Epigenetic scores for the circulating proteome as tools for disease prediction

Gadd, Danni A; Hillary, Robert F.; McCartney, Daniel L.; Zaghlool, Shaza B.; Stevenson, Anna J.; Cheng, Yipeng; Fawns-Ritchie, Chloë; Nangle, Clifford; Campbell, Archie; Flaig, Robin; Harris, Sarah E.; Walker, Rosie M.; Shi, Liu; Tucker‐Drob, Elliot M.; Gieger, Christian; Peters, Annette; Waldenberger, Mélanie; Graumann, Johannes; McRae, Allan F.; Deary, Ian J.; Porteous, David J.; Hayward, Caroline; Visscher, Peter M.; Cox, Simon R.; Evans, Kathryn; McIntosh, Andrew M.; Suhre, Karsten; Marioni, Riccardo E.

doi:10.7554/elife.71802

Cited by 60 publications

(97 citation statements)

References 81 publications

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“…Additionally, the DNAm signatures of proteins we quantify represent widespread differences across blood cells that are related to circulating protein levels and are therefore not derived from the same cell-types as proteins. Despite this limitation, previous work supports DNAm scores for proteins as useful markers of brain health, suggesting there is merit in integrating DNAm signatures of protein levels in disease stratification 18 .…”

Section: Discussionmentioning

confidence: 99%

“…Additionally, DNAm accounts for inter-individual variability in circulating protein levels 14 – 16 . Recently, through integration of DNAm and protein data, we have shown that epigenetic scores for plasma protein levels—known as EpiScores—associate with brain morphology and cognitive ageing markers 17 and predict the onset of neurological diseases 18 . These studies highlight that while datasets that allow for integration of proteomic, epigenetic and phenotypic information are rarely-available, they hold potential to advance risk stratification.…”

Section: Introductionmentioning

confidence: 99%

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Integrated methylome and phenome study of the circulating proteome reveals markers pertinent to brain health

et al. 2022

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Characterising associations between the methylome, proteome and phenome may provide insight into biological pathways governing brain health. Here, we report an integrated DNA methylation and phenotypic study of the circulating proteome in relation to brain health. Methylome-wide association studies of 4058 plasma proteins are performed (N = 774), identifying 2928 CpG-protein associations after adjustment for multiple testing. These are independent of known genetic protein quantitative trait loci (pQTLs) and common lifestyle effects. Phenome-wide association studies of each protein are then performed in relation to 15 neurological traits (N = 1,065), identifying 405 associations between the levels of 191 proteins and cognitive scores, brain imaging measures or APOE e4 status. We uncover 35 previously unreported DNA methylation signatures for 17 protein markers of brain health. The epigenetic and proteomic markers we identify are pertinent to understanding and stratifying brain health.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Integrated methylome and phenome study of the circulating proteome reveals markers pertinent to brain health

et al. 2022

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“…DNAm can precisely track ageing through predictors termed “epigenetic clocks” [ 2 – 8 ]. DNAm has also been found to capture other components of health, such as smoking status [ 9 , 10 ], alcohol consumption [ 11 , 12 ], obesity [ 11 , 13 ], and protein levels [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

Refining epigenetic prediction of chronological and biological age

et al. 2023

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Background Epigenetic 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. Methods First, 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). Results Through 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. Conclusions The 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.

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“…We developed a combined score, DNAmCVDscore , predictive of future CVD events by regressing the time-to-CVD event on 60 candidate DNAm surrogates: the nine newly developed within this study, 32 DNAm surrogates for blood measured (mainly inflammatory) proteins produced by Gadd and colleagues [ 23 , 26 ]; three epigenetic clocks (HorvathDNAmAge, HannumDNAmAgem, and DNAmPhenoAge) [ 20 ]; two DNAm surrogates for lead exposure [ 22 ]; six ‘Houseman’ DNAm surrogates for white blood cell (WBC) proportion [ 38 ]; and the nine components of the DNAmGrimAge clock (DNAm surrogates for smoking pack-years, telomere length, and seven blood measured proteins) [ 11 ].…”

Section: Resultsmentioning

confidence: 99%

“…The best performing epigenetic clock, called DNAGrimAge, incorporates DNAm scores for seven circulating proteins and smoking pack-years, and it has been consistently associated with longevity and numerous age-related diseases, and functional and cognitive outcomes [ 11 , 19 , 21 ]. Other examples of DNAm surrogate of exposures and risk factors include the DNAm biomarkers identified by Colicino and colleagues for cumulative lead exposure [ 22 ], the one derived by Marioni and colleagues for several longevity-related and inflammatory proteins [ 23 – 26 ], the classification by Guida and colleagues of current, former (including time since smoking cessation) and never smokers based on blood DNAm biomarkers [ 7 ], and the recent characterisation of electronic health records phenotypes by Thompson and colleagues [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

A blood DNA methylation biomarker for predicting short-term risk of cardiovascular events

et al. 2022

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Background Recent evidence highlights the epidemiological value of blood DNA methylation (DNAm) as surrogate biomarker for exposure to risk factors for non-communicable diseases (NCD). DNAm surrogate of exposures predicts diseases and longevity better than self-reported or measured exposures in many cases. Consequently, disease prediction models based on blood DNAm surrogates may outperform current state-of-the-art prediction models. This study aims to develop novel DNAm surrogates for cardiovascular diseases (CVD) risk factors and develop a composite biomarker predictive of CVD risk. We compared the prediction performance of our newly developed risk score with the state-of-the-art DNAm risk scores for cardiovascular diseases, the ‘next-generation’ epigenetic clock DNAmGrimAge, and the prediction model based on traditional risk factors SCORE2. Results Using data from the EPIC Italy cohort, we derived novel DNAm surrogates for BMI, blood pressure, fasting glucose and insulin, cholesterol, triglycerides, and coagulation biomarkers. We validated them in four independent data sets from Europe and the USA. Further, we derived a DNAmCVDscore predictive of the time-to-CVD event as a combination of several DNAm surrogates. ROC curve analyses show that DNAmCVDscore outperforms previously developed DNAm scores for CVD risk and SCORE2 for short-term CVD risk. Interestingly, the performance of DNAmGrimAge and DNAmCVDscore was comparable (slightly lower for DNAmGrimAge, although the differences were not statistically significant). Conclusions We described novel DNAm surrogates for CVD risk factors useful for future molecular epidemiology research, and we described a blood DNAm-based composite biomarker, DNAmCVDscore, predictive of short-term cardiovascular events. Our results highlight the usefulness of DNAm surrogate biomarkers of risk factors in epigenetic epidemiology to identify high-risk populations. In addition, we provide further evidence on the effectiveness of prediction models based on DNAm surrogates and discuss methodological aspects for further improvements. Finally, our results encourage testing this approach for other NCD diseases by training and developing DNAm surrogates for disease-specific risk factors and exposures.

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Epigenetic scores for the circulating proteome as tools for disease prediction

Cited by 60 publications

References 81 publications

Integrated methylome and phenome study of the circulating proteome reveals markers pertinent to brain health

Integrated methylome and phenome study of the circulating proteome reveals markers pertinent to brain health

Refining epigenetic prediction of chronological and biological age

A blood DNA methylation biomarker for predicting short-term risk of cardiovascular events

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