Age-related changes in DNA methylation in a sample of elderly Brazilians
Bibliographic record
Abstract
Abstract Background Age-related changes in DNA methylation (DNAm) play a critical role in regulating gene expression. However, most epigenome-wide association studies have predominantly focused on individuals of European descent. This study aims to characterize longitudinal changes in DNAm patterns in a cohort of elderly Brazilian participants. Methods DNAm profiles were collected approximately nine years apart from 23 elderly Brazilian individuals using the Illumina Infinium MethyationEPIC BeadChip. Using mixed-effects models, we examined changes in DNAm patterns using both quantitative age and binary timepoint (e.g., baseline vs. follow-up) as predictors of interest to identify differentially methylated positions (DMPs). Significant DMPs were compared with a list of previously identified age-related DMPs. Differentially methylated regions (DMRs) were also identified using DMRcate. Gene ontology (GO) pathway enrichment analyses were performed to explore the functional significance of identified DMPs and DMRs. Results Of the 586,229 autosomal probes included in the differential methylation analyses, 2768 significant (FDR < 0.05) age-associated DMPs (aDMPs) and 2757 significant (FDR < 0.05) timepoint-associated DMPs (tpDMPs) were identified. Of the 2768 aDMPs, 1471 were replicated from previous studies. Of the 1297 non-replicated CpGs, 77.4% were exclusive to the EPIC array. The DMR analyses identified 305 age-associated DMRs (aDMRs) and 372 timepoint-associated DMRs (tpDMRs). Both aDMPs and aDMRs exhibited age-related hypermethylation within CpG islands and promoter regions of the genome, whereas hypomethylation predominantly occurred in interCGI and intergenic regions and introns. The GO enrichment analyses identified several neurological and cognition-related pathways enriched for hypermethylated CpG islands, many of which were mapped near transcription start sites and first exon regions. Conclusions This longitudinal study identified age-associated and timepoint-associated DMPs and DMRs in a sample of elderly Brazilians. Most of the non-replicated CpGs were found to be on the new EPIC array, suggesting that more age-related studies using the EPIC array are required to validate these CpGs. The GO pathway enrichment analyses identified age-related enrichment of several gene sets related to cognitive and physical decline in elderly populations. The enrichment of these sites could provide evidence for age-related neurodegeneration and cognitive decline in elderly populations.
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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) | 0.734 | 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".