Delany, S. J. | Search bioRxiv

bioRxiv2025

Analysis of blood-based DNA Methylation signatures of aging and disease progression in IBD patients

BackgroundInflammatory bowel diseases (IBDs) are chronic inflammatory disorders with a dysregulated immune response partly influenced by environmental factors. DNA methylation (DNAm), a key epigenetic mechanism, is implicated in the etiology of complex diseases, including IBD. Epigenetic clocks, which use DNAm patterns to estimate biological aging, have been increasingly linked to various health and disease states. Previous studies have associated DNAm with IBD, and first- and second-generation epigenetic clocks with IBD subtypes. ResultsIn a discovery IBD cohort (n=149) with 8-year clinical follow-up data, we explored the relationship between DNAm variation, second- and third-generation epigenetic clocks, and IBD clinicopathological outcomes, including disease subtype, activity, and recurrence. One CpG site was significantly differentially methylated (Benjamini-Hochberg adjusted p-value<0.05) in patients with clinical recurrence of disease over the long term (i.e., after the first year of study) compared to non-recurrence (no treatment escalation after 8 years). Next, we assessed DNAm aging signatures and IBD outcomes using logistic regression. Individuals with IBD exhibited significantly increased epigenetic aging, as measured by GrimAge, GrimAge2, and DunedinPACE, compared with controls. These associations were replicated in two independent IBD cohorts (GSE87648 (n=377) and GSE112611 (n=238)). Additionally, in UC patients, the active disease group was associated with higher age acceleration (GrimAge (U=669, p=0.003)) and higher pace of aging (DunedinPACE (t=3.233, 0.002)) compared to the inactive group. In the discovery cohort, DunedinPACE outperforms CRP measures in discriminating activity in UC patients with an AUC, sensitivity and specificity of 0.71, 69.5% and 68.7% respectively, highlighting its potential as a useful biomarker of activity in UC. ConclusionsOverall, we present strong evidence that dynamic age-related DNAm changes can be used to differentiate between IBD (including separately by subtype) and controls. Furthermore, our study provides important new evidence that DunedinPACE may have utility as a biomarker for monitoring disease recurrence in IBD patients and may be a strong marker of disease activity in UC patients. Overall, this suggests that blood-based DNAm signatures could serve as biomarkers for detection and monitoring of IBD.

bioinformatics↗

bioRxiv2022

A comparison of feature selection methodologies and learning algorithms in the development of a DNA methylation-based telomere length estimator

The field of epigenomics holds great promise in understanding and treating disease with advances in machine learning (ML) and artificial intelligence being vitally important in this pursuit. Increasingly, research now utilises DNA methylation measures at cytosine-guanine dinucleotides (CpG) to detect disease and estimate biological traits such as aging. Given the high dimensionality of DNA methylation data, feature-selection techniques are commonly employed to reduce dimensionality and identify the most important subset of features. In this study, we test and compare a range of feature-selection methods and ML algorithms in the development of a novel DNA methylation-based telomere length (TL) estimator. We found that principal component analysis in advance of elastic net regression led to the overall best performing estimator when evaluated using a nested cross-validation analysis and two independent test cohorts. In contrast, the baseline model of elastic net regression with no prior feature reduction stage performed worst - suggesting a prior feature-selection stage may have important utility. The variance in performance across tested approaches shows that estimators are sensitive to data set heterogeneity and the development of an optimal DNA methylation-based estimator should benefit from the robust methodological approach used in this study. Additionally, we observed that different DNA methylation-based TL estimators, which have few common CpGs, are associated with many of the same biological entities. Moreover, our methodology which utilises a range of feature-selection approaches and ML algorithms could be applied to other biological markers and disease phenotypes, to examine their relationship with DNA methylation and predictive value.

bioinformatics↗