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Record W4390199682 · doi:10.1002/alz.079678

Transcriptomics similarities between Alzheimer’s Disease and Cardiovascular Disease

2023· article· en· W4390199682 on OpenAlexaff
Lucas Uglione Da Ros, Marco Antônio De Bastiani, Lara Willers Lobato, Eduardo R. Zimmer

Bibliographic record

VenueAlzheimer s & Dementia · 2023
Typearticle
Languageen
FieldMedicine
TopicGDF15 and Related Biomarkers
Canadian institutionsMcGill University
Fundersnot available
KeywordsCoronary artery diseaseTranscriptomeCADDiseaseDementiaInternal medicineMedicineCardiologyBioinformaticsBiologyGeneGene expressionGenetics

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.732
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.041
GPT teacher head0.265
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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