2015
Quantification of biological aging in young adults
Belsky DW, Caspi A, Houts R, Cohen HJ, Corcoran DL, Danese A, Harrington H, Israel S, Levine ME, Schaefer JD, Sugden K, Williams B, Yashin AI, Poulton R, Moffitt TE. Quantification of biological aging in young adults. Proceedings Of The National Academy Of Sciences Of The United States Of America 2015, 112: e4104-e4110. PMID: 26150497, PMCID: PMC4522793, DOI: 10.1073/pnas.1506264112.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgingBiomarkersCognitionCross-Sectional StudiesHumansLife ExpectancyLongitudinal StudiesMiddle AgedRegression AnalysisTime FactorsConceptsYoung adultsYoung humansSame chronological ageSelf-reported bad healthCognitive declineOlder adultsBiological agingYoung individualsChronological ageMultiple organ systemsBrain agingLongitudinal measuresAge-related diseasesChronic diseasesAdultsFourth decadeBirth cohortOrgan systemsWorse healthIndividualsRejuvenation therapyTime pointsHuman agingMultiple biomarkersTherapy
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