Alzheimer’s genetic risk effects on cerebral blood flow are spatially consistent and proximal to gene expression across the lifespan
Bibliographic record
Abstract
Abstract Cerebrovascular dysregulation is a hallmark feature of Alzheimer’s disease (AD), where alterations in cerebral blood flow (CBF) are observed decades prior to symptom onset. Genome-wide association studies (GWAS) show that AD has a polygenic aetiology, providing a tool for studying AD susceptibility across the lifespan. Here, we ascertain whether AD genetic risk effects on CBF previously observed (Chandler et al., 2019) remain consistent across the lifespan. We further provide a causal mechanism to AD genetic risk scores (AD-GRS) effects by establishing spatial convergence between AD-GRS associated regional reductions in CBF and mRNA expression of the proximal AD transcripts using independent data from the Allen Brain Atlas. We analysed grey matter (GM) CBF in a young cohort (N=75; aged 18-35) and an older cohort (N=90; aged 55-85). Critically, we observed that AD-GRS was negatively associated with whole brain GM CBF in the older cohort (standardised β −0.38 [−0.68 – −0.09], P = 0.012), consistent with our prior observation in younger healthy adults (Chandler et al., 2019). We then demonstrate that the regional impact of AD-GRS on GM CBF was spatially consistent across the younger and older samples (r = 0.233, P = 0.035). Finally, we show that CBF across the cortex was related to the regional expression of the genes proximal to SNP’s used to estimate AD-GRS in both younger and older cohorts (Z TWO-TAILED = −1.99, P= 0.047; Z TWO-TAILED = −2.153 P = 0.032, respectively). These observations collectively demonstrate that AD risk alleles have a negative influence on brain vascular function and likely contribute to cerebrovascular changes preceding the onset of clinical symptoms, potentially driven by regional expression of proximal AD risk genes across the brain. Our observations suggest that reduced CBF is an early antecedent of AD and a key modifiable target for therapeutic intervention in individuals with a higher cumulative genetic risk for AD. This study will further enable identification of key molecular processes that underpin AD genetic risk related reductions in CBF that could be targeted decades prior to the onset of neurodegeneration.
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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 | 0.001 | 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".