Comparative validation of an epigenetic mortality risk score with three aging biomarkers for predicting mortality risks among older adult males.

BACKGROUND A 'mortality risk score' (MS) based on ten prominent mortality-related cytosine-phosphate-guanine (CpG) sites was previously associated with all-cause mortality, but has not been verified externally. We aimed to validate the association of MS with mortality and to compare MS with three aging biomarkers: telomere length (TL), DNA methylation age (DNAmAge) and phenotypic age (DNAmPhenoAge) to explore whether MS can serve as a reliable measure of biological aging and mortality. METHODS Among 534 males aged 55-85 years from the US Normative Aging Study, the MS, DNAmAge and DNAmPhenoAge were derived from blood DNA methylation profiles from the Illumina HumanMethylation450 BeadChip, and TL was measured by quantitative real-time polymerase chain reaction (qRT-PCR). RESULTS A total of 147 participants died during a median follow-up of 9.4 years. The MS showed strong associations with all-cause, cardiovascular disease (CVD) and cancer mortality. After controlling for all potential covariates, participants with high MS (>5 CpG sites with aberrant methylation) had almost 4-fold all-cause mortality (hazard ratio: 3.84, 95% confidence interval: 1.92-7.67) compared with participants with a low MS (0-1 CpG site with aberrant methylation). Similar patterns were observed with respect to CVD and cancer mortality. MS was associated with TL and DNAmPhenoAge acceleration but not with DNAmAge acceleration. Although the MS and DNAmPhenoAge acceleration were independently associated with all-cause mortality, the former exhibited a higher predictive accuracy of mortality than the latter. CONCLUSIONS MS has the potential to be a prominent predictor of mortality that could enhance survival prediction in clinical settings.

[1]  F. Kronenberg,et al.  Telomere Length , 2020, Definitions.

[2]  E. Colicino,et al.  Impacts of air pollution, temperature, and relative humidity on leukocyte distribution: An epigenetic perspective. , 2019, Environment international.

[3]  E. Colicino,et al.  Accelerated DNA methylation age and the use of antihypertensive medication among older adults , 2018, Aging.

[4]  H. Brenner,et al.  Leukocyte telomere length and epigenetic-based mortality risk score: associations with all-cause mortality among older adults , 2018, Epigenetics.

[5]  H. Brenner,et al.  Vitamin D status and epigenetic-based mortality risk score: strong independent and joint prediction of all-cause mortality in a population-based cohort study , 2018, Clinical Epigenetics.

[6]  J. Long,et al.  Dose Response and Prediction Characteristics of a Methylation Sensitive Digital PCR Assay for Cigarette Consumption in Adults , 2018, Front. Genet..

[7]  Steve Horvath,et al.  DNA methylation-based biomarkers and the epigenetic clock theory of ageing , 2018, Nature Reviews Genetics.

[8]  M. Levine,et al.  An epigenetic biomarker of aging for lifespan and healthspan , 2018, bioRxiv.

[9]  H. Brenner,et al.  Methylomic survival predictors, frailty, and mortality , 2018, Aging.

[10]  H. Brenner,et al.  Associations of self-reported smoking, cotinine levels and epigenetic smoking indicators with oxidative stress among older adults: a population-based study , 2017, European Journal of Epidemiology.

[11]  Jonathan A. Heiss,et al.  DNA methylation signatures in peripheral blood strongly predict all-cause mortality , 2017, Nature Communications.

[12]  M. Levine,et al.  Epigenetic clock analysis of diet, exercise, education, and lifestyle factors , 2017, Aging.

[13]  Paolo Vineis,et al.  Epigenetic Signatures of Cigarette Smoking , 2016, Circulation. Cardiovascular genetics.

[14]  M. Levine,et al.  DNA methylation-based measures of biological age: meta-analysis predicting time to death , 2016, Aging.

[15]  M. Levine,et al.  An epigenetic clock analysis of race/ethnicity, sex, and coronary heart disease , 2016, Genome Biology.

[16]  E. Colicino,et al.  Telomere Length, Long-Term Black Carbon Exposure, and Cognitive Function in a Cohort of Older Men: The VA Normative Aging Study , 2016, Environmental Health Perspectives.

[17]  H. Brenner,et al.  Epigenetic age acceleration predicts cancer, cardiovascular, and all-cause mortality in a German case cohort , 2016, Clinical Epigenetics.

[18]  H. Brenner,et al.  Relationship of tobacco smoking and smoking-related DNA methylation with epigenetic age acceleration , 2016, OncoTarget.

[19]  R. Marioni,et al.  The epigenetic clock and telomere length are independently associated with chronological age and mortality , 2016, International journal of epidemiology.

[20]  E. Colicino,et al.  Blood Epigenetic Age may Predict Cancer Incidence and Mortality , 2016, EBioMedicine.

[21]  Simone Wahl,et al.  Genome-Wide Analysis of DNA Methylation and Fine Particulate Matter Air Pollution in Three Study Populations: KORA F3, KORA F4, and the Normative Aging Study , 2016, Environmental health perspectives.

[22]  S. Horvath,et al.  Increased epigenetic age and granulocyte counts in the blood of Parkinson's disease patients , 2015, Aging.

[23]  M. Levine,et al.  Epigenetic age of the pre-frontal cortex is associated with neuritic plaques, amyloid load, and Alzheimer’s disease related cognitive functioning , 2015, Aging.

[24]  P. Vokonas,et al.  Exposure to sub-chronic and long-term particulate air pollution and heart rate variability in an elderly cohort: the Normative Aging Study , 2015, Environmental Health.

[25]  H. Brenner,et al.  DNA methylation changes of whole blood cells in response to active smoking exposure in adults: a systematic review of DNA methylation studies , 2015, Clinical Epigenetics.

[26]  H. Brenner,et al.  Smoking-Associated DNA Methylation Biomarkers and Their Predictive Value for All-Cause and Cardiovascular Mortality , 2015, Environmental health perspectives.

[27]  S. Horvath,et al.  Longitudinal changes of telomere length and epigenetic age related to traumatic stress and post-traumatic stress disorder , 2015, Psychoneuroendocrinology.

[28]  S. Horvath,et al.  DNA methylation age of blood predicts all-cause mortality in later life , 2015, Genome Biology.

[29]  Steve Horvath,et al.  The epigenetic clock is correlated with physical and cognitive fitness in the Lothian Birth Cohort 1936 , 2015, International journal of epidemiology.

[30]  Steve Horvath,et al.  Obesity accelerates epigenetic aging of human liver , 2014, Proceedings of the National Academy of Sciences.

[31]  S. Horvath DNA methylation age of human tissues and cell types , 2013, Genome Biology.

[32]  Francesco Marabita,et al.  A beta-mixture quantile normalization method for correcting probe design bias in Illumina Infinium 450 k DNA methylation data , 2012, Bioinform..

[33]  Devin C. Koestler,et al.  DNA methylation arrays as surrogate measures of cell mixture distribution , 2012, BMC Bioinformatics.

[34]  F. Tylavsky,et al.  Racial differences in gene-specific DNA methylation levels are present at birth. , 2011, Birth defects research. Part A, Clinical and molecular teratology.

[35]  M. Pencina,et al.  On the C‐statistics for evaluating overall adequacy of risk prediction procedures with censored survival data , 2011, Statistics in medicine.

[36]  F. Kronenberg,et al.  Telomere length and risk of incident cancer and cancer mortality. , 2010, JAMA.

[37]  François Mariotti,et al.  Dose‐response analyses using restricted cubic spline functions in public health research , 2010, Statistics in medicine.

[38]  S. Humphries,et al.  Telomere length in atherosclerosis and diabetes , 2010, Atherosclerosis.

[39]  M. Fenech,et al.  Telomere length in white blood cells, buccal cells and brain tissue and its variation with ageing and Alzheimer's disease , 2008, Mechanisms of Ageing and Development.

[40]  M. Blasco Telomeres and human disease: ageing, cancer and beyond , 2005, Nature Reviews Genetics.

[41]  Richard Doll,et al.  Mortality in relation to smoking: 22 years' observations on female British doctors. , 1980, British medical journal.

[42]  F. Harrell,et al.  Prognostic/Clinical Prediction Models: Multivariable Prognostic Models: Issues in Developing Models, Evaluating Assumptions and Adequacy, and Measuring and Reducing Errors , 2005 .

[43]  J. Schwartz,et al.  Cigarette smoking and peripheral blood leukocyte differentials. , 1994, Annals of epidemiology.

[44]  Albert Damon,et al.  The Normative Aging Study: An Interdisciplinary and Longitudinal Study of Health and Aging , 1972 .

[45]  Annual smoking-attributable mortality, years of potential life lost, and economic costs--United States, 1995-1999. , 2002, MMWR. Morbidity and mortality weekly report.

[46]  C. Greider,et al.  Telomere length regulation. , 1996, Annual review of biochemistry.

[47]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .