Dataset Information

Epigenetic biomarkers of ageing are predictive of mortality risk in a longitudinal clinical cohort of individuals diagnosed with oropharyngeal cancer.

ABSTRACT:

Background

Epigenetic clocks are biomarkers of ageing derived from DNA methylation levels at a subset of CpG sites. The difference between age predicted by these clocks and chronological age, termed "epigenetic age acceleration", has been shown to predict age-related disease and mortality. We aimed to assess the prognostic value of epigenetic age acceleration and a DNA methylation-based mortality risk score with all-cause mortality in a prospective clinical cohort of individuals with head and neck cancer: Head and Neck 5000. We investigated two markers of intrinsic epigenetic age acceleration (IEAAHorvath and IEAAHannum), one marker of extrinsic epigenetic age acceleration (EEAA), one optimised to predict physiological dysregulation (AgeAccelPheno), one optimised to predict lifespan (AgeAccelGrim) and a DNA methylation-based predictor of mortality (ZhangScore). Cox regression models were first used to estimate adjusted hazard ratios (HR) and 95% confidence intervals (CI) for associations of epigenetic age acceleration with all-cause mortality in people with oropharyngeal cancer (n = 408; 105 deaths). The added prognostic value of epigenetic markers compared to a clinical model including age, sex, TNM stage and HPV status was then evaluated.

Results

IEAAHannum and AgeAccelGrim were associated with mortality risk after adjustment for clinical and lifestyle factors (HRs per standard deviation [SD] increase in age acceleration = 1.30 [95% CI 1.07, 1.57; p = 0.007] and 1.40 [95% CI 1.06, 1.83; p = 0.016], respectively). There was weak evidence that the addition of AgeAccelGrim to the clinical model improved 3-year mortality prediction (area under the receiver operating characteristic curve: 0.80 vs. 0.77; p value for difference = 0.069).

Conclusion

In the setting of a large, clinical cohort of individuals with head and neck cancer, our study demonstrates the potential of epigenetic markers of ageing to enhance survival prediction in people with oropharyngeal cancer, beyond established prognostic factors. Our findings have potential uses in both clinical and non-clinical contexts: to aid treatment planning and improve patient stratification.

SUBMITTER: Beynon RA

PROVIDER: S-EPMC8725548 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

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Publications

Epigenetic biomarkers of ageing are predictive of mortality risk in a longitudinal clinical cohort of individuals diagnosed with oropharyngeal cancer.

Beynon Rhona A RA Ingle Suzanne M SM Langdon Ryan R May Margaret M Ness Andy A Martin Richard M RM Suderman Matthew M Ingarfield Kate K Marioni Riccardo E RE McCartney Daniel L DL Waterboer Tim T Pawlita Michael M Relton Caroline C Smith George Davey GD Richmond Rebecca C RC

Clinical epigenetics 20220103 1

<h4>Background</h4>Epigenetic clocks are biomarkers of ageing derived from DNA methylation levels at a subset of CpG sites. The difference between age predicted by these clocks and chronological age, termed "epigenetic age acceleration", has been shown to predict age-related disease and mortality. We aimed to assess the prognostic value of epigenetic age acceleration and a DNA methylation-based mortality risk score with all-cause mortality in a prospective clinical cohort of individuals with hea ...[more]

PMID: 34980250

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Project description: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.

Project description:Accurate prediction of cardiovascular disease (CVD) mortality is essential for effective treatment decisions and risk management. Current models often lack comprehensive integration of key biomarkers, limiting their predictive power. This study aims to develop a predictive model for CVD-related mortality using a machine learning-based feature selection algorithm and assess its performance compared to existing models. We analyzed data from a cohort of 4,882 adults recruited between 1999 and 2004, followed for up to 20 years. After applying the Boruta algorithm for feature selection, key biomarkers including NT-proBNP, cardiac troponins, and homocysteine were identified as significant predictors of CVD mortality. Predictive models were built using these biomarkers alongside demographic and clinical variables. Model performance was evaluated using the concordance index (C-index), sensitivity, specificity, and accuracy, with internal validation conducted through bootstrap sampling. Additionally, decision curve analysis (DCA) was performed to assess clinical benefit. The combined model, incorporating both biomarkers and demographic variables, demonstrated superior predictive performance with a C-index of 0.9205 (95% CI: 0.9129-0.9319), outperforming models with demographic variables alone (C-index: 0.9030 (95% CI: 0.8938-0.9147)) or biomarkers alone (C-index: 0.8659 (95% CI: 0.8519-0.8826)). Cox regression analysis further identified key predictors of CVD mortality, including elevated AST/ALT, TyG, BUN, and systolic blood pressure, with protective factors such as higher chloride and iron levels. Nomogram construction and DCA confirmed that the combined model provided substantial net benefit across various time points. The integration of cardiac biomarkers, lipid profiles, and inflammatory markers significantly improves the accuracy of predictive models for CVD-related mortality. This novel approach offers enhanced prognostication, with potential for further optimization through the inclusion of additional clinical and lifestyle data.

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Dataset Information

Epigenetic biomarkers of ageing are predictive of mortality risk in a longitudinal clinical cohort of individuals diagnosed with oropharyngeal cancer.

Background

Results

Conclusion

Publications

Epigenetic biomarkers of ageing are predictive of mortality risk in a longitudinal clinical cohort of individuals diagnosed with oropharyngeal cancer.

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