Associations of DNA methylation algorithms of aging and cancer risk: Results from a prospective cohort study

Associations of DNA Methylation Algorithms of Aging With Cardiovascular Disease and Mortality Risk Among US Older Adults

BACKGROUND

Several DNA methylation (DNAm) algorithms have recently emerged as robust predictors of aging and adverse health outcomes in older adults, offering valuable insights into cardiovascular disease (CVD) risk stratification. However, their predictive performance for CVD varies significantly. This study aimed to systematically investigate the associations of 12 widely used DNAm algorithms with CVD and mortality risk.

METHODS

Data from the NHANES (National Health and Nutrition Examination Survey) 1999 to 2002 were used to assess 12 DNAm algorithms (eg, HannumAgeacc, PhenoAgeacc, GrimAgeMortacc, GrimAge2Mortacc) in relation to CVD risk and mortality. Two cohorts were analyzed: one for CVD risk (n=1230) and another for CVD mortality risk (n=1606). DNAm was measured using the Infinium Methylation EPIC BeadChip kit (Illumina). Odds ratios (ORs) and hazard ratios (HRs), along with 95% CIs per SD increase of these DNAm algorithms, were calculated.

RESULTS

Significant associations were observed for GrimAgeMortacc and GrimAge2Mortacc with coronary heart disease and heart attack, with multivariable-adjusted ORs per SD increase ranging from 2.15 to 2.76. However, several algorithms exhibited no significant association with self-reported prevalent CVD. For mortality risk, HannumAgeacc, PhenoAgeacc, ZhangAgeacc, GrimAgeMortacc, and GrimAge2Mortacc were significantly associated with CVD mortality. The multivariable-adjusted HRs per SD increase were 1.19 (95% CIs, 1.05-1.34), 1.13 (95% CIs, 1.01-1.26), 1.63 (95% CI, 1.08-2.47), 1.90 (95% CIs, 1.51-2.40), and 1.87 (95% CIs, 1.51-2.32), respectively. These associations were consistent across biological sex, age (≥50 and <65 versus ≥65 years), and race and ethnicity groups.

CONCLUSIONS

DNAm algorithms, particularly GrimAgeMortacc and GrimAge2Mortacc, may serve as valuable tools for CVD risk stratification and mortality risk assessment.

Impact of DNA-Methylation Age Acceleration on Long-Term Mortality Among US Adults With Cardiovascular-Kidney-Metabolic Syndrome

BACKGROUND

The association between DNA methylation age acceleration (DNAmAA) and cardiovascular-kidney-metabolic (CKM) syndrome stages and long-term mortality in the population with CKM syndrome remains unclear.

METHODS AND RESULTS

This cohort study included 1889 participants from the National Health and Nutrition Examination Survey (1999-2002) with CKM stages and DNA methylation age data. DNAmAA was calculated as residuals from the regression of DNA methylation age on chronological age. The primary outcome was all-cause mortality, with cardiovascular and noncardiovascular mortality as secondary outcomes. Proportional odds models assessed the associations between DNAmAAs and CKM stages, and Cox proportional hazards regression models estimated the associations between DNAmAAs and mortality. Significant associations were found between DNAmAAs and advanced CKM stages, particularly for GrimAge2Mort acceleration (GrimAA) (odds ratio [OR], 1.547 [95% CI, 1.316-1.819]). Over an average follow-up of 14 years, 1015 deaths occurred. Each 5-unit increase in GrimAA was associated with a 50% increase in all-cause mortality (95% CI, 1.39-1.63), a 77% increase in cardiovascular mortality (95% CI, 1.46-2.15), and a 42% increase in noncardiovascular mortality (95% CI, 1.27-1.59). With the lowest GrimAA tertile as a reference, the highest GrimAA tertile showed hazard ratios of 1.95 (95% CI, 1.56-2.45) for all-cause mortality, 3.06 (95% CI, 2.13-4.40) for cardiovascular mortality, and 1.65 (95% CI, 1.20-2.29) for noncardiovascular mortality. Mediation analysis indicated that GrimAA mediates the association between various exposures (including physical activity, Healthy Eating Index-2015 score, hemoglobin A1c, etc.) and mortality.

CONCLUSIONS

GrimAA may serve as a valuable biomarker for assessing CKM stages and mortality risk in individuals with CKM syndrome, thereby informing personalized management strategies.

Smoking-independent DNA methylation markers for lung cancer risk: External validation in a large population-based cohort study

Smoking-associated epigenetic changes have been linked to lung cancer (LC) risk; however, the role of epigenetic alterations independent of smoking is yet to be fully understood. This study aimed to validate 16 previously reported CpG sites that are independent of smoking yet associated with LC risk within a population-based prospective cohort. Using the Infinium Methylation EPIC BeadChip kit or the Infinium HumanMethylation450K BeadChip Assay, DNA methylation (DNAm) in whole blood was assessed in four subsets (n = 736, 1027, 997, and 312) of a population-based cohort from Germany. The DNAm levels of the 16 smoking-independent CpG sites were analyzed. Hazard ratios (HRs) and their 95% confidence intervals (95% CIs) were calculated to assess associations of DNAm at the 16 CpG sites with LC risk, adjusting for multiple covariates, including smoking habits and a smoking-associated DNAm score. Over 17 years of follow-up, a total of 199 LCs were observed. Among the 16 CpGs, cg02211449 showed a negative association with LC risk (HR [95% CI] per SD increase, = 0.70 [0.63-0.78]), while cg11385536 (1.04 [1.01-1.07]), cg09736286 (1.64 [1.10-2.44]), cg19907023 (1.64 [1.01-2.66]), and cg22032485 (1.52 [1.04-2.21]) displayed positive associations with LC risk. Five of the 16 suggested smoking-independent CpGs could be externally validated as predictors of LC risk. Further research should address their potential contribution to enhanced LC risk stratification.

Methylation Biomarkers of Lung Cancer Risk: A Systematic Review and Meta-Analysis

Background: Lung cancer (LC) is the leading cause of cancer deaths worldwide among both men and women, and represents a major public health challenge. DNA methylation (DNAm) has shown potential in identifying individuals at higher risk of LC, but the overall evidence has not been systematically evaluated. This review and meta-analysis aims to evaluate and summarize existing research on the association between blood DNAm levels and LC risk. Methods: Searches were conducted in PubMed, Web of Science, and Scopus for studies published until February 2024, following PRISMA and MOOSE guidelines. Eleven studies met the eligibility criteria. Results: Using a random effects model, our pooled analysis showed a significant association between increased DNAm levels and LC risk (OR 1.24, 95% CI 1.10-1.39; I2 = 93.90%, p = 0.0001). Stratifying the results by study design showed a stronger association in two prospective cohort studies (OR 1.61; 95% CI 1.36-1.90; I2 = 14.42%, p = 0.32), while case-control studies showed a weaker association (OR 1.05; 95% CI 0.99-1.11; I2 = 70.57%, p = 0.0001). Sensitivity analyses indicated that omitting individual studies did not significantly alter the LC risk estimates. Conclusions: These findings suggest that higher blood DNAm levels are associated with an increased risk of LC, especially in long-term cohort studies. Further research is recommended to explore the potential of DNAm as a screening biomarker for LC and to clarify the role of other influencing factors.

Older Age as a Worrisome Feature in Patients With Intraductal Papillary Mucinous Neoplasms: A Long-Term Surveillance Study

INTRODUCTION

Aging has been implicated in the development of various cancer types. No study has specifically investigated age at intraductal papillary mucinous neoplasm (IPMN) diagnosis in relation to the long-term risk of pancreatic carcinogenesis.

METHODS

Within a prospective cohort of 4,104 patients diagnosed with pancreatic cysts, we identified 3,142 patients with IPMNs and examined an association of age at IPMN diagnosis with the incidence of pancreatic carcinoma. Using the multivariable competing-risks proportional hazards regression model, we estimated subdistribution hazard ratios (SHRs) and 95% confidence intervals (CIs) for pancreatic carcinoma incidence according to age at IPMN diagnosis.

RESULTS

During 22,187 person-years of follow-up, we documented 130 patients diagnosed with pancreatic carcinoma (64 with IPMN-derived carcinoma and 66 with concomitant ductal adenocarcinoma). Older age at IPMN diagnosis was associated with a higher risk of pancreatic cancer incidence ( Ptrend = 0.002). Compared with patients younger than 55 years, patients aged 55-64, 65-74, and ≥ 75 years had adjusted SHRs of 1.80 (95% CI, 0.75-4.32), 2.56 (95% CI, 1.10-5.98), and 3.31 (95% CI, 1.40-7.83), respectively. Patients aged 70 years and older had a numerically similar adjusted SHR compared with patients younger than 70 years with worrisome features defined by the international consensus guidelines (1.73 [95% CI, 1.01-2.97] and 1.66 [95% CI, 0.89-3.10], respectively).

DISCUSSION

Older patients with IPMNs were at a higher risk of developing pancreatic carcinoma during surveillance. Surgically fit elderly patients may be good candidates for periodic surveillance aimed at a reduction of pancreatic cancer-related deaths.

Epigenetic Ageing and Breast Cancer Risk: A Systematic Review

BACKGROUND

Age is one of the strongest risk factors for breast cancer. Measures of biological age based on DNA methylation have gained popularity for their strong association with risk of many diseases, including cancer, which may help to identify high-risk subgroups for targeted prevention.

METHODS

We carried out a systematic review of prospective studies that examined the association of methylation-based markers of ageing with risk of invasive breast cancer in healthy (breast cancer-free) women, published up to May 2023. The search of three databases (MEDLINE, EMBASE and Web of Science) identified 2913 individual abstracts eligible for screening. Risk of bias assessment was conducted using ROBINS-E.

RESULTS

Ten prospective studies met the eligibility criteria, and these were heterogeneous in design and findings. The most frequently assessed epigenetic ageing measures were Horvath's first-generation clock, PhenoAge and GrimAge. Four studies reported mainly positive associations, five null associations and one reported a negative association. These associations were generally weak and the results were not consistent across epigenetic ageing measures.

CONCLUSION

The summarised evidence is insufficient to support a role for current epigenetic ageing measures to stratify breast cancer risk. PROSPERO Registration: This systematic review was registered in the International Prospective Register of Systematic Reviews (PROSPERO: CRD42023417559).

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.

Towards a Novel Frontier in the Use of Epigenetic Clocks in Epidemiology

Health problems associated with aging are a major public health concern for the future. Aging is a complex process with wide intervariability among individuals. Therefore, there is a need for innovative public health strategies that target factors associated with aging and the development of tools to assess the effectiveness of these strategies accurately. Novel approaches to measure biological age, such as epigenetic clocks, have become relevant. These clocks use non-sequential variable information from the genome and employ mathematical algorithms to estimate biological age based on DNA methylation levels. Therefore, in the present study, we comprehensively review the current status of the epigenetic clocks and their associations across the human phenome. We emphasize the potential utility of these tools in an epidemiological context, particularly in evaluating the impact of public health interventions focused on promoting healthy aging. Our review describes associations between epigenetic clocks and multiple traits across the life and health span. Additionally, we highlighted the evolution of studies beyond mere associations to establish causal mechanisms between epigenetic age and disease. We explored the application of epigenetic clocks to measure the efficacy of interventions focusing on rejuvenation.

Identification of RFC4 as a potential biomarker for pan-cancer involving prognosis, tumour immune microenvironment and drugs

RFC4 is required for DNA polymerase δ and DNA polymerase ε to initiate DNA template expansion. Downregulated RFC4 inhibits tumour proliferation by causing S-phase arrest and inhibiting mitosis, resulting in the reduction of tumour cells. RFC4 has been implicated that it plays an important role in the initiation and progression of cancers, but a comprehensive analysis of the role of RFC4 in cancer has not been performed. We comprehensively analysed the expression, prognosis, methylation level, splicing level, relationship of RFC4 and immune infiltration, and pan-cancer immunotherapy response used various databases (including TCGA, GTEx, UALCAN, Oncosplicing, TIDE, TISCH, HPA and CAMOIP), and experimented its biological function in HCC. Through pan-cancer analysis, we found that RFC4 is significantly upregulated in most tumours. The tumour patients with high expression of RFC4 have poor prognosis. The methylation level and variable splicing level of RFC4 were abnormal in most tumours compared with the adjacent tissues. Furthermore, RFC4 was closely associated with immune cell infiltration in various cancers. RFC4 was significantly co-expressed with immune checkpoints and other immune-related genes. The expression of RFC4 could indicate the immunotherapy efficacy of some tumours. The RFC4 expression was associated with sensitivity to specific small molecule drugs. Cell experiments have shown that downregulated RFC4 can inhibit cell cycle and tumour cell proliferation. We conducted a systematic pan-cancer analysis of RFC4, and the results showed that RFC4 can serve as a biomarker for cancer diagnosis and prognosis. These findings open new perspectives for precision medicine.

Associations of combined phenotypic aging and genetic risk with incident cancer: A prospective cohort study

Background

Age is the most important risk factor for cancer, but aging rates are heterogeneous across individuals. We explored a new measure of aging-Phenotypic Age (PhenoAge)-in the risk prediction of site-specific and overall cancer.

Methods

Using Cox regression models, we examined the association of Phenotypic Age Acceleration (PhenoAgeAccel) with cancer incidence by genetic risk group among 374,463 participants from the UK Biobank. We generated PhenoAge using chronological age and nine biomarkers, PhenoAgeAccel after subtracting the effect of chronological age by regression residual, and an incidence-weighted overall cancer polygenic risk score (CPRS) based on 20 cancer site-specific polygenic risk scores (PRSs).

Results

Compared with biologically younger participants, those older had a significantly higher risk of overall cancer, with hazard ratios (HRs) of 1.22 (95% confidence interval, 1.18-1.27) in men, and 1.26 (1.22-1.31) in women, respectively. A joint effect of genetic risk and PhenoAgeAccel was observed on overall cancer risk, with HRs of 2.29 (2.10-2.51) for men and 1.94 (1.78-2.11) for women with high genetic risk and older PhenoAge compared with those with low genetic risk and younger PhenoAge. PhenoAgeAccel was negatively associated with the number of healthy lifestyle factors (Beta = -1.01 in men, p<0.001; Beta = -0.98 in women, p<0.001).

Conclusions

Within and across genetic risk groups, older PhenoAge was consistently related to an increased risk of incident cancer with adjustment for chronological age and the aging process could be retarded by adherence to a healthy lifestyle.

Funding

This work was supported by the National Natural Science Foundation of China (82230110, 82125033, 82388102 to GJ; 82273714 to MZ); and the Excellent Youth Foundation of Jiangsu Province (BK20220100 to MZ).

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.

Household air pollution and epigenetic aging in Xuanwei, China

BACKGROUND

Household air pollution (HAP) from indoor combustion of solid fuel is a global health burden linked to lung cancer. In Xuanwei, China, lung cancer rate for nonsmoking women is among the highest in the world and largely attributed to high levels of polycyclic aromatic hydrocarbons (PAHs) that are produced from combustion of smoky (bituminous) coal used for cooking and heating. Epigenetic age acceleration (EAA), a DNA methylation-based biomarker of aging, has been shown to be highly correlated with biological processes underlying the susceptibility of age-related diseases. We aim to assess the association between HAP exposure and EAA.

METHODS

We analyzed data from 106 never-smoking women from Xuanwei, China. Information on fuel type was collected using a questionnaire, and validated exposure models were used to predict levels of 43 HAP constituents. Exposure clusters were identified using hierarchical clustering. EAA was derived for five epigenetic clocks defined as the residuals resulting from regressing each clock on chronological age. We used generalized estimating equations to test associations between exposure clusters derived from predicted levels of HAP exposure, ambient 5-methylchrysene (5-MC), a PAH previously found to be associated with risk of lung cancer, and EAA, while accounting for repeated-measurements and confounders.

RESULTS

We observed an increase in GrimAge EAA for clusters with 31 and 33 PAHs reflecting current (β = 0.77 y per standard deviation (SD) increase, 95 % CI:0.36,1.19) and childhood (β = 0.92 y per SD, 95 % CI:0.40,1.45) exposure, respectively. 5-MC (ng/m3-year) was found to be associated with GrimAge EAA for current (β = 0.15 y, 95 % CI:0.05,0.25) and childhood (β = 0.30 y, 95 % CI:0.13,0.47) exposure.

CONCLUSIONS

Our findings suggest that exposure to PAHs from indoor smoky coal combustion, particularly 5-MC, is associated with GrimAge EAA, a biomarker of mortality.

Biomarkers of aging through the life course: A Recent Literature Update

Purpose of review

The development of biomarkers of aging has greatly advanced epidemiological studies of aging processes. However, much debate remains on the timing of aging onset and the causal relevance of these biomarkers. In this review, we discuss the most recent biomarkers of aging that have been applied across the life course.

Recent findings

The most recently developed aging biomarkers that have been applied across the life course can be designated into three categories: epigenetic clocks, epigenetic markers of chronic inflammation, and mitochondrial DNA copy number. While these have been applied at different life stages, the development, validation, and application of these markers has been largely centered on populations of older adults. Few studies have examined trajectories of aging biomarkers across the life course. As the wealth of molecular and biochemical data increases, emerging biomarkers may be able to capture complex and system-specific aging processes. Recently developed biomarkers include novel epigenetic clocks; clocks based on ribosomal DNA, transcriptomic profiles, proteomics, metabolomics, and inflammatory markers; clonal hematopoiesis of indeterminate potential gene mutations; and multi-omics approaches.

Summary

Attention should be placed on aging at early and middle life stages to better understand trajectories of aging biomarkers across the life course. Additionally, novel biomarkers will provide greater insight into aging processes. The specific mechanisms of aging reflected by these biomarkers should be considered when interpreting results.

Identifying TME signatures for cervical cancer prognosis based on GEO and TCGA databases

The mortality rate from cervical cancer (CESC), a malignant tumor that affects women, has increased significantly globally in recent years. The discovery of biomarkers points to a direction for the diagnosis of cervical cancer with the advancement of bioinformatics technology. The goal of this study was to look for potential biomarkers for the diagnosis and prognosis of CESC using the GEO and TCGA databases. Because of the high dimension and small sample size of the omic data, or the use of biomarkers generated from a single omic data, the diagnosis of cervical cancer may be inaccurate and unreliable. The purpose of this study was to search the GEO and TCGA databases for potential biomarkers for the diagnosis and prognosis of CESC. We begin by downloading CESC (GSE30760) DNA methylation data from GEO, then perform differential analysis on the downloaded methylation data and screen out the differential genes. Then, using estimation algorithms, we score immune cells and stromal cells in the tumor microenvironment and perform survival analysis on the gene expression profile data and the most recent clinical data of CESC from TCGA. Then, using the 'limma' package and Venn plot in R language to perform differential analysis of genes and screen out overlapping genes, these overlapping genes were then subjected to GO and KEGG functional enrichment analysis. The differential genes screened by the GEO methylation data and the differential genes screened by the TCGA gene expression data were intersected to screen out the common differential genes. A protein-protein interaction (PPI) network of gene expression data was then created in order to discover important genes. The PPI network's key genes were crossed with previously identified common differential genes to further validate them. The Kaplan-Meier curve was then used to determine the prognostic importance of the key genes. Survival analysis has shown that CD3E and CD80 are important for the identification of cervical cancer and can be considered as potential biomarkers for cervical cancer.