[{"publication_status":"published","acknowledgement":"We are grateful to all the families who took part, the general practitioners, and the Scottish School of Primary Care for their help in recruiting them and the whole GS team that includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists, healthcare assistants, and nurses.","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","day":"28","publisher":"Springer Nature","article_number":"12","has_accepted_license":"1","status":"public","isi":1,"tmp":{"short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","image":"/images/cc_by.png"},"external_id":{"pmid":["36855161"],"isi":["000940286600001"]},"pmid":1,"citation":{"ama":"Bernabeu E, Mccartney DL, Gadd DA, et al. Refining epigenetic prediction of chronological and biological age. <i>Genome Medicine</i>. 2023;15. doi:<a href=\"https://doi.org/10.1186/s13073-023-01161-y\">10.1186/s13073-023-01161-y</a>","apa":"Bernabeu, E., Mccartney, D. L., Gadd, D. A., Hillary, R. F., Lu, A. T., Murphy, L., … Marioni, R. E. (2023). Refining epigenetic prediction of chronological and biological age. <i>Genome Medicine</i>. Springer Nature. <a href=\"https://doi.org/10.1186/s13073-023-01161-y\">https://doi.org/10.1186/s13073-023-01161-y</a>","mla":"Bernabeu, Elena, et al. “Refining Epigenetic Prediction of Chronological and Biological Age.” <i>Genome Medicine</i>, vol. 15, 12, Springer Nature, 2023, doi:<a href=\"https://doi.org/10.1186/s13073-023-01161-y\">10.1186/s13073-023-01161-y</a>.","short":"E. Bernabeu, D.L. Mccartney, D.A. Gadd, R.F. Hillary, A.T. Lu, L. Murphy, N. Wrobel, A. Campbell, S.E. Harris, D. Liewald, C. Hayward, C. Sudlow, S.R. Cox, K.L. Evans, S. Horvath, A.M. Mcintosh, M.R. Robinson, C.A. Vallejos, R.E. Marioni, Genome Medicine 15 (2023).","ieee":"E. Bernabeu <i>et al.</i>, “Refining epigenetic prediction of chronological and biological age,” <i>Genome Medicine</i>, vol. 15. Springer Nature, 2023.","chicago":"Bernabeu, Elena, Daniel L. Mccartney, Danni A. Gadd, Robert F. Hillary, Ake T. Lu, Lee Murphy, Nicola Wrobel, et al. “Refining Epigenetic Prediction of Chronological and Biological Age.” <i>Genome Medicine</i>. Springer Nature, 2023. <a href=\"https://doi.org/10.1186/s13073-023-01161-y\">https://doi.org/10.1186/s13073-023-01161-y</a>.","ista":"Bernabeu E, Mccartney DL, Gadd DA, Hillary RF, Lu AT, Murphy L, Wrobel N, Campbell A, Harris SE, Liewald D, Hayward C, Sudlow C, Cox SR, Evans KL, Horvath S, Mcintosh AM, Robinson MR, Vallejos CA, Marioni RE. 2023. Refining epigenetic prediction of chronological and biological age. Genome Medicine. 15, 12."},"oa_version":"Published Version","_id":"12719","file_date_updated":"2023-03-14T10:29:47Z","title":"Refining epigenetic prediction of chronological and biological age","ddc":["570"],"publication":"Genome Medicine","intvolume":"        15","type":"journal_article","scopus_import":"1","article_type":"original","abstract":[{"lang":"eng","text":"Background\r\nEpigenetic clocks can track both chronological age (cAge) and biological age (bAge). The latter is typically defined by physiological biomarkers and risk of adverse health outcomes, including all-cause mortality. As cohort sample sizes increase, estimates of cAge and bAge become more precise. Here, we aim to develop accurate epigenetic predictors of cAge and bAge, whilst improving our understanding of their epigenomic architecture.\r\n\r\nMethods\r\nFirst, we perform large-scale (N = 18,413) epigenome-wide association studies (EWAS) of chronological age and all-cause mortality. Next, to create a cAge predictor, we use methylation data from 24,674 participants from the Generation Scotland study, the Lothian Birth Cohorts (LBC) of 1921 and 1936, and 8 other cohorts with publicly available data. In addition, we train a predictor of time to all-cause mortality as a proxy for bAge using the Generation Scotland cohort (1214 observed deaths). For this purpose, we use epigenetic surrogates (EpiScores) for 109 plasma proteins and the 8 component parts of GrimAge, one of the current best epigenetic predictors of survival. We test this bAge predictor in four external cohorts (LBC1921, LBC1936, the Framingham Heart Study and the Women’s Health Initiative study).\r\n\r\nResults\r\nThrough the inclusion of linear and non-linear age-CpG associations from the EWAS, feature pre-selection in advance of elastic net regression, and a leave-one-cohort-out (LOCO) cross-validation framework, we obtain cAge prediction with a median absolute error equal to 2.3 years. Our bAge predictor was found to slightly outperform GrimAge in terms of the strength of its association to survival (HRGrimAge = 1.47 [1.40, 1.54] with p = 1.08 × 10−52, and HRbAge = 1.52 [1.44, 1.59] with p = 2.20 × 10−60). Finally, we introduce MethylBrowsR, an online tool to visualise epigenome-wide CpG-age associations.\r\n\r\nConclusions\r\nThe integration of multiple large datasets, EpiScores, non-linear DNAm effects, and new approaches to feature selection has facilitated improvements to the blood-based epigenetic prediction of biological and chronological age."}],"quality_controlled":"1","oa":1,"date_updated":"2025-04-23T08:49:38Z","volume":15,"doi":"10.1186/s13073-023-01161-y","article_processing_charge":"No","date_published":"2023-02-28T00:00:00Z","file":[{"file_size":4275987,"content_type":"application/pdf","success":1,"checksum":"833b837910c4db42fb5f0f34125f77a7","creator":"cchlebak","date_created":"2023-03-14T10:29:47Z","file_name":"2023_GenomeMed_Bernabeu.pdf","relation":"main_file","date_updated":"2023-03-14T10:29:47Z","file_id":"12722","access_level":"open_access"}],"author":[{"first_name":"Elena","last_name":"Bernabeu","full_name":"Bernabeu, Elena"},{"full_name":"Mccartney, Daniel L.","last_name":"Mccartney","first_name":"Daniel L."},{"full_name":"Gadd, Danni A.","last_name":"Gadd","first_name":"Danni A."},{"full_name":"Hillary, Robert F.","last_name":"Hillary","first_name":"Robert F."},{"first_name":"Ake T.","last_name":"Lu","full_name":"Lu, Ake T."},{"first_name":"Lee","last_name":"Murphy","full_name":"Murphy, Lee"},{"first_name":"Nicola","full_name":"Wrobel, Nicola","last_name":"Wrobel"},{"first_name":"Archie","full_name":"Campbell, Archie","last_name":"Campbell"},{"full_name":"Harris, Sarah E.","last_name":"Harris","first_name":"Sarah E."},{"full_name":"Liewald, David","last_name":"Liewald","first_name":"David"},{"full_name":"Hayward, Caroline","last_name":"Hayward","first_name":"Caroline"},{"first_name":"Cathie","last_name":"Sudlow","full_name":"Sudlow, Cathie"},{"full_name":"Cox, Simon R.","last_name":"Cox","first_name":"Simon R."},{"first_name":"Kathryn L.","full_name":"Evans, Kathryn L.","last_name":"Evans"},{"full_name":"Horvath, Steve","last_name":"Horvath","first_name":"Steve"},{"last_name":"Mcintosh","full_name":"Mcintosh, Andrew M.","first_name":"Andrew M."},{"id":"E5D42276-F5DA-11E9-8E24-6303E6697425","first_name":"Matthew Richard","last_name":"Robinson","orcid":"0000-0001-8982-8813","full_name":"Robinson, Matthew Richard"},{"first_name":"Catalina A.","last_name":"Vallejos","full_name":"Vallejos, Catalina A."},{"first_name":"Riccardo E.","full_name":"Marioni, Riccardo E.","last_name":"Marioni"}],"language":[{"iso":"eng"}],"department":[{"_id":"MaRo"}],"date_created":"2023-03-12T23:01:02Z","month":"02","publication_identifier":{"eissn":["1756-994X"]},"year":"2023"},{"pmid":1,"related_material":{"record":[{"status":"public","id":"9706","relation":"research_data"}]},"citation":{"short":"R.F. Hillary, D. Trejo-Banos, A. Kousathanas, D.L. Mccartney, S.E. Harris, A.J. Stevenson, M. Patxot, S.E. Ojavee, Q. Zhang, D.C. Liewald, C.W. Ritchie, K.L. Evans, E.M. Tucker-Drob, N.R. Wray, A.F. Mcrae, P.M. Visscher, I.J. Deary, M.R. Robinson, R.E. Marioni, Genome Medicine 12 (2020).","mla":"Hillary, Robert F., et al. “Multi-Method Genome- and Epigenome-Wide Studies of Inflammatory Protein Levels in Healthy Older Adults.” <i>Genome Medicine</i>, vol. 12, no. 1, 60, Springer Nature, 2020, doi:<a href=\"https://doi.org/10.1186/s13073-020-00754-1\">10.1186/s13073-020-00754-1</a>.","chicago":"Hillary, Robert F., Daniel Trejo-Banos, Athanasios Kousathanas, Daniel L. Mccartney, Sarah E. Harris, Anna J. Stevenson, Marion Patxot, et al. “Multi-Method Genome- and Epigenome-Wide Studies of Inflammatory Protein Levels in Healthy Older Adults.” <i>Genome Medicine</i>. Springer Nature, 2020. <a href=\"https://doi.org/10.1186/s13073-020-00754-1\">https://doi.org/10.1186/s13073-020-00754-1</a>.","ieee":"R. F. Hillary <i>et al.</i>, “Multi-method genome- and epigenome-wide studies of inflammatory protein levels in healthy older adults,” <i>Genome Medicine</i>, vol. 12, no. 1. Springer Nature, 2020.","ista":"Hillary RF, Trejo-Banos D, Kousathanas A, Mccartney DL, Harris SE, Stevenson AJ, Patxot M, Ojavee SE, Zhang Q, Liewald DC, Ritchie CW, Evans KL, Tucker-Drob EM, Wray NR, Mcrae AF, Visscher PM, Deary IJ, Robinson MR, Marioni RE. 2020. Multi-method genome- and epigenome-wide studies of inflammatory protein levels in healthy older adults. Genome Medicine. 12(1), 60.","ama":"Hillary RF, Trejo-Banos D, Kousathanas A, et al. Multi-method genome- and epigenome-wide studies of inflammatory protein levels in healthy older adults. <i>Genome Medicine</i>. 2020;12(1). doi:<a href=\"https://doi.org/10.1186/s13073-020-00754-1\">10.1186/s13073-020-00754-1</a>","apa":"Hillary, R. F., Trejo-Banos, D., Kousathanas, A., Mccartney, D. L., Harris, S. E., Stevenson, A. J., … Marioni, R. E. (2020). Multi-method genome- and epigenome-wide studies of inflammatory protein levels in healthy older adults. <i>Genome Medicine</i>. Springer Nature. <a href=\"https://doi.org/10.1186/s13073-020-00754-1\">https://doi.org/10.1186/s13073-020-00754-1</a>"},"oa_version":"Published Version","_id":"8133","file_date_updated":"2020-07-22T06:27:38Z","title":"Multi-method genome- and epigenome-wide studies of inflammatory protein levels in healthy older adults","publication_status":"published","user_id":"ba8df636-2132-11f1-aed0-ed93e2281fdd","day":"08","article_number":"60","publisher":"Springer Nature","status":"public","has_accepted_license":"1","tmp":{"short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","image":"/images/cc_by.png"},"isi":1,"external_id":{"isi":["000551778400001"],"pmid":["32641083"]},"file":[{"date_created":"2020-07-22T06:27:38Z","creator":"dernst","success":1,"content_type":"application/pdf","file_size":1136983,"access_level":"open_access","file_id":"8145","date_updated":"2020-07-22T06:27:38Z","relation":"main_file","file_name":"2020_GenomeMedicine_Hillary.pdf"}],"date_created":"2020-07-19T22:00:58Z","language":[{"iso":"eng"}],"department":[{"_id":"MaRo"}],"author":[{"first_name":"Robert F.","last_name":"Hillary","full_name":"Hillary, Robert F."},{"full_name":"Trejo-Banos, Daniel","last_name":"Trejo-Banos","first_name":"Daniel"},{"first_name":"Athanasios","last_name":"Kousathanas","full_name":"Kousathanas, Athanasios"},{"last_name":"Mccartney","full_name":"Mccartney, Daniel L.","first_name":"Daniel L."},{"last_name":"Harris","full_name":"Harris, Sarah E.","first_name":"Sarah E."},{"first_name":"Anna J.","full_name":"Stevenson, Anna J.","last_name":"Stevenson"},{"full_name":"Patxot, Marion","last_name":"Patxot","first_name":"Marion"},{"first_name":"Sven Erik","last_name":"Ojavee","full_name":"Ojavee, Sven Erik"},{"first_name":"Qian","full_name":"Zhang, Qian","last_name":"Zhang"},{"first_name":"David C.","last_name":"Liewald","full_name":"Liewald, David C."},{"first_name":"Craig W.","last_name":"Ritchie","full_name":"Ritchie, Craig W."},{"last_name":"Evans","full_name":"Evans, Kathryn L.","first_name":"Kathryn L."},{"full_name":"Tucker-Drob, Elliot M.","last_name":"Tucker-Drob","first_name":"Elliot M."},{"last_name":"Wray","full_name":"Wray, Naomi R.","first_name":"Naomi R."},{"first_name":"Allan F.","full_name":"Mcrae, Allan F.","last_name":"Mcrae"},{"last_name":"Visscher","full_name":"Visscher, Peter M.","first_name":"Peter M."},{"first_name":"Ian J.","full_name":"Deary, Ian J.","last_name":"Deary"},{"id":"E5D42276-F5DA-11E9-8E24-6303E6697425","first_name":"Matthew Richard","last_name":"Robinson","orcid":"0000-0001-8982-8813","full_name":"Robinson, Matthew Richard"},{"full_name":"Marioni, Riccardo E.","last_name":"Marioni","first_name":"Riccardo E."}],"issue":"1","month":"07","publication_identifier":{"eissn":["1756-994X"]},"year":"2020","corr_author":"1","intvolume":"        12","publication":"Genome Medicine","ddc":["570"],"scopus_import":"1","type":"journal_article","article_type":"original","quality_controlled":"1","abstract":[{"lang":"eng","text":"The molecular factors which control circulating levels of inflammatory proteins are not well understood. Furthermore, association studies between molecular probes and human traits are often performed by linear model-based methods which may fail to account for complex structure and interrelationships within molecular datasets.In this study, we perform genome- and epigenome-wide association studies (GWAS/EWAS) on the levels of 70 plasma-derived inflammatory protein biomarkers in healthy older adults (Lothian Birth Cohort 1936; n = 876; Olink® inflammation panel). We employ a Bayesian framework (BayesR+) which can account for issues pertaining to data structure and unknown confounding variables (with sensitivity analyses using ordinary least squares- (OLS) and mixed model-based approaches). We identified 13 SNPs associated with 13 proteins (n = 1 SNP each) concordant across OLS and Bayesian methods. We identified 3 CpG sites spread across 3 proteins (n = 1 CpG each) that were concordant across OLS, mixed-model and Bayesian analyses. Tagged genetic variants accounted for up to 45% of variance in protein levels (for MCP2, 36% of variance alone attributable to 1 polymorphism). Methylation data accounted for up to 46% of variation in protein levels (for CXCL10). Up to 66% of variation in protein levels (for VEGFA) was explained using genetic and epigenetic data combined. We demonstrated putative causal relationships between CD6 and IL18R1 with inflammatory bowel disease and between IL12B and Crohn’s disease. Our data may aid understanding of the molecular regulation of the circulating inflammatory proteome as well as causal relationships between inflammatory mediators and disease."}],"date_updated":"2026-04-02T14:28:33Z","oa":1,"volume":12,"doi":"10.1186/s13073-020-00754-1","date_published":"2020-07-08T00:00:00Z","article_processing_charge":"No"}]