DNA methylome analysis identifies accelerated epigenetic ageing associated with postmenopausal breast cancer susceptibility

Methods in DNA methylation array dataset analysis: A review

Understanding the intricate relationships between gene expression levels and epigenetic modifications in a genome is crucial to comprehending the pathogenic mechanisms of many diseases. With the advancement of DNA Methylome Profiling techniques, the emphasis on identifying Differentially Methylated Regions (DMRs/DMGs) has become crucial for biomarker discovery, offering new insights into the etiology of illnesses. This review surveys the current state of computational tools/algorithms for the analysis of microarray-based DNA methylation profiling datasets, focusing on key concepts underlying the diagnostic/prognostic CpG site extraction. It addresses methodological frameworks, algorithms, and pipelines employed by various authors, serving as a roadmap to address challenges and understand changing trends in the methodologies for analyzing array-based DNA methylation profiling datasets derived from diseased genomes. Additionally, it highlights the importance of integrating gene expression and methylation datasets for accurate biomarker identification, explores prognostic prediction models, and discusses molecular subtyping for disease classification. The review also emphasizes the contributions of machine learning, neural networks, and data mining to enhance diagnostic workflow development, thereby improving accuracy, precision, and robustness.

Chemotherapy-induced acceleration of DNA methylation-based biological age in breast cancer

Breast cancer is the most common cancer diagnosed in women and is often treated with chemotherapy. Although previous studies have demonstrated increasing biological age in patients who receive chemotherapy, evaluation of this association with DNA methylation-based markers of biological ageing may provide novel insight into the role of chemotherapy on the ageing process. We therefore sought to investigate the association between chemotherapy and markers of biological ageing as estimated from DNA methylation in women with breast cancer. DNA methylation profiling was performed on peripheral blood collected from 18 patients before and after the first cycle of chemotherapy using the Infinium HumanMethylation450 BeadChip. Six markers of biological age acceleration were estimated from DNA methylation levels. Multiple linear regression analyses were performed to evaluate the association between each metric of biological age acceleration and chemotherapy. After adjusting for chronological age and race, intrinsic epigenetic age acceleration (p = 0.041), extrinsic epigenetic age acceleration (p = 0.050), PhenoAge acceleration (p = 0.001), GrimAge acceleration (p < 0.001), and DunedinPACE (p = 0.006) were significantly higher and telomere length (p = 0.027) was significantly lower following the first cycle of chemotherapy compared to before treatment initiation. These results demonstrate greater biological ageing as estimated from DNA methylation following chemotherapy in women with breast cancer. Our findings illustrate that cytotoxic therapies may modulate the ageing process among breast cancer patients and may also have implications for age-related health conditions in cancer survivors.

Exploring the potential of epigenetic clocks in aging research

The process of aging is a notable risk factor for numerous age-related illnesses. Hence, a reliable technique for evaluating biological age or the pace of aging is crucial for understanding the aging process and its influence on the progression of disease. Epigenetic alterations are recognized as a prominent biomarker of aging, and epigenetic clocks formulated on this basis have been shown to provide precise estimations of chronological age. Extensive research has validated the effectiveness of epigenetic clocks in determining aging rates, identifying risk factors for aging, evaluating the impact of anti-aging interventions, and predicting the emergence of age-related diseases. This review provides a detailed overview of the theoretical principles underlying the development of epigenetic clocks and their utility in aging research. Furthermore, it explores the existing obstacles and possibilities linked to epigenetic clocks and proposes potential avenues for future studies in this field.

Epigenetic aging differentially impacts breast cancer risk by self-reported race

BACKGROUND

Breast cancer (BrCa) is the most common cancer for women globally. BrCa incidence varies by age and differs between racial groups, with Black women having an earlier age of onset and higher mortality compared to White women. The underlying biological mechanisms of this disparity remain uncertain. Here, we address this knowledge gap by examining the association between overall epigenetic age acceleration and BrCa initiation as well as the mediating role of race.

RESULTS

We measured whole-genome methylation (866,238 CpGs) using the Illumina EPIC array in blood DNA extracted from 209 women recruited from University Hospitals Cleveland Medical Center. Overall and intrinsic epigenetic age acceleration was calculated-accounting for the estimated white blood cell distribution-using the second-generation biological clock GrimAge. After quality control, 149 BrCa patients and 42 disease-free controls remained. The overall chronological mean age at BrCa diagnosis was 57.4 ± 11.4 years and nearly one-third of BrCa cases were self-reported Black women (29.5%). When comparing BrCa cases to disease-free controls, GrimAge acceleration was 2.48 years greater (p-value = 0.0056), while intrinsic epigenetic age acceleration was 1.72 years higher (p-value = 0.026) for cases compared to controls. After adjusting for known BrCa risk factors, we observed BrCa risk increased by 14% [odds ratio (OR) = 1.14; 95% CI: 1.05, 1.25] for a one-year increase in GrimAge acceleration. The stratified analysis by self-reported race revealed differing ORs for GrimAge acceleration: White women (OR = 1.17; 95% CI: 1.03, 1.36), and Black women (OR = 1.08; 95% CI: 0.96, 1.23). However, our limited sample size failed to detect a statistically significant interaction for self-reported race (p-value >0.05) when examining GrimAge acceleration with BrCa risk.

CONCLUSIONS

Our study demonstrated that epigenetic age acceleration is associated with BrCa risk, and the association suggests variation by self-reported race. Although our sample size is limited, these results highlight a potential biological mechanism for BrCa risk and identifies a novel research area of BrCa health disparities requiring further inquiry.

Molecular and cell phenotype programs in oral epithelial cells directed by co-exposure to arsenic and smokeless tobacco

Chronic arsenic exposure can lead to various health issues, including cancer. Concerns have been mounting about the enhancement of arsenic toxicity through co-exposure to various prevalent lifestyle habits. Smokeless tobacco products are commonly consumed in South Asian countries, where their use frequently co-occurs with exposure to arsenic from contaminated groundwater. To decipher the in vitro molecular and cellular responses to arsenic and/or smokeless tobacco, we performed temporal multi-omics analysis of the transcriptome and DNA methylome remodelling in exposed hTERT-immortalized human normal oral keratinocytes (NOK), as well as arsenic and/or smokeless tobacco genotoxicity and mutagenicity investigations in NOK cells and in human p53 knock-in murine embryonic fibroblasts (Hupki MEF). RNAseq results from acute exposures to arsenic alone and in combination with smokeless tobacco extract revealed upregulation of genes with roles in cell cycle changes, apoptosis and inflammation responses. This was in keeping with global DNA hypomethylation affecting genes involved in the same processes in response to chronic treatment in NOK cells. At the phenotypic level, we observed a dose-dependent decrease in NOK cell viability, induction of DNA damage, cell cycle changes and increased apoptosis, with the most pronounced effects observed under arsenic and SLT co-exposure conditions. Live-cell imaging experiments indicated that the DNA damage likely resulted from induction of apoptosis, an observation validated by a lack of exome-wide mutagenesis in response to chronic exposure to arsenic and/or smokeless tobacco. In sum, our integrative omics study provides novel insights into the acute and chronic responses to arsenic and smokeless tobacco (co-)exposure, with both types of responses converging on several key mechanisms associated with cancer hallmark processes. The generated rich catalogue of molecular programs in oral cells regulated by arsenic and smokeless tobacco (co-)exposure may provide bases for future development of biomarkers for use in molecular epidemiology studies of exposed populations at risk of developing oral cancer.

The d3GHR carrier epigenome in Druze clan longevity

The Druze are a distinct group known for their close community, traditions, and consanguineous marriages, dating back to the eleventh century. This practice has led to unique genetic variations, impacting both pathology and gene-associated phenotypes. Some Druze clans, particularly those with exceptional long-lived family heads (ELLI), attracted attention. Given that the bulk of these ELLI were men, the d3GHR polymorphism was the first obvious possibility. Among the 73 clan members, 8.2% carried the d3GHR isoform, with nearly 11% being males. There was a significant age-related increase (p = 0.04) in this isoform among males, leading to examination of potential environmental mediators affecting gene regulation among these carriers during life (namely epigenetic). We focused on DNA methylation due to its crucial role in gene regulation, development, and disease progression. We analyzed DNA samples from 14 clan members with different GHR genotypes, finding a significant (p < 0.05) negative correlation between DNA methylation levels and age. Employing a biological age clock, we observed a significant + 4.229 years favoring the d3GHR group over the WT and heterozygous groups. In conclusion, this study highlights the advantage of d3GHR carriers among this unique Druze clan and underscores the importance of genotype-environment interaction in epigenetic regulation and its impact on health.

Epigenetic age acceleration and mortality risk prediction in U.S. adults

Background

Epigenetic clocks have emerged as novel measures of biological age and potential predictors of mortality. We aimed to test whether epigenetic age acceleration (EAA) estimated using different epigenetic clocks predict long-term overall, cardiovascular or cancer mortality.

Methods

We analyzed data from 2,105 participants to the 1999-2002 National Health and Nutrition Examination Survey aged ≥50 years old who were followed for mortality through 2019. EAAs was calculated from the residuals of Horvath, Hannum, SkinBlood, Pheno, Zhang, Lin, Weidner, Vidal-Bralo and Grim epigenetic clocks regressed on chronological age. Using cox proportional hazards regression, we estimated the hazard ratio (HR) and 95% confidence interval (CI) for the association of EAA (per 5-year) and the DunedinPoAm pace of aging (per 10% increase) with overall, cardiovascular and cancer mortality, adjusting for covariates and white blood cell composition.

Results

During a median follow-up of 17.5 years, 998 deaths occurred, including 272 from cardiovascular disease and 209 from cancer. Overall mortality was most significantly predicted by Grim EAA (P < 0.0001; HR: 1.50, 95% CI: 1.32-1.71) followed by Hannum (P = 0.001; HR: 1.16, 95% CI: 1.07-1.27), Pheno (P = 0.001; HR: 1.13, 95% CI: 1.05-1.21), Horvath (P = 0.007; HR: 1.13, 95% CI: 1.04-1.22) and Vidal-Bralo (P = 0.008; HR: 1.13, 95% CI: 1.03-1.23) EAAs. Grim EAA predicted cardiovascular mortality (P < 0.0001; HR: 1.55, 95% CI: 1.29-1.86), whereas Hannum (P = 0.006; HR: 1.24, 95% CI: 1.07-1.44), Horvath (P = 0.02; HR: 1.18, 95% CI: 1.02-1.35) and Grim (P = 0.049; HR: 1.37, 95% CI: 1.00-1.87) EAAs predicted cancer mortality. DunedinPoAm pace of aging was associated with overall (P = 0.003; HR: 1.23, 95% CI: 1.08-1.38) and cardiovascular (P = 0.04; HR: 1.25, 95% CI: 1.01-1.55) mortality.

Conclusions

In a U.S. representative sample, Horvath, Hannum, Pheno, Vidal-Bralo and Grim EAA all predicted overall mortality but only Grim EAA predicted cardiovascular mortality and Horvath, Hannum or Grim EAA predicted cancer mortality. Pace of aging predicted overall and cardiovascular mortality.

Potential Role of Glyphosate, Glyphosate-Based Herbicides, and AMPA in Breast Cancer Development: A Review of Human and Human Cell-Based Studies

The potential connection between exposure to glyphosate and glyphosate-based herbicides (GBHs) and breast cancer risk is a topic of research that is rapidly gaining the public's attention due to the conflicting reports surrounding glyphosate's potential carcinogenicity. In this review, we synthesize the current published biomedical literature works that have explored associations of glyphosate, its metabolite, aminomethylphosphonic acid (AMPA), and GBHs with breast cancer risk in humans and human cell-based models. Using PubMed as our search engine, we identified a total of 14 articles that were included in this review. In the four human studies, urinary glyphosate and/or AMPA were associated with breast cancer risk, endocrine disruption, oxidative stress biomarkers, and changes in DNA methylation patterns. Among most of the 10 human cell-based studies, glyphosate exhibited endocrine disruption, induced altered gene expression, increased DNA damage, and altered cell viability, while GBHs were more cytotoxic than glyphosate alone. In summary, numerous studies have shown glyphosate, AMPA, and GBHs to have potential carcinogenic, cytotoxic, or endocrine-disruptive properties. However, more human studies need to be conducted in order for more definitive and supported conclusions to be made on their potential effects on breast cancer risk.

Bidirectional relationship between epigenetic age and brain health events

Chronological age offers an imperfect estimate of the molecular changes that occur with aging. Epigenetic age, which is derived from DNA methylation data, provides a more nuanced representation of aging-related biological processes. This study examines the bidirectional relationship between epigenetic age and the occurrence of brain health events (stroke, dementia, and late-life depression). Using data from the Health and Retirement Study, we analyzed blood samples from over 4,000 participants to determine how epigenetic age relates to past and future brain health events. Study participants with a prior brain health event prior to blood collection were 4% epigenetically older (beta 0.04, SE 0.01), suggesting that these conditions are associated with faster aging than that captured by chronological age. Furthermore, a one standard deviation increase in epigenetic age was associated with 70% higher odds of experiencing a brain health event in the next four years after blood collection (OR 1.70, 95%CI 1.16-2.50), indicating that epigenetic age is not just a consequence but also a predictor of poor brain health. Both results were replicated through Mendelian Randomization analyses, supporting their causal nature. Our findings support the utilization of epigenetic age as a useful biomarker to evaluate the role of interventions aimed at preventing and promoting recovery after a brain health event.

Targeting the epigenetically older individuals for geroprotective trials: the use of DNA methylation clocks

Chronological age is the most important risk factor for the incidence of age-related diseases. The pace of ageing determines the magnitude of that risk and can be expressed as biological age. Targeting fundamental pathways of human aging with geroprotectors has the potential to lower the biological age and therewith prolong the healthspan, the period of life one spends in good health. Target populations for geroprotective interventions should be chosen based on the ageing mechanisms being addressed and the expected effect of the geroprotector on the primary outcome. Biomarkers of ageing, such as DNA methylation age, can be used to select populations for geroprotective interventions and as a surrogate outcome. Here, the use of DNA methylation clocks for selecting target populations for geroprotective intervention is explored.

KDM5 family as therapeutic targets in breast cancer: Pathogenesis and therapeutic opportunities and challenges

Breast cancer (BC) is the most frequent malignant cancer diagnosis and is a primary factor for cancer deaths in women. The clinical subtypes of BC include estrogen receptor (ER) positive, progesterone receptor (PR) positive, human epidermal growth factor receptor 2 (HER2) positive, and triple-negative BC (TNBC). Based on the stages and subtypes of BC, various treatment methods are available with variations in the rates of progression-free disease and overall survival of patients. However, the treatment of BC still faces challenges, particularly in terms of drug resistance and recurrence. The study of epigenetics has provided new ideas for treating BC. Targeting aberrant epigenetic factors with inhibitors represents a promising anticancer strategy. The KDM5 family includes four members, KDM5A, KDM5B, KDM5C, and KDMD, all of which are Jumonji C domain-containing histone H3K4me2/3 demethylases. KDM5 proteins have been extensively studied in BC, where they are involved in suppressing or promoting BC depending on their specific upstream and downstream pathways. Several KDM5 inhibitors have shown potent BC inhibitory activity in vitro and in vivo, but challenges still exist in developing KDM5 inhibitors. In this review, we introduce the subtypes of BC and their current therapeutic options, summarize KDM5 family context-specific functions in the pathobiology of BC, and discuss the outlook and pitfalls of KDM5 inhibitors in this disease.

Cell-free DNA methylation patterns in aging and their association with inflamm-aging

Aim: Liquid biopsies analyzing cell-free DNA (cfDNA) methylation in plasma offer a noninvasive diagnostic for diseases, with the potential of aging biomarkers underexplored. Methods: Utilizing enzymatic methyl-seq (EM-seq), this study assessed cfDNA methylation patterns in aging with blood from 35 healthy individuals. Results: It found aging signatures, including higher cfDNA levels and variations in fragment sizes, plus approximately 2000 age-related differentially methylated CpG sites. A biological age predictive model based on 48 CpG sites showed a strong correlation with chronological age, verified by two datasets. Age-specific epigenetic shifts linked to inflammation were revealed through differentially methylated regions profiling and Olink proteomics. Conclusion: These findings suggest cfDNA methylation as a potential aging biomarker and might exacerbate immunoinflammatory reactivity in older individuals.

Joint association of biological aging and lifestyle with risks of cancer incidence and mortality: A cohort study in the UK Biobank

BACKGROUND

Aging is a risk factor for cancer incidence and mortality. Biological aging can reflect the aging degree of the body better than chronological age and can be aggravated by unhealthy lifestyle factors. We aimed to assess the joint effect of biological aging and lifestyle with risks of cancer incidence and mortality.

METHODS

This study included a total of 281,889 participants aged 37 to 73 from the UK Biobank database. Biological age was derived from chronological age and 9 clinical blood indicators, and lifestyle score was constructed by body mass index, smoking status, alcohol consumption, physical activity, and diet. Multivariate Cox hazard proportional regression model was used to analyze the independent and joint association of biological aging and lifestyle with risks of cancer incidence and mortality, respectively.

RESULTS

Over a median follow-up period of 12.3 years, we found that older biological age was associated with increased risks of overall cancer, digestive system cancers, lung, breast and renal cancers incidence and mortality (HRs: 1.12-2.25). In the joint analysis of biological aging and lifestyle with risks of cancer incidence and mortality, compared with unhealthy lifestyle and younger biological age, individuals with healthy lifestyle and older biological age had decreased risks of incidence (8% ∼ 60%) and mortality (20% ∼ 63%) for overall, esophageal, colorectal, pancreatic and lung cancers.

CONCLUSIONS

Biological aging may be an important risk factor for cancer morbidity and mortality. A healthier lifestyle is more likely to mitigate the adverse effects of biological aging on overall cancer and some site-specific cancers.

Accelerated epigenetic clock aging in maternal peripheral blood and preterm birth

BACKGROUND

Epigenetic clocks use CpG DNA methylation to estimate biological age. Acceleration is associated with cancer, heart disease, and shorter life span. Few studies evaluate DNA methylation age and pregnancy outcomes. AgeAccelGrim is a novel epigenetic clock that combines 7 DNA methylation components.

OBJECTIVE

This study aimed to determine whether maternal biological aging (via AgeAccelGrim) is associated with early preterm birth.

STUDY DESIGN

A prospective cohort of patients with singleton pregnancies and at high risk of spontaneous preterm birth delivering at a tertiary university hospital were included in this study. Genome-wide CpG methylation was measured using the Illumina EPIC BeadChip (Illumina, Inc, San Diego, CA) from maternal blood samples obtained at <28 weeks of gestation. AgeAccelGrim and its 7 DNA methylation components were estimated by the Horvath DNA methylation age online tool. Positive values are associated with accelerated biological aging, whereas negative values are associated with slower biological aging relative to each subject's age. The primary outcome was preterm birth at <34 weeks of gestation (any indication). The secondary outcomes were preterm birth at <37 and <28 weeks of gestation. AgeAccelGrim was analyzed as a continuous variable and in quartiles. Exploratory analyses evaluated each of the 7 DNA methylation components included in the composite AgeAccelGrim. Data were analyzed by chi-square test, t test, rank-sum test, logistic regression (controlling a priori for maternal age, cell counts, low socioeconomic status, and gestational age at the time of sample collection), and Kaplan-Meier survival analyses. The log-rank test was used to test the equality of the survival functions.

RESULTS

Overall, 163 patients met the inclusion criteria. Of the patients, 48%, 39%, and 21% delivered at <37, <34, and <28 weeks of gestation, respectively. The median AgeAccelGrim was -0.35 years (interquartile range, -2.24 to 1.31) for those delivering at term. Those delivering preterm had higher AgeAccelGrim values that were inversely proportional to delivery gestational age (preterm birth at <37 weeks of gestation: +0.40 years [interquartile range: -1.21 to +2.28]; preterm birth at <34 weeks of gestation: +0.51 years [interquartile range: -1.05 to +2.67]; preterm birth at <28 weeks of gestation: +1.05 years [interquartile range: -0.72 to +2.72]). Estimated DNA methylation of the 7 epigenetic clock component values was increased among those with preterm birth at <34 weeks of gestation, although the differences were only significant for DNA methylation of plasminogen activation inhibitor 1. In regression models, AgeAcccelGrim was associated with an elevated risk of preterm birth with increasing magnitude for increasing severity of preterm birth. For each 1-year increase in the AgeAccelGrim value (ie, each 1-year increase in biological age compared with chronologic age), the adjusted odds of preterm birth were 11% (adjusted odds ratio, 1.11; 95% confidence interval, 1.00-1.24), 13% (adjusted odds ratio, 1.13; 95% confidence interval, 1.01-1.26), and 18% (adjusted odds ratio, 1.18; 95% confidence interval, 1.04-1.35) higher for preterm birth at <37, <34, and <28 weeks of gestation, respectively. Similarly, individuals with accelerated biological aging (≥75th percentile AgeAccelGrim) had more than double the odds of preterm birth at <34 weeks of gestation (adjusted odds ratio, 2.36; 95% confidence interval, 1.10-5.08) and more than triple the odds of preterm birth at <28 weeks of gestation (adjusted odds ratio, 3.89; 95% confidence interval, 1.61-9.38). The adjusted odds ratio for preterm birth at <37 weeks of gestation was 1.73 but spanned the null (adjusted odds ratio, 1.73; 95% confidence interval, 0.81-3.69). In Kaplan-Meier survival analyses, those in the highest AgeAccelGrim quartile delivered the earliest (log-rank P value of <.001).

CONCLUSION

Accelerated biological aging was associated with preterm birth among high-risk patients. Future research confirming these findings and elucidating factors that slow biological aging may improve birth outcomes.

Uncovering Forensic Evidence: A Path to Age Estimation through DNA Methylation

Age estimation is a critical aspect of reconstructing a biological profile in forensic sciences. Diverse biochemical processes have been studied in their correlation with age, and the results have driven DNA methylation to the forefront as a promising biomarker. DNA methylation, an epigenetic modification, has been extensively studied in recent years for developing age estimation models in criminalistics and forensic anthropology. Epigenetic clocks, which analyze DNA sites undergoing hypermethylation or hypomethylation as individuals age, have paved the way for improved prediction models. A wide range of biomarkers and methods for DNA methylation analysis have been proposed, achieving different accuracies across samples and cell types. This review extensively explores literature from the past 5 years, showing scientific efforts toward the ultimate goal: applying age prediction models to assist in human identification.

Epigenetic age estimation of wild mice using faecal samples

Age is a key parameter in population ecology, with a myriad of biological processes changing with age as organisms develop in early life then later senesce. As age is often hard to accurately measure with non-lethal methods, epigenetic methods of age estimation (epigenetic clocks) have become a popular tool in animal ecology and are often developed or calibrated using captive animals of known age. However, studies typically rely on invasive blood or tissue samples, which limit their application in more sensitive or elusive species. Moreover, few studies have directly assessed how methylation patterns and epigenetic age estimates compare across environmental contexts (e.g. captive or laboratory-based vs. wild animals). Here, we built a targeted epigenetic clock from laboratory house mice (strain C57BL/6, Mus musculus) using DNA from non-invasive faecal samples, and then used it to estimate age in a population of wild mice (Mus musculus domesticus) of unknown age. This laboratory mouse-derived epigenetic clock accurately predicted adult wild mice to be older than juveniles and showed that wild mice typically increased in epigenetic age over time, but with wide variation in epigenetic ageing rate among individuals. Our results also suggested that, for a given body mass, wild mice had higher methylation across targeted CpG sites than laboratory mice (and consistently higher epigenetic age estimates as a result), even among the smallest, juvenile mice. This suggests wild and laboratory mice may display different CpG methylation levels from very early in life and indicates caution is needed when developing epigenetic clocks on laboratory animals and applying them in the wild.

Causal association of obesity with epigenetic aging and telomere length: a bidirectional mendelian randomization study

BACKGROUND

In observational studies, there exists an association between obesity and epigenetic age as well as telomere length. However, varying and partially conflicting outcomes have notably arisen from distinct studies on this topic. In the present study, two-way Mendelian randomization was used to identify potential causal associations between obesity and epigenetic age and telomeres.

METHODS

A genome-wide association study was conducted using data from individuals of European ancestry to investigate bidirectional Mendelian randomization (MR) regarding the causal relationships between obesity, as indicated by three obesity indicators (body mass index or BMI, waist circumference adjusted for BMI or WCadjBMI, and waist-to-hip ratio adjusted for BMI or WHRadjBMI), and four epigenetic age measures (HannumAge, HorvathAge, GrimAge, PhenoAge), as well as telomere length. To assess these causal associations, various statistical methods were employed, including Inverse Variance Weighted (IVW), Weighted Median, MR Egger, Weighted Mode, and Simple Mode. To address the issue of multiple testing, we applied the Bonferroni correction. These methods were used to determine whether there is a causal link between obesity and epigenetic age, as well as telomere length, and to explore potential bidirectional relationships. Forest plots and scatter plots were generated to show causal associations between exposures and outcomes. For a comprehensive visualization of the results, leave-one-out sensitivity analysis plots, individual SNP-based forest plots for MR analysis, and funnel plots were included in the presentation of the results.

RESULTS

A strong causal association was identified between obesity and accelerated HannumAge, GrimAge, PhenoAge and telomere length shrinkage. The causal relationship between WCadjBMI and PhenoAge acceleration (OR: 2.099, 95%CI: 1.248-3.531, p = 0.005) was the strongest among them. However, only the p-values for the causal associations of obesity with GrimAge, PhenoAge, and telomere length met the criteria after correction using the Bonferroni multiple test. In the reverse MR analysis, there were statistically significant causal associations between HorvathAge, PhenoAge and GrimAge and BMI, but these associations exhibited lower effect sizes, as indicated by their Odds Ratios (ORs). Notably, sensitivity analysis revealed the robustness of the study results.

CONCLUSIONS

The present findings reveal a causal relationship between obesity and the acceleration of epigenetic aging as well as the reduction of telomere length, offering valuable insights for further scientific investigations aimed at developing strategies to mitigate the aging process in humans.

Machine learning-based establishment and validation of age-related patterns for predicting prognosis in non-small cell lung cancer within the context of the tumor microenvironment

Lung cancer (LC) is a leading cause of cancer-related mortality worldwide, with non-small cell lung cancer (NSCLC) accounting for over 80% of cases. The impact of aging on clinical outcomes in NSCLC remains poorly understood, particularly with respect to the immune response. In this study, we explored the effects of aging on NSCLC using 307 genes associated with human aging from the Human Ageing Genomic Resources. We identified 53 aging-associated genes that significantly correlate with overall survival of NSCLC patients, including the clinically validated gene BUB1B. Furthermore, we developed an aging-associated enrichment score to categorize patients based on their aging subtypes and evaluated their prognostic and therapeutic response values in LC. Our analyses yielded two aging-associated subtypes with unique profiles in the tumor microenvironment, demonstrating varying responses to immunotherapy. Consensus clustering based on transcriptome profiles provided insights into the effects of aging on NSCLC and highlighted the potential of personalized therapeutic approaches tailored to aging subtypes. Our findings provide a new target and theoretical support for personalized therapeutic approaches in patients with NSCLC, offering insights into the potential impact of aging on cancer outcomes.

Comprehensive single cell aging atlas of mammary tissues reveals shared epigenomic and transcriptomic signatures of aging and cancer

Aging is the greatest risk factor for breast cancer; however, how age-related cellular and molecular events impact cancer initiation is unknown. We investigate how aging rewires transcriptomic and epigenomic programs of mouse mammary glands at single cell resolution, yielding a comprehensive resource for aging and cancer biology. Aged epithelial cells exhibit epigenetic and transcriptional changes in metabolic, pro-inflammatory, or cancer-associated genes. Aged stromal cells downregulate fibroblast marker genes and upregulate markers of senescence and cancer-associated fibroblasts. Among immune cells, distinct T cell subsets (Gzmk+, memory CD4+, γδ) and M2-like macrophages expand with age. Spatial transcriptomics reveal co-localization of aged immune and epithelial cells in situ. Lastly, transcriptional signatures of aging mammary cells are found in human breast tumors, suggesting mechanistic links between aging and cancer. Together, these data uncover that epithelial, immune, and stromal cells shift in proportions and cell identity, potentially impacting cell plasticity, aged microenvironment, and neoplasia risk.

DNA methylation at quantitative trait loci (mQTLs) varies with cell type and nonheritable factors and may improve breast cancer risk assessment

To individualise breast cancer (BC) prevention, markers to follow a person's changing environment and health extending beyond static genetic risk scores are required. Here, we analysed cervical and breast DNA methylation (n = 1848) and single nucleotide polymorphisms (n = 1442) and demonstrate that a linear combination of methylation levels at 104 BC-associated methylation quantitative trait loci (mQTL) CpGs, termed the WID™-qtBC index, can identify women with breast cancer in hormone-sensitive tissues (AUC = 0.71 [95% CI: 0.65-0.77] in cervical samples). Women in the highest combined risk group (high polygenic risk score and WID™-qtBC) had a 9.6-fold increased risk for BC [95% CI: 4.7-21] compared to the low-risk group and tended to present at more advanced stages. Importantly, the WID™-qtBC is influenced by non-genetic BC risk factors, including age and body mass index, and can be modified by a preventive pharmacological intervention, indicating an interaction between genome and environment recorded at the level of the epigenome. Our findings indicate that methylation levels at mQTLs in relevant surrogate tissues could enable integration of heritable and non-heritable factors for improved disease risk stratification.

Aging measures and cancer: Findings from the Health and Retirement Study

Background

Compared to cancer-free persons, cancer survivors of the same chronological age (CA) have increased physiological dysfunction, i.e., higher biological age (BA), which may lead to higher morbidity and mortality. We estimated BA using eight aging metrics: BA computed by Klemera Doubal method (KDM-BA), phenotypic age (PhenoAge), five epigenetic clocks (ECs, Horvath, Hannum, Levine, GrimAge, and pace of aging (POA)), and subjective age (SA). We tested if aging constructs were associated with total cancer prevalence and all-cause mortality in cancer survivors and controls, i.e., cancer-free persons, in the Health and Retirement Study (HRS), a large population-based study.

Methods

In 2016, data on BA-KDM, PhenoAge, and SA were available for 946 cancer survivors and 4,555 controls; data for the five ECs were available for 582 cancer survivors and 2,805 controls. Weighted logistic regression was used to estimate the association between each aging construct and cancer prevalence (odds ratio, OR, 95%CI). Weighted Cox proportional hazards regression was used to estimate the associations between each aging construct and cancer incidence as well as all-cause mortality (hazard ratio, HR, 95%CI). To study all BA metrics (except for POA) independent of CA, we estimated age acceleration as residuals of BA regressed on CA.

Results

Age acceleration for each aging construct and POA were higher in cancer survivors than controls. In a multivariable-adjusted model, five aging constructs (age acceleration for Hannum, Horvath, Levine, GrimAge, and SA) were associated with cancer prevalence. Among all cancer survivors, age acceleration for PhenoAge and four ECs (Hannum, Horvath, Levine, and GrimAge), was associated with higher all-cause mortality over 4 years of follow-up. PhenoAge, Hannum, and GrimAge were also associated with all-cause mortality in controls. The highest HR was observed for GrimAge acceleration in cancer survivors: 2.03 (95% CI, 1.58-2.60). In contrast, acceleration for KDM-BA and POA was significantly associated with mortality in controls but not in cancer survivors. When all eight aging constructs were included in the same model, two of them (Levine and GrimAge) were significantly associated with mortality among cancers survivors. None of the aging constructs were associated with cancer incidence.

Conclusion

Variations in the associations between aging constructs and mortality in cancer survivors and controls suggests that aging constructs may capture different aspects of aging and that cancer survivors may be experiencing age-related physiologic dysfunctions differently than controls. Future work should evaluate how these aging constructs predict mortality for specific cancer types.

Using proteomics and metabolomics to identify therapeutic targets for senescence mediated cancer: genetic complementarity method

Background

Senescence have emerged as potential factors of lung cancer risk based on findings from many studies. However, the underlying pathogenesis of lung cancer caused by senescence is not clear. In this study, we try to explain the potential pathogenesis between senescence and lung cancer through proteomics and metabonomics. And try to find new potential therapeutic targets in lung cancer patients through network mendelian randomization (MR).

Methods

The genome-wide association data of this study was mainly obtained from a meta-analysis and the Transdisciplinary Research in Cancer of the Lung Consortium (TRICL), respectively.And in this study, we mainly used genetic complementarity methods to explore the susceptibility of aging to lung cancer. Additionally, a mediation analysis was performed to explore the potential mediating role of proteomics and metabonomics, using a network MR design.

Results

GNOVA analysis revealed a shared genetic structure between HannumAge and lung cancer with a significant genetic correlation estimated at 0.141 and 0.135, respectively. MR analysis showed a relationship between HannumAge and lung cancer, regardless of smoking status. Furthermore, genetically predicted HannumAge was consistently associated with the proteins C-type lectin domain family 4 member D (CLEC4D) and Retinoic acid receptor responder protein 1 (RARR-1), indicating their potential role as mediators in the causal pathway.

Conclusion

HannumAge acceleration may increase the risk of lung cancer, some of which may be mediated by CLEC4D and RARR-1, suggestion that CLEC4D and RARR-1 may serve as potential drug targets for the treatment of lung cancer.

Epigenetic aging in older breast cancer survivors and noncancer controls: preliminary findings from the Thinking and Living with Cancer Study

BACKGROUND

Cancer and its treatments may accelerate aging in survivors; however, research has not examined epigenetic markers of aging in longer term breast cancer survivors. This study examined whether older breast cancer survivors showed greater epigenetic aging than noncancer controls and whether epigenetic aging related to functional outcomes.

METHODS

Nonmetastatic breast cancer survivors (n = 89) enrolled prior to systemic therapy and frequency-matched controls (n = 101) ages 62 to 84 years provided two blood samples to derive epigenetic aging measures (Horvath, Extrinsic Epigenetic Age [EEA], PhenoAge, GrimAge, Dunedin Pace of Aging) and completed cognitive (Functional Assessment of Cancer Therapy-Cognitive Function) and physical (Medical Outcomes Study Short Form-12) function assessments at approximately 24 to 36 and 60 months after enrollment. Mixed-effects models tested survivor-control differences in epigenetic aging, adjusting for age and comorbidities; models for functional outcomes also adjusted for racial group, site, and cognitive reserve.

RESULTS

Survivors were 1.04 to 2.22 years biologically older than controls on Horvath, EEA, GrimAge, and DunedinPACE measures (p = .001-.04) at approximately 24 to 36 months after enrollment. Survivors exposed to chemotherapy were 1.97 to 2.71 years older (p = .001-.04), and among this group, an older EEA related to worse self-reported cognition (p = .047) relative to controls. An older epigenetic age related to worse physical function in all women (p < .001-.01). Survivors and controls showed similar epigenetic aging over time, but Black survivors showed accelerated aging over time relative to non-Hispanic White survivors.

CONCLUSION

Older breast cancer survivors, particularly those exposed to chemotherapy, showed greater epigenetic aging than controls that may relate to worse outcomes. If replicated, measurement of biological aging could complement geriatric assessments to guide cancer care for older women.

Methylation scores for smoking, alcohol consumption and body mass index and risk of seven types of cancer

Methylation marks of exposure to health risk factors may be useful markers of cancer risk as they might better capture current and past exposures than questionnaires, and reflect different individual responses to exposure. We used data from seven case-control studies nested within the Melbourne Collaborative Cohort Study of blood DNA methylation and risk of colorectal, gastric, kidney, lung, prostate and urothelial cancer, and B-cell lymphoma (N cases = 3123). Methylation scores (MS) for smoking, body mass index (BMI), and alcohol consumption were calculated based on published data as weighted averages of methylation values. Rate ratios (RR) and 95% confidence intervals for association with cancer risk were estimated using conditional logistic regression and expressed per SD increase of the MS, with and without adjustment for health-related confounders. The contribution of MS to discriminate cases from controls was evaluated using the area under the curve (AUC). After confounder adjustment, we observed: large associations (RR = 1.5-1.7) with lung cancer risk for smoking MS; moderate associations (RR = 1.2-1.3) with urothelial cancer risk for smoking MS and with mature B-cell neoplasm risk for BMI and alcohol MS; moderate to small associations (RR = 1.1-1.2) for BMI and alcohol MS with several cancer types and cancer overall. Generally small AUC increases were observed after inclusion of several MS in the same model (colorectal, gastric, kidney, urothelial cancers: +3%; lung cancer: +7%; B-cell neoplasms: +8%). Methylation scores for smoking, BMI and alcohol consumption show independent associations with cancer risk, and may provide some improvements in risk prediction.

Buffy coat signatures of breast cancer risk in a prospective cohort study

BACKGROUND

Epigenetic alterations are a near-universal feature of human malignancy and have been detected in malignant cells as well as in easily accessible specimens such as blood and urine. These findings offer promising applications in cancer detection, subtyping, and treatment monitoring. However, much of the current evidence is based on findings in retrospective studies and may reflect epigenetic patterns that have already been influenced by the onset of the disease.

METHODS

Studying breast cancer, we established genome-scale DNA methylation profiles of prospectively collected buffy coat samples (n = 702) from a case-control study nested within the EPIC-Heidelberg cohort using reduced representation bisulphite sequencing (RRBS).

RESULTS

We observed cancer-specific DNA methylation events in buffy coat samples. Increased DNA methylation in genomic regions associated with SURF6 and REXO1/CTB31O20.3 was linked to the length of time to diagnosis in the prospectively collected buffy coat DNA from individuals who subsequently developed breast cancer. Using machine learning methods, we piloted a DNA methylation-based classifier that predicted case-control status in a held-out validation set with 76.5% accuracy, in some cases up to 15 years before clinical diagnosis of the disease.

CONCLUSIONS

Taken together, our findings suggest a model of gradual accumulation of cancer-associated DNA methylation patterns in peripheral blood, which may be detected long before clinical manifestation of cancer. Such changes may provide useful markers for risk stratification and, ultimately, personalized cancer prevention.

Prediction of chronological age and its applications in forensic casework: methods, current practices, and future perspectives

Estimating an individual's age can be relevant in several areas primarily related to the clinical and forensic fields. In the latter, estimation of an individual's chronological age from biological material left by the perpetrator at a crime scene may provide helpful information for police investigation. Estimation of age is also beneficial in immigration cases, where age can affect the person's protection status under the law, or in disaster victim identification to narrow the list of potential missing persons. In the last decade, research has focused on establishing new approaches for age prediction in the forensic field. From the first forensic age estimations based on morphological inspections of macroscopic changes in bone and teeth, the focus has shifted to molecular methods for age estimation. These methods allow the use of samples from human biological material that does not contain morphological age features and can, in theory, be investigated in traces containing only small amounts of biological material. Molecular methods involving DNA analyses are the primary choice and estimation of DNA methylation levels at specific sites in the genome is the most promising tool. This review aims to provide an overview of the status of forensic age prediction using molecular methods, with particular focus in DNA methylation. The frequent challenges that impact forensic age prediction model development will be addressed, together with the importance of validation efforts within the forensic community.

Clinical biomarker-based biological aging and risk of cancer in the UK Biobank

BACKGROUND

Despite a clear link between aging and cancer, there has been inconclusive evidence on how biological age (BA) may be associated with cancer incidence.

METHODS

We studied 308,156 UK Biobank participants with no history of cancer at enrolment. Using 18 age-associated clinical biomarkers, we computed three BA measures (Klemera-Doubal method [KDM], PhenoAge, homeostatic dysregulation [HD]) and assessed their associations with incidence of any cancer and five common cancers (breast, prostate, lung, colorectal, and melanoma) using Cox proportional-hazards models.

RESULTS

A total of 35,426 incident cancers were documented during a median follow-up of 10.9 years. Adjusting for common cancer risk factors, 1-standard deviation (SD) increment in the age-adjusted KDM (hazard ratio = 1.04, 95% confidence interval = 1.03-1.05), age-adjusted PhenoAge (1.09, 1.07-1.10), and HD (1.02, 1.01-1.03) was significantly associated with a higher risk of any cancer. All BA measures were also associated with increased risks of lung and colorectal cancers, but only PhenoAge was associated with breast cancer risk. Furthermore, we observed an inverse association between BA measures and prostate cancer, although it was attenuated after removing glycated hemoglobin and serum glucose from the BA algorithms.

CONCLUSIONS

Advanced BA quantified by clinical biomarkers is associated with increased risks of any cancer, lung cancer, and colorectal cancer.

Analysis of Pregnancy Complications and Epigenetic Gestational Age of Newborns

Importance

Preeclampsia, gestational hypertension, and gestational diabetes, the most common pregnancy complications, are associated with substantial morbidity and mortality in mothers and children. Little is known about the biological processes that link the occurrence of these pregnancy complications with adverse child outcomes; altered biological aging of the growing fetus up to birth is one molecular pathway of increasing interest.

Objective

To evaluate whether exposure to each of these 3 pregnancy complications (gestational diabetes, gestational hypertension, and preeclampsia) is associated with accelerated or decelerated gestational biological age in children at birth.

Design, Setting, and Participants

Children included in these analyses were born between 1998 and 2018 and spanned multiple geographic areas of the US. Pregnancy complication information was obtained from maternal self-report and/or medical record data. DNA methylation measures were obtained from blood biospecimens collected from offspring at birth. The study used data from the national Environmental Influences on Child Health Outcomes (ECHO) multisite cohort study collected and recorded as of the August 31, 2021, data lock date. Data analysis was performed from September 2021 to December 2022.

Exposures

Three pregnancy conditions were examined: gestational hypertension, preeclampsia, and gestational diabetes.

Main Outcomes and Measures

Accelerated or decelerated biological gestational age at birth, estimated using existing epigenetic gestational age clock algorithms.

Results

A total of 1801 child participants (880 male [48.9%]; median [range] chronological gestational age at birth, 39 [30-43] weeks) from 12 ECHO cohorts met the analytic inclusion criteria. Reported races included Asian (49 participants [2.7%]), Black (390 participants [21.7%]), White (1026 participants [57.0%]), and other races (92 participants [5.1%]) (ie, American Indian or Alaska Native, Native Hawaiian or other Pacific Islander, multiple races, and other race not specified). In total, 524 participants (29.0%) reported Hispanic ethnicity. Maternal ages ranged from 16 to 45 years of age with a median of 29 in the analytic sample. A range of maternal education levels, from less than high school (260 participants [14.4%]) to Bachelor's degree and above (629 participants [34.9%]), were reported. In adjusted regression models, prenatal exposure to maternal gestational diabetes (β, -0.423; 95% CI, -0.709 to -0.138) and preeclampsia (β, -0.513; 95% CI, -0.857 to -0.170), but not gestational hypertension (β, 0.003; 95% CI, -0.338 to 0.344), were associated with decelerated epigenetic aging among exposed neonates vs those who were unexposed. Modification of these associations, by sex, was observed with exposure to preeclampsia (β, -0.700; 95% CI, -1.189 to -0.210) and gestational diabetes (β, -0.636; 95% CI, -1.070 to -0.200), with associations observed among female but not male participants.

Conclusions and Relevance

This US cohort study of neonate biological changes related to exposure to maternal pregnancy conditions found evidence that preeclampsia and gestational diabetes delay biological maturity, especially in female offspring.

Within subject cross-tissue analyzes of epigenetic clocks in substance use disorder postmortem brain and blood

There is a possible accelerated biological aging in patients with substance use disorders (SUD). The evaluation of epigenetic clocks, which are accurate estimators of biological aging based on DNA methylation changes, has been limited to blood tissue in patients with SUD. Consequently, the impact of biological aging in the brain of individuals with SUD remains unknown. In this study, we evaluated multiple epigenetic clocks (DNAmAge, DNAmAgeHannum, DNAmAgeSkinBlood, DNAmPhenoAge, DNAmGrimAge, and DNAmTL) in individuals with SUD (n = 42), including alcohol (n = 10), opioid (n = 19), and stimulant use disorder (n = 13), and controls (n = 10) in postmortem brain (prefrontal cortex) and blood tissue obtained from the same individuals. We found a higher DNAmPhenoAge (β = 0.191, p-value = 0.0104) and a nominally lower DNAmTL (β = -0.149, p-value = 0.0603) in blood from individuals with SUD compared to controls. SUD subgroup analysis showed a nominally lower brain DNAmTL in subjects with alcohol use disorder, compared to stimulant use disorder and controls (β = 0.0150, p-value = 0.087). Cross-tissue analyzes indicated a lower blood DNAmTL and a higher blood DNAmAge compared to their respective brain values in the SUD group. This study highlights the relevance of tissue specificity in biological aging studies and suggests that peripheral measures of epigenetic clocks in SUD may depend on the specific type of drug used.

Bariatric surgery-induced weight loss and associated genome-wide DNA-methylation alterations in obese individuals

BACKGROUND

Obesity is a multifactorial and chronic condition of growing universal concern. It has recently been reported that bariatric surgery is a more successful treatment for severe obesity than other noninvasive interventions, resulting in rapid significant weight loss and associated chronic disease remission. The identification of distinct epigenetic patterns in patients who are obese or have metabolic imbalances has suggested a potential role for epigenetic alterations in causal or mediating pathways in the development of obesity-related pathologies. Specific changes in the epigenome (DNA methylome), associated with metabolic disorders, can be detected in the blood. We investigated whether such epigenetic changes are reversible after weight loss using genome-wide DNA methylome analysis of blood samples from individuals with severe obesity (mean BMI ~ 45) undergoing bariatric surgery.

RESULTS

Our analysis revealed 41 significant (Bonferroni p < 0.05) and 1169 (false discovery rate p < 0.05) suggestive differentially methylated positions (DMPs) associated with weight loss due to bariatric surgery. Among the 41 significant DMPs, 5 CpGs were replicated in an independent cohort of BMI-discordant monozygotic twins (the heavier twin underwent diet-induced weight loss). The effect sizes of these 5 CpGs were consistent across discovery and replication sets (p < 0.05). We also identified 192 differentially methylated regions (DMRs) among which SMAD6 and PFKFB3 genes were the top hypermethylated and hypomethylated regions, respectively. Pathway enrichment analysis of the DMR-associated genes showed that functional pathways related to immune function and type 1 diabetes were significant. Weight loss due to bariatric surgery also significantly decelerated epigenetic age 12 months after the intervention (mean =  - 4.29; p = 0.02).

CONCLUSIONS

We identified weight loss-associated DNA-methylation alterations targeting immune and inflammatory gene pathways in blood samples from bariatric-surgery patients. The top hits were replicated in samples from an independent cohort of BMI-discordant monozygotic twins following a hypocaloric diet. Energy restriction and bariatric surgery thus share CpGs that may represent early indicators of response to the metabolic effects of weight loss. The analysis of bariatric surgery-associated DMRs suggests that epigenetic regulation of genes involved in endothelial and adipose tissue function is key in the pathophysiology of obesity.

Epigenetic clocks provide clues to the mystery of uterine ageing

BACKGROUND

Rising maternal ages and age-related fertility decline are a global challenge for modern reproductive medicine. Clinicians and researchers pay specific attention to ovarian ageing and hormonal insufficiency in this regard. However, uterine ageing is often left out of the picture, with the majority of reproductive clinicians being close to unanimous on the absence of age-related functional decline in the uterine tissues. Therefore, most existing techniques to treat an age-related decline in implantation rates are based primarily on hormonal supplementation and oocyte donation. Solving the issue of uterine ageing might lead to an adjustment to these methods.

OBJECTIVE AND RATIONALE

A focus on uterine ageing and the possibility of slowing it emerged with the development of the information theory of ageing, which identifies genomic instability and erosion of the epigenetic landscape as important drivers of age-related decline in the functionality of most cells and tissues. Age-related smoothing of this landscape and a decline in tissue function can be assessed by measuring the ticking of epigenetic clocks. Within this review, we explore whether the uterus experiences age-related alterations using this elegant approach. We analyse existing data on epigenetic clocks in the endometrium, highlight approaches to improve the accuracy of the clocks in this cycling tissue, speculate on the endometrial pathologies whose progression might be predicted by the altered speed of epigenetic clocks and discuss the possibilities of slowing down the ticking of these clocks.

SEARCH METHODS

Data for this review were identified by searches of Medline, PubMed and Google Scholar. References from relevant articles using the search terms 'ageing', 'maternal age', 'female reproduction', 'uterus', 'endometrium', 'implantation', 'decidualization', 'epigenetic clock', 'biological age', 'DNA methylation', 'fertility' and 'infertility' were selected. A total of 95 articles published in English between 1985 and 2022 were included, six of which describe the use of the epigenetic clock to evaluate uterine/endometrium ageing.

OUTCOMES

Application of the Horvath and DNAm PhenoAge epigenetic clocks demonstrated a poor correlation with chronological age in the endometrium. Several approaches were suggested to enhance the predictive power of epigenetic clocks for the endometrium. The first was to increase the number of samples in the training dataset, as for the Zang clock, or to use more sophisticated clock-building algorithms, as for the AltumAge clock. The second method is to adjust the clocks according to the dynamic nature of the endometrium. Using either approach revealed a strong correlation with chronological age in the endometrium, providing solid evidence for age-related functional decline in this tissue. Furthermore, age acceleration/deceleration, as estimated by epigenetic clocks, might be a promising tool to predict or to gain insights into the origin of various endometrial pathologies, including recurrent implantation failure, cancer and endometriosis. Finally, there are several strategies to slow down or even reverse epigenetic clocks that might be applied to reduce the risk of age-related uterine impairments.

WIDER IMPLICATIONS

The uterine factor should be considered, along with ovarian issues, to correct for the decline in female fertility with age. Epigenetic clocks can be tested to gain a deeper understanding of various endometrial disorders.

Associations between accelerated parental biologic age, autism spectrum disorder, social traits, and developmental and cognitive outcomes in their children

Parental age is a known risk factor for autism spectrum disorder (ASD), however, studies to identify the biologic changes underpinning this association are limited. In recent years, "epigenetic clock" algorithms have been developed to estimate biologic age and to evaluate how the epigenetic aging impacts health and disease. In this study, we examined the relationship between parental epigenetic aging and their child's prospective risk of ASD and autism related quantitative traits in the Early Autism Risk Longitudinal Investigation study. Estimates of epigenetic age were computed using three robust clock algorithms and DNA methylation measures from the Infinium HumanMethylation450k platform for maternal blood and paternal blood specimens collected during pregnancy. Epigenetic age acceleration was defined as the residual of regressing chronological age on epigenetic age while accounting for cell type proportions. Multinomial logistic regression and linear regression models were completed adjusting for potential confounders for both maternal epigenetic age acceleration (n = 163) and paternal epigenetic age acceleration (n = 80). We found accelerated epigenetic aging in mothers estimated by Hannum's clock was significantly associated with lower cognitive ability and function in offspring at 12 months, as measured by Mullen Scales of Early Learning scores (β = -1.66, 95% CI: -3.28, -0.04 for a one-unit increase). We also observed a marginal association between accelerated maternal epigenetic aging by Horvath's clock and increased odds of ASD in offspring at 36 months of age (aOR = 1.12, 95% CI: 0.99, 1.26). By contrast, fathers accelerated aging was marginally associated with decreased ASD risk in their offspring (aOR = 0.83, 95% CI: 0.68, 1.01). Our findings suggest epigenetic aging could play a role in parental age risks on child brain development.

A pan-tissue DNA-methylation epigenetic clock based on deep learning

Several age predictors based on DNA methylation, dubbed epigenetic clocks, have been created in recent years, with the vast majority based on regularized linear regression. This study explores the improvement in the performance and interpretation of epigenetic clocks using deep learning. First, we gathered 142 publicly available data sets from several human tissues to develop AltumAge, a neural network framework that is a highly accurate and precise age predictor. Compared to ElasticNet, AltumAge performs better for within-data set and cross-data set age prediction, being particularly more generalizable in older ages and new tissue types. We then used deep learning interpretation methods to learn which methylation sites contributed to the final model predictions. We observe that while most important CpG sites are linearly related to age, some highly-interacting CpG sites can influence the relevance of such relationships. Using chromatin annotations, we show that the CpG sites with the highest contribution to the model predictions were related to gene regulatory regions in the genome, including proximity to CTCF binding sites. We also found age-related KEGG pathways for genes containing these CpG sites. Lastly, we performed downstream analyses of AltumAge to explore its applicability and compare its age acceleration with Horvath’s 2013 model. We show that our neural network approach predicts higher age acceleration for tumors, for cells that exhibit age-related changes in vitro, such as immune and mitochondrial dysfunction, and for samples from patients with multiple sclerosis, type 2 diabetes, and HIV, among other conditions. Altogether, our neural network approach provides significant improvement and flexibility compared to current epigenetic clocks for both performance and model interpretability.

Centenarians consistently present a younger epigenetic age than their chronological age with four epigenetic clocks based on a small number of CpG sites

Aging is a progressive time-dependent biological process affecting differentially individuals, who can sometimes present exceptional longevity. Epigenetic alterations are one of the hallmarks of aging, which comprise the epigenetic drift and clock at DNA methylation level. In the present study, we estimated the DNA methylation-based age (DNAmage) using four epigenetic clocks based on a small number of CpGs in French centenarians and semi-supercentenarians (CSSC, n=214) as well as nonagenarians' and centenarians' offspring (NCO, n=143) compared to individuals from the French general population (CG, n=149). DNA methylation analysis of the nine CpGs included in the epigenetic clocks showed high correlation with chronological age (-0.66>R>0.54) and also the presence of an epigenetic drift for four CpGs that was only visible in CSSC. DNAmage analysis showed that CSSC and to a lesser extend NCO present a younger DNAmage than their chronological age (15-28.5 years for CSSC, 4.4-11.5 years for NCO and 4.2-8.2 years for CG), which were strongly significant in CSSC compared to CG (p-values<2.2e-16). These differences suggest that epigenetic aging and potentially biological aging are slowed in exceptionally long-lived individuals and that epigenetic clocks based on a small number of CpGs are sufficient to reveal alterations of the global epigenetic clock.

Differences in DNA Methylation-Based Age Prediction Within Twin Pairs Discordant for Cancer

DNA methylation-based age acceleration (DNAmAA) is associated with cancer, with both cancer tissue and blood showing increased DNAmAA. We aimed to investigate whether DNAmAA is associated with cancer risk within twin pairs discordant for cancer, and whether DNAmAA has the potential to serve as a biomarker for such. The study included 47 monozygotic and 48 same-sex-dizygotic cancer-discordant twin pairs from the Finnish Twin Cohort study with blood samples available between 17 and 31 years after the cancer diagnosis. We studied all cancers (95 pairs), then separately breast cancer (24 pairs) and all sites other than breast cancer (71 pairs). DNAmAA was calculated for seven models: Horvath, Horvath intrinsic epigenetic age acceleration, Hannum, Hannum intrinsic epigenetic age acceleration, Hannum extrinsic epigenetic age acceleration, PhenoAge and GrimAge. Within-pair differences in DNAmAA were analyzed by paired t tests and linear regression. Twin pairs sampled before cancer diagnosis did not differ significantly in DNAmAA. However, the within-pair differences in DNAmAA before cancer diagnosis increased significantly the closer the cancer diagnosis was, and this acceleration extended for years after the diagnosis. Pairs sampled after the diagnosis differed for DNAmAA with the Horvath models capturing cancer diagnosis-associated DNAmAA across all three cancer groupings. The results suggest that DNAmAA in blood is associated with cancer diagnosis. This may be due to epigenetic alterations in relation to cancer, its treatment or associated lifestyle changes. Based on the current study, the biomarker potential of DNAmAA in blood appears to be limited.

Methylation-based markers of aging and lifestyle-related factors and risk of breast cancer: a pooled analysis of four prospective studies

BACKGROUND

DNA methylation in blood may reflect adverse exposures accumulated over the lifetime and could therefore provide potential improvements in the prediction of cancer risk. A substantial body of research has shown associations between epigenetic aging and risk of disease, including cancer. Here we aimed to study epigenetic measures of aging and lifestyle-related factors in association with risk of breast cancer.

METHODS

Using data from four prospective case-control studies nested in three cohorts of European ancestry participants, including a total of 1,655 breast cancer cases, we calculated three methylation-based measures of lifestyle factors (body mass index [BMI], tobacco smoking and alcohol consumption) and seven measures of epigenetic aging (Horvath-based, Hannum-based, PhenoAge and GrimAge). All measures were regression-adjusted for their respective risk factors and expressed per standard deviation (SD). Odds ratios (OR) and 95% confidence intervals (CI) were calculated using conditional or unconditional logistic regression and pooled using fixed-effects meta-analysis. Subgroup analyses were conducted by age at blood draw, time from blood sample to diagnosis, oestrogen receptor-positivity status and tumour stage.

RESULTS

None of the measures of epigenetic aging were associated with risk of breast cancer in the pooled analysis: Horvath 'age acceleration' (AA): OR per SD = 1.02, 95%CI: 0.95-1.10; AA-Hannum: OR = 1.03, 95%CI:0.95-1.12; PhenoAge: OR = 1.01, 95%CI: 0.94-1.09 and GrimAge: OR = 1.03, 95%CI: 0.94-1.12, in models adjusting for white blood cell proportions, body mass index, smoking and alcohol consumption. The BMI-adjusted predictor of BMI was associated with breast cancer risk, OR per SD = 1.09, 95%CI: 1.01-1.17. The results for the alcohol and smoking methylation-based predictors were consistent with a null association. Risk did not appear to substantially vary by age at blood draw, time to diagnosis or tumour characteristics.

CONCLUSION

We found no evidence that methylation-based measures of aging, smoking or alcohol consumption were associated with risk of breast cancer. A methylation-based marker of BMI was associated with risk and may provide insights into the underlying associations between BMI and breast cancer.

Interrelationships and determinants of aging biomarkers in cord blood

BACKGROUND

Increasing evidence supports the concept of prenatal programming as an early factor in the aging process. DNA methylation age (DNAm age), global genome-wide DNA methylation (global methylation), telomere length (TL), and mitochondrial DNA content (mtDNA content) have independently been shown to be markers of aging, but their interrelationship and determinants at birth remain uncertain.

METHODS

We assessed the inter-correlation between the aging biomarkers DNAm age, global methylation, TL and mtDNA content using Pearson's correlation in 190 cord blood samples of the ENVIRONAGE birth cohort. TL and mtDNA content was measured via qPCR, while the DNA methylome was determined using the human 450K methylation Illumina microarray. Subsequently, DNAm age was calculated according to Horvath's epigenetic clock, and mean global, promoter, gene-body, and intergenic DNA methylation were determined. Path analysis, a form of structural equation modeling, was performed to disentangle the complex causal relationships among the aging biomarkers and their potential determinants.

RESULTS

DNAm age was inversely correlated with global methylation (r = -0.64, p < 0.001) and mtDNA content (r = - 0.16, p = 0.027). Cord blood TL was correlated with mtDNA content (r = 0.26, p < 0.001) but not with global methylation or DNAm age. Path analysis showed the strongest effect for global methylation on DNAm age with a decrease of 0.64 standard deviations (SD) in DNAm age for each SD (0.01%) increase in global methylation (p < 0.001). Among the applied covariates, newborn sex and season of delivery were the strongest determinants of aging biomarkers.

CONCLUSIONS

We provide insight into molecular aging signatures at the start of life, including their interrelations and determinants, showing that cord blood DNAm age is inversely associated with global methylation and mtDNA content but not with newborn telomere length. Our findings demonstrate that cord blood TL and DNAm age relate to different pathways/mechanisms of biological aging and can be influenced by environmental factors already at the start of life. These findings are relevant for understanding fetal programming and for the early prevention of noncommunicable diseases.

Harnessing the Full Potential of Multi-Omic Analyses to Advance the Study and Treatment of Chronic Kidney Disease

Chronic kidney disease (CKD) was the 12th leading cause of death globally in 2017 with the prevalence of CKD estimated at ~9%. Early detection and intervention for CKD may improve patient outcomes, but standard testing approaches even in developed countries do not facilitate identification of patients at high risk of developing CKD, nor those progressing to end-stage kidney disease (ESKD). Recent advances in CKD research are moving towards a more personalised approach for CKD. Heritability for CKD ranges from 30% to 75%, yet identified genetic risk factors account for only a small proportion of the inherited contribution to CKD. More in depth analysis of genomic sequencing data in large cohorts is revealing new genetic risk factors for common diagnoses of CKD and providing novel diagnoses for rare forms of CKD. Multi-omic approaches are now being harnessed to improve our understanding of CKD and explain some of the so-called 'missing heritability'. The most common omic analyses employed for CKD are genomics, epigenomics, transcriptomics, metabolomics, proteomics and phenomics. While each of these omics have been reviewed individually, considering integrated multi-omic analysis offers considerable scope to improve our understanding and treatment of CKD. This narrative review summarises current understanding of multi-omic research alongside recent experimental and analytical approaches, discusses current challenges and future perspectives, and offers new insights for CKD.

Counteracting aged DNA methylation states to combat ageing and age-related diseases

DNA methylation (DNAm) overwrites information about multiple extrinsic factors on the genome. Age is one of these factors. Age causes characteristic DNAm changes that are thought to be not only major drivers of normal ageing but also precursors to diseases, cancer being one of these. Although there is still much to learn about the relationship between ageing, age-related diseases and DNAm, we now know how to interpret some of the effects caused by age in the form of changes in methylation marks at specific loci. In fact, these changes form the basis of the so called "epigenetic clocks", which translate the genomic methylation profile into an "epigenetic age". Epigenetic age does not only estimate chronological age but can also predict the risk of chronic diseases and mortality. Epigenetic age is believed to be one of the most accurate metrics of biological age. Initial evidence has recently been gathered pointing to the possibility that the rate of epigenetic ageing can be slowed down or even reversed. In this review, we discuss some of the most relevant advances in this field. Expected outcome is that this approach can provide insights into how to preserve health and reduce the impact of ageing diseases in humans.

DNA Methylation, Aging, and Cancer Risk: A Mini-Review

Accumulation of somatic mutations and genomic instability are hallmarks of both aging and cancer. Epigenetic alterations occur across cell types and tissues with advancing age. DNA methylation-based estimates of biologic age can predict important age-related outcomes, including risk of frailty and mortality, and most recently have been shown to be associated with risk of developing cancer. In this mini-review, we examine pathways known to exhibit altered methylation in aging tissues, pre-malignant lesions, and tumors and review methodologies of epigenetic clocks that reliably predict cancer risk, including those derived from methylation studies of peripheral blood, as well as those methylation levels from within the tissues at high risk of cancer.

eClock: An ensemble-based method to accurately predict ages with a biased distribution from DNA methylation data

DNA methylation is closely related to senescence, so it has been used to develop statistical models, called clock models, to predict chronological ages accurately. However, because the training data always have a biased age distribution, the model performance becomes weak for the samples with a small age distribution density. To solve this problem, we developed the R package eClock, which uses a bagging-SMOTE method to adjust the biased distribution and predict age with an ensemble model. Moreover, it also provides a bootstrapped model based on bagging only and a traditional clock model. The performance on three datasets showed that the bagging-SMOTE model significantly improved rare sample age prediction. In addition to model construction, the package also provides other functions such as data visualization and methylation feature conversion to facilitate the research in relevant areas.

DNA Methylation - and Telomere - Based Biological Age Estimation as Markers of Biological Aging in Donors Kidneys

The biological age of an organ may represent a valuable tool for assessing its quality, especially in the elder. We examined the biological age of the kidneys [right (RK) and left kidney (LK)] and blood leukocytes in the same subject and compared these to assess whether blood mirrors kidney biological aging. Biological age was studied in n = 36 donors (median age: 72 years, range: 19-92; male: 42%) by exploring mitotic and non-mitotic pathways, using telomere length (TL) and age-methylation changes (DNAmAge) and its acceleration (AgeAcc). RK and LK DNAmAge are older than blood DNAmAge (RK vs. Blood, p = 0.0271 and LK vs. Blood, p = 0.0245) and RK and LK AgeAcc present higher score (this mean the AgeAcc is faster) than that of blood leukocytes (p = 0.0271 and p = 0.0245) in the same donor. TL of RK and LK are instead longer than that of blood (p = 0.0011 and p = 0.0098) and the increase in Remuzzi-Karpinski score is strongly correlated with kidney TL attrition (p = 0.0046). Finally, blood and kidney TL (p < 0.01) and DNAmAge (p < 0.001) were correlated. These markers can be evaluated in further studies as indicators of biological age of donor organ quality and increase the usage of organs from donors of advanced age therefore offering a potential translational research inkidney transplantation.

HIV, pathology and epigenetic age acceleration in different human tissues

Epigenetic clocks based on patterns of DNA methylation have great importance in understanding aging and disease; however, there are basic questions to be resolved in their application. It remains unknown whether epigenetic age acceleration (EAA) within an individual shows strong correlation between different primary tissue sites, the extent to which tissue pathology and clinical illness correlate with EAA in the target organ, and if EAA variability across tissues differs according to sex. Considering the outsized role of age-related illness in Human Immunodeficiency Virus-1 (HIV), these questions were pursued in a sample enriched for tissue from HIV-infected individuals. We used a custom methylation array to generate DNA methylation data from 661 samples representing 11 human tissues (adipose, blood, bone marrow, heart, kidney, liver, lung, lymph node, muscle, spleen and pituitary gland) from 133 clinically characterized, deceased individuals, including 75 infected with HIV. We developed a multimorbidity index based on the clinical disease history. Epigenetic age was moderately correlated across tissues. Blood had the greatest number and degree of correlation, most notably with spleen and bone marrow. However, blood did not correlate with epigenetic age of liver. EAA in liver was weakly correlated with EAA in kidney, adipose, lung and bone marrow. Clinically, hypertension was associated with EAA in several tissues, consistent with the multiorgan impacts of this illness. HIV infection was associated with positive age acceleration in kidney and spleen. Male sex was associated with increased epigenetic acceleration in several tissues. Preliminary evidence indicates that amyotrophic lateral sclerosis is associated with positive EAA in muscle tissue. Finally, greater multimorbidity was associated with greater EAA across all tissues. Blood alone will often fail to detect EAA in other tissues. While hypertension is associated with increased EAA in several tissues, many pathologies are associated with organ-specific age acceleration.

Assessing the causal role of epigenetic clocks in the development of multiple cancers: a Mendelian randomization study

Background

Epigenetic clocks have been associated with cancer risk in several observational studies. Nevertheless, it is unclear whether they play a causal role in cancer risk or if they act as a non-causal biomarker.

Methods

We conducted a two-sample Mendelian randomization (MR) study to examine the genetically predicted effects of epigenetic age acceleration as measured by HannumAge (nine single-nucleotide polymorphisms (SNPs)), Horvath Intrinsic Age (24 SNPs), PhenoAge (11 SNPs), and GrimAge (4 SNPs) on multiple cancers (i.e. breast, prostate, colorectal, ovarian and lung cancer). We obtained genome-wide association data for biological ageing from a meta-analysis (N = 34,710), and for cancer from the UK Biobank (N cases = 2671-13,879; N controls = 173,493-372,016), FinnGen (N cases = 719-8401; N controls = 74,685-174,006) and several international cancer genetic consortia (N cases = 11,348-122,977; N controls = 15,861-105,974). Main analyses were performed using multiplicative random effects inverse variance weighted (IVW) MR. Individual study estimates were pooled using fixed effect meta-analysis. Sensitivity analyses included MR-Egger, weighted median, weighted mode and Causal Analysis using Summary Effect Estimates (CAUSE) methods, which are robust to some of the assumptions of the IVW approach.

Results

Meta-analysed IVW MR findings suggested that higher GrimAge acceleration increased the risk of colorectal cancer (OR = 1.12 per year increase in GrimAge acceleration, 95% CI 1.04-1.20, p = 0.002). The direction of the genetically predicted effects was consistent across main and sensitivity MR analyses. Among subtypes, the genetically predicted effect of GrimAge acceleration was greater for colon cancer (IVW OR = 1.15, 95% CI 1.09-1.21, p = 0.006), than rectal cancer (IVW OR = 1.05, 95% CI 0.97-1.13, p = 0.24). Results were less consistent for associations between other epigenetic clocks and cancers.

Conclusions

GrimAge acceleration may increase the risk of colorectal cancer. Findings for other clocks and cancers were inconsistent. Further work is required to investigate the potential mechanisms underlying the results.

Funding

FMB was supported by a Wellcome Trust PhD studentship in Molecular, Genetic and Lifecourse Epidemiology (224982/Z/22/Z which is part of grant 218495/Z/19/Z). KKT was supported by a Cancer Research UK (C18281/A29019) programme grant (the Integrative Cancer Epidemiology Programme) and by the Hellenic Republic's Operational Programme 'Competitiveness, Entrepreneurship & Innovation' (OΠΣ 5047228). PH was supported by Cancer Research UK (C18281/A29019). RMM was supported by the NIHR Biomedical Research Centre at University Hospitals Bristol and Weston NHS Foundation Trust and the University of Bristol and by a Cancer Research UK (C18281/A29019) programme grant (the Integrative Cancer Epidemiology Programme). RMM is a National Institute for Health Research Senior Investigator (NIHR202411). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. GDS and CLR were supported by the Medical Research Council (MC_UU_00011/1 and MC_UU_00011/5, respectively) and by a Cancer Research UK (C18281/A29019) programme grant (the Integrative Cancer Epidemiology Programme). REM was supported by an Alzheimer's Society project grant (AS-PG-19b-010) and NIH grant (U01 AG-18-018, PI: Steve Horvath). RCR is a de Pass Vice Chancellor's Research Fellow at the University of Bristol.

Deconvolution of the epigenetic age discloses distinct inter-personal variability in epigenetic aging patterns

BACKGROUND

The epigenetic age can now be extrapolated from one of several epigenetic clocks, which are based on age-related changes in DNA methylation levels at specific multiple CpG sites. Accelerated aging, calculated from the discrepancy between the chronological age and the epigenetic age, has shown to predict morbidity and mortality rate. We assumed that deconvolution of epigenetic age to its components could shed light on the diversity of epigenetic, and by inference, on inter-individual variability in the causes of biological aging.

RESULTS

Using the Horvath original epigenetic clock, we identified several CpG sites linked to distinct genes that quantitatively explain much of the inter-personal variability in epigenetic aging, with CpG sites related to secretagogin and malin being the most variable. We show that equal epigenetic age in different subjects can result from variable contribution size of the same CpG sites to the total epigenetic age. In a healthy cohort, the most variable CpG sites are responsible for accelerated and decelerated epigenetic aging, relative to chronological age.

CONCLUSIONS

Of the 353 CpG sites that form the basis for the Horvath epigenetic age, we have found the CpG sites that are responsible for accelerated and decelerated epigenetic aging in healthy subjects. However, the relative contribution of each site to aging varies between individuals, leading to variable personal aging patterns. Our findings pave the way to form personalized aging cards allowing the identification of specific genes related to CpG sites, as aging markers, and perhaps treatment of these targets in order to hinder undesirable age drifting.

Evidence of accelerated epigenetic aging of breast tissues in patients with breast cancer is driven by CpGs associated with polycomb-related genes

Purpose

Age is one of the strongest risk factors for the development of breast cancer, however, the underlying etiology linking age and breast cancer remains unclear. We have previously observed links between epigenetic aging signatures in breast/tumor tissue and breast cancer risk/prevalence. However, these DNA methylation-based aging biomarkers capture diverse epigenetic phenomena and it is not known to what degree they relate to breast cancer risk, and/or progression.

Methods

Using six epigenetic clocks, we analyzed whether they distinguish normal breast tissue adjacent to tumor (cases) vs normal breast tissue from healthy controls (controls).

Results

The Levine (p = 0.0037) and Yang clocks (p = 0.023) showed significant epigenetic age acceleration in cases vs controls in breast tissue. We observed that much of the difference between cases and controls is driven by CpGs associated with polycomb-related genes. Thus, we developed a new score utilizing only CpGs associated with polycomb-related genes and demonstrated that it robustly captured epigenetic age acceleration in cases vs controls (p = 0.00012). Finally, we tested whether this same signal could be seen in peripheral blood. We observed no difference in cases vs. controls and no correlation between matched tissue/blood samples, suggesting that peripheral blood is not a good surrogate marker for epigenetic age acceleration.

Conclusions

Moving forward, it will be critical for studies to elucidate whether epigenetic age acceleration in breast tissue precedes breast cancer diagnosis and whether methylation changes at CpGs associated with polycomb-related genes can be used to assess the risk of developing breast cancer among unaffected individuals.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13148-022-01249-z.

Susceptibility to hormone-mediated cancer is reflected by different tick rates of the epithelial and general epigenetic clock

Background

A variety of epigenetic clocks utilizing DNA methylation changes have been developed; these clocks are either tissue-independent or designed to predict chronological age based on blood or saliva samples. Whether discordant tick rates between tissue-specific and general epigenetic clocks play a role in health and disease has not yet been explored.

Results

Here we analyze 1941 cervical cytology samples, which contain a mixture of hormone-sensitive cervical epithelial cells and immune cells, and develop the WID general clock (Women’s IDentification of risk), an epigenetic clock that is shared by epithelial and immune cells and optimized for cervical samples. We then develop the WID epithelial clock and WID immune clock, which define epithelial- and immune-specific clocks, respectively. We find that the WID-relative-epithelial-age (WID-REA), defined as the difference between the epithelial and general clocks, is significantly reduced in cervical samples from pre-menopausal women with breast cancer (OR 2.7, 95% CI 1.28-5.72). We find the same effect in normal breast tissue samples from pre-menopausal women at high risk of breast cancer and show that potential risk reducing anti-progesterone drugs can reverse this. In post-menopausal women, this directionality is reversed. Hormone replacement therapy consistently leads to a significantly lower WID-REA in cancer-free women, but not in post-menopausal women with breast or ovarian cancer.

Conclusions

Our findings imply that there are multiple epigenetic clocks, many of which are tissue-specific, and that the differential tick rate between these clocks may be an informative surrogate measure of disease risk.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13059-022-02603-3.

The WID-BC-index identifies women with primary poor prognostic breast cancer based on DNA methylation in cervical samples

Genetic and non-genetic factors contribute to breast cancer development. An epigenome-based signature capturing these components in easily accessible samples could identify women at risk. Here, we analyse the DNA methylome in 2,818 cervical, 357 and 227 matched buccal and blood samples respectively, and 42 breast tissue samples from women with and without breast cancer. Utilising cervical liquid-based cytology samples, we develop the DNA methylation-based Women’s risk IDentification for Breast Cancer index (WID-BC-index) that identifies women with breast cancer with an AUROC (Area Under the Receiver Operator Characteristic) of 0.84 (95% CI: 0.80–0.88) and 0.81 (95% CI: 0.76–0.86) in internal and external validation sets, respectively. CpGs at progesterone receptor binding sites hypomethylated in normal breast tissue of women with breast cancer or in BRCA mutation carriers are also hypomethylated in cervical samples of women with poor prognostic breast cancer. Our data indicate that a systemic epigenetic programming defect is highly prevalent in women who develop breast cancer. Further studies validating the WID-BC-index may enable clinical implementation for monitoring breast cancer risk.

Breast cancer is most commonly diagnosed via a needle biopsy. In this study, the authors show that cervical samples from women with breast cancer have a methylation signature different to that of healthy controls.

Maternal dietary fatty acid composition and newborn epigenetic aging-a geometric framework approach

BACKGROUND

Maternal nutrition is associated with epigenetic and cardiometabolic risk factors in offspring. Research in humans has primarily focused on assessing the impact of individual nutrients.

OBJECTIVES

We sought to assess the collective impact of maternal dietary MUFAs, PUFAs, and SFAs on epigenetic aging and cardiometabolic risk markers in healthy newborn infants using a geometric framework approach.

METHODS

Body fatness (n = 162), aortic intima-media thickness (aIMT; n = 131), heart rate variability (n = 118), and epigenetic age acceleration (n = 124) were assessed in newborn infants. Maternal dietary intake was cross-sectionally assessed in the immediate postpartum period via a validated 80-item self-administered FFQ. Generalized additive models were used to explore interactive associations of nutrient intake, with results visualized as response surfaces.

RESULTS

After adjustment for total energy intake, maternal age, gestational age, and sex there was a 3-way interactive association of MUFAs, PUFAs, and SFAs (P = 0.001) with newborn epigenetic aging. This suggests that the nature of each fat class association depends upon one another. Response surfaces revealed MUFAs were positively associated with newborn epigenetic age acceleration only at proportionately lower intakes of SFAs or PUFAs. We also demonstrate a potential beneficial association of omega-3 (n-3) PUFAs with newborn epigenetic age acceleration (P = 0.008). There was no significant association of fat class with newborn aIMT, heart rate variability, or body fatness.

CONCLUSIONS

In this study, we demonstrated an association between maternal dietary fat class composition and epigenetic aging in newborns. Future research should consider other characteristics such as the source of maternal dietary fatty acids.

Common Pathogenetic Mechanisms Underlying Aging and Tumor and Means of Interventions

Recently, there has been an increase in the incidence of malignant tumors among the older population. Moreover, there is an association between aging and cancer. During the process of senescence, the human body suffers from a series of imbalances, which have been shown to further accelerate aging, trigger tumorigenesis, and facilitate cancer progression. Therefore, exploring the junctions of aging and cancer and searching for novel methods to restore the junctions is of great importance to intervene against aging-related cancers. In this review, we have identified the underlying pathogenetic mechanisms of aging-related cancers by comparing alterations in the human body caused by aging and the factors that trigger cancers. We found that the common mechanisms of aging and cancer include cellular senescence, alterations in proteostasis, microbiota disorders (decreased probiotics and increased pernicious bacteria), persistent chronic inflammation, extensive immunosenescence, inordinate energy metabolism, altered material metabolism, endocrine disorders, altered genetic expression, and epigenetic modification. Furthermore, we have proposed that aging and cancer have common means of intervention, including novel uses of common medicine (metformin, resveratrol, and rapamycin), dietary restriction, and artificial microbiota intervention or selectively replenishing scarce metabolites. In addition, we have summarized the research progress of each intervention and revealed their bidirectional effects on cancer progression to compare their reliability and feasibility. Therefore, the study findings provide vital information for advanced research studies on age-related cancers. However, there is a need for further optimization of the described methods and more suitable methods for complicated clinical practices. In conclusion, targeting aging may have potential therapeutic effects on aging-related cancers.