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. 2025 Feb 5;17(1):17.
doi: 10.1186/s13148-025-01821-3.

Age-related changes in DNA methylation in a sample of elderly Brazilians

Hayley Welsh  1 , Caio M P F Batalha  2 , Weili Li  3 , Nadja C Souza-Pinto  2 , Yeda A O Duarte  4   5 , Michel S Naslavsky  6 , Esteban J Parra  7
Affiliations

Age-related changes in DNA methylation in a sample of elderly Brazilians

Hayley Welsh et al. Clin Epigenetics. .

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.

Keywords: DNA methylation; Illumina EPIC array; Longitudinal study; Molecular aging.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: The SABE study was approved by the IRB from the Public Health School at the University of São Paulo (FSP-USP), protocol 2044, approval CAAE 47683115.4.0000.5421. All individuals enrolled in the SABE cohort provided written informed consent and the ethic protocols were approved by local and national institutional review boards COEP/FSP/USP OF.COEP/23/10, CONEP 2044/2014, CEP HIAE 1263‐10, University of Toronto RIS 39685. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Distributions of CpG and genic annotations for significant aDMPs and aDMRs. A Distribution of CpG annotations for significant aDMPs, B Distribution of genic annotations for significant aDMPs using fractional counting, C Distribution of CpG annotations for significant aDMRs, D Distribution of genic annotations for significant aDMRs using fractional counting
Fig. 2
Fig. 2
Compares raw (unadjusted) p-values between aDMPs and tpDMPs. DMPs significant after FDR adjusted are highlighted (FDR < 0.05). Orange represents significant DMPs only identified using timepoint as the predictor, blue represents significant DMPs only identified using quantitative age as the predictor, and purple represents the DMPs that overlap within both models
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References

    1. Belsky DW, Caspi A, Arseneault L, et al. Quantification of the pace of biological aging in humans through a blood test, the DunedinPoAm DNA methylation algorithm. Elife. 2020;9: e54870. - PMC - PubMed
    1. Moore LD, Le T, Fan G. DNA methylation and its basic function. Neuropsychopharmacol. 2012;38(1):23–38. - PMC - PubMed
    1. Smith ZD, Meissner A. DNA methylation: roles in mammalian development. Nat Rev Genet. 2013;14(3):204–20. - PubMed
    1. Al-Hasani K, Mathiyalagan P, El-Osta A. Epigenetics, cardiovascular disease, and cellular reprogramming. J Mol Cell Cardiol. 2019;128:129–33. - PubMed
    1. Johnson AA, Akman K, Calimport SR, et al. The role of DNA methylation in aging, rejuvenation, and age-related disease. Rejuvenation Res. 2012;15(5):483–94. - PMC - PubMed

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