Evidence map›Paper›PMID 32321578›Full record

ArticleClinical epigenetics2020

Epigenetic prediction of complex traits and mortality in a cohort of individuals with oropharyngeal cancer.

Ryan J Langdon, Rhona A Beynon, Kate Ingarfield, Riccardo E Marioni, Daniel L McCartney, Richard M Martin, Andy R Ness, Michael Pawlita, Tim Waterboer, Caroline Relton and 2 more

Open access · goldAbstract readMulticenter Study
In one paragraph

Article in Clinical epigenetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
0.8field-weighted citation impact, top 31% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Review
  4. Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors at 6 institutions in 2 countries.

Ryan J LangdonMRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.
Rhona A BeynonMRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.
Kate IngarfieldNIHR Bristol Biomedical Research Centre, University Hospitals Bristol and University of Bristol, Bristol, UK.
Riccardo E MarioniMRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Crewe Road, Edinburgh, Scotland, EH4 2XU, UK.
Daniel L McCartneyMRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Crewe Road, Edinburgh, Scotland, EH4 2XU, UK.
Richard M MartinMRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.
Andy R NessNIHR Bristol Biomedical Research Centre, University Hospitals Bristol and University of Bristol, Bristol, UK.
Michael PawlitaInfections and Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
Tim WaterboerInfections and Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
Caroline ReltonMRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.
Steven J ThomasNIHR Bristol Biomedical Research Centre, University Hospitals Bristol and University of Bristol, Bristol, UK.
Rebecca C RichmondMRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK. rebecca.richmond@bristol.ac.uk.
MRC Epidemiology Unit · GBUniversity of Bristol · GBHeidelberg University · DENIHR Bristol Biomedical Research Centre · GBMRC Institute of Genetics and Molecular Medicine · GBUniversity of Edinburgh · GB

Funding

Cancer Research UK 15954Cancer Research UK 19169Cancer Research UK 25350Cancer Research UK C18281/A19169Cancer Research UK C18281/A20988Medical Research Council MC_UU_00011/1Medical Research Council MC_UU_00011/5Medical Research Council MC_UU_12013/1Medical Research Council MC_UU_12013/2Medical Research Council MC_UU_12013/3Wellcome TrustWellcome Trust WT099874MA
6 · The paper itself

Abstract

backgroundDNA methylation (DNAm) variation is an established predictor for several traits. In the context of oropharyngeal cancer (OPC), where 5-year survival is ~ 65%, DNA methylation may act as a prognostic biomarker. We examined the accuracy of DNA methylation biomarkers of 4 complex exposure traits (alcohol consumption, body mass index [BMI], educational attainment and smoking status) in predicting all-cause mortality in people with OPC.

resultsDNAm predictors of alcohol consumption, BMI, educational attainment and smoking status were applied to 364 individuals with OPC in the Head and Neck 5000 cohort (HN5000; 19.6% of total OPC cases in the study), followed up for median 3.9 years; inter-quartile range (IQR) 3.3 to 5.2 years (time-to-event-death or censor). The proportion of phenotypic variance explained in each trait was as follows: 16.5% for alcohol consumption, 22.7% for BMI, 0.4% for educational attainment and 51.1% for smoking. We then assessed the relationship between each DNAm predictor and all-cause mortality using Cox proportional-hazard regression analysis. DNAm prediction of smoking was most consistently associated with mortality risk (hazard ratio [HR], 1.38 per standard deviation (SD) increase in smoking DNAm score; 95% confidence interval [CI] 1.04 to 1.83; P 0.025, in a model adjusted for demographic, lifestyle, health and biological variables). Finally, we examined the accuracy of each DNAm predictor of mortality. DNAm predictors explained similar levels of variance in mortality to self-reported phenotypes. Receiver operator characteristic (ROC) curves for the DNAm predictors showed a moderate discrimination of alcohol consumption (area under the curve [AUC] 0.63), BMI (AUC 0.61) and smoking (AUC 0.70) when predicting mortality. The DNAm predictor for education showed poor discrimination (AUC 0.57). Z tests comparing AUCs between self-reported phenotype ROC curves and DNAm score ROC curves did not show evidence for difference between the two (alcohol consumption P 0.41, BMI P 0.62, educational attainment P 0.49, smoking P 0.19).

conclusionsIn the context of a clinical cohort of individuals with OPC, DNAm predictors for smoking, alcohol consumption, educational attainment and BMI exhibit similar predictive values for all-cause mortality compared to self-reported data. These findings may have translational utility in prognostic model development, particularly where phenotypic data are not available.

Indexed as

DNA MethylationAdultAgedAged, 80 and overAlcohol DrinkingBiomarkers, TumorBody Mass IndexCohort StudiesEducational StatusEpigenesis, GeneticFemaleHumansMaleMiddle AgedOropharyngeal NeoplasmsPrognosisBiomarkers, Tumor

Identifiers

PMID32321578
PMCPMC7178612
OpenAlexW3016150166

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.