed mood (β= -1.2, p=0.05), lean mass (β= -1.2, p=0.04), and attenuated notably after adjusting for antidepressant use (β= -1.0, p=0.11). Findings support the need to further explore racial differences in cognitive decline and potentially related anti-depressant underuse.
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 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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".