MétaCan
Menu
← Back to cohort
Record W4406222516 · doi:10.1002/alz.094863

Association of Blood‐based DNA Methylation Markers with Cognition in Alzheimer’s Disease

2024· article· en· W4406222516 on OpenAlexaboutno aff
Kok Pin Ng, Ling Wang, Rajkumar Dorajoo, Marie Loh, Adeline Su Lyn Ng, Shahul Hameed, Simon Kang Seng Ting, Nagaendran Kandiah, Jianjun Liu

Bibliographic record

VenueAlzheimer s & Dementia · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEpigenetics and DNA Methylation
Canadian institutionsnot available
Fundersnot available
KeywordsDNA methylationAssociation (psychology)CognitionDiseaseAlzheimer's diseaseMethylationBiologyGeneticsMedicineDNAComputational biologyPsychologyNeurosciencePathologyGene

Abstract

fetched live from OpenAlex

Abstract Background DNA methylation is an epigenetic change characterized by the addition of methyl groups to DNA, typically in the cytosine‐ phosphate‐guanine (CpG) nucleotide base pairings. Given that DNA methylation alterations are shown to be associated with Alzheimer’s Disease (AD) pathology in autopsied brains, blood‐based DNA methylation changes are increasingly being studied as a potential peripheral biomarker for AD. However, the role of blood‐based DNA methylation changes as a marker of cognitive impairment in AD remains unclear. In this study, we aim to identify the blood‐based DNA methylation signatures that are associated with worse cognitive performance and brain atrophy in AD individuals. Methods 277 AD patients of Chinese ethnicity were recruited from a tertiary memory clinic (National Neuroscience Institute, Singapore). All participants underwent the Montreal Cognitive Assessment (MoCA) test and blood collection. CpG probe methylation was measured using the Illumina Infinium MethylationEPIC BeadChip. A subset of participants (n = 218, 78.7%) underwent MRI brain scan and medial temporal lobe atrophy (MTA) score was measured visually. Regression analysis evaluated the associations of each methylation marker with MoCA and MTA scores, corrected for age, gender and years of education. Results We identified two methylation markers (cg11103255 and cg10845701) to be associated with MoCA scores. Hypermethymation at the cg11103255 site was associated with lower MoCA scores (p = 2.86e‐07) while hypomethylation at the cg10845701 site was associated with lower MoCA scores (p = 8.18e‐07). Furthermore, hypomethylation at the cg10845701 site was associated with higher MTA scores. cg11103255 is mapped to the Microtubule Crosslinking Factor 1 (MTCL1) gene, a protein coding gene that enables microtubule binding activity. cg10845701 is mapped to the Solute Carrier Family 44 Member 5 (SLC44A5) gene, a protein coding gene that enables transmembrane transporter activity. While the role of MTCL1 gene on AD is not known, SLC44A5 was previously reported to be associated with brain atrophy in an AD GWAS. Conclusion Our findings demonstrate the potential role of blood‐based DNA methylation markers as measures of worse cognitive performance among AD. Further longitudinal studies in an independent cohort are needed to validate these findings.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.014
GPT teacher head0.261
Teacher spread0.247 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueAlzheimer s & Dementia→Same topicEpigenetics and DNA Methylation→French-language works237,207→