Have you noticed that some people seem to get older faster than others? Scientists have previously found that a chemical tag on DNA known as DNA methylation can be used to predict an individual’s chronological age. However, age predicted using DNA methylation (also known as biological or epigenetic age) does not always perfectly correspond to chronological age. Indeed, some people’s biological age is higher than their years, while other people’s is lower.

When an individual’s biological age is higher than their chronological age, they are said to be experiencing ‘epigenetic age acceleration’. This type of accelerated ageing, which can be measured with ‘epigenetic clocks’ based on DNA methylation, has been associated with several adverse health outcomes, including cancer. This means that epigenetic clocks may improve our ability to predict cancer risk and detect cancer early. However, it is still unclear whether accelerated biological ageing causes cancer, or whether it simply correlates with the disease.

Morales-Berstein et al. wanted to investigate whether epigenetic age acceleration, as measured by epigenetic clocks, plays a role in the development of several cancers. To do so, they used an approach known as Mendelian randomization. Using genetic variants as natural experiments, they studied the effect of different measures of epigenetic age acceleration on cancer risk.

Their work focused on five types of cancer: breast, colorectal, prostate, ovarian and lung cancer. They used genetic association data from people of European ancestry to determine whether genetic variants that are strongly associated with accelerated ageing are also strongly associated with cancer. The results showed that one of the DNA methylation markers used as an estimate of biological ageing could be directly related to the risk of developing colorectal cancer.

This work provides new insights into the relationship between markers of biological ageing and cancer. Similar relationships should also be studied in other groups of people and for other cancer sites. The results suggest that reversing biological ageing by altering DNA methylation could prevent or delay the development of colorectal cancer.

DNA methylation (DNAm) at specific cytosine-phosphate-guanine (CpG) sites has been found to be strongly correlated with chronological age. Biological age, as predicted by DNAm patterns at specific CpG sites, may differ from chronological age on an individual basis. Observational evidence suggests that epigenetic age acceleration (i.e. when an individual’s biological age is greater than their chronological age) may be associated with an increased risk of mortality and age-related diseases, including cancer (Fransquet et al., 2019).

Epigenetic clocks are heritable indicators of biological ageing derived from DNAm data. Each clock is based on DNAm levels measured at a different set of CpG sites, which capture distinctive features of epigenetic ageing (Liu et al., 2020). ‘First-generation’ epigenetic clocks, such as HannumAge (Hannum et al., 2013) and Intrinsic HorvathAge (Horvath, 2013), have been derived from DNAm levels at CpG sites found to be strongly associated with chronological age. HannumAge is trained on 71 age-related CpGs found in blood (Hannum et al., 2013), while Intrinsic HorvathAge is based on 353 age-related CpGs found in several human tissues and cell types, and is further adjusted for blood cell counts (Horvath, 2013). More recently, ‘second-generation’ epigenetic clocks, such as, PhenoAge (Levine et al., 2018) and GrimAge (Lu et al., 2019a), have been developed to predict age-related morbidity and mortality. PhenoAge incorporates data from 513 CpGs associated with mortality and nine clinical biomarkers (i.e. albumin, creatinine, serum glucose, C-reactive protein, lymphocyte percentage, mean corpuscular volume, red cell distribution width, alkaline phosphatase and leukocyte count) (Levine et al., 2018), and GrimAge includes data from 1,030 CpGs associated with smoking pack-years and seven plasma proteins (i.e. cystatin C, leptin, tissue inhibitor metalloproteinases 1, adrenomedullin, beta-2-microglobulin, growth differentiation factor 15, and plasminogen activation inhibitor 1 (PAI-1)) (Lu et al., 2019a). Due to differences in their composition, HannumAge and Intrinsic HorvathAge are better predictors of chronological age (Hannum et al., 2013; Horvath, 2013), while PhenoAge and GrimAge stand out for their ability to predict health and lifespan (Levine et al., 2018; Lu et al., 2019a; McCrory et al., 2021).

Several studies suggest that HannumAge, Intrinsic HorvathAge, PhenoAge and GrimAge acceleration are associated with cancer risk (Levine et al., 2018; Ambatipudi et al., 2017; Levine et al., 2015; Dugue et al., 2021; Kresovich et al., 2019b; Kresovich et al., 2019a; Zheng et al., 2016). In contrast, others indicate that evidence in support of this claim is weak or non existent (Dugué et al., 2018; Hillary et al., 2020; Durso et al., 2017; Wang et al., 2021). This lack of consensus could be explained by biases that often affect observational research, such as reverse causation (e.g. cancer influencing the epigenome and not the other way around) and residual confounding (e.g. unmeasured, or imprecisely measured confounders of the association between epigenetic age acceleration and cancer) (Relton and Davey Smith, 2012).

The strength of the associations between epigenetic age acceleration and different cancers has also been found to vary across epigenetic clocks. For instance, positive associations between epigenetic age acceleration and colorectal cancer seem to be much stronger when biological age is estimated using second-generation clocks (i.e. PhenoAge and GrimAge) (Dugue et al., 2021) rather than first-generation clocks (i.e. HannumAge and Intrinsic HorvathAge) (Dugué et al., 2018; Durso et al., 2017). Lack of consensus across epigenetic clocks could be explained by differences in their algorithms (which may reflect different mechanisms of biological ageing), as well as heterogeneity in study designs (Fransquet et al., 2019). Furthermore, even if there were a consensus, it would still be unclear whether age-related DNA methylation plays a causal role in cancer risk or if it merely acts as a non-causal prognostic biomarker.

Mendelian randomization (MR), a method that uses genetic variants as instrumental variables to infer causality between a modifiable exposure and an outcome, is less likely to be affected by residual confounding and reverse causation than traditional observational methods (Davey Smith and Ebrahim, 2003). A recent genome-wide association study (GWAS) meta-analysis has revealed 137 genetic loci associated with epigenetic age acceleration (as measured by six epigenetic biomarkers) that may be used within an MR framework (McCartney et al., 2021).

McCartney et al., 2021 used IVW MR, MR-Egger, weighted median and weighted mode methods to explore the genetically predicted effects of HannumAge, Intrinsic HorvathAge, PhenoAge and GrimAge acceleration on breast, ovarian, and lung cancer. Here, we extend this analysis to include colorectal and prostate cancer (two of the most common cancers worldwide Sung et al., 2021) and use additional methods and datasets to verify the robustness of our findings.

The aim of this two-sample MR study was to examine the genetically predicted effects of epigenetic age acceleration (as measured by HannumAge Hannum et al., 2013, Horvath Intrinsic Age Horvath, 2013, PhenoAge Levine et al., 2018 and GrimAge Lu et al., 2019a) on multiple cancers (i.e., breast, prostate, colorectal, ovarian and lung cancer) using summary genetic association data from (1) McCartney et al. (N = 34,710) (McCartney et al., 2021), (2) the UK Biobank (N cases = 2671–13,879; N controls = 173,493–372,016), (3) FinnGen (N cases = 719–8401; N controls = 74,685–174,006) and (4) several international cancer genetic consortia (N cases = 11,348–122,977; N controls = 15,861–105,974).

In this comprehensive two-sample MR study of epigenetic age acceleration and multiple cancers, we found evidence to suggest that genetically predicted GrimAge acceleration may increase the risk of colorectal cancer in both males and females. Among subtypes, effects appeared to be stronger in relation to colon than rectal cancer. Our MR results also suggested that genetically predicted GrimAge acceleration may decrease the risk of prostate cancer and that genetically predicted Intrinsic HorvathAge acceleration may be protective against lung cancer. Nevertheless, these did not pass multiple testing correction. Finally, we found no consistent evidence for other measures of epigenetic age acceleration and cancers.

Our MR estimates for the association between GrimAge and colorectal cancer were consistent with those reported in Dugue et al., 2021, an observational nested case-control study in the Melbourne Collaborative Cohort Study (RR = 1.04 per year increase in GrimAge acceleration, 95% CI 1.01–1.07, p = 0.02). However, our findings contrast with those highlighted in Hillary et al., 2020, an observational cohort study that used Generation Scotland data. The latter authors observed no evidence of an association between GrimAge acceleration and colorectal cancer after correcting for multiple testing. Nevertheless, it is possible that their analyses were underpowered, as their sample only included 63 colorectal cancer cases (0.66%). More importantly, the direction of the reported estimate is consistent with our findings and those presented in Dugue et al., 2021.

Observational evidence for the association between other measures of epigenetic ageing and cancer is inconclusive (the pre-existing evidence has been summarised in Supplementary file 1 — Table s17). For instance, epigenetic clock acceleration has been positively associated with breast (Ambatipudi et al., 2017; Kresovich et al., 2019b; Kresovich et al., 2019a) and lung cancer (Levine et al., 2018; Levine et al., 2015; Dugue et al., 2021) in some studies. However, (Durso et al., 2017, Hillary et al., 2020) and (Dugué et al., 2018) did not find strong evidence to support this. In some cases, observational evidence is stronger for some clocks than it is for others. For example, for colorectal cancer, evidence of a positive association is much stronger for second-generation clocks (Dugue et al., 2021) than for first-generation clocks (Dugué et al., 2018; Durso et al., 2017). In the case of prostate cancer, as in our study, apart from weak evidence of an inverse association with GrimAge, no other associations have been observed (Dugue et al., 2021; Dugué et al., 2018). To date, the association between epigenetic age acceleration and ovarian cancer has not been explored observationally. Although our findings were less susceptible to biases that often influence observational research, they still provide no compelling evidence of a causality between several measures of epigenetic clock acceleration and cancer.

This MR study had several strengths. For instance, we pooled results from multiple sources using fixed effect meta-analysis to improve the precision of the MR estimates presented in McCartney et al., 2021. We also conducted extra sensitivity analyses, such as MR of negative control outcomes, MR of cancer risk factors, single-SNP MR and CAUSE analyses, to assess the validity of the MR assumptions. Moreover, we performed subtype-specific MR analyses and sought to corroborate our results using UK Biobank GWAS data on parental history of cancer and LD Score regression. Additionally, our findings contribute to the identification of modifiable targets for future interventions aimed at reversing epigenetic ageing for the prevention of cancer. Compared to clinical trials, MR provides a cheaper, quicker, and ethical way of assessing the long-term impact of interventions on epigenetic ageing. This is especially relevant while attempts to develop interventions which reverse epigenetic ageing are still in early stages (Fahy et al., 2019; Fitzgerald et al., 2021; Gensous et al., 2020; Chen et al., 2019).

The findings from this study should be interpreted in light of its limitations. We only identified four genetic instruments for GrimAge acceleration, which explained 0.47% of the variance in the trait. This could lead to two issues: low statistical power and horizontal pleiotropy. First, our GrimAge analyses were underpowered to detect ORs < 1.20 for colorectal cancer. Therefore, it is possible that our findings do not reflect a true effect (we identified an OR = 1.12 for colorectal). Similarly, our study was underpowered to detect genetically predicted effects of GrimAge acceleration on cancer subtypes and cancers with smaller sample sizes (i.e. ovarian and lung cancer). Some of our sensitivity analyses, such as the MR-Egger intercept test used to detect uncorrelated horizontal pleiotropy, also had low power, resulting in imprecise estimates. The weighted mode method may also be misleading in this context, as its use is limited in the presence of very few SNPs. Although these limitations potentially undermine the validity of our results, it is reassuring that point estimates for the genetically predicted effect of GrimAge acceleration on colorectal cancer were consistent across MR methods and study populations. However, since CAUSE analyses did not provide evidence against confounding by correlated horizontal pleiotropy, it is possible that the genetically predicted effects identified are attributed to correlated pleiotropy (whereby SNPs are associated with epigenetic age acceleration and cancer through a shared heritable factor) rather than a causal effect of GrimAge on cancer risk.

One could argue that because the results for GrimAge acceleration were inconsistent with those obtained for other measures of epigenetic age acceleration, chance and horizontal pleiotropy are more likely explanations for our findings. However, inconsistencies across epigenetic ageing measures do not necessarily invalidate our results. They may simply reflect differences in how clocks were trained (i.e. they were trained on different outcomes, tissues, and populations). Different clocks may capture information on distinct underlying biological ageing mechanisms (Liu et al., 2020). For example, GrimAge was trained on mortality and smoking (factors which are closely related to cancer risk), which may explain why it outperforms other measures of epigenetic ageing in predicting time-to-cancer (Lu et al., 2019a).

Although little is known about the underlying mechanisms, GrimAge may plausibly influence cancer risk through hormonal, inflammatory and metabolic processes (Yu et al., 2020; Bottazzi et al., 2018; Lau and Robinson, 2021). In bidirectional MR analyses, we found evidence that genetically predicted GrimAge acceleration may be a cause of telomere shortening, another marker of biological ageing. Shorter telomeres have been shown to lower cancer risk in prior MR analyses (Telomeres Mendelian Randomization Collaboration et al., 2017; Gao et al., 2020; Kuo et al., 2019), so it is plausible that GrimAge acceleration decreases cancer risk, at least in part, via its effect on telomere length. GrimAge acceleration may still increase cancer risk via pathways other than those related to cellular division. The positive effect of GrimAge acceleration on cancer via these other pathways may counteract the negative effects mediated via telomere length, resulting in null MR results for GrimAge acceleration and breast, ovarian, prostate and lung cancer, and positive MR results for GrimAge acceleration and colorectal cancer. To better understand the biology of ageing, future studies should consider running MR analyses using data on DNAm-predicted telomere length, since DNAm telomere length is independent of telomerase activity and has been more strongly associated with age than measured telomere length (Lu et al., 2019b).

Although promising in terms of consistency and biological plausibility, further research is required to confirm our findings. For example, multivariable MR (Burgess and Thompson, 2015; Sanderson et al., 2019) could be used to disentangle the causal effects of GrimAge acceleration on cancer from shared heritable factors such as and blood cell composition. Additionally, our analyses could be replicated using other large independent cancer datasets. Although we conducted MR analyses on parental history of cancer, their effect estimates are not directly comparable to those obtained in the main analyses due to cases in the GWAS-by-proxy of parental endpoints being defined as either or both parents reportedly having a type of cancer. Furthermore, it would also be useful to replicate our analyses once a larger GWAS of epigenetic ageing with more genetic instruments for GrimAge acceleration is available. This would allow for a more rigorous assessment of horizontal pleiotropy and may be used to assess clustering of genetic variants to reveal distinct biological mechanisms underlying the effects (Foley et al., 2021). In spite of these suggestions, we acknowledge that it may be challenging to get access to suitable datasets for replication purposes in the short term.

The selection of ‘super controls’ (e.g. in UK Biobank, FinnGen and GECCO), with no other cancers, related lesions (i.e. benign, in situ, uncertain or unspecified behaviour neoplasms) or reported family history of cancer, could have inflated cancer GWAS effect sizes (and our MR estimates), because ‘super controls’ are healthier than the general population and are less likely to be genetically predisposed to develop cancer.

Another limitation is that we did not have access to individual level data. Therefore, we were unable to stratify the analyses by potential effect modifiers, such as sex, smoking, and menopausal status. Moreover, we did not have sex-specific instruments for sex-specific cancers. However, it is unlikely that the genetic architecture of epigenetic clock acceleration differs across sexes, as DNAm levels at individual clock CpGs are highly correlated between males and females (Grodstein et al., 2020; Tajuddin et al., 2019).

Finally, to reduce bias due to population stratification, this study was conducted using data from participants of European ancestry only. The GWAS data used for the analyses had been adjusted for the top genetic principal components for the same reason. Furthermore, our MR of negative control outcomes suggests that our MR results are unlikely to be biased by residual population stratification. Despite this, confounding due to population stratification, dynastic effects and assortative mating cannot be ruled out completely, as it is not possible to test the second MR assumption (i.e. independence assumption). Furthermore, more research is required to see if our results could translate to other ancestries.

From a public health perspective, our work provides potentially relevant findings. Observational and Mendelian randomization studies suggest that GrimAge acceleration may be influenced by several cancer risk factors, such as obesity and smoking (Lu et al., 2019a; McCartney et al., 2021). If GrimAge acceleration is a causal mediator between these risk factors and colorectal cancer, the GrimAge clock may be a treatable intermediary when targeting the underlying risk factors is not feasible or too difficult to accomplish. It could also be targeted in populations at high-risk of colorectal cancer. Nevertheless, we think it may be too early to make claims regarding the clinical utility of our findings. The GrimAge clock has only recently been created and very few studies have assessed its association with colorectal cancer. More research is required to corroborate our results and to evaluate whether GrimAge acceleration can be modified through lifestyle or clinical interventions.

In conclusion, our findings suggest that genetically predicted GrimAge acceleration may increase the risk of colorectal cancer. Findings were less consistent for other epigenetic clocks and cancers. Further work is required to investigate the potential mechanisms underlying the genetically predicted effects identified in this study.

Summary statistics for epigenetic age acceleration measures of HannumAge, Intrinsic HorvathAge, PhenoAge and GrimAge were downloaded from: https://datashare.ed.ac.uk/handle/10283/3645 (Datasets used: European-ancestries meta-analysis summary statistics: Hannum (645.4Mb), European-ancestries meta-analysis summary statistics: IEAA (645.7Mb), European-ancestries meta-analysis summary statistics: GrimAge (645.7Mb), European-ancestries meta-analysis summary statistics: PhenoAge (645.7Mb)). Summary statistics for international cancer genetic consortiums were obtained from their respective data repositories. Colorectal cancer data were obtained following the submission of a written request to the GECCO committee, which may be contacted by email at kafdem@fredhutch.org/upeters@fredhutch.org. Breast, ovarian, prostate and lung cancer data were accessed via MR-Base (http://app.mrbase.org/), which holds complete GWAS summary data from BCAC, OCAC, PRACTICAL and ILCCO. Breast cancer subtype data were obtained from BCAC and can be downloaded from: http://bcac.ccge.medschl.cam.ac.uk/bcacdata/oncoarray/oncoarray-and-combined-summary-result/gwas-summary-associations-breast-cancer-risk-2020/. Data on breast and ovarian cancer in BRCA1 and BRCA2 carriers were obtained from CIMBA and can be downloaded from: http://cimba.ccge.medschl.cam.ac.uk/oncoarray-complete-summary-results/. Prostate cancer subtype data are not publicly available through MR-Base but can be accessed upon request. These data are managed by the PRACTICAL committee, which may be contacted by email at practical@icr.ac.uk. FinnGen data is publicly available and can be accessed here: https://www.finngen.fi/en/access_results (Datasets used from release 5: Malignant neoplasm of breast (all cancers excluded), Malignant neoplasm of bronchus and lung (all cancers excluded), Colorectal cancer (all cancers excluded), Malignant neoplasm of ovary (all cancers excluded), Malignant neoplasm of prostate (all cancers excluded)). UK Biobank data can be accessed through the MR-Base platform. Parental history of cancer data were obtained from the UK Biobank study under application #15825 and can be accessed via an approved application to the UK Biobank (https://www.ukbiobank.ac.uk/enable-your-research/apply-for-access). GWAS data for negative control outcomes and potential confounders were obtained via the MR-Base platform (GWAS IDs for negative control outcomes: "ukb-b-19560", "ukb-b-533"; GWAS IDs for confounders: "ukb-b-10831", "ukb-b-13702", "ukb-b-6134", "ieu-a-1239", "ukb-b-5779", "ieu-a-835", "ieu-a-61"). GWAS data for measured telomere length used in bidirectional MR analyses were also obtained via the MR-Base platform (GWAS ID: "ieu-b-4879").

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Author details

1. Fernanda Morales Berstein

   1. MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
   2. Population Health Sciences, Bristol Medical School, Bristol, United Kingdom

   Contribution

   Conceptualization, Formal analysis, Visualization, Writing – original draft, Writing – review and editing

   For correspondence

   dy20206@bristol.ac.uk

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8237-2021

2. Daniel L McCartney

   Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, United Kingdom

   Contribution

   Writing – review and editing

   Competing interests

   No competing interests declared

3. Ake T Lu

   Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, United States

   Contribution

   Writing – review and editing

   Competing interests

   declares that UC Regents filed the patent "DNA METHYLATION BASED BIOMARKERS FOR LIFE EXPECTANCY AND MORBIDITY" (International Application Number PCT/US2019/055444; in pending status) and that the Epigenetic Clock Development Foundation and Foxo Labs hold licenses

4. Konstantinos K Tsilidis

   1. Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
   2. Department of Hygiene and Epidemiology, School of Medicine, University of Ioannina, Ioannina, Greece

   Contribution

   Writing – review and editing

   Competing interests

   No competing interests declared

5. Emmanouil Bouras

   Department of Hygiene and Epidemiology, School of Medicine, University of Ioannina, Ioannina, Greece

   Contribution

   Formal analysis, Writing – review and editing

   Competing interests

   No competing interests declared

6. Philip Haycock

   1. MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
   2. Population Health Sciences, Bristol Medical School, Bristol, United Kingdom

   Contribution

   Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5001-3350

7. Kimberley Burrows

   1. MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
   2. Population Health Sciences, Bristol Medical School, Bristol, United Kingdom

   Contribution

   Writing – review and editing

   Competing interests

   No competing interests declared

8. Amanda I Phipps

   1. Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, United States
   2. Department of Epidemiology, School of Public Health, University of Washington, Seattle, United States

   Contribution

   Writing – review and editing

   Competing interests

   No competing interests declared

9. Daniel D Buchanan

   Department of Clinical Pathology, Melbourne Medical School, University of Melbourne, Parkville, Australia

   Contribution

   Writing – review and editing

   Competing interests

   No competing interests declared

10. Iona Cheng

    Cancer Prevention Institute of California, Fremont, United States

    Contribution

    Writing – review and editing

    Competing interests

    No competing interests declared

11. the PRACTICAL consortium

    Contribution

    Contributions to the acquisition of data, critical revision of the manuscript and final approval of the version to be published

    Competing interests

    No competing interests declared

    1. Rosalind A Eeles, The Institute of Cancer Research, London, United Kingdom
    2. Christopher A Haiman, Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, United States
    3. Zsofia Kote-Jarai, The Institute of Cancer Research, London, United Kingdom
    4. Fredrick R Schumacher, Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, United States
    5. Sara Benlloch, The Institute of Cancer Research, London, United Kingdom
    6. Ali Amin Al Olama, Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, United Kingdom
    7. Kenneth R Muir, Division of Population Health, Health Services Research and Primary Care, University of Manchester, Manchester, United Kingdom
    8. Sonja I Berndt, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, United States
    9. David V Conti, Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, United States
    10. Fredrik Wiklund, Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
    11. Stephen Chanock, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, United States
    12. Ying Wang, Department of Population Science, American Cancer Society, Atlanta, United States
    13. Catherine M Tangen, SWOG Statistical Center, Fred Hutchinson Cancer Research Center, Seattle, United States
    14. Jyotsna Batra, Australian Prostate Cancer Research Centre-Qld, Institute of Health and Biomedical Innovation and School of Biomedical Sciences, Queensland University of Technology, Brisbane, Australia
    15. Judith A Clements, Australian Prostate Cancer Research Centre-Qld, Institute of Health and Biomedical Innovation and School of Biomedical Sciences, Queensland University of Technology, Brisbane, Australia
    16. A, Translational Research Institute, Brisbane, Australia
    17. Henrik Grönberg, Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
    18. Nora Pashayan, Department of Applied Health Research, University College London, London, United Kingdom
    19. Johanna Schleutker, Institute of Biomedicine, University of Turku, Turku, Finland
    20. Demetrius Albanes, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, United States
    21. Stephanie Weinstein, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, United States
    22. Alicja Wolk, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
    23. Catharine ML West, Division of Cancer Sciences, University of Manchester, Manchester Academic Health Science Centre, Radiotherapy Related Research, The Christie Hospital NHS Foundation Trust, Manchester, United Kingdom
    24. Lorelei A Mucci, Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, United States
    25. Géraldine Cancel-Tassin, CeRePP, Tenon Hospital, Paris, France
    26. Stella Koutros, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, United States
    27. Karina Dalsgaard Sørensen, Department of Molecular Medicine, Aarhus University Hospital, Aarhus, Denmark
    28. Eli Marie Grindedal, Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
    29. David E Neal, Nuffield Department of Surgical Sciences, University of Oxford, Room 6603, Level 6, John Radcliffe Hospital, Headley Way, Oxford, United Kingdom
    30. Freddie C Hamdy, Nuffield Department of Surgical Sciences, University of Oxford, Oxford, United Kingdom
    31. Jenny L Donovan, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
    32. Ruth C Travis, Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
    33. Robert J Hamilton, Dept. of Surgical Oncology, Princess Margaret Cancer Centre, Toronto, Canada
    34. Sue Ann Ingles, Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, United States
    35. Barry S Rosenstein, Department of Radiation Oncology and Department of Genetics and Genomic Sciences, Box 1236, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, United States
    36. Yong-Jie Lu, Centre for Cancer Biomarker and Biotherapeutics, Barts Cancer Institute, Queen Mary University of London, John Vane Science Centre, Charterhouse Square, London, United Kingdom
    37. Graham G Giles, Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Australia
    38. Robert J MacInnis, Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Australia
    39. Adam S Kibel, Division of Urologic Surgery, Brigham and Womens Hospital, Boston, United States
    40. Ana Vega, Fundación Pública Galega Medicina Xenómica, Santiago de Compostela, Spain
    41. Manolis Kogevinas, ISGlobal, Barcelona, Spain
    42. Kathryn L Penney, Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital/Harvard Medical School, Boston, United States
    43. Jong Y Park, Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, United States
    44. Janet L Stanford, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, United States
    45. Cezary Cybulski, International Hereditary Cancer Center, Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
    46. Børge G Nordestgaard, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
    47. Sune F Nielsen, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
    48. Hermann Brenner, Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
    49. Christiane Maier, Humangenetik Tuebingen, Tuebingen, Germany
    50. UJeri Kim, The University of Texas M. D. Anderson Cancer Center, Department of Genitourinary Medical Oncology, Houston, United States
    51. Esther M John, Departments of Epidemiology & Population Health and of Medicine, Division of Oncology, Stanford Cancer Institute, Stanford University School of Medicine, Stanford, United States
    52. Manuel R Teixeira, Department of Genetics, Portuguese Oncology Institute of Porto (IPO-Porto), Porto, Portugal
    53. Susan L Neuhausen, Department of Population Sciences, Beckman Research Institute of the City of Hope, Duarte, United States
    54. Kim De Ruyck, Ghent University, Faculty of Medicine and Health Sciences, Basic Medical Sciences, Ghent, Belgium
    55. Azad Razack, Department of Surgery, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
    56. Lisa F Newcomb, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, United States
    57. Davor Lessel, Institute of Human Genetics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
    58. Radka Kaneva, Molecular Medicine Center, Department of Medical Chemistry and Biochemistry, Medical University of Sofia, Sofia, Bulgaria
    59. Nawaid Usmani, Department of Oncology, Cross Cancer Institute, University of Alberta, Edmonton, Canada
    60. Frank Claessens, Molecular Endocrinology Laboratory, Department of Cellular and Molecular Medicine, Leuven, Belgium
    61. Paul A Townsend, Division of Cancer Sciences, Manchester Cancer Research Centre, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, NIHR Manchester Biomedical Research Centre, Health Innovation Manchester, Univeristy of Manchester, Manchester, United Kingdom
    62. Jose Esteban Castelao, Genetic Oncology Unit, CHUVI Hospital, Complexo Hospitalario Universitario de Vigo, Instituto de Investigación Biomédica Galicia Sur (IISGS), Vigo, Spain
    63. Monique J Roobol, Department of Urology, Erasmus University Medical Center, Rotterdam, Netherlands
    64. Florence Menegaux, "Exposome and Heredity", CESP (UMR 1018), Faculté de Médecine, Université Paris-Saclay, Villejuif, France
    65. Kay-Tee Khaw, Clinical Gerontology Unit, University of Cambridge, Cambridge, United Kingdom
    66. ULisa Cannon-Albright, Division of Epidemiology, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, United States
    67. Hardev Pandha, The University of Surrey, Guildford, Surrey, United Kingdom
    68. Stephen N Thibodeau, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, United States
    69. David J Hunter, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
    70. Peter Kraft, Program in Genetic Epidemiology and Statistical Genetics, Department of Epidemiology, Harvard School of Public Health, Boston, United States
    71. William J Blot, Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, United States
    72. Elio Riboli, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom

12. Richard M Martin

    1. MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
    2. Population Health Sciences, Bristol Medical School, Bristol, United Kingdom
    3. National Institute for Health Research (NIHR) Bristol Biomedical Research Centre, University Hospitals Bristol and Weston NHS Foundation Trust and the University of Bristol, Bristol, United Kingdom

    Contribution

    Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-7992-7719

13. George Davey Smith

    1. MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
    2. Population Health Sciences, Bristol Medical School, Bristol, United Kingdom

    Contribution

    Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-1407-8314

14. Caroline L Relton

    1. MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
    2. Population Health Sciences, Bristol Medical School, Bristol, United Kingdom

    Contribution

    Writing – review and editing

    Competing interests

    declares that UC Regents filed the patent "DNA METHYLATION BASED BIOMARKERS FOR LIFE EXPECTANCY AND MORBIDITY" (International Application Number PCT/US2019/055444; in pending status) and that the Epigenetic Clock Development Foundation and Foxo Labs hold licenses. SH receives consulting fees from the Epigenetic Clock Development Foundation and royalties for patents involving epigenetic clocks

    "This ORCID iD identifies the author of this article:" 0000-0003-2052-4840

15. Steve Horvath

    1. Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, United States
    2. Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, United States

    Contribution

    Writing – review and editing

    Competing interests

    has received a speaker fee from Illumina and is an advisor to the Epigenetic Clock Development Foundation

16. Riccardo E Marioni

    Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, United Kingdom

    Contribution

    Writing – review and editing

    Competing interests

    is employed part time by Novo Nordisk outside of this work

    "This ORCID iD identifies the author of this article:" 0000-0003-4430-4260

17. Tom G Richardson

    1. MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
    2. Population Health Sciences, Bristol Medical School, Bristol, United Kingdom
    3. Novo Nordisk Research Centre, Oxford, United Kingdom

    Contribution

    Conceptualization, Formal analysis, Supervision, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-7918-2040

18. Rebecca C Richmond

    1. MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
    2. Population Health Sciences, Bristol Medical School, Bristol, United Kingdom

    Contribution

    Conceptualization, Supervision, Writing – review and editing

    Competing interests

    No competing interests declared

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