Epigenetics

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.

Abstract. Introduction: Preterm birth drives adverse perinatal maternal and infant health outcomes through heterogeneous symptoms, severity, and etiologies. Delivery prior to reaching 37 weeks of gestation may result from medically indicated intervention for pregnancy complications or spontaneously in the absence of prior symptoms. Placental tissue collected following preterm birth exhibits differential DNA methylation compared to full-term placentas and may indicate pregnancy health during gestation. Placental DNA currently has limited utility for assessing health of ongoing pregnancy, as sampling placental tissue during gestation increases the risk of infection and miscarriage. Risks associated with placental sampling during pregnancy limit the use of DNA methylation in clinical preterm birth prediction. Assessing preterm birth risk during gestation requires non-invasive methods for characterizing placental DNA methylation. Results: We quantified genome-wide DNA methylation patterns of hypermethylated cell-free DNA in pregnant (n = 99) and non-pregnant (n = 93) plasma using cell-free methylated DNA immunoprecipitation sequencing (cfMeDIP-seq). In each sample, we assessed DNA methylation status in 300-bp genomic windows, examining both sequencing read counts and calculated absolute molar DNA amount. Known hypermethylated placental regions, including RASSF1, STAT5A, and ERG promoters showed significantly increased odds of detection in pregnant samples, suggesting enrichment of cell-free placental DNA. Of the 536,444 300-bp windows examined, 173,071 (32%) showed significant enrichment in pregnant plasma. Linear modeling identified 107,505 differentially methylated regions (DMRs) associated with pregnancies later diagnosed with intrauterine growth restriction (IUGR) (n = 22). Alu elements showed increased representation in these DMRs than expected, while other repetitive elements exhibited underrepresentation. Discussion: These results demonstrate cfMeDIP-seq's ability to enrich for cell-free placental DNA and characterize cell-free DNA methylation signatures of pregnancies complicated by IUGR. Enrichment of cell-free placental DNA enables non-invasive profiling of placental DNA methylation from maternal plasma. Detectable epigenetic signatures in maternal plasma may identify pregnancies at elevated risk for preterm birth before clinical symptoms appear. Our findings further highlight the potential of cell-free placental DNA for monitoring pregnancy health.

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.

The existence and functional relevance of mitochondrial DNA methylation remain controversial. Here, we systematically profiled cytosine methylation and hydroxymethylation across human brain and blood tissues spanning healthy and malignant states using orthogonal sequencing approaches that avoid chemical conversion during library preparation. While nuclear DNA exhibited canonical methylation patterns, mitochondrial DNA consistently showed negligible signal, indistinguishable from background technical noise. By mapping cytosine-guanine sites between mitochondrial DNA and nuclear-embedded mitochondrial sequences, we demonstrate the potential of these nuclear counterparts to confound not only cytosine methylation but also hydroxymethylation measurements, corroborating and extending prior findings implicating nuclear contamination as a potential source of apparent mitochondrial epigenetic signals. Additional technical factors that inflate apparent mtDNA methylation signals were identified, including sequence context biases, flow cell chemistries, and coverage-dependent discrepancies between the heavy and light strands. Collectively, these results provide convergent evidence against the presence of biologically meaningful cytosine methylation or hydroxymethylation in mitochondrial DNA. These findings caution against interpreting apparent mtDNA methylation signals in human adult tissues as meaningful without rigorous orthogonal validation and comprehensive consideration of technical and analytical confounding factors.

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.

Analyses of 3,370 normal human tissues of ectodermal, endodermal and mesodermal origins identified 12,587 regions averaging [~]585 bp with significant differences in DNA methylation levels within identical tissues. These methylation variants (MeVars) occurred in 8,037 genes enriched in neurological disorders and cancers of which, majority were tissue-specific rather than being systemic. This somatic variation was reduced by reprogramming in vitro into iPSCs and in vivo during spermatogenesis. Analysis of prefrontal cortices showed a higher incidence of MeVars in the candidate genes in controls than schizophrenia patients wherein a subset showed significantly altered transcript levels. Similar effects were observed for oral tissues and skin fibroblast cells. MeVars showed significant association with SINE1, simple and low complexity repeats, H3K27me3, H3k9me3 and H3K4me1 modifications and EZH2, SUZ12 and REST binding sites. Collectively, MeVars have postzygotic origins with an ability to reset during reprogramming, adding a new dimension in the form of epigenetic diversity and its relevance to disease susceptibility in humans.

The X chromosome is often excluded from studies analyzing associations between traits and DNA methylation. In females, one copy of most genes on the X is inactivated (X-chromosome inactivation; XCI) through DNA methylation of the gene promoter on the inactive X. This leads to challenges in analyzing and interpreting DNA methylation data patterns. Particularly for sex-biased diseases and traits, there may be many loci of interest on the X chromosome, which contains about 5% of the genome. To address the need for appropriate analysis of DNA methylation data on the X chromosome, we develop a statistical approach to infer locus-specific escape from XCI sensitive to phenotype or covariate values. Performance of this method is illustrated by analysis of data from two sex-biased traits: rheumatoid arthritis which is 3-fold more common in females, and recurrent venous thromboembolism which occurs 2.5 times more often in males. Analyses of these two datasets identify new trait-associated loci on the X chromosome, demonstrate the capabilities of the new method for both bisulfite sequencing data and Illumina EPIC data, suggest at least one locus where variable escape may explain a sex-specific disease association, and rule out variable escape as a potential explanation at other loci. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=176 HEIGHT=200 SRC="FIGDIR/small/732395v1_ufig1.gif" ALT="Figure 1"> View larger version (52K): org.highwire.dtl.DTLVardef@1fd6c70org.highwire.dtl.DTLVardef@da4ee8org.highwire.dtl.DTLVardef@729512org.highwire.dtl.DTLVardef@98edb1_HPS_FORMAT_FIGEXP M_FIG C_FIG Created with BioRender (bioRender.com)

CD4+FOXP3+ regulatory T (Treg) cells maintain self-tolerance, restrain immune responses during inflammatory stimuli, and promote tissue function and repair. Treg cell lineage identity, stability, and function depend on specific DNA methylation patterns maintained by the epigenetic regulator, UHRF1. Aging disrupts DNA methylation patterns necessary for Treg cell-mediated lung repair in a cell-autonomous manner. Nevertheless, whether maintenance DNA methylation is necessary for age-related Treg cell transcriptional and methylation programs is unknown. Here, we performed transcriptional and DNA methylation profiling on young and old Treg cells isolated from mice with chimeric Treg cell-specific loss of UHRF1. We observed cell-autonomous, age-related alterations in transcriptional and DNA methylation signatures that were dependent on UHRF1. We conclude that maintenance DNA methylation is required for age-related alterations in Treg cell transcriptional and DNA methylation signatures.

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.

The information content of the genome has been extensively analyzed. However, a comparable quantitative framework for DNA methylation is still lacking. Without such quantification, the magnitude of this regulatory and dynamic epigenetic structure remains conceptually imprecise, even though methylation dysregulation is strongly linked to disease-related phenotypes and altered cellular identity. Here we address this gap by applying Shannon information theory to DNA methylation. We first consider methylation marks as binary or probabilistic regulatory states and estimate the theoretical upper-bound information capacity of the human methylome under simplifying assumptions. We then progressively refine this estimate by incorporating biologically relevant constraints, including methylation bias, bimodal methylation distributions, local CpG correlation, genomic regulatory class, and cell-type-discriminative methylation patterns. This approach allows us to distinguish between theoretical methylation capacity, statistical methylation entropy, and biologically interpretable regulatory information. Finally, we consider methylation information from a discriminative perspective, analyzing its contribution to distinguishing cell types and regulatory cellular states. Within this framework, mutual information between methylation patterns and cell identity provides a biologically constrained estimate of methylations role as an epigenetic identity code. Our layered analysis reconciles megabit-scale methylome capacity with compact, biologically interpretable identity signatures. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/735086v1_ufig1.gif" ALT="Figure 1"> View larger version (73K): org.highwire.dtl.DTLVardef@f0f0fdorg.highwire.dtl.DTLVardef@5d8a1eorg.highwire.dtl.DTLVardef@116debdorg.highwire.dtl.DTLVardef@79530e_HPS_FORMAT_FIGEXP M_FIG C_FIG

Background Complex traits are influenced by numerous variants, most of which have regulatory effects on gene expression that can be mediated by DNA methylation. Molecular QTL mapping is an approach that aims to dissect these effects. However, obtaining molecular phenotypes on a large scale is challenging, particularly in livestock species. In cattle, an epigenotyping array called EpiChip has recently been developed in the European RUMIGEN project. The EpiChip, which contains 43,317 CpG sites distributed all over the bovine genome, enables large-scale measurement of DNA methylation. This study aims to characterize the genetic determinism of blood DNA methylation in cows by estimating heritability and mapping cis- and trans-methylation QTLs (meQTLs). Results Whole blood samples from 4,457 genotyped Holstein cows were epigenotyped. Across all CpG sites, the heritability estimates averaged 24.6%. The local meQTL mapping at sequence-level for variable CpG sites (SD > 2.5%; n = 28,806) detected cis-meQTLs for 80.1% of the CpG sites, with sentinel SNPs located close to their associated CpGs. A two-step analysis was also conducted to identify long-range associations, with a particular focus on trans-meQTL hotspots. First, we identified CpG-SNP trans-associations using medium-density genotypes (50k SNPs) that revealed 31,846 SNPs with significant effects on 1 to 530 trans-CpG sites. Then, regions associated with at least 34 independent trans-CpGs were retained defining 31 hotpots. For each hotspot, a local sequence-level GWAS was conducted using the first principal component derived from the associated trans-CpGs. Out of the 31 detected hotspots, three were located close to transcription factor genes (RUNX1, NFIC and FOXA3) for which the associated trans-CpGs were enriched for the corresponding binding motif. Two other hotspots were located within KDM5A and KDM5B, and their corresponding trans-CpGs were strongly overrepresented in H3K4me3 narrow peaks in blood as well as in other tissues. Conclusions By identifying functional candidate genes associated with blood DNA methylation in cattle, these findings provide new insights into the regulatory architecture of DNA methylation in mammals, highlighting the value of large-scale molecular data from livestock populations.

BackgroundAdversity during pregnancy is associated with alterations in offspring brain development. DNA methylation (DNAm) is a type of epigenetic modification is a putative mechanism for the intergenerational transfer of prenatal adversity on developmental outcomes. We examined the effects of prenatal social disadvantage (PSD) and prenatal psychosocial stress (PSS) on offspring epigenome-wide DNAm at four time points from infancy to age 4, and tested whether persistent DNAm signals mediated associations between PSD, PSS, and child psychopathology at ages 4-6. MethodsLongitudinal DNAm data (birth, Y1, Y2, Y3) was derived from salivary tissue of 281 infants (43.7% female) in the eLABE study. We examined epigenome-wide associations between DNAm and PSD and PSS. Linear models were adjusted for age, child sex, child race, maternal tobacco smoking, cell type composition, and batch effects. Mediation analyses tested whether birth DNAm mediated associations between prenatal exposures and internalizing and externalizing symptoms at ages 4-6. ResultsPSD was associated with 47 FDR-significant CpGs at birth and 3 at year 1. PSS was associated with 3 FDR-significant CpGs at birth. Thirteen CpGs showed PSD-associated significance across all four timepoints, including two CpGs associated with genes involved with neuronal maturation (BCL11B) and one CpG associated with a gene implicated in brain vascular health (ZNF474). Exploratory mediation analyses revealed an indirect effect of methylation at ZNF474 (cg19980369; p=0.028; pFDR=0.360) on the association between PSD and year 4-6 externalizing symptoms. ConclusionsPSD was associated with epigenetic signatures at birth, with a subset of associations persisting across early childhood and converging on cellular stress response biology. PSS showed minimal epigenetic associations, suggesting differential biological embedding of structural versus psychological dimensions of adversity.

Background: Epigenetic clocks are markers of biological aging that may vary in their sensitivity to environmental stressors and lifestyle modifiers. To evaluate the utility of these biomarkers as sensors of the human exposome, we investigated how they respond to two powerful and opposing exposures: smoking, a source of oxidative stress, and the antioxidant-rich Mediterranean diet. Objectives: We assessed the sensitivity of eleven epigenetic clocks to diet and smoking and evaluated whether Mediterranean diet adherence modifies associations between smoking and epigenetic aging. Methods: We analysed 928 participants (mean age 41 years, 59% male) from the Airwave Health Monitoring Study. Linear regression models assessed associations between Mediterranean Diet Score (MDS) and epigenetic age acceleration (EAA), alongside smoking status and blood cotinine. Interaction terms between smoking status and MDS were included to detect dietary attenuation of smoking-related EAA. Models were adjusted for demographic, socioeconomic, lifestyle, and psychological covariates. Results: Higher MDS was associated with lower EAA for GrimAge ({beta} = -0.07 SD; 95% CI: -0.13, -0.01) and Bernabeu ({beta} = -0.08 SD; 95% CI: -0.14, -0.02) after false discovery rate correction. Smoking was strongly associated with increased EAA, particularly for GrimAge, Bernabeu, and DunedinPACE. Among current smokers, effect sizes were greater in those with lower dietary adherence (e.g. GrimAge: 1.79 SD, 95% CI: 1.54, 2.04) compared with those with higher adherence (1.35 SD, 95% CI: 1.01, 1.68; P_interaction < 0.001). Similar attenuation patterns were observed for Bernabeu. Higher intake of fruits, vegetables, and whole grains contributed most to the attenuation of smoking-related EAA. Conclusions: Our findings indicate that certain epigenetic clocks effectively capture the tension between harmful and protective exposures within the exposome. Rather than suggesting that diet neutralises the risks of tobacco, these results demonstrate that specific clocks are sensitive enough to monitor how lifestyle factors modify molecular responses to environmental toxins. This highlights the value of second-generation clocks in quantifying biological resilience.

BackgroundPrenatal growth restriction has been associated with adverse neonatal and long- term health outcomes, yet the epigenetic mechanisms by which an adverse intrauterine environment shapes fetal immune development remain incompletely understood. Monozygotic dichorionic-diamniotic twins with selective fetal growth restriction (sFGR) provide a unique human model for investigating environmentally driven developmental programming independent of genetic variation and inter-twin placental vascular anastomoses. MethodsUmbilical cord blood buffy coat samples were collected from three sFGR and two gestational age-matched concordant control twin pairs. Bulk RNA sequencing and genome-wide DNA methylation analysis were performed, followed by differential expression, pathway enrichment, hematopoietic and immune module analyses, differential methylation, and integrative transcriptomic-epigenomic analyses. ResultsCompared with concordant control twin pairs, discordant twins exhibited broad attenuation of immune and inflammatory transcriptional programs alongside enrichment of erythroid- and hypoxia-related pathways, consistent with adaptive hematopoietic responses to intrauterine stress. Within discordant twin pairs, the growth-restricted co-twins displayed marked transcriptional asymmetry characterized by selective enrichment of cytotoxic lymphoid signatures despite global suppression of myeloid and antigen-presenting cell-associated programs. Integrated transcriptomic and epigenomic analyses further revealed coordinated epigenetic remodeling, with hypomethylated regions in growth-restricted twins enriched for immune regulatory pathways, including T cell differentiation and leukocyte activation. At selected loci, concordant hypomethylation and increased gene expression suggested a potential epigenetic basis for the observed immune remodeling. ConclusionsThese findings suggest that intrauterine growth restriction is associated with coordinated hematopoietic and immune reprogramming at birth, consistent with both compositional and cell-intrinsic alterations. In genetically identical twins, relative growth divergence was associated with polarized transcriptional states, highlighting how intrauterine environmental differences may shape early immune development independent of genetic background.

Mammalian preimplantation development requires precisely coordinated lineage decisions to establish the trophectoderm (TE), inner cell mass (ICM), epiblast (EPI), and primitive endoderm (PrE). Glucose metabolism and epigenetic regulation are increasingly recognised as key determinants of lineage specification during preimplantation development. However, how glucose-dependent metabolic cues interface with epigenetic mechanisms to regulate embryonic cell fate remains poorly understood. Here, we investigated the role of glucose in regulating protein methylation by protein arginine methyltransferases (PRMT) in bovine preimplantation development. PRMT1 and its associated histone mark H4R3me2a were detected throughout bovine oocyte maturation and embryo development. Pharmacological inhibition of Type I PRMTs using two structurally distinct inhibitors, GSK3368715 and MS023, markedly reduced global protein asymmetric dimethylarginine (ADMA) and H4R3me2a levels. PRMT inhibition impaired blastocyst cell proliferation, reduced total cell number, and disrupted both first and second lineage decisions, as demonstrated by decreased CDX2- and SOX2-positive TE and ICM cells and reduced NANOG- and GATA6-positive EPI and PrE cell allocation. Mechanistically, Type I PRMT inhibition downregulated key components of the Hippo-associated TE programme, including YAP, TEAD4, and TFAP2C. Consistent effects were observed in mouse embryos, where MS023 treatment reduced ADMA, CDX2, YAP, and TFAP2C expression and impaired TE and ICM allocation. Collectively, our findings identify Type I PRMT-mediated ADMA as an essential epigenetic regulator of early mammalian lineage specification and support a conserved ADMA-Hippo regulatory axis linking arginine methylation to embryonic cell fate decisions. In briefType I protein arginine methyltransferase (PRMT)-mediated asymmetric dimethylarginine (ADMA) is required for proper lineage specification during mammalian preimplantation development. ADMA depletion disrupts Hippo signalling, cell proliferation, and trophectoderm and inner cell mass allocation in bovine and mouse embryos.

Background: Contemporary cardiovascular disease (CVD) risk equations may not fully capture cumulative biological aging or long-term exposure burden. DNA methylation (DNAm) biomarkers may capture aging- and exposure-related biology, but their incremental prognostic value beyond clinical risk-factor models like PREVENT remains uncertain. To our knowledge, no prior study has benchmarked DNAm-based biomarkers with PREVENT. Methods: In a population-based cohort study, we analyzed NHANES 1999-2002 participants with DNAm biomarkers and mortality follow-up. We derived a DNAmScore from candidate DNAm biomarkers using elastic-net Cox regression with repeated nested cross-validation. A PREVENT-like clinical model was defined as a Cox model fit in NHANES using PREVENT predictors. Weighted Cox models estimated the association between DNAmScore and mortality after adjustment for PREVENT-like clinical predictors. We then compared the PREVENT-like clinical model, DNAmScore alone, and a combined model (PREVENT-like clinical predictors plus DNAmScore) using cross-fitted C-index, time-dependent AUC, calibration, and Brier score. Results: Our cohort included 2,282 participants; 597 and 937 deaths occurred by 10 and 15 years, respectively. After adjustment for PREVENT-like clinical predictors, the cross-fitted DNAmScore was strongly associated with all-cause mortality (HR per 1-SD increase, 2.43; 95% CI, 1.97?2.99). At 10 years, AUCs were 0.791 for the PREVENT-like model, 0.791 for DNAmScore, and 0.803 for the combined model. At 15 years, corresponding AUCs were 0.825, 0.822, and 0.835. Compared with the PREVENT-like model, the combined model improved AUC by 0.013 (95% CI, 0.006?0.020) at 10 years and 0.010 (95% CI, 0.004?0.015) at 15 years. The combined model had lower Brier scores at all three horizons with similar calibration. DNAmScore remained associated with CVD mortality after clinical adjustment. Conclusions: DNAmScore identified residual biological risk beyond PREVENT-like clinical predictors, with strong independent mortality associations and modest, consistent improvements in cross-fitted prediction performance. These findings support development and external validation of CVD-specific DNAm biomarkers.

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.

Background: The placenta has a unique transcriptomic profile, including microRNAs that are secreted into maternal circulation throughout pregnancy. MicroRNAs are small, non-coding RNA that post-transcriptionally regulate gene expression. Spontaneous preterm birth (sPTB) is associated with substantial differences in both placental pathophysiology and placental gene expression compared to term birth. We aimed to generate microRNA signatures of sPTB and map them to target genes using a microRNA-mRNA network. Methods: This study was conducted within the Conditions Affecting Neurocognitive Development and Learning in Early childhood (CANDLE) study. Placental samples were collected at delivery, and RNA was isolated for mRNA and microRNA sequencing. To investigate sPTB, this study excluded placental samples of participants with iatrogenic indications for PTB or induced labor. We examined differences in microRNA expression in participants who delivered before 37 weeks (N=35) compared to term participants (N=404) in a series of covariate-adjusted linear regression models. We used paired placental microRNA and mRNA expression data from this cohort to validate associations between computationally predicted microRNA-mRNA pairs and establish a microRNA-mRNA network. Results: Expression of 7 microRNAs were increased in sPTB (FDR<0.05) and were inversely correlated with sPTB-associated genes involved in immune signaling. Expression of 12 microRNAs were decreased in sPTB, including 4 members of the maternally expressed chromosome 14 microRNA cluster (miR-376a-3p, miR-376c-3p, miR-377-3p, and miR-381-3p). These microRNAs were predicted to negatively regulate oxidative phosphorylation genes that were increased in sPTB. The associations between miR-376c-3p and miR-377-3p and oxidative phosphorylation were confirmed in microRNA knockdown experiments. Conclusions: This study highlights potential biological mechanisms by which placental microRNA dysfunction might contribute to sPTB and highlights putative sPTB biomarkers that may be detectable in maternal circulation.

Objectives: Oral health conditions impact a significant proportion of the global population. Chronological age is a known risk factor; however, characterization of epigenetic age remains limited and is expected to provide additional insight into biological mechanisms. Materials and Methods: The National Health and Nutrition Examination Survey (NHANES) was used to analyze the effect of epigenetic age measures of DunedinPoAm, and epigenetic age acceleration (EAA) of Horvath, Hannum, Weidner, Lin, VidalBralo, PhenoAge, GrimAge, and GrimAge2, on various oral health outcomes from survey and examination results. Univariable and multivariable logistic regression were performed, adjusting for sex, race-ethnicity, education, poverty income ratio categories, and dental insurance coverage status. Results: DunedinPoAm was associated with the last dental appointment being for an existing issue (p=0.0093), poor general oral condition (p=0.0226), limiting food due to teeth problems (p=0.0031), and recommendation to see a dentist within the next two weeks (p=0.0171). EAAs for PhenoAge, GrimAge, and GrimAge2, were associated with a smaller number of oral health outcomes, whereas EAAs for Horvath, Hannum, Weidner, Lin, and Vidal-Bralo showed no associations. Conclusions: In a representative U.S. population, DunedinPoAm was most consistently positively associated with different adverse oral health outcomes compared with other epigenetic aging measures. Tracking specific epigenetic ages such as DunedinPoAm, EAA GrimAge, EAA GrimAge2, and PhenoAge, may aid in additional monitoring of oral health outcomes. Understanding specific aging-related CpGs associated with oral health may aid in elucidating underlying molecular mechanisms.

Accurate epigenetic age prediction from DNA methylation profiles is intrinsically high-dimensional, creating a need for parsimonious models that preserve predictive performance while reducing the number of assayed cytosine-phosphate-guanine (CpG) loci. This study introduces MOSurvivor, a population-based multi-objective search framework that jointly optimizes a weight-threshold CpG selector and eight XGBoost hyperparameters. Experiments used the GSE40279 whole-blood cohort (656 individuals profiled on the Illumina HumanMethylation450 platform). After retaining 1,000 age-correlated CpGs, five strategies were evaluated on the same 30 seeded 80:20 train/test splits: fixed-parameter XGBoost using all 1,000 CpGs, random search, a genetic algorithm, particle swarm optimization, and MOSurvivor. Internal fitness was estimated using three-fold cross-validation on each training set. Across the 30 held-out test sets, MOSurvivor achieved a mean absolute error (MAE) of 4.149 {+/-} 0.300 years, root mean squared error of 5.545 {+/-} 0.392 years, and R2 of 0.855{+/-} 0.027 while retaining 211.6 {+/-} 54.8 CpGs. Relative to full-feature XGBoost (MAE 4.095 {+/-} 0.285 years), MOSurvivor reduced the feature set by 78.8% at an MAE increase of only 0.054 years (1.3%). Paired Wilcoxon tests found no significant accuracy difference between MOSurvivor and any comparator (all unadjusted p > 0.05; all Holm-adjusted p [&ge;] 0.476). The most recurrent locus, cg16867657, appeared in 29 runs, whereas mean pairwise Jaccard similarity was 0.124, indicating a small stable core embedded in multiple near-equivalent feature subsets. MOSurvivor thus offers a competitive accuracy-parsimony trade-off rather than superior absolute accuracy. External validation and leakage-free nested feature preselection remain necessary before biological or clinical translation. Keywords: epigenetic clock, DNA methylation, feature selection, multi-objective optimization, XGBoost, metaheuristics, biological aging.