2016
DNA methylation-based measures of biological age: meta-analysis predicting time to death
Chen BH, Marioni RE, Colicino E, Peters MJ, Ward-Caviness CK, Tsai PC, Roetker NS, Just AC, Demerath EW, Guan W, Bressler J, Fornage M, Studenski S, Vandiver AR, Moore AZ, Tanaka T, Kiel DP, Liang L, Vokonas P, Schwartz J, Lunetta KL, Murabito JM, Bandinelli S, Hernandez DG, Melzer D, Nalls M, Pilling LC, Price TR, Singleton AB, Gieger C, Holle R, Kretschmer A, Kronenberg F, Kunze S, Linseisen J, Meisinger C, Rathmann W, Waldenberger M, Visscher PM, Shah S, Wray NR, McRae AF, Franco OH, Hofman A, Uitterlinden AG, Absher D, Assimes T, Levine ME, Lu AT, Tsao PS, Hou L, Manson JE, Carty CL, LaCroix AZ, Reiner AP, Spector TD, Feinberg AP, Levy D, Baccarelli A, van Meurs J, Bell JT, Peters A, Deary IJ, Pankow JS, Ferrucci L, Horvath S. DNA methylation-based measures of biological age: meta-analysis predicting time to death. Aging 2016, 8: 1844-1859. PMID: 27690265, PMCID: PMC5076441, DOI: 10.18632/aging.101020.Peer-Reviewed Original ResearchConceptsCause mortalityBlood cell compositionRisk factorsTraditional risk factorsBlood cell countAdditional risk factorsChronological ageEpigenetic ageCell compositionBiological ageEpigenetic age accelerationStudy ACell countEthnic groupsSignificant associationHuman cohortsRobust biomarkersMortalityTotal sample sizeMethylation-based measuresDNA methylation-based measuresEpigenetic age estimatesAgeAge accelerationDifferent cohorts
2015
DNA methylation age of blood predicts future onset of lung cancer in the women's health initiative
Levine ME, Hosgood HD, Chen B, Absher D, Assimes T, Horvath S. DNA methylation age of blood predicts future onset of lung cancer in the women's health initiative. Aging 2015, 7: 690-700. PMID: 26411804, PMCID: PMC4600626, DOI: 10.18632/aging.100809.Peer-Reviewed Original ResearchConceptsIntrinsic epigenetic age accelerationWomen's Health InitiativeLung cancer incidenceLung cancer susceptibilityLung cancerHealth initiativesCancer incidenceCox proportional hazards modelCancer susceptibilityLung cancer casesProportional hazards modelCurrent smokersAge-related declineAge-associated diseasesAge-related diseasesFuture onsetCancer casesCigarette smokeHazards modelUseful biomarkerEpigenetic age accelerationCandidate biomarkersOlder individualsDNA methylation ageGenotoxic carcinogens
2014
Not All Smokers Die Young: A Model for Hidden Heterogeneity within the Human Population
Levine M, Crimmins E. Not All Smokers Die Young: A Model for Hidden Heterogeneity within the Human Population. PLOS ONE 2014, 9: e87403. PMID: 24520332, PMCID: PMC3919713, DOI: 10.1371/journal.pone.0087403.Peer-Reviewed Original ResearchConceptsLung function levelsProportional hazards modelMost age groupsCurrent smokersSimilar inflammationNHANES IIIMortality riskSmokersAge 50Age 80Hazards modelExtreme old ageAge groupsMeans of biomarkersOlder ageResilient phenotypeHigh exposureFunction levelUnderstanding of heterogeneityDamaging factorsLongevity extensionAging processBiological advantagesSmokingInflammation