The Asian Cohort for Alzheimer's Disease (ACAD) Study: 2025 Update
Bibliographic record
Abstract
BACKGROUND: Asian Americans and Asian Canadians (ASACs) are the fastest-growing minority group in the US and Canada, yet they are under-representative in Alzheimer's disease (AD) research. To address the need of culturally appropriate clinical protocols and community-based recruitment approaches for ASACs, the Asian Cohort for Alzheimer's Disease (ACAD), the first large dementia genetics cohort focusing on Chinese, Korean, and Vietnamese, launched in 2021 to investigate genetic and non-genetic risk factors for AD among ASACs. Our clinical and community-based participatory research (CPBR) scientists have a long collaborative history and diverse cultural and scientific training backgrounds: both are critical in leading AD and CBPR research. METHOD: Upon receipt of an NIA U19 grant in 2023, ACAD has expanded to 9 recruiting sites, a coordinating site, and an analysis site with a centralized data management system. Gathering feedback from its pilot phase, ACAD updated the study protocol including community outreach and recruitment strategies, the data collection packet, pre-screening and sample collection procedures, and in English, Chinese (Mandarin and Cantonese), Korean, and Vietnamese. The updated DCP includes more neuropsychological tests and cultural tailored lifestyle questionnaires with an emphasis on immigration experiences. To ensure consistency, ACAD implemented a training curriculum for data/sample collect and for culturally appropriate recruitment approaches in collaboration with community partners, clinics, and nursing homes serving Asian communities. RESULT: As of January 2025, ACAD has consented 1,173 participants and has additional 1,920 people on the interest list. The follow-up visits are expected to begin in Spring 2025, providing insights into conversion to dementia and disease progression. ACAD has developed a cultural appropriate data collection protocol, which is robust for both in-person and virtual assessments. A comprehensive training curriculum covering outreach, prescreening, consenting, data and sample collects, and data entry is in place to ensure study rigor. CONCLUSION: The ACAD team (including community partners) have demonstrated the feasibility of recruiting ASACs in clinical research. With an expansion plan and in collaboration with other AD research focuses on racial minority populations, insights from ACAD may identify potential novel, population-specific therapeutic pathways for AD.
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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".