Clinical Epigenetics

Aberrant DNA methylation is a hallmark of cancer, but its clinical interpretation remains debated. UHRF1, a key epigenetic adaptor for DNA methylation maintenance and chromatin bivalency regulation in embryonic stem cells, is frequently overexpressed yet shows context-dependent prognostic behaviour. By integrating bulk and single-cell transcriptomics, CpG-resolution methylation, developmental chromatin states, immune profiling and clinical outcomes across gastric (STAD), clear-cell renal (KIRC) and adrenal (ACC) carcinomas, we identified a four-class UHRF1-embryonic morphogenesis (UHRF1-EM) framework resolving this paradox. This axis revealed an inverse prognostic pattern: whilst across all three tumours EM-low and EM-high states mark better or worse prognosis, respectively, UHRF1-high levels associate with favourable outcome in STAD (UH-EML), and unfavourable in KIRC and ACC (UH-EMH). The classification proved reproducible and independently prognostic after adjustment for stage and molecular subtypes, outperforming existing classifiers and exceeding pathological stage in KIRC and ACC. Multivariable models incorporating UHRF1-EM yielded uniformly positive {Delta}C-indices. Hypermethylation associated with the UHRF1-EM axis was enriched at ESC bivalent developmental loci (EM and oncofoetal genes), but not at housekeeping cell-cycle sites. In STAD, this pattern was related to oncofoetal gene downregulation and best prognosis, whereas in KIRC and ACC it matched with gene-body/enhancer methylation, higher EM expression, immunosuppressive microenvironments and worst prognosis. Together, these findings establish the UHRF1-EM axis as a clinically robust molecular classifier and support a mechanistic model in which tumour-specific epigenetic engagement of developmental loci may contribute to the prognostic inversion, providing a foundation for further mechanistic experimental validation.

Background: Psychosocial stress is a key risk factor for coronary heart disease (CHD), particularly in postmenopausal women who face both a high stress burden and elevated cardiovascular risk. DNA methylation (DNAm), a critical epigenetic modification bridging environment and health, remains understudied as a contributor to stress-related CHD. Methods: We conducted an epigenome-wide association study (EWAS) of stress in the Women's Health Initiative (WHI), an ancestrally diverse cohort of postmenopausal women (n=3,857). At screening visit, participants completed a questionnaire assessing stressful life events and provided whole blood for DNAm. Incident CHD was then longitudinally ascertained (follow-up mean/SD: 16.7/8.4 years), and DNAm signatures were evaluated as CHD predictors using Cox regression. Predictive models were independently validated in the Jackson Heart Study (JHS; n=3,053) and Multi-Ethnic Study of Atherosclerosis (MESA; n=870). The bulk-level DNAm associations were computationally deconvolved at the cell-type-specific level using tensor composition analysis (TCA). Results: The EWAS in WHI identified 841 stress-related DNAm sites (99 hypermethylated, 742 hypomethylated with stress) after FDR correction, with 13 significant after Bonferroni correction, including sites located on immune and CHD-related genes (e.g., TNF, ALDH2). Methylation risk scores (MRSs) integrating the 841 FDR-significant sites (MRS841) and 13 Bonferroni-significant sites (MRS13) predicted incident CHD (HR=1.33-1.37; p[≤]0.0008) and mediated 16.5-17.7% of the association between stress and CHD. In JHS and MESA, MRS13 independently predicted CHD (HR=1.34; p=0.036), whereas MRS841 was suggestively associated with CHD (HR=1.27; p=0.087). TCA indicated that the greatest number of stress-related sites predictive of CHD was specifically in monocytes (133 total), with directions consistent with bulk-level associations (9 hypermethylated, 124 hypomethylated with stress). Conclusion: Our study supports methylation risk scores as novel biomarkers of stress-related CHD and uncovers epigenetic regulation in monocytes as a potential underlying mechanism. These findings highlight biological pathways linking stress and disease and may promote personalized interventions in high-risk populations.

Background: Suicide is a major public health concern and a highly complex, heterogeneous phenotype. Increasing evidence implicates epigenetic mechanisms, particularly DNA methylation (DNAm), in suicidal behavior. Methods: Building on previous epigenome-wide association studies (EWASs), we conducted the largest EWAS to date in postmortem dorsolateral prefrontal cortex (Brodmann area 9), analyzing DNAm and epigenetic aging (EA) in 199 suicide decedents (SD) and 190 age- and sex-matched non-psychiatric controls (NPC) using the Infinium MethylationEPIC BeadChip Array v2.0. Results: Bulk tissue analysis identified no significant differentially methylated positions or regions. In contrast, cell type-specific analysis using DNAm-deconvoluted cell proportions identified 605 differentially methylated cytosines in individual cell types (DMCTs) in excitatory neurons, 10 in inhibitory neurons, and 28 in oligodendrocyte precursor cells. Sex-stratified analyses identified mainly male-specific DMCTs, most of which were found in excitatory neurons, while comparison of violent and non-violent suicide identified additional DMCTs in glial cell types. Excitatory neuron DMCTs were enriched for synaptic, small GTPase signaling, and neurodevelopmental pathways, and overlapped genes previously associated with suicidal behavior, including MAD1L1. No significant differences in EA acceleration were observed overall or by sex or suicide mechanism. Conclusions: These findings indicate that suicide-associated DNAm patterns are primarily neuron-specific and may remain undetectable in bulk tissue, highlighting the importance of cell type-specific approaches to elucidate biological mechanisms underlying suicide.

Abstract Background: Peripheral artery disease (PAD) is a common atherosclerotic condition with incompletely understood molecular drivers. DNA methylation is a key epigenetic regulator influenced by smoking, aging, and metabolic risk factors, yet genome-wide methylation patterns specifically associated with PAD remain understudied. Objective: To characterize genome-wide DNA methylation patterns associated with peripheral artery disease (PAD) using the Infinium Methylation EPIC v2.0 platform. Design: Single-center pilot epigenome-wide association study using pooled whole blood genomic DNA and pool-level bioinformatic analysis. Setting: Academic medical center. Patients: Ten adults patients diagnosed with PAD and 22 healthy controls. Interventions: None. Methods: In this prospective single-center pilot study, we performed genome-wide DNA methylation profiling on blood DNA from 10 clinically diagnosed patients with PAD (mean age 72.7 {+/-} 11.2 years; 50% female) and 22 age- and sex ratio-matched controls (mean age 74.6 {+/-} 7.8) using the Illumina Infinium MethylationEPIC v2.0 BeadChip (>935,000 CpG sites). Data was processed with the SeSAMe pipeline. Differentially methylated loci (DMLs) and regions (DMRs) were identified at FDR [≤] 0.01 and |{Delta}{beta}| [≥] 0.10. Enrichment analysis of associated genes was performed using clusterProfiler (Gene Ontology, GO, and Kyoto Encyclopedia of Genes and Genomes, KEGG, computational methods). Results: Following quality control, 933,942 probes were analyzed. We observed that a majority of significant DMLs exhibited hypomethylation in PAD. Intersection of DML- and DMR-supported genes yielded 16,854 high-confidence genes. GO analysis revealed a strong enrichment for muscle system processes, regulation of membrane potential, embryonic organ development, DNA-binding transcription activator activity, actin binding, and metal ion transmembrane transporter activity. Cellular component terms highlighted cell leading edge, focal adhesion, and cell cortex. KEGG pathways were dominated by calcium signaling, cadherin signaling, MAPK signaling, and cytoskeleton in muscle cells. Conclusions: Blood DNA methylation patterns in a cohort of PAD patients are enriched in developmental vascular, muscle, and cell-motility programs that are characteristically reactivated in adult vascular pathology (endothelial dysfunction, vascular smooth muscle cell (VSMC) phenotypic switching, pathological angiogenesis). These findings generate a hypothesis for biomarker development and epigenetic therapeutic targeting in PAD. Larger longitudinal and multi-ethnic validation studies are warranted.

Long term associations of early life experiences with later life DNA methylation are understudied. In the U.S. Health and Retirement Study, participants self-reported early life experiences, including years in school, smoking during childhood, growing up in a rural area, and living with a grandparent. Later life DNA methylation was measured in blood for participants with a mean age of 69.6 years at 731,474 sites. We tested for associations between each early life experience with DNA methylation age acceleration, global and site-specific methylation, and enriched biological pathways. We compared results across early life experiences. Participants (N = 3,562) were 58.8% female and 68.1% non-Hispanic White. They reported 13 mean years in school, 18.3% smoked during childhood, 42.7% grew up in a rural area, and 26.8% lived with a grandparent. Fewer years in school (0.10, 95% CI: 0.06, 0.14) and smoking during childhood (0.65, 95% CI: 0.32, 0.97) were associated with accelerated GrimAge in later life, while living in a rural area and living with a grandparent were not associated. Early life experiences were associated (p<1x10-4) with distinct DNA methylation sites, specifically 574 sites for years in school, 20 for smoking during childhood, 49 for growing up in a rural area, and 23 for living with a grandparent. For example, one fewer year in school was associated with 0.47 (p-value = 4.61x10-15) lower percent methylation at cg07318158 in OTUD7B. Sites associated with our early life exposures were enriched for unique pathways. Years in school was enriched for embryonic development and cell structure pathways, smoking during childhood was enriched for nervous system development, exocytosis, and cell adhesion and structure pathways, growing up in a rural area was enriched for cell and vesicle processing pathways, and living with a grandparent was enriched for hormone regulation and protein breakdown pathways. Findings suggest our early life exposures are associated with unique DNA methylation patterns in later life, which can potentially allow for separate biomarker opportunities aimed at early intervention of adverse later life outcomes associated with these exposures.

Background DNA methylation (DNAm) based age acceleration is an indicator of biological ageing. However, it is unclear how age acceleration is linked to mental health in adolescents. We examined the longitudinal associations between DNAm age acceleration and depression and anxiety in adolescents. We also triangulated the evidence using Mendelian Randomisation (MR). Methods We analysed a subsample of adolescents (n=261) from a London-based adolescent cohort study. DNAm profile was generated from saliva samples collected at ages 11-12 years. DNAm age acceleration measures were derived from Horvath, PhenoAge, and PedBE clocks. Depressive and anxiety symptoms were measured at ages 13-15 years, using the Patient Health Questionnaire and Generalised Anxiety Disorder scale, respectively. The associations between DNAm age acceleration and depressive and anxiety symptoms were assessed using logistic regression. We also conducted a two-sample MR to investigate the potential causal relationship between Horvath and PhenoAge acceleration and depression and anxiety. Results We found that PhenoAge age acceleration was associated with higher severity levels of depressive symptoms (OR=1.04, 95% CI 1.01, 1.08) and clinically significant depressive symptoms (OR=1.05, 95% CI 1.01, 1.10). None of these three clocks was associated with anxiety outcomes. MR suggested a potential causal relationship between PhenoAge age acceleration and depression (Inverse variance weighted OR = 1.01, 95% CI 1.00, 1.02). Conclusions PhenoAge acceleration at ages 11-12 years was associated with depressive symptoms at ages 13-15 years. The association patterns are consistent between longitudinal and MR analyses. PhenoAge acceleration may represent a potential early biomarker for subsequent depressive symptoms in adolescents.

IntroductionFetal development represents a critical window during which genetic and environmental influences shape lifelong health. DNA methylation (DNAm) is a candidate underlying mechanism. While individual prenatal exposures have been related to DNAm, no studies have investigated the broader prenatal exposome, nor incorporated genetics with the exposome. Here, we integrated the prenatal exposome and genetics as predictors of DNAm at birth. MethodsWe used data from the Dutch Generation R (n=2282) and English Avon Longitudinal Study of Parents and Children (ALSPAC; n=809) cohorts. We performed epigenome-wide elastic net regression, using Generation R for model development/internal validation and ALSPAC for external validation, to predict DNAm at each CpG site. We used three models: Model 1 included 42 prenatal exposures, Model 2 additionally included child sex, gestational age and birth weight, and Model 3 further included meQTLs. ResultsIn Model 1, the prenatal exposome explained on average 0.7% of DNAm variation across 347 validated CpGs (0.1% of tested CpGs). This increased to 40,044 CpGs (10.2%) with 1.3% of variation explained in Model 2, and 91,305 CpGs (23.2%) with 3.0% of variation explained in Model 3. In Model 1, prenatal smoking was the largest predictor, followed by delivery characteristics, among which meconium-stained amniotic fluid was a novel finding. In Model 3, typically both SNPs and multiple prenatal exposures were selected. DiscussionWe find that genomic associations with cord blood DNAm are stronger and more widespread than prenatal exposures, although typically, the prenatal exposome explains additional variation in DNAm beyond genetic influences.

Background. DNA methylation patterns in cord blood are robustly associated with birthweight in the general population. However, it remains unknown whether these associations extend to clinically relevant populations, such as preterm neonates or those born small for gestational age, and whether they directly reflect birthweight or are driven indirectly by genetic, familial, maternal, and obstetric factors. Methods. We calculated a birthweight methylation profile score (MPSBW) using weights of 835 CpGs previously associated with birthweight in the general population and evaluated its association with birthweight in 67 monochorionic (MC) twin pairs including 134 neonates (97% born preterm) from the Twinlife study. MC twin pairs are identical twins sharing a single placenta, often unequally, which can result in unequal resource distribution and differential fetal growth. Results. We examined the association between within-pair differences in birthweight and MPSBW, thereby estimating the association independent of factors shared equally by co-twins. A 500-gram increase in birthweight was associated with a 0.256 SD increase in MPSBW (p<0.005) in this population of preterm neonates. Adjustment for polygenic score for birthweight (PGSBW) confirmed that the observed epigenetic associations were not driven by common genetic variation underlying birthweight. Interestingly, a similar effect size (0.226 SD per 500 g birthweight increase; p<0.05) was observed in the within-pair analysis, which controls for all shared influences within a twin pair. Conclusion DNA methylation is associated with individual differences in birthweight in a high-risk clinical population of MC twins, independent of shared genetic, familial or maternal influences.

Prostate cancer remains one of the most common cancers among men globally. While significant strides have been made in diagnosis and treatment, understanding the complex genetic and epigenetic underpinnings of the disease remains crucial for guiding intervention and developing more personalized and effective therapies. The importance of DNA methylation in prostate cancer has been known for some time, but important facets of the modulation of the epigenome during carcinogenesis remain obscure, partly because the bulk of cancer methylation data have been produced using microarray technologies. Here we utilise the TruSeq methyl capture method (EPICseq) to profile the, previously defined, UK Prostate ICGC cohort of well-annotated primary prostate cancers. To this we add methylation sequencing of benign tissue from the same men. These data allow us to identify differentially methylated regions distinguishing cancerous and non-cancerous prostate tissue, while identifying numerous genes whose methylation profiles can perform that task as well as distinguishing between classes of prostate cancer. We describe a describe a methylation-based control mechanism for prostate-cancer-associated SNPs, and show that this seems a likely mechanism of action for a SNP near the MMP7 gene. We describe three novel molecular signatures that arise from different aspects of the biology of prostate cancer revealed by sequencing. Each is shown to be an independent classifier of cancers into groups with different expected times to relapse. These consist of patterns in driver gene methylation, strand-specific methylation, and signal arising in mitochondrial reads. We show that these signatures, combined with existing molecular tools, provide a powerful predictor of time to recurrence. By substantially enhancing understanding of prostate cancer risk, detection, and prognosis, we pave the way for the development of clinical practices that will benefit patients and improve outcomes.

Background: DNA methylation (DNAm) may capture cumulative genetic, environmental, and lifestyle influences on cardiovascular health. Composite DNAm score based on the American Heart Association Life's Essential 8 (LE8) framework have been linked to clinical events, but their association with early vascular changes and intergenerational effects is unclear. Methods: We studied up to 1432 participants from the multigenerational Young Finns Study (YFS-3G), including parents (G0) and adult offspring (G1). DNAm was measured using Illumina EPIC arrays in 2011 and/or 2018, and carotid intima--media thickness (cIMT) was assessed in 2018. The LE8 DNAm score was calculated as a weighted sum of methylation levels. Associations with cIMT were evaluated in intergenerational, prospective, and cross-sectional settings, adjusting for demographic, technical, and biological covariates and conventional cardiovascular risk factors. Results: Higher parental LE8 DNAm score was associated with lower offspring cIMT ({beta} = -0.022 mm/SD; p-value = 0.02), although the association was attenuated after adjustment for parental cardiovascular risk factors. In G1, a higher baseline DNAm score was associated with lower cIMT measured seven years later ({beta} = -0.030 mm/SD; p-value = 1.1 x 10-5). This association remained significant after adjustment for follow-up cardiovascular risk factors (p-value=0.009) but not after additional adjustment for prior cIMT. Cross-sectionally, higher DNAm score was associated with lower cIMT in both generations, with attenuation after risk factor adjustment in G1 but not G0. Associations with carotid plaque were not significant. Genes associated with the DNAm score were enriched for immune and inflammatory pathways. Conclusions: An LE8-derived DNAm score was associated with lower cIMT across the life course and, to a lesser extent, across generations. These findings suggest that blood DNAm reflects cumulative cardiovascular health and vascular burden and may complement conventional cardiovascular risk assessment.

Quantifying the differentiation state of cells is an important task in regenerative medicine, cellular reprogramming and in elucidating how differentiation hierarchies are altered in aging and disease. For DNA methylation data (DNAm) there is however an accute shortage of methods for quantifying differentiation state. Here we use machine learning to build EpiDiff, a predictor of differentiation that yields a continuous absolute differentiation index (DI) when applied to a DNAm sample. We extensively validate EpiDiff in single-cells, sorted cells and bulk tissue, as well as in in-vitro differentiation, organoid, reprogramming and cellular rejuvenation experiments. In mixed samples, EpiDiff measures the extent of differentiation across all cellular lineages. In sorted cell populations, we find that DI increases with age and, underpinning this increase, we discover that many cell-types undergo a developmental epigenetic drift that is most prominent towards the mesenchymal lineage. When applied to normal-tissues, EpiDiff is predictive of carcinogenic exposure and cancer risk. In summary, EpiDiff is a unique, general and versatile tool that can accurately quantify the global differentiation state of a DNAm sample with broad applicability to many disciplines.

Background: Human aging does not follow a single trajectory. Epigenetic changes offer insight into the heterogeneity in aging by reflecting the combined influence of genetic, environmental, and lifestyle factors on the timing and progression of age-related changes beyond what chronological age alone can explain. Recent studies in cancer and aging underscore the importance of methylation variability as a marker of biological dysregulation. Methods: We investigated the role of DNA methylation in aging heterogeneity by performing epigenome-wide differential methylation and variance association analyses in blood samples from 1,445 Canadians aged 45 to 85 from the Canadian Longitudinal Study on Aging. Results: We identified 448 differentially methylated regions and 488 differentially variable regions associated with health decline as measured by the health deficit accumulation Frailty Index, cognitive function, and physical function. These two classes of regions showed minimal overlap, with distinct gene coverage, suggesting that variability contributes a complementary signal to aging heterogeneity. Genes overlapped by differentially methylated regions were enriched for immune and inflammation-related pathways, whereas differentially variable regions highlighted additional localized, CpG-island-enriched signals shared across health domains, consistent with regionally structured rather than diffuse dysregulation. By integrating significant CpGs from both analyses, we constructed an epigenetic biomarker. The biomarker was associated with all-cause mortality and showed higher discrimination than biomarkers constructed from differential methylation or variability alone, with a similar pattern reproduced in the Baltimore Longitudinal Study of Aging. Conclusions: These findings suggest that DNA methylation variability may provide a complementary dimension of epigenetic aging and support further evaluation in larger cohorts with more mortality events.

Endometriosis is a chronic inflammatory disease associated with pelvic pain, infertility, and delayed diagnosis. Growing evidence suggests that altered DNA methylation contributes to disease development and could serve as a biomarker for disease. We developed a leakage-safe machine learning pipeline to classify endometriosis case-control status and menstrual cycle phase using genome-wide DNA methylation data from eutopic endometrial tissue. The dataset consisted of 984 samples profiled using the Illumina Infinium MethylationEPIC array, with measurements across approximately 759,000 CpG sites. Technical variation was corrected using SmartSVA batch correction. Ridge logistic regression models were trained using stratified 80/20 train-test splits, with regularization strength selected via stratified cross-validation. Feature selection approaches included ridge coefficient ranking, per-CpG t-tests, and univariate logistic regression with FDR correction. Model validity was evaluated using label-shuffling analyses. Menstrual cycle phase classification showed strong performance (mean cross-validation AUROC: 0.971, held-out test AUROC: 0.989), reflecting genome-wide hormonally driven methylation. Ridge regression produced lower but meaningful performance for endometriosis classification (mean cross-validation AUROC: 0.854, held-out test AUROC: 0.875). Ridge coefficient-based feature selection identified compact predictive CpG sets, supporting the hypothesis that endometriosis-associated methylation signal is distributed across many loci rather than a few highly predictive CpGs. Pathway enrichment analyses identified substantial enrichment for menstrual cycle phase but limited enrichment for disease status following FDR correction, consistent with a diffuse endometriosis-associated signal. These findings demonstrate that ridge regression can detect methylation patterns associated with both endometriosis and menstrual cycle phase, highlighting the importance of accounting for cycle-related epigenetic variation in endometrial DNA methylation studies.

Objective: To investigate sex-specific associations between DNA methylation (DNAm) of neurodevelopmental, stress-response, and dopaminergic pathway genes and need for pharmacological treatment for Neonatal Opioid Withdrawal Syndrome (NOWS) among opioid-exposed newborns. Study Design: Buccal swabs were collected at birth from 181 infants in the multi-site Child and Family Study (CAFS). DNAm was measured via pyrosequencing at CpG sites within promoter regions of neurodevelopmental (BDNF), stress-response (AVP, NR3C1, OXTR, FKBP5, SLC6A4), dopaminergic (DRD2, DRD4), and opioid signaling (OPRM1) genes. Need for pharmacological treatment for NOWS was obtained from medical records. We estimated robust linear regression models to investigate sex-specific associations of DNAm and need for NOWS treatment. Estimated marginal means quantified sex-specific DNAm differences by treatment status. Results: After correction for multiple testing, five CpG sites within DRD4 were significantly associated with need for pharmacological treatment, with four sites showing higher DNAm in treated infants and one showing lower DNAm. Significant sex-specific associations were identified within DRD4 and AVP. Conclusions: Epigenetic variation across multiple biological pathways contributes to variability in NOWS severity among infants with prenatal opioid exposure. The relationship between epigenetic variation and need for NOWS treatment may vary depending on infant sex.

DNA methylation is a key epigenetic mechanism regulating the expression of genes involved in numerous developmental and pathological processes. However, the contribution of DNA methylation of microRNA genes to atherosclerosis remains poorly understood. In this study, we profiled DNA methylation patterns of both the regulatory elements and gene bodies of five microRNA genes (MIR10B, MIR21, MIR100, MIR127, and MIR143) in vascular tissues and paired peripheral blood cells (PBC) of 92 patients with advanced carotid atherosclerosis and 32 PBC of control participants by targeted bisulfite sequencing. We identified distinct tissue-specific DNA methylation patterns for all five microRNA genes in patients with advanced carotid atherosclerosis. The regulatory regions of MIR10B, MIR127, and MIR100 were moderately hypomethylated in carotid atherosclerotic plaques compared with intact vascular tissues. We further integrated our findings with lab-internal and publicly available epigenome-wide methylation datasets and evaluated the influence of vascular and blood cell composition using computational deconvolution approaches. After adjustment for cellular heterogeneity in vascular tissues, DNA methylation at CpG sites in MIR100 and MIR127 remained independently associated with atherosclerosis. Increased DNA methylation at a single CpG site (chr11:122025143, GRCh37/hg19) located within the MIR100 E-box region was associated with metabolic syndrome. Moreover, DNA methylation levels of MIR10B, MIR21, and MIR127 in atherosclerotic plaques were linked with indicators of histological instability and history of acute cerebrovascular events. In peripheral blood, we observed moderate hypomethylation of the regulatory regions of MIR10B, MIR21, and the MIR100 E-box region in patients compared with the control group. However, only the MIR10B remained robust against blood cell composition. In blood, MIR10B and MIR143 methylation correlated with lipid metabolism and carotid stenosis, while the MIR21 CpG island showed strong blood-plaque concordance, confirming its potential as a surrogate biomarker. Overall, advanced carotid atherosclerosis is characterized by specific tissue-altering DNA methylation patterns of microRNA genes, where alterations mainly occur in the regulatory regions, predominantly featuring hypomethylation. The results underscore the complex, cell- and tissue-specific nature of DNA methylation of microRNA genes in both the regulatory elements and gene bodies in vascular tissue and blood, highlighting the critical need to decipher these intricate epigenetic landscapes to identify reliable, robust biomarkers for assessing plaque instability and cardiovascular risk.

Residential environmental exposures might be associated with biological ageing, but evidence has largely focused on a limited set of single exposures. We investigated associations between the multidomain residential external exposome and epigenetic ageing in 3307 adults from the nationally representative UK Understanding Society study. We linked 30 exposures across six domains spanning social, built, physico-chemical, retail, health and weather environments, to residential postcodes and examined associations with first-, second- and third-generation epigenetic clocks. Across four complementary approaches capturing individual, joint, domain-level and clustered exposures, adverse social, built and physico-chemical environments showed the most consistent associations with biological ageing. Area deprivation, neighbourhood crime, lower social cohesion and greenness, air pollution and higher temperatures were associated with faster pace of ageing measured by third-generation clocks (particularly DunedinPACE) and, to a lesser extent, with PhenoAge. Greater distance to leisure services was associated with older biological age on Horvath s clock and PhenoAge. Clustering identified urban and rural exposure profiles, with faster ageing in the urban profile. Residential environmental exposures are associated with epigenetic ageing, with later-generation clocks appearing more sensitive to the biological imprint of the external residential exposome.

Background: Current recommendations limit the use of menopausal hormone therapy (MHT) principally to the management of menopausal symptoms, largely because of concerns regarding increased breast cancer (BC) risk. The impact of MHT use on systemic biological aging and blood methylome and its overlap with that of menopause timing remains incompletely understood. Methods: In females (N=78,209) of the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial, we used Cox models to assess the associations of menopause timing (ordinal age at menopause; binary early-M [<45 years] vs non-early-M [[&ge;]45 years]) and MHT use with BC incidence, mortality, and survival. In a nested case-control subset with blood DNA methylome (DNAm; 758 cases, 745 controls), we related both exposures to epigenetic clocks and DNAm-based telomere length (DNAmTL) and used polygenic instruments to test both causal directions. Results: Ever MHT use was associated with higher BC incidence (aHR 1.17, 95% CI 1.10-1.24), particularly for hormone-receptor positive (HR+) subtypes (1.19, 1.11-1.27), whereas early-M was associated with lower HR+ incidence (0.88, 0.81-0.96) but not with overall incidence after adjustment (0.94, 0.88-1.01). However, ev-er MHT use was associated with lower BC mortality (0.72, 0.62-0.83) and better overall survival (0.87, 0.81-0.95). In the DNAm subset, non-early-M was associated with slower epigenetic aging and longer DNAmTL par-ticularly in cases. Higher cumulative aging and pace-of-aging markers predicted worse survival and partially mediated the early-M associations. Genetic analyses supported shared polygenic architecture between meno-pause timing and DNAm aging biomarkers rather than simple causation. MHT showed no broad epigenetic age acceleration and its profound methylome and immune-marker signatures largely did not overlap with those of menopause timing. Conclusions: Ever MHT use was associated with lower BC mortality and better overall survival despite higher BC incidence. Systemic aging detected more than a decade after menopausal transition correlates with early menopause. MHT use shows molecular signatures distinct to those seen for menopause timing.

DNA methylation-based epigenetic clocks estimate biological age from methylation profiles, and the difference between predicted biological age and chronological age is commonly described as age acceleration (AA). We compared AA across eight cancer types, lung, colorectal, breast, thyroid, bone marrow and blood, kidney, uterus, and head and neck, using seven epigenetic clocks and 5,528 publicly available samples. Across the 56 cancer type clock combinations, tumor tissues showed higher average AA than normal tissues in 44 comparisons. The uterus cohort showed the clearest deviation from this overall trend, with normal samples exhibiting higher AA for six of seven clocks. Analyses of paired normal and tumor samples generally showed higher predicted ages and greater variability in tumor samples. We additionally examined age-associated methylation changes and the ability of clock CpGs to distinguish tumor from normal tissue. Several discriminatory CpGs were shared across cancer types and frequently showed tumor-associated hypermethylation at cancer-related loci. Small subsets of top-ranked CpGs captured substantial discriminatory information. Age-stratified subsampling preserved the main AA patterns, suggesting that chronological-age differences did not explain the observed tumor-normal differences. Overall, these findings highlight broad cancer-associated alterations in epigenetic aging together with substantial cancer type- and clock-specific heterogeneity.

Trained immunity and clonal hematopoiesis are two newly identified immunological phenomena that contribute to the pathophysiology of atherosclerotic cardiovascular disease. These two phenomena share some convergent molecular mechanisms, such as IL-1{beta} being a central regulator and involvement of epigenetic enzymes. Therefore, we hypothesize that presence of clonal hematopoiesis driver mutations (CHDMs) can predispose to an increased capacity to build trained immunity. We previously characterized how the presence of CHDMs relates to immune cell function and vasculometabolic complications in a cohort of older individuals with overweight and obesity. From this cohort we now selected 17 individuals with CH due to DNMT3A mutations and 15 without any known CHDMs. We performed in depth immune characterization via flow cytometry, functional assays with monocytes and neutrophils, and we measured the capacity to build trained immunity using {beta}-glucan and oxLDL as stimuli. We corroborated our previous findings of lower ex vivo cytokine production capacity of PBMCs from individuals with DNMT3A mutations. Importantly, presence of DNMT3A CHDMs associated with higher trained immunity response. Moreover, we demonstrated that individuals with DNMT3A mutations were characterized with higher CD10+ mature neutrophils and a lower neutrophil MPO release upon TLR2 stimulation. In conclusion, presence of DNMT3A CHDMs is associated with increased susceptibility to build a hyperresponsive trained monocyte phenotype. The exact molecular mechanisms behind this phenomena requires further investigation.

Background: Colorectal cancer (CRC) is a major cause of cancer-related mortality, with distant metastasis strongly associated with poor clinical outcomes. Integrating transcriptomic and epigenomic data through machine learning may improve the molecular characterization of metastatic CRC. Methods: We analyzed 518 primary tumors from the TCGA-COAD/READ cohort (436 non-metastatic [M0] and 82 metastatic [M1]) with matched RNA-seq and DNA methylation data. Five machine learning classifiers were evaluated for discrimination of metastatic status using stratified nested cross-validation. Model explainability was assessed using model coefficients for linear classifiers and SHAP-based feature importance for tree-based models. Differential expression, functional enrichment, methylation-expression correlation, immune-related transcriptional scoring, statistical mediation, and survival analyses were subsequently performed. Results: Integrated RNA-seq and DNA methylation showed the strongest discrimination between M0 and M1 tumors, with SVM reaching a ROC-AUC of 0.787 +/- 0.047. Six features - ARC, ASPDH, C13orf15, C4orf23, GPATCH3, and cg12040555 - ranked among the top 20 predictors across four models with consistent directions. cg10057218 methylation was inversely correlated with GSDMB expression (Spearman rho = -0.589) and increased in M1 tumors. Statistical mediation indicated a significant indirect association between M1 status and reduced GSDMB expression through cg10057218 methylation (indirect effect = -0.370; 95% CI [-0.519, -0.224]; proportion mediated = 82.1%). M1 tumors also exhibited reduced immune-related transcriptional activity. A 20-feature canonical molecular score separated M1 from M0 tumors (in-sample ROC-AUC = 0.918), while the corresponding Logistic Regression model achieved a nested cross-validated ROC-AUC of 0.781 +/- 0.046. Higher scores were associated with shorter overall survival (log-rank p = 1.67 x 10^-7). Conclusions: Transcriptomic and DNA methylation integration identified a consistently prioritized multiomics feature set associated with metastatic CRC. The findings highlight coordinated molecular and immune-related differences between M0 and M1 tumors and identify cg10057218-associated GSDMB repression as a candidate epigenetic feature of metastatic disease.