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PubMed2026

Methylation profiling of normal tissue adjacent to breast tumors reveals two distinct groups with divergent tumor microenvironment features.

We previously identified diverse genetic evolutionary patterns in whole-genome sequencing of paired normal tissue adjacent to tumor (NAT) and tumor tissues from Hong Kong breast cancer (HKBC) patients. Here, we investigated whether DNA methylation (DNAm) contributes to NAT heterogeneity and shapes the tumor microenvironment (TME). Genome-wide DNAm profiling was performed on paired NAT and tumor tissues from 188 HKBC patients using the Infinium 850 K array. RNA-seq data were available for 76 NATs and 177 tumors. Cellular composition was inferred using MethylCIBERSORT, CIBERSORTx, and EpiDISH, and histopathologic features were assessed on 115 H&E-stained sections. Unsupervised clustering identified two distinct NAT subtypes with divergent TME characteristics. Cluster 1 (N = 139) showed higher epithelial and fibroblast content and enrichment of estrogen response pathways. Cluster 2 (N = 49) exhibited an immune-metabolic phenotype characterized by increased fat and immune cells, stromal disruption, inflammatory pathway activation, and greater macrophage infiltration. Cluster 2 patients also demonstrated significantly younger epigenetic age estimated using multiple epigenetic clocks. These DNAm-defined NAT subtypes and associated TME features were validated in 97 NAT samples from TCGA breast cancer patients. Overall, our findings identify DNAm-driven NAT heterogeneity with distinct TME landscapes, providing new insights into field cancerization and tumor evolution in breast cancer.

Journal Article↗

PubMed2026

GT-Mamba: a Topology-Aware Graph-State space model for robust and interpretable epigenetic age prediction.

MOTIVATION: Current epigenetic clocks face a trade-off between predictive accuracy and biological interpretability, often relying on dataset-specific correction to generalize across cohorts. We propose GT-Mamba, a novel architecture that integrates a Structure-Aware Graph Transformer with the Mamba state space model. This design captures CpG topological correlations and genome-wide long-range dependencies. RESULTS: GT-Mamba demonstrates strong out-of-the-box robustness across heterogeneous independent validation cohorts, achieving a weighted average MAE of 4.43 years. Notably, it effectively generalizes to EPIC 850k arrays despite partial feature missingness, and maintains consistent performance across homologous age distribution shifts (MAE 2.94 years in a young cohort). Ablation studies confirm that graph topology contributes to improved robustness against noise. Mechanistic analysis suggests that the model captures methylation patterns associated with both developmental and functional processes. AVAILABILITY: Source code and pre-trained models are freely available at https://github.com/NENUBioCompute/GT-Mamba and archived on Zenodo (DOI: 10.5281/zenodo.19703155).

Epigenesis, Genetic↗

PubMed2026

Understanding and making sense of epigenetic age misalignment across different aging clocks.

The output of an epigenetic aging clock can vary depending on the training method utilized, cell type composition, the nature of the training dataset, the technology used to generate the methylomic data, acute stressors, and other factors. On an individual level, epigenetic age can fluctuate across different clocks purely due to differences in model training. Among aging clock researchers, it is well-known that the epigenetic age of a single sample can vary across different models. Based on our observations and conversations with longevity scientists and stakeholders, however, this fact is often unappreciated among non-aging clock experts. To help bring more awareness to this important topic, we highlight key literature and, as an illustrative example, use eight blood-trained clocks to show that epigenetic age is frequently misaligned in a publicly available whole blood dataset. Our simple analysis revealed that the average sample difference between the youngest and oldest predicted ages across these clocks was 17 years. The smallest and largest individual-level differences observed were 4 and 45 years, respectively. Clock misalignment has implications for choosing which clock to utilize, interpreting the impact of an intervention on epigenetic age, personalized tracking, and relating epigenetic age to the abstract concept of biological age.

Humans↗

PubMed2026

The effects of tildrakizumab in the epigenetic aging deviation of psoriasis: A 52-week open-label study.

BACKGROUND: While biologic therapies targeting interleukin-23 control cutaneous inflammation in psoriasis, their impact on epigenetic aging has not been previously demonstrated. OBJECTIVES: To evaluate the effects of tildrakizumab treatment in the epigenetic aging deviation of moderate-to severe psoriasis. METHODS: In an open-label 52-week clinical trial, 20 adults with psoriasis were treated with tildrakizumab-asmn 100 mg injections until week 28. Ten age-matched controls without psoriasis were enrolled. Genome-wide DNA methylation was profiled in peripheral blood leukocyte DNA (MethylationEPICv2.0, Illumina) to calculate epigenetic aging clocks predictive of all-cause-mortality, phenotypic age, chronological age, pace of aging, and telomere length. Epigenetic age deviation was calculated as the residuals against chronological age. RESULTS: Psoriasis patients had increased epigenetic age deviation in clocks predictive of mortality: PCGrimAge (P = .008), cytosine-phosphate-guanine (CpG) PTPCGrimAge3 (P = .019), CpGPTGrimAge3 (P = .019), GrimAge2 (P = .049). PCGrimAge was reversed by 0.3 years (week 28, P = .005) and 0.5 years (week 52, P = .04) after the use of tildrakizumab-asmn. The pace of aging was increased in psoriasis patients: DunedinPACE (P = .049). LIMITATIONS: Pilot study (small sample size). CONCLUSIONS: Psoriasis patients presented accelerated epigenetic aging in mortality-predictive clocks. Treatment with tildrakizumab-asmn (interleukin-23 inhibition) showed partial reversal of those clocks in 28 weeks. (Funded by Sun Pharmaceutical Industries, Inc; ClinicalTrials.gov number, NCT05110313).

DNA methylation clocks↗

PubMed2005

Age-related human small intestine methylation: evidence for stem cell niches.

BACKGROUND: The small intestine is constructed of many crypts and villi, and mouse studies suggest that each crypt contains multiple stem cells. Very little is known about human small intestines because mouse fate mapping strategies are impractical in humans. However, it is theoretically possible that stem cell histories are inherently written within their genomes. Genomes appear to record histories (as exemplified by use of molecular clocks), and therefore it may be possible to reconstruct somatic cell dynamics from somatic cell errors. Recent human colon studies suggest that random somatic epigenetic errors record stem cell histories (ancestry and total numbers of divisions). Potentially age-related methylation also occurs in human small intestines, which would allow characterization of their stem cells and comparisons with the colon. METHODS: Methylation patterns in individual crypts from 13 small intestines (17 to 78 years old) were measured by bisulfite sequencing. The methylation patterns were analyzed by a quantitative model to distinguish between immortal or niche stem cell lineages. RESULTS: Age-related methylation was observed in the human small intestines. Crypt methylation patterns were more consistent with stem cell niches than immortal stem cell lineages. Human large and small intestine crypt niches appeared to have similar stem cell dynamics, but relatively less methylation accumulated with age in the small intestines. There were no apparent stem cell differences between the duodenum and ileum, and stem cell survival did not appear to decline with aging. CONCLUSION: Crypt niches containing multiple stem cells appear to maintain human small intestines. Crypt niches appear similar in the colon and small intestine, and the small intestinal stem cell mitotic rate is the same as or perhaps slower than that of the colon. Although further studies are needed, age-related methylation appears to record somatic cell histories, and a somatic epigenetic molecular clock strategy may potentially be applied to other human tissues to reconstruct otherwise occult stem cell histories.

Adolescent↗

PubMed2025

Multiomic clocks to predict phenotypic age in mice.

Biological age refers to a person's overall health in aging, as distinct from their chronological age. Diverse measures of biological age, referred to as "clocks," have been developed in recent years and enable risk assessments and an estimation of the efficacy of longevity interventions in animals and humans. Although most clocks are trained to predict chronological age, clocks have been developed to predict more complex composite biological age outcomes, at least in humans. These composite outcomes can be made up of a combination of phenotypic data, chronological age, and disease or mortality risk. Here, we develop the first such composite biological age measure for mice: the mouse phenotypic age model (Mouse PhenoAge). This outcome is based on frailty measures, complete blood counts, and mortality risk in a longitudinally assessed cohort of male and female C57BL/6 mice. We then develop clocks to predict Mouse PhenoAge, based on multiomic models using metabolomic and DNA methylation data. Our models accurately predict Mouse PhenoAge, and residuals of the models are associated with remaining lifespan, even for mice of the same chronological age. These methods offer novel ways to accurately predict mortality in laboratory mice, thus reducing the need for lengthy and costly survival studies.

Animals↗

PubMed2026

Differences between mitotically old and young endometrial tumors.

Human tumors likely differ in their mitotic ages, reflecting how many divisions elapse between the final tumor progenitor cell and surgical removal. We used a rapidly fluctuating CpG (fCpG) methylation clock to infer relative endometrial adenocarcinomas (EAC) mitotic ages. Experimentally, young tumors initiated from single cells show low-diversity, high-variance fCpG distributions with trimodal peaks near 0%, 50%, and 100%, reflecting inherited progenitor methylation states. fCpG methylation becomes polymorphic with divisions, and older tumors exhibit higher diversity, lower variance, and unimodal distributions centered around 50%. Mitotic ages varied across EAC samples. Synchronous hyperplasia and invasive regions generally shared similar ages, and primary-metastatic EAC pairs showed both synchronous and stepwise progression. The Cancer Genome Atlas (TCGA) EACs also showed variable mitotic ages: younger tumors were enriched for proliferation pathways, whereas older tumors showed more immune infiltration, immune-pathway activation, and evidence of T-cell exhaustion. These results show that human tumors can be ranked by mitotic age and suggest that the growth of older cancers is restrained by immune surveillance. © 2026 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Humans↗

PubMed2005

Counting human somatic cell replications: methylation mirrors endometrial stem cell divisions.

Cell proliferation may be altered in many diseases, but it is uncertain exactly how to measure total numbers of divisions. Although it is impossible to count every division directly, potentially total numbers of stem cell divisions since birth may be inferred from numbers of somatic errors. The idea is that divisions are surreptitiously recorded by random errors that occur during replication. To test this "molecular clock" hypothesis, epigenetic errors encoded in certain methylation patterns were counted in glands from 30 uteri. Endometrial divisions can differ among women because of differences in estrogen exposures or numbers of menstrual cycles. Consistent with an association between mitotic age and methylation, there was an age-related increase in methylation with stable levels after menopause, and significantly less methylation was observed in lean or older multiparous women. Methylation patterns were diverse and more consistent with niche rather than immortal stem cell lineages. There was no evidence for decreased stem cell survival with aging. An ability to count lifetime numbers of stem cell divisions covertly recorded by random replication errors provides new opportunities to link cell proliferation with aging and cancer.

Adolescent↗

PubMed2026

Spatial niche remodeling of senescent liver-resident immune cells and its role in chronic liver diseases.

The liver serves the triple functions of metabolism, detoxification, and immune surveillance. Its unique immune microenvironment is shaped by continuous exposure to gut-derived antigens, pathogen-associated molecular patterns (PAMPs), and metabolites arriving via the portal vein, necessitating a delicate equilibrium between immune tolerance and effector activation. This equilibrium relies on the coordinated activities of diverse liver-resident immune cell populations-including Kupffer cells (KCs), liver sinusoidal endothelial cells (LSECs), hepatic stellate cells (HSCs), dendritic cells (DCs), tissue-resident memory T cells (TRM), innate-like T cells, including mucosal-associated invariant T (MAIT) cells, natural killer T (NKT) cells, and γδ T cells, innate lymphoid cells (ILCs, encompassing conventional NK cells and helper ILC subsets), and neutrophils. With advancing age and chronic injury, these resident immune cell populations undergo profound senescence-associated phenotypic reprogramming that is spatially organized along the portal-to-central axis of the hepatic lobule. Key mechanisms include: telomere dysfunction and DNA damage accumulation driving persistent activation of p53/p21 and p16/Rb pathways; mitochondrial dysfunction with mitochondrial DNA (mtDNA) leakage fueling the senescence-associated secretory phenotype (SASP) via the cyclic GMP-AMP synthase (cGAS)-stimulator of interferon genes (STING) pathway; epigenetic age acceleration, including genome-wide H3K27me3 heterochromatinization; and metabolic reprogramming toward glycolysis and lipid accumulation. This review proposes a "spatial niche remodeling" framework to integrate these cell-intrinsic senescence programs with their lobular context, intercellular communication network rewiring, and pathogenic roles across the spectrum of chronic liver disease-from steatosis through steatohepatitis, fibrosis, cirrhosis, to hepatocellular carcinoma. We critically evaluate emerging senotherapeutic strategies targeting specific liver-resident immune cell subsets, discuss the barriers to clinical translation, and identify priority areas for future investigation, including the application of spatial multi-omics, humanized models, and epigenetic clock-guided clinical trials.

Kupffer cells↗

PubMed2026

Ensemble DNA methylation clock demonstrates Immune-metabolic aging signatures associated with mortality.

Aging is a multifactorial process that is best described in terms of the progressive acquisition of multiple layers of phenotypic changes, such as epigenetic modifications, inflammation, and metabolic dysregulation. DNA methylation clocks have been extensively used to construct epigenetic clocks based on the DNAm profiles that can be used to estimate biological age and predict age-associated outcomes. Nevertheless, the vast majority of clocks constructed so far have been based on linear models, which are unlikely to fully account for the heterogeneity and non-linearity of survival-related DNAm signatures. In this work, we constructed a heterogeneous stacked ensemble survival model based on DNAm data obtained from the Framingham Heart Study. We first identified 190 CpG loci using elastic net Cox regression and subsequently constructed a survival prediction model based on the fusion of five complementary survival models by means of a neural network meta-learner. The prediction power of the survival model was evaluated in an external validation cohort, where we observed strong performance for predicting all-cause mortality that significantly exceeded PhenoAge and was statistically comparable to GrimAge. These performance estimates were derived in cohorts of European ancestry and externally validated in postmenopausal women aged 50-79 years, and should therefore be interpreted as applicable only to demographically similar populations.

Humans↗

PubMed2026

Age-associated epigenomic heterogeneity in papillary tumors of the pineal region: a multicenter YoungNOA investigation.

BACKGROUND: Papillary tumors of the pineal region (PTPR) are rare CNS neoplasms with adult and pediatric presentations, but whether age defines distinct molecular biology is unclear. METHODS: We assembled a multicenter retrospective cohort of 86 histologically confirmed PTPR with genome-wide DNA methylation data, comprising 62 adult and 24 pediatric tumors. Molecular subgroup, array platform, sex, and tumor purity were incorporated into multivariable models. Analyses included DNA methylation class assignment, differential methylation, copy-number variation (CNV), epigenetic mitotic-clock scores, methylation-based tumor microenvironment deconvolution, and descriptive survival evaluation. RESULTS: Adult and pediatric tumors mapped within the established PTPR-A and PTPR-B methylation framework rather than forming age-defined methylation classes. Pediatric tumors were enriched for PTPR-B (22 of 24 tumors [91.7%]) compared with adult tumors (39 of 62 [62.9%]). After adjustment for methylation-based subgroup as well as technical and biological covariates, 2,923 CpG probes were associated with age at a false discovery rate (FDR) threshold below 0.05, and 530 also met the prespecified effect-size threshold. Global methylation summaries were similar between age groups. CNV patterns were dominated by molecular subgroup; adjusted genomic CNV load was not independently associated with pediatric age. In contrast, epiTOC2 intrinsic rate score and the methylation signature represented by the first principal component (PC1) showed age-associated effects independent of molecular subgroup. Methylation-based deconvolution suggested a limited microenvironmental signal, with neutrophil fraction showing the most consistent adjusted association. CONCLUSIONS: Adult and pediatric PTPR share the established PTPR-A/PTPR-B framework. Pediatric tumors, particularly within PTPR-B, showed age-associated DNA methylation differences and higher epigenetic mitotic-clock (epiTOC2) scores in this retrospective cohort. These tissue-level associations do not establish clinical risk or treatment implications and require prospective clinical annotation and orthogonal validation.

Humans↗

PubMed2026

Epigenetic aging of colorectal mucosa in cancer development.

BACKGROUND: The past decade has seen the development of epigenetic models of aging that accurately estimate chronological age and predict disease incidence and mortality. These estimates are modulated by lifestyle and environmental factors linked to carcinogenesis, but to date this has primarily been studied in blood. METHODS: We examined epigenetic aging in normal colonic tissue (n = 96), adjacent mucosa (n = 245) and tumors (n = 208), using models trained on age (Horvath, Hannum, Zhang), mortality (PhenoAge, GrimAge), aging rate (DunedinPACE), cellular mitotic history (EpiTOC, epiTOC2, miAGe), and telomere length (DNAmTL). RESULTS: The Horvath model was the most accurate estimator of chronological age in normal colonic mucosa, with high correlation (r > 0.70) between the Horvath, Hannum, Zhang, PhenoAge and GrimAge models, and between mitotic clocks (r > 0.94). All models showed similar performance in normal tissue and adjacent mucosa, but substantially more variation in estimates in tumors. Significant differences in age acceleration were present between normal and adjacent mucosa by six models (Hannum, Zhang, PhenoAge, EpiTOC, epiTOC2 and miAge), while tumors showed highly significant differences by all models. Age acceleration differed by region of the colon, with varying patterns by model type. Physical activity (PhenoAge), smoking history (GrimAge), and alcohol consumption (Horvath, mitotic clocks) were associated with epigenetic aging in adjacent mucosa, while smoking history, smoking intensity, and alcohol consumption were associated with DNAmTL in tumors. CONCLUSIONS: Our study reveals an impact of tissue type, region, and lifestyle factors on epigenetic aging, but also highlights significant heterogeneity between models and the need for careful consideration within study design.

DNA methylation↗

PubMed2006

Human hair genealogies and stem cell latency.

BACKGROUND: Stem cells divide to reproduce themselves and produce differentiated progeny. A fundamental problem in human biology has been the inability to measure how often stem cells divide. Although it is impossible to observe every division directly, one method for counting divisions is to count replication errors; the greater the number of divisions, the greater the numbers of errors. Stem cells with more divisions should produce progeny with more replication errors. METHODS: To test this approach, epigenetic errors (methylation) in CpG-rich molecular clocks were measured from human hairs. Hairs exhibit growth and replacement cycles and "new" hairs physically reappear even on "old" heads. Errors may accumulate in long-lived stem cells, or in their differentiated progeny that are eventually shed. RESULTS: Average hair errors increased until two years of age, and then were constant despite decades of replacement, consistent with new hairs arising from infrequently dividing bulge stem cells. Errors were significantly more frequent in longer hairs, consistent with long-lived but eventually shed mitotic follicle cells. CONCLUSION: Constant average hair methylation regardless of age contrasts with the age-related methylation observed in human intestine, suggesting that error accumulation and therefore stem cell latency differs among tissues. Epigenetic molecular clocks imply similar mitotic ages for hairs on young and old human heads, consistent with a restart with each new hair, and with genealogies surreptitiously written within somatic cell genomes.

Biological Clocks↗

PubMed2024

The effect of low birth weight as an intrauterine exposure on the early onset of sarcopenia through possible molecular pathways.

Sarcopenia, a musculoskeletal disease characterized by the progressive loss of skeletal muscle mass, strength, and physical performance, presents significant challenges to global public health due to its adverse effects on mobility, morbidity, mortality, and healthcare costs. This comprehensive review explores the intricate connections between sarcopenia and low birth weight (LBW), emphasizing the developmental origins of health and disease (DOHaD) hypothesis, inflammatory processes (inflammaging), mitochondrial dysfunction, circadian rhythm disruptions, epigenetic mechanisms, and genetic variations revealed through genome-wide studies (GWAS). A systematic search strategy was developed using PubMed to identify relevant English-language publications on sarcopenia, LBW, DOHaD, inflammaging, mitochondrial dysfunction, circadian disruption, epigenetic mechanisms, and GWAS. The publications consist of 46.2% reviews, 21.2% cohort studies, 4.8% systematic reviews, 1.9% cross-sectional studies, 13.4% animal studies, 4.8% genome-wide studies, 5.8% epigenome-wide studies, and 1.9% book chapters. The review identified key factors contributing to sarcopenia development, including the DOHaD hypothesis, LBW impact on muscle mass, inflammaging, mitochondrial dysfunction, the influence of clock genes, the role of epigenetic mechanisms, and genetic variations revealed through GWAS. The DOHaD theory suggests that LBW induces epigenetic alterations during foetal development, impacting long-term health outcomes, including the early onset of sarcopenia. LBW correlates with reduced muscle mass, grip strength, and lean body mass in adulthood, increasing the risk of sarcopenia. Chronic inflammation (inflammaging) and mitochondrial dysfunction contribute to sarcopenia, with LBW linked to increased oxidative stress and dysfunction. Disrupted circadian rhythms, regulated by genes such as BMAL1 and CLOCK, are associated with both LBW and sarcopenia, impacting lipid metabolism, muscle mass, and the ageing process. Early-life exposures, including LBW, induce epigenetic modifications like DNA methylation (DNAm) and histone changes, playing a pivotal role in sarcopenia development. Genome-wide studies have identified candidate genes and variants associated with lean body mass, muscle weakness, and sarcopenia, providing insights into genetic factors contributing to the disorder. LBW emerges as a potential early predictor of sarcopenia development, reflecting the impact of intrauterine exposures on long-term health outcomes. Understanding the complex interplay between LBW with inflammaging, mitochondrial dysfunction, circadian disruption, and epigenetic factors is essential for elucidating the pathogenesis of sarcopenia and developing targeted interventions. Future research on GWAS and the underlying mechanisms of LBW-associated sarcopenia is warranted to inform preventive strategies and improve public health outcomes.

Humans↗

PubMed2026

Developmental roles of LSD1/KDM1A-like (LDL) proteins in plants.

LYSINE-SPECIFIC DEMETHYLASE 1-like (LDL) proteins are conserved FAD-dependent amine oxidases that serve as pivotal regulators in plants. While animal systems typically rely on a single LSD1/KDM1A enzyme, the Arabidopsis thaliana genome encodes an expanded family of LDL homologues (FLD, LDL1, LDL2, and LDL3), resulting in substantial subfunctionalization and specialized recruitment mechanisms. This review explores the diverse developmental roles of plant LDLs, ranging from flowering time and circadian clock regulation to heterochromatin maintenance and epigenetic regulation. We discuss the redundant roles of FLD, LDL1, and LDL2 in repressing the floral repressor FLC and their nonredundant specialized function within the CCA1/LHY-TOC1 circadian feedback loop. A central focus of our review is the emerging mechanism of transcription-coupled demethylation, in which LDLs associate with the phosphorylated C-terminal domain of RNA polymerase II to modify chromatin cotranscriptionally within gene bodies. By integrating findings from Arabidopsis thaliana and crops such as tomato and soybean, we illustrate how the diversified LDL-mediated regulatory toolkit facilitates precise, gene-specific regulation. Ultimately, the LDL family represents a cornerstone of the sophisticated epigenetic strategies that regulate plant phenotypic plasticity in response to developmental and environmental cues.

Circadian clock↗

PubMed1993

Intracellular coordination by the ultradian clock.

Activity Cycles↗

PubMed1998

Circadian and ultradian clock-controlled rhythms in unicellular microorganisms.

The time structure of a biological system is at least as intricate as its spatial structure. Whereas we have detailed information about the latter, our understanding of the former is still rudimentary. As techniques for monitoring intracellular processes continuously in single cells become more refined, it becomes increasingly evident that periodic behaviour abounds in all time domains. Circadian timekeeping dominates in natural environments. Here the free-running period is about 24 h. Circadian rhythms in eukaryotes and prokaryotes allow predictive matching of intracellular states with environmental changes during the daily cycles. Unicellular organisms provide excellent systems for the study of these phenomena, which pervade all higher life forms. Intracellular timekeeping is essential. The presence of a temperature-compensated oscillator provides such a timer. The coupled outputs (epigenetic oscillations) of this ultradian clock constitute a special class of ultradian rhythm. These are undamped and endogenously driven by a device which shows biochemical properties characteristic of transcriptional and translational elements. Energy-yielding processes, protein turnover, motility and the timing of the cell-division cycle processes are all controlled by the ultradian clock. Different periods characterize different species, and this indicates a genetic determinant. Periods range from 30 min to 4 h. Mechanisms of clock control are being elucidated; it is becoming evident that many different control circuits can provide these functions.

Activity Cycles↗

PubMed2026

Clonal haematopoiesis of indeterminate potential and epigenetic age acceleration: Systematic review and meta-analysis.

Clonal haematopoiesis of indeterminate potential (CHIP) represents somatic mutations in haematopoietic stem cells that drive clonal expansion. Epigenetic age acceleration (EAA), estimated from DNA methylation (DNAm) clocks, may capture age-related changes in haematopoiesis. This systematic review and meta-analysis was conducted to synthesise evidence on associations between CHIP and EAA and explore shared biological mechanisms that may underlie this relationship. Six databases were searched from January 1, 2011, to June 6, 2025, adhering to PRISMA 2020. Random-effects meta-analyses were performed. Five studies comprising 7483 individuals (ages 55-79, 67.1% female) assessing associations between CHIP and DNAm clocks were included. Across studies, CHIP individuals had higher EAA than no-CHIP individuals, and larger clones were associated with higher EAA. Meta-analysis of three cross-sectional studies (n = 6946) showed that CHIP had higher EAA versus no-CHIP for Horvath1Age IEAA (mean difference, MD=2.84 years, 95% confidence interval, CI: 1.49-4.19), HannumAge EEAA (MD=2.31 years, 95% CI: 1.14-3.49), PhenoAge (MD=1.84 years, 95% CI: 0.96-2.71), and GrimAge (MD=1.20 years, 95% CI: 0.80-1.61). Both DNMT3A- and TET2-mutated CHIP were associated with higher EAA with TET2-mutated CHIP showing larger effect sizes and more consistent associations than DNMT3A-mutated CHIP across DNAm clocks tested. Higher EAA may also act as an effect modifier for morbidity and mortality in CHIP. Larger longitudinal studies are needed to verify a temporal relationship and determine whether EAA provides incremental prognostic value for morbidity and mortality in CHIP.

Humans↗