The Caribbean‐Hispanic Alzheimer’s brain transcriptome reveals ancestry‐specific disease mechanisms
Bibliographic record
Abstract
Abstract Background Ethnicity substantially impacts risk for Alzheimer’s disease, with elevated risk in Caribbean‐Hispanics (CH). However, the mechanisms of this population‐specific risk are unknown. Here we report the first transcriptomic analysis of brain tissue from a population of CH Alzheimer's disease (AD) patients. Method RNA sequencing was performed on postmortem dorsolateral prefrontal cortex tissue from A) 38 genetically‐confirmed CH subjects (nAD = 20, nnon‐AD = 18), and B) 596 non‐Hispanic Caucasians (nAD = 359, nnon‐AD = 238). Robust regression was used to identify AD‐associated genes in both samples and transcriptome‐wide results were compared to identify genes with concordant vs. discordant associations. Result In CH, 893/17 301 genes were associated with AD status. Of these 893 genes, 107 were also significantly associated with AD in Caucasians after correction (all but 2 with the same direction of effect). Genome‐wide, association t‐statistics were positively correlated between samples (rho = 0.37). Rank‐based gene ontology enrichment analysis revealed several processes discordantly (signal recognition particle (SRP)‐dependent protein targeting and cell killing) and concordantly (neurotransmitter secretion) associated with AD between populations. Conclusion The CH‐specific upregulation of SRP‐related genes in AD suggests unique etiopathogenic mechanisms in this population and links to the P.gingivalis hypothesis of AD. This is the first step toward identifying AD‐related disease mechanisms in CH as we have identified target genes and pathways for further investigation.
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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 | 0.000 |
| 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) | 0.001 | 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".