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PubMed2026

Liquid biopsy-based diagnostic evaluation of hypermethylated CpG sites for ovarian cancer diagnosis.

Ovarian cancer is a heterogeneous gynaecological malignancy characterised by high mortality and an absence of reliable biomarkers for detection. In this study, a CpG-specific, ARMS-PCR approach was employed to evaluate the methylation status of six diagnostically relevant CpG sites in 65 epithelial ovarian cancer tissues and 35 healthy controls. Based on methylation frequency, the top three CpG sites were selected and evaluated in two diagnostic panels. A TaqMan-based MethyLight assay incorporating cg02957270, cg00480298 and Col2A1 (as endogenous control) was developed for tissue and serum cell-free DNA cohort analysis. ARMS-PCR demonstrated diagnostic sensitivities of 80%, 73.3% and 82.3% for singleplex and multiplex panels, respectively. However, the multiplex MethyLight assay achieved 86% sensitivity and 90% specificity, with an AUC of 0.97 in the serum cohort. Furthermore, while ARMS-PCR panels displayed limited clinicopathological correlations, MethyLight showed significant correlations (P < 0.05). Overall, this pilot study highlights the promise of liquid biopsy-based diagnostics using independent hypermethylated and hypomethylated CpG biomarkers for ovarian cancer detection.

Humans↗

PubMed2025

A Protocol for Detecting DNA Methylation Changes at CpG Sites of Stemness-Related Genes in Aging Stem Cells.

Aging adversely affects the self-renewal and differentiation capabilities of stem cells, which impairs tissue regeneration as well as the homeostasis. Epigenetic mechanisms, specifically DNA methylation, play a key role in the maintenance of pluripotency in stem cells and regulation of pluripotency-related gene expression. Age-related modifications in methylation patterns could influence the expression of genes critical for stem cell potency maintenance, including transcription factors Nanog and Sox2. The following chapter describes a step-by-step bisulfite sequencing protocol for detection of methylation changes in the aging stem cells and provides valuable insights into the stem cells epigenetic profile. Further, the methodology describes the steps of genomic DNA extraction, bisulfite conversion, real-time PCR amplification, and sequencing for an in-depth view of the epigenetic profile derived from aging stem cells.

DNA Methylation↗

PubMed2022

Temporal associations between leukocytes DNA methylation and blood lipids: a longitudinal study.

BACKGROUND: The associations between blood lipids and DNA methylation have been investigated in epigenome-wide association studies mainly among European ancestry populations. Several studies have explored the direction of the association using cross-sectional data, while evidence of longitudinal data is still lacking. RESULTS: We tested the associations between peripheral blood leukocytes DNA methylation and four lipid measures from Illumina 450 K or EPIC arrays in 1084 participants from the Chinese National Twin Registry and replicated the result in 988 participants from the China Kadoorie Biobank. A total of 23 associations of 19 CpG sites were identified, with 4 CpG sites located in or adjacent to 3 genes (TMEM49, SNX5/SNORD17 and CCDC7) being novel. Among the validated associations, we conducted a cross-lagged analysis to explore the temporal sequence and found temporal associations of methylation levels of 2 CpG sites with triglyceride and 2 CpG sites with high-density lipoprotein-cholesterol (HDL-C) in all twins. In addition, methylation levels of cg11024682 located in SREBF1 at baseline were temporally associated with triglyceride at follow-up in only monozygotic twins. We then performed a mediation analysis with the longitudinal data and the result showed that the association between body mass index and HDL-C was partially mediated by the methylation level of cg06500161 (ABCG1), with a mediation proportion of 10.1%. CONCLUSIONS: Our study indicated that the DNA methylation levels of ABCG1, AKAP1 and SREBF1 may be involved in lipid metabolism and provided evidence for elucidating the regulatory mechanism of lipid homeostasis.

Humans↗

PubMed2025

Detection of cell-type-specific differentially methylated regions in epigenome-wide association studies.

MOTIVATION: DNA methylation at cytosine-phosphate-guanine (CpG) sites is one of the most important epigenetic markers. Therefore, epidemiologists are interested in investigating DNA methylation in large cohorts through epigenome-wide association studies (EWAS). However, the observed EWAS data are bulk data with signals aggregated from distinct cell types. Deconvolution of cell-type-specific signals from EWAS data is challenging because phenotypes can affect both cell-type proportions and cell-type-specific methylation levels. Recently, there has been active research on detecting cell-type-specific risk CpG sites for EWAS data. However, existing methods all assume that the methylation levels of different CpG sites are independent and perform association detection for each CpG site separately. Although these methods significantly improve the detection at the aggregated-level-identifying a CpG site as a risk CpG site as long as it is associated with the phenotype in any cell type, they have low power in detecting cell-type-specific associations for EWAS with typical sample sizes. RESULTS: Here, we develop a new method, Fine-scale inference for Differentially Methylated Regions (FineDMR), to borrow strengths of nearby CpG sites to improve the cell-type-specific association detection. Via a Bayesian hierarchical model built upon Gaussian process functional regression, FineDMR takes advantage of the spatial dependencies between CpG sites. FineDMR can provide cell-type-specific association detection as well as output subject-specific and cell-type-specific methylation profiles for each subject. Simulation studies and real data analysis show that FineDMR substantially improves the power in detecting cell-type-specific associations for EWAS data. AVAILABILITY AND IMPLEMENTATION: FineDMR is freely available at https://github.com/JiaRuofan/Detection-of-Cell-type-specific-DMRs-in-EWAS.

DNA Methylation↗

PubMed2025

Epigenetic mechanisms underlying variation of IL-6, a well-established inflammation biomarker and risk factor for cardiovascular disease.

BACKGROUND AND AIMS: Cardiovascular disease (CVD) is one of the leading causes of morbidity and mortality worldwide, yet the underlying molecular mechanisms remain less understood. Chronic low-grade inflammation is a complex immune response contributing to the pathophysiology of cardiovascular disease. This response is signaled in part by interleukin-6 (IL-6), a pleiotropic, pro-inflammatory cytokine. Phenotypic variance in circulating IL-6 level may be explained in part by DNA methylation which is increasingly being associated with cardiovascular effects. METHODS: In this study we evaluated methylated DNA (CpG sites) associated with blood IL-6 levels across ∼4,400 ancestrally diverse individuals (81 % self-reported White; 9 % Black or African American, 8 % Hispanic or Latino/a, and 2 % Chinese American). RESULTS: We identified 178 CpG sites associated with IL-6 (p<0.05/∼395,000). Among the sites, cg04437762 is located within the transcription unit of IL6R, a current therapeutic target for inflammatory disease, and cg26692003 and cg00464927 were significant for IL6 and IL6ST trans-CpG-gene transcripts. Functional gene expression downstream of methylation identified cellular response to IL-6 and B-cell regulation and activation pathways. Four genes were linked with both a genetic component of cardiovascular disease and an IL-6 associated CpG site. Three CpG sites identified through Mendelian randomization analyses supported inference of a causal effect on IL-6 levels, including the LYN gene that regulates immune cell signaling and has been previously associated with atherosclerosis. CONCLUSIONS: Overall, we identified several novel IL-6-CpG sites and downstream pathways affected by methylation. Follow-up functional studies including the regulation of IL-6 would complement current knowledge of CVD pathophysiology and potential therapeutic targets.

Humans↗

PubMed2025

Alterations in DNA Methylation, Proteomic, and Metabolomic Profiles in African Ancestry Populations with APOL1 Risk Alleles.

KEY POINTS: We aimed to elucidate potential methylation, proteomic, and metabolomic mechanisms by which APOL1 variants may be linked to kidney disease. We report distinct methylation profiling between APOL1 risk allele carriers and noncarriers, many near APOL gene family. We report higher APOL1 protein and lower C18:1 cholesteryl ester in two risk allele carriers. BACKGROUND: The APOL1 high-risk haplotype has been associated with CKD and the deterioration of kidney function, particularly in populations with West African ancestry. However, the mechanisms by which APOL1 risk variants increase the risk for kidney disease and its progression have not been fully elucidated. METHODS: We compared methylation (N=3191; 715 [22%] carriers), proteomic (N=1240; 169 [14%] carriers), and metabolomic (N=6309; 674 [11%] carriers) profiles in African and Hispanic/Latino carriers of two APOL1 high-risk alleles (G1/G1, G2/G2, G1/G2) and noncarriers (G0/G0), excluding heterozygotes (G0/G1, G0/G2), from the Population Architecture using Genomics and Epidemiology Consortium and UK Biobank. In each study, the associations between the APOL1 high-risk haplotype and up to 722,719 cytosine-phosphate-guanine (CpG) sites, 2923 proteins, or 836 metabolites were estimated using covariate-adjusted linear regression models, followed by fixed-effects sample size–weighted meta-analyses. RESULTS: Significant associations were observed between APOL1 high-risk haplotype and methylation at 52 CpG sites, with 48 located on chromosome 22 and 18 in the vicinity of APOL1–4 and MYH9. All significant CpG sites near APOL2 were hypomethylated, whereas those near APOL3 and APOL4 were hypermethylated. APOL1-associated CpG sites were also identified in genes involved in ion transport and mitochondrial stress pathways. Sensitivity analyses indicated consistent yet attenuated effects among heterozygotes, supporting an additive effect of APOL1 risk alleles. Further analyses of the 52 CpG sites identified two near APOL4 exhibiting G1-specific effects, eight associated with CKD but none with eGFR, and three showing heterogeneity by CKD status. In addition, carrying two APOL1 risk alleles was associated with higher plasma APOL1 protein (β=1.12, PFDR = 2.26e-70) and lower C18:1 cholesteryl ester metabolite (Z=−4.50, PFDR = 4.83e-3). CONCLUSIONS: Our results demonstrate differential methylation, proteomic, and metabolomic profiles associated with APOL1 high-risk haplotypes.

APOL1↗

PubMed2026

Machine Learning-Based Identification of Survival-Associated CpG Biomarkers in Pancreatic Ductal Adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is an exceptionally aggressive cancer with a 5-year survival rate of less than 10%, driven by late-stage diagnosis, limited treatment options, and a lack of reliable biomarkers for early detection and prognosis. In this study, we integrated DNA methylation data from TCGA and ICGC cohorts, categorizing samples based on survival time, and identified 688 differentially methylated CpG sites, along with 224 CpG biomarkers significantly associated with patient survival through statistical and machine learning-based analyses. We developed a random forest model to predict patient survival, achieving 85.2% accuracy for short-survival patients and 70.0% for long-survival patients in the validation set. External dataset validation further confirmed the model's robustness and accuracy. De novo motif analysis of genomic regions surrounding the 224 CpG biomarkers identified TWIST1 and FOXA2 as key transcriptional regulators enriched in survival-associated CpG sites, linking their activity to patient survival outcomes. Collectively, our findings highlight valuable epigenetic biomarkers and provide a predictive model to assess PDAC risk levels post-surgery, offering the potential for improved patient stratification and personalized therapeutic strategies.

DNA methylation↗

PubMed2025

Machine Learning-Based Identification of Survival-Associated CpG Biomarkers in Pancreatic Ductal Adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is an exceptionally aggressive cancer with a 5-year survival rate of less than 10%, driven by late-stage diagnosis, limited treatment options, and a lack of reliable biomarkers for early detection and prognosis. In this study, we integrated DNA methylation data from TCGA and ICGC cohorts, categorizing samples based on survival time, and identified 684 differentially methylated CpG sites, along with 224 CpG biomarkers significantly associated with patient survival through statistical and machine learning-based analyses. We developed a random forest model to predict patient survival, achieving 85.2% accuracy for short-survival patients and 70.0% for long-survival patients in the validation set. External dataset validation further confirmed the model's robustness and accuracy. De novo motif analysis of genomic regions surrounding the 224 CpG biomarkers identified TWIST1 and FOXA2 as key transcriptional regulators enriched in survival-associated CpG sites, linking their activity to patient survival outcomes. Collectively, our findings highlight valuable epigenetic biomarkers and provide a predictive model to assess PDAC risk levels post-surgery, offering the potential for improved patient stratification and personalized therapeutic strategies.

Journal Article↗

PubMed2026

Plasma cfDNA hypermethylation at SNCA intron 1 as a potential blood-based epigenetic signal in Parkinson's disease and multiple system atrophy.

BACKGROUND: The accumulation of α-synuclein (SNCA) in the central nervous system is a hallmark of Parkinson's disease (PD) and multiple system atrophy (MSA). SNCA intron 1 methylation is implicated in SNCA transcriptional regulation and may serve as a peripheral epigenetic signal in synucleinopathies. However, studies of SNCA methylation in leukocyte-derived DNA have yielded inconsistent results. We aimed to evaluate whether cell-free DNA (cfDNA)-based SNCA intron 1 methylation differs in PD or MSA compared with normal controls (NC). METHODS: Plasma cfDNA was collected from 105 patients with PD, 50 with MSA, and 114 NC. DNA methylation at CpG sites 10-17 was quantified by bisulfite pyrosequencing. Multivariable linear and logistic regression models, adjusted for age, sex, and education, were used to compare methylation levels and estimate odds ratios (ORs). RESULTS: Patients with PD exhibited hypermethylation at CpG site 14 and higher mean methylation across CpG sites 10-17 compared with NC. Patients with MSA showed hypermethylation at CpG sites 10, 12, 13, and 17 and elevated mean methylation. Elevated mean methylation was also observed in drug-naïve de novo PD and early-stage PD patients. Compared with the lowest tertile, the highest mean methylation tertile was associated with increased odds of PD (OR, 2.49; 95% CI, 1.08-5.92) and MSA (OR, 5.02; 95% CI, 1.58-18.00). CONCLUSION: Plasma cfDNA SNCA intron 1 hypermethylation is associated with PD and MSA and detectable in drug-naïve and early-stage PD. It may represent a peripheral epigenetic alteration and warrants evaluation as an adjunctive signal for early screening.

Humans↗

PubMed2026

DNA methylation as a driver of lung fibroblast senescence in COPD.

Cellular senescence is increasingly recognized as a hallmark of chronic obstructive pulmonary disease (COPD), with higher levels in lung fibroblasts from COPD patients. Upon senescence, both hypomethylation and hypermethylation have been described but not in COPD-derived fibroblasts yet. This study investigated whether altered DNA methylation can be a driver of fibroblast senescence in COPD. Genome-wide gene expression and DNA methylation data were generated from primary lung fibroblasts of 11 COPD stage IV patients and 10 matched controls. Gene expression of six well-known senescence genes was compared between COPD and control. COPD-associated senescence genes were correlated with their related CpG sites in an expression quantitative trait methylation (eQTM) analysis. Methylation levels of significant eQTMs were compared between COPD and control fibroblasts. A causal relationship between altered DNA methylation and senescence was validated in 5-Aza-2'-deoxycytidine (5-Aza-2'-dC)-treated primary lung fibroblasts. Gene expression of CDKN1A, CDKN2A, and CDKN2B was higher, while LMNB1 expression was lower in COPD-derived fibroblasts compared to controls. A total of 19 eQTMs were found for the COPD-associated senescence genes CDKN1A (9), CDKN2A (1), and LMNB1 (9). Among these, seven CpG sites (4 for CDKN1A and 3 for LMNB1) exhibited differential methylation between COPD and control. Treatment with 5-Aza-2'-dC led to global demethylation and increased senescence and, importantly, confirmed the association between senescence and hypomethylation of the COPD-associated CpG site cg04924375. Altered DNA methylation is linked to fibroblast senescence in COPD, and seven CpG sites are identified as potential epigenetic regulators of the senescence genes CDKN1A and LMNB1.NEW & NOTEWORTHY This study identifies DNA methylation as a mechanistic contributor to lung fibroblast senescence in chronic obstructive pulmonary disease (COPD). By integrating DNA methylation data with the transcriptomic data of senescence-related genes, we uncovered seven COPD-associated CpG sites linked to the senescence regulators CDKN1A and LMNB1. Pharmacological demethylation induces fibroblast senescence and is consistent with a functional role for hypomethylation at cg04924375, providing new insight into epigenetic regulation of cellular senescence in COPD lung fibroblasts.

Humans↗

PubMed2024

Epigenetic signature of very low birth weight in young adult life.

BACKGROUND: Globally, one in ten babies is born preterm (<37 weeks), and 1-2% preterm at very low birth weight (VLBW, <1500 g). As adults, they are at increased risk for a plethora of health conditions, e.g., cardiometabolic disease, which may partly be mediated by epigenetic regulation. We compared blood DNA methylation between young adults born at VLBW and controls. METHODS: 157 subjects born at VLBW and 161 controls born at term, from the Helsinki Study of Very Low Birth Weight Adults, were assessed for peripheral venous blood DNA methylation levels at mean age of 22 years. Significant CpG-sites (5'-C-phosphate-G-3') were meta-analyzed against continuous birth weight in four independent cohorts (pooled n = 2235) with cohort mean ages varying from 0 to 31 years. RESULTS: In the discovery cohort, 66 CpG-sites were differentially methylated between VLBW adults and controls. Top hits were located in HIF3A, EBF4, and an intergenic region nearest to GLI2 (distance 57,533 bp). Five CpG-sites, all in proximity to GLI2, were hypermethylated in VLBW and associated with lower birth weight in the meta-analysis. CONCLUSION: We identified differentially methylated CpG-sites suggesting an epigenetic signature of preterm birth at VLBW present in adult life. IMPACT: Being born preterm at very low birth weight has major implications for later health and chronic disease risk factors. The mechanism linking preterm birth to later outcomes remains unknown. Our cohort study of 157 very low birth weight adults and 161 controls found 66 differentially methylated sites at mean age of 22 years. Our findings suggest an epigenetic mark of preterm birth present in adulthood, which opens up opportunities for mechanistic studies.

Humans↗

PubMed2025

Development and validation of a machine learning prognostic model based on an epigenomic signature in patients with pancreatic ductal adenocarcinoma.

BACKGROUND: In Pancreatic Ductal Adenocarcinoma (PDAC), current prognostic scores are unable to fully capture the biological heterogeneity of the disease. While some approaches investigating the role of multi-omics in PDAC are emerging, the analysis of methylation data is under exploited. MATERIALS AND METHODS: We analyzed CpG sites from two publicly available datasets, the TCGA-PAAD used as discovery set and the CPTAC-PDA as external test set. Single mutations and co-mutation of KRAS and TP53 genes were identified as targets, and differentially methylated CpG sites (DMC) were detected accordingly. We trained and validated Random Forest (RF) models to predict each target. Area Under the Receiver Operating Characteristic curve (AUROC) and Area Under the Precision-Recall curve (AUPRC) were used as performance metrics. Then, we performed consensus clustering from the DMCs to identify novel patients' profiles. Finally, we trained and validated a combination of eXtreme Gradient Boosting (XGB) and tree models to select an epigenomic prognostic determinant. RESULTS: From 598 DMCs extracted, an RF model predicted KRAS and TP53 co-mutation on the external test set with AUROC of 0.77 and AUPRC of 0.87. The consensus clustering allowed us to identify 4 clusters (C1, C2, C3, and C4) of patients. The C4 cluster captured a subgroup of patients with favorable Overall Survival (OS) with respect to others. The XGB model perfectly predicted C4 vs other clusters on the discovery set. In both cohorts, patients were stratified into two risk groups according to methylation levels of cg16854533, individuated as the most important CpG site. CONCLUSION: We analyzed methylation data to develop a classifier for the TP53 and KRAS mutational status. Four prognostic clusters were pointed out and a prognostic model using a CpG site was validated in an independent cohort. Our results evidence that the proposed use of methylation data facilitates risk stratification for PDAC.

Humans↗

PubMed2026

DNA hypomethylation of the OLFM1 gene in patients with depression.

OBJECTIVE: Depression is a heterogeneous psychiatric disorder and a growing public health concern, characterized by its high prevalence, recurrence rate, and association with suicide. There is evidence suggesting that both genetic susceptibility and environmental factors can regulate gene expression through DNA methylation, thereby influencing the occurrence and development of depression. The olfactory sensory neuropeptide 1 (OLFM1) protein is a risk factor for mental disorders. However, there are no reports yet regarding the correlation between the OLFM1 gene and depression, nor have there been any studies on the association between OLFM1 gene DNA methylation and depression. METHODS: Genomic DNA was extracted from peripheral blood samples of patients with depression (n = 100) and healthy controls (n = 100) using the QIAamp DNA Blood Mini Kit. Subsequently, the extracted genomic DNA was subjected to bisulfite treatment using the EZ DNA Methylation-Gold™ kit. DNA methylation levels of 107 CpG sites in six fragments of OLFM1 exon 1 and its downstream were detected by the Illumina HiSeq platform using MethylTarget™ technology. RESULTS: Methylation levels across the overall OLFM1 CpG island and its six fragments (OLFM1-1 to OLFM1-6) were significantly reduced in the depression group relative to controls. Analysis of the OLFM1 gene fragments revealed that 84 of 107 CpG sites were significantly hypomethylated in depressed individuals. When patients were divided by sex, male patients displayed hypomethylation at 65 CpG sites, substantially more than the 37 sites found in females. CONCLUSION: OLFM1 hypomethylation is associated with depression and may serve as a potential epigenetic biomarker.

Humans↗

PubMed2026

Chronic heart failure and GPX3 promoter methylation: A clinical-epigenetic analysis.

BACKGROUND: Selenoprotein GPX3 is linked to Chronic Heart Failure (CHF), but its promoter methylation patterns in CHF remain unclear. OBJECTIVE: To explore CpG methylation in the GPX3 promoter region and its association with clinical parameters in CHF. METHODS: Twenty CHF patients and twenty healthy controls were included. Methylation levels of CpG sites within the GPX3_FA28 promoter region were quantified. Group differences were assessed using appropriate statistical tests. Restricted cubic spline (RCS) models were applied to explore dose-response associations between differentially methylated CpG sites and clinical indicators across multiple physiological systems. RESULTS: Significant locus-specific methylation alterations were identified in CHF patients. CpG_5 showed hypermethylation (P = 0.017), while CpG_9 (P = 0.045) and CpG_19 (P = 0.008) were hypomethylated compared with controls. Patients with NYHA class I/II exhibited higher methylation at CpG_1 (P = 0.028) and CpG_2 (P = 0.040). CpG_5 methylation displayed nonlinear associations (P < 0.05) with total bilirubin (inverted U-shape), carbon dioxide (triphasic), total cholesterol (U-shape), and plateletcrit (wave-like). CpG_9 correlated with activated partial thromboplastin time and hematopoietic markers, while CpG_19 was linked to eosinophil percentage and erythrocyte parameters. CONCLUSIONS: GPX3 promoter methylation displays apparent locus specificity in CHF. Different CpG sites may contribute to CHF pathophysiology through distinct epigenetic mechanisms. These findings highlight the potential of GPX3 methylation as a stratified biomarker in CHF.

Humans↗

PubMed2026

eQTM (expression quantitative trait methylation) Atlas: a comprehensive resource of over 11 million DNA methylation-gene expression associations through across 11 tissues and 4 diseases.

MOTIVATION: Epigenome-wide association studies (EWAS) have identified numerous DNA methylation (DNAm) CpG sites associated with complex traits and diseases, but interpretation of those CpG sites remains challenging because in EWAS, CpGs are mostly linked to nearby genes based only on genomic proximity. Expression quantitative trait methylation (eQTM) analyses connect DNAm CpGs with statistically associated gene expression levels. However, a comprehensive, searchable resource integrating eQTMs across diverse tissues and disease contexts has been lacking. RESULTS: We developed the eQTM Atlas, a web-based resource that manually curates more than 11 million DNAm-gene expression associations from eight cohorts, covering 11 tissue types, four broad disease contexts, 173,886 unique CpG probes and 20,231 unique genes. The Atlas supports gene- or CpG- searches by tissue or disease type and finding associated CpG or genes, visualization of cis- and trans-eQTMs through genome browser, heatmap interfaces across various tissues, and cohort-level data downloads. By integrating eQTM results with EWAS resources, the eQTM Atlas enables users to connect disease- or trait-associated CpGs to statistically associated genes rather than relying solely on proximity-based gene annotation, supporting functional interpretation of EWAS findings and generation of disease-specific regulatory hypotheses. AVAILABILITY AND IMPLEMENTATION: The eQTM Atlas is freely available at https://shiny.crc.pitt.edu/eqtm_browser/. The web interface is implemented in R Shiny and hosted through the University of Pittsburgh Center for Research Computing (CRC). Source code is available at https://github.com/ads303/eQTM-Atlas.

DNA methylation↗

PubMed2024

An epigenome-wide study of selenium status and DNA methylation in the Strong Heart Study.

BACKGROUND: Selenium (Se) is an essential nutrient linked to adverse health endpoints at low and high levels. The mechanisms behind these relationships remain unclear and there is a need to further understand the epigenetic impacts of Se and their relationship to disease. We investigated the association between urinary Se levels and DNA methylation (DNAm) in the Strong Heart Study (SHS), a prospective study of cardiovascular disease (CVD) among American Indians adults. METHODS: Selenium concentrations were measured in urine (collected in 1989-1991) using inductively coupled plasma mass spectrometry among 1,357 participants free of CVD and diabetes. DNAm in whole blood was measured cross-sectionally using the Illumina MethylationEPIC BeadChip (850 K) Array. We used epigenome-wide robust linear regressions and elastic net to identify differentially methylated cytosine-guanine dinucleotide (CpG) sites associated with urinary Se levels. RESULTS: The mean (standard deviation) urinary Se concentration was 51.8 (25.1) μg/g creatinine. Across 788,368 CpG sites, five differentially methylated positions (DMP) (hypermethylated: cg00163554, cg18212762, cg11270656, and hypomethylated: cg25194720, cg00886293) were significantly associated with Se in linear regressions after accounting for multiple comparisons (false discovery rate p-value: 0.10). The top hypermethylated DMP (cg00163554) was annotated to the Disco Interacting Protein 2 Homolog C (DIP2C) gene, which relates to transcription factor binding. Elastic net models selected 425 hypo- and hyper-methylated DMPs associated with urinary Se, including three sites (cg00163554 [DIP2C], cg18212762 [MAP4K2], cg11270656 [GPIHBP1]) identified in linear regressions. CONCLUSIONS: Urinary Se was associated with minimal changes in DNAm in adults from American Indian communities across the Southwest and the Great Plains in the United States, suggesting that other mechanisms may be driving health impacts. Future analyses should explore other mechanistic biomarkers in human populations, determine these relationships prospectively, and investigate the potential role of differentially methylated sites with disease endpoints.

Humans↗

PubMed2025

Altered mitochondrial DNA methylation in blood in individuals with mild cognitive impairment.

BACKGROUND: Previous studies reported that altered mitochondrial methylation in Alzheimer's disease (AD), however, whether epigenetic modifications in mitochondrial genomes contribute to preclinical AD remains unclear. This study aimed to investigate mitochondrial methylation changes in individuals with cognitive decline. RESEARCH DESIGN AND METHODS: We examined whole mitochondrial genome methylation in 50 individuals with mild cognitive impairment (MCI) and 50 individuals without MCI, using bisulfite amplicon sequencing, assessing methylation at 366 Cytosine-guanine oligodeoxynucleotide (CpG) sites. RESULTS: We found the overall methylation level of mitochondrial DNA (mtDNA) in each subject was relatively low, ranging from 0% to 15%. Global methylation was significantly higher in individuals with cognitive decline compared to controls (3.86% vs. 3.46%, p = 0.037), with 34 differentially methylated CpG sites identified. Methylation differences (MD) between cognitive decline individuals and controls were 22.93 ± 5.60% at chrM6465 (Q = 0.013), 12.55 ± 3.02% at chrM9612 (Q = 0.013), 11.45 ± 3.88% at chrM11762 (Q = 0.159) and 11.03 ± 3.88% at chrM11766 (Q = 0.172), respectively, while the level of MD at chrM15812 was -13.11 ± 4.31% (Q = 0.159) after Benjamini-Hochberg FDR adjusted. Furthermore, Methylation at specific sites were significantly correlated with Mini-Mental State Examination scores, distinguishing individuals with cognitive decline from controls. CONCLUSIONS: Our study provides an mtDNA methylation map and suggests a role for these sites in preclinical AD pathogenesis.

Humans↗

PubMed2024

A methylation risk score for chronic kidney disease: a HyperGEN study.

Chronic kidney disease (CKD) impacts about 1 in 7 adults in the United States, but African Americans (AAs) carry a disproportionately higher burden of disease. Epigenetic modifications, such as DNA methylation at cytosine-phosphate-guanine (CpG) sites, have been linked to kidney function and may have clinical utility in predicting the risk of CKD. Given the dynamic relationship between the epigenome, environment, and disease, AAs may be especially sensitive to environment-driven methylation alterations. Moreover, risk models incorporating CpG methylation have been shown to predict disease across multiple racial groups. In this study, we developed a methylation risk score (MRS) for CKD in cohorts of AAs. We selected nine CpG sites that were previously reported to be associated with estimated glomerular filtration rate (eGFR) in epigenome-wide association studies to construct a MRS in the Hypertension Genetic Epidemiology Network (HyperGEN). In logistic mixed models, the MRS was significantly associated with prevalent CKD and was robust to multiple sensitivity analyses, including CKD risk factors. There was modest replication in validation cohorts. In summary, we demonstrated that an eGFR-based CpG score is an independent predictor of prevalent CKD, suggesting that MRS should be further investigated for clinical utility in evaluating CKD risk and progression.

Humans↗