Transcriptomics similarities between Alzheimer’s Disease and Cardiovascular Disease
Bibliographic record
Abstract
Abstract Background Cardiovascular disease, such as coronary artery disease (CAD), is considered a risk factor for the development of dementia, especially due to Alzheimer’s Disease (AD). The mechanisms through which these two entities interact are not totally elucidated, even with some evidence indicating a role of microinfarcts and white matter lesions. CAD is a severe form of cardiovascular disease, in which the arteries that irrigate the heart are compromised. Here, we aimed to assess altered biological processes that are shared between CAD and AD using blood transcriptomics. Method We used the GEO repository to search for available transcriptomics studies of whole peripheral blood for CAD and AD; for CAD we selected GSE20680, GSE20681, GSE98583, GSE42148 and GSE12288, and for AD GSE63063 and GSE85426, together with the ADNI database. All CAD patients had angiographic confirmation of coronary stenosis. The data was downloaded using the GEOquery package and the differentially expressed genes (DEG) were defined as having p‐value < 0.05 and logFC > 0.27. We then proceeded to determine the intersection of DEGs present both in CAD and AD and these DEGs were submitted to Gene Ontology (GO) enrichment analysis using the clusterProfiler package (v3.16.1) and enrichGO function. GO terms were clustered using semantic similarity with the package GOSemSim. All analyses were performed in R. Result We found a total of 3561 DEGs for AD and 1873 for CAD, with an intersection of 352 of those. After the functional enrichment analysis, these were translated into 32 GO terms enriched in both diseases. These terms were clustered based on semantic similarity into 9 groups: myeloid cell differentiation; homeostasis of number of cells; positive regulation of I kappa B kinase/NF‐κB signaling; regulation of immune effector process; positive regulation of cell adhesion; positive regulation of proteolysis; positive regulation of cytokine production; phospholipid biosynthesis process; miscellaneous biological processes. Conclusion Here we provide evidence that shared systemic pathological changes, including inflammatory processes, may also contribute to the development of both cardiovascular disease and dementia.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".