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Record W4406209889 · doi:10.1002/alz.092675

Associations between peripheral blood DNA methylation and FDG‐PET signal in AD individuals

2024· article· en· W4406209889 on OpenAlexaff
Lavínia Perquim, Marco Antônio De Bastiani, Luiza Santos Machado, Wyllians Vendramini Borelli, Guilherme Povala, Thomas Hugentobler Schlickmann, Tharick A. Pascoal, Pedro Rosa‐Neto, Alexandre S. Cristino, Eduardo R. Zimmer

Bibliographic record

VenueAlzheimer s & Dementia · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEpigenetics and DNA Methylation
Canadian institutionsMcGill University
Fundersnot available
KeywordsPeripheral bloodDNA methylationPeripheralMethylationDNASIGNAL (programming language)MedicineBiologyComputational biologyCancer researchOncologyInternal medicineComputer scienceGeneticsGeneGene expression

Abstract

fetched live from OpenAlex

Abstract Background Epigenetics plays a crucial role in regulating genetic transcription and responding to environmental and lifestyle changes without altering the DNA sequence. Their dysregulation is associated with AD, presenting potential as blood biomarkers. However, no study has evaluated whether peripheral blood (PB) epigenetic biomarkers are associated with brain metabolism, indexed by FDG‐PET, a classic Imaging AD biomarker. Thus, we explore the associations between PB DNA methylation and FDG‐PET signal in the brain of cognitively unimpaired (CU) and AD individuals. Method We evaluated CU=43 and AD=122 individuals from the ADNI cohort who underwent FDG‐PET imaging and PB DNA methylation analysis. Methylation data were analyzed using the minfi R package. Correlation analysis was performed with the statistically significant differentially methylated regions (DMRs) (p<0.005) and the regional FDG‐PET standardized uptake value ratio (SUVRs) values extracted with the DKT atlas. Voxel‐wise associations between FDG‐PET and DMRs were tested using linear regressions accounting for group, gender, age, and APOE4 status. The analysis was corrected for multiple comparisons using cluster‐wise RFT (p<0.05). Result We identified 478 DMRs (Figure 1), multiple of them significantly associated with regional FDG‐PET SUVRs (Figure 2). The voxel‐based analysis demonstrated that DMR cg02041677, located in the ATE1 gene, was negatively associated with FDG‐PET signal in the left hippocampus, right orbitofrontal gyrus, and right medial temporal gyrus (tmax=‐4.28, ‐4.04, ‐3.58, respectively; p‐value<0.001). The cg11128212, in the intron, nearby two lncRNA (ENSG00000289046, ENSG00000274591), was positively associated with brain metabolism in the left hippocampus, left temporal pole, and left middle temporal gyrus (tmax=4.08, 3.98, 3.80, respectively; p‐value<0.001) while the cg11901271, located in intron, nearby of a LncRNA (ENSG00000287358), showed positive correlations with FDG‐PET in the Left hippocampus (tmax=5.35, respectively; p‐value<0.001) (Figure 3). Conclusion Here, we show that PB DMRs exhibited a significant pattern of association with brain glucose metabolism in vulnerable AD regions. LncRNAs are important transcriptional regulators, the methylation could impact gene expression in AD and present potential as blood biomarkers.

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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.021
GPT teacher head0.285
Teacher spread0.264 · 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

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