2019
Midlife Study of the Louisville Twins: Connecting Cognitive Development to Biological and Cognitive Aging
Beam CR, Turkheimer E, Finkel D, Levine ME, Zandi E, Guterbock TM, Giangrande EJ, Ryan L, Pasquenza N, Davis DW. Midlife Study of the Louisville Twins: Connecting Cognitive Development to Biological and Cognitive Aging. Behavior Genetics 2019, 50: 73-83. PMID: 31820295, PMCID: PMC7033012, DOI: 10.1007/s10519-019-09983-6.Peer-Reviewed Original ResearchConceptsLouisville Twin StudyCognitive developmentCognitive agingCognitive functioningCognitive developmental trajectoriesLongitudinal Twin StudyTwin studiesPhysical health factorsEpisodic memoryLower biological ageFunctional ability measuresIQ measuresAbility measuresDevelopmental trajectoriesFSIQ scoresMidlife phaseMidlife studyPhysical functioningFunctional abilityChronological ageFunctioningPsychiatric outcomesSecond pilot studySecond studyIQ
2018
A new aging measure captures morbidity and mortality risk across diverse subpopulations from NHANES IV: A cohort study
Liu Z, Kuo PL, Horvath S, Crimmins E, Ferrucci L, Levine M. A new aging measure captures morbidity and mortality risk across diverse subpopulations from NHANES IV: A cohort study. PLOS Medicine 2018, 15: e1002718. PMID: 30596641, PMCID: PMC6312200, DOI: 10.1371/journal.pmed.1002718.Peer-Reviewed Original ResearchConceptsPhenotypic AgeNHANES IVCause mortalityMortality riskHealth behaviorsRepresentative US adult populationDisease-free personsOld-old adultsChronological ageRisk of deathAge 85 yearsCause-specific mortalityCause of deathProportional hazards modelUS adult populationHealth behavior characteristicsDisease countsPotential biological mechanismsEfficacy of interventionsRace/ethnicityNormal BMICohort studyDiverse subpopulationsHazards modelRisk individualsIs 60 the New 50? Examining Changes in Biological Age Over the Past Two Decades
Levine ME, Crimmins EM. Is 60 the New 50? Examining Changes in Biological Age Over the Past Two Decades. Demography 2018, 55: 387-402. PMID: 29511995, PMCID: PMC5897168, DOI: 10.1007/s13524-017-0644-5.Peer-Reviewed Original ResearchConceptsBiological ageModifiable health behaviorsModifiable risk factorsLife expectancySex-specific changesNHANES IVMedication useNHANES IIIRisk factorsDegree of improvementHealth behaviorsOlder groupOlder adultsPace of agingAgeSex groupsGreater declineGreater improvementChronological ageContribution of changesHealthExpectancy
2017
Biological Age, Not Chronological Age, Is Associated with Late-Life Depression
Brown PJ, Wall MM, Chen C, Levine ME, Yaffe K, Roose SP, Rutherford BR. Biological Age, Not Chronological Age, Is Associated with Late-Life Depression. The Journals Of Gerontology Series A 2017, 73: 1370-1376. PMID: 28958059, PMCID: PMC6132120, DOI: 10.1093/gerona/glx162.Peer-Reviewed Original ResearchConceptsLate-life depressionCES-D scoresDepressive symptomsBaseline CES-D scoreBiological ageChronological ageCovariate-adjusted regression modelsOlder biological ageEpidemiologic Studies Depression ScaleSignificant depressive symptomsBody Composition StudyMean chronological ageAge-associated changesNumerous physiological systemsAge-related processesKidney functioningLife depressionDepression ScaleDepression groupBrain disordersHealth AgingLongitudinal associationsSymptomsAgeRegression models
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
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 ResearchConceptsYoung 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
Evidence of accelerated aging among African Americans and its implications for mortality
Levine ME, Crimmins EM. Evidence of accelerated aging among African Americans and its implications for mortality. Social Science & Medicine 2014, 118: 27-32. PMID: 25086423, PMCID: PMC4197001, DOI: 10.1016/j.socscimed.2014.07.022.Peer-Reviewed Original ResearchConceptsBiological ageNutrition Examination SurveyThird National HealthHigher biological ageMajor age-related diseasesChronological ageCancer mortalityExamination SurveyAge-related diseasesNational HealthEarly deathAge 60Age 30MortalityAge accountRacial disparitiesAgePremature declineWhite participantsAfrican AmericansCurrent studyMortality selectionHealthWhitesAging process
2012
Modeling the Rate of Senescence: Can Estimated Biological Age Predict Mortality More Accurately Than Chronological Age?
Levine ME. Modeling the Rate of Senescence: Can Estimated Biological Age Predict Mortality More Accurately Than Chronological Age? The Journals Of Gerontology Series A 2012, 68: 667-674. PMID: 23213031, PMCID: PMC3660119, DOI: 10.1093/gerona/gls233.Peer-Reviewed Original Research