2019
Changing Disease Prevalence, Incidence, and Mortality Among Older Cohorts: The Health and Retirement Study
Crimmins EM, Zhang YS, Kim JK, Levine ME. Changing Disease Prevalence, Incidence, and Mortality Among Older Cohorts: The Health and Retirement Study. The Journals Of Gerontology Series A 2019, 74: s21-s26. PMID: 31724057, PMCID: PMC6853787, DOI: 10.1093/gerona/glz075.Peer-Reviewed Original ResearchConceptsMyocardial infarctionHeart diseaseOlder cohortDisease prevalencePopulation healthPrevalence of cancerPrevalence of peopleYounger cohortsRetirement StudyStart of observationCardiovascular conditionsAge 70CohortOlder personsInfarctionPrevalenceIncidenceDeath rateStrokeDiseaseImportant signCancerMortalityHealthDiabetes
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 individuals
2017
Eleven Telomere, Epigenetic Clock, and Biomarker-Composite Quantifications of Biological Aging: Do They Measure the Same Thing?
Belsky DW, Moffitt TE, Cohen AA, Corcoran DL, Levine ME, Prinz JA, Schaefer J, Sugden K, Williams B, Poulton R, Caspi A. Eleven Telomere, Epigenetic Clock, and Biomarker-Composite Quantifications of Biological Aging: Do They Measure the Same Thing? American Journal Of Epidemiology 2017, 187: 1220-1230. PMID: 29149257, PMCID: PMC6248475, DOI: 10.1093/aje/kwx346.Peer-Reviewed Original ResearchMeSH KeywordsAgingBiological ClocksBiomarkersCohort StudiesFemaleHumansMaleMiddle AgedTelomere HomeostasisEpigenetic clock analysis of diet, exercise, education, and lifestyle factors
Quach A, Levine ME, Tanaka T, Lu AT, Chen BH, Ferrucci L, Ritz B, Bandinelli S, Neuhouser ML, Beasley JM, Snetselaar L, Wallace RB, Tsao PS, Absher D, Assimes TL, Stewart JD, Li Y, Hou L, Baccarelli AA, Whitsel EA, Horvath S. Epigenetic clock analysis of diet, exercise, education, and lifestyle factors. Aging 2017, 9: 419-437. PMID: 28198702, PMCID: PMC5361673, DOI: 10.18632/aging.101168.Peer-Reviewed Original ResearchConceptsIntrinsic epigenetic age accelerationExtrinsic epigenetic age accelerationModerate alcohol consumptionMetabolic syndromeLifestyle factorsAlcohol consumptionEpigenetic age accelerationHealth initiativesItalian cohort studyPostmenopausal female participantsFirst-line medicationWomen's Health InitiativeBlood carotenoid levelsType 2 diabetesAge accelerationAge-related functional declineHealth-related outcomesEpigenetic clock analysisFemale participantsIndicator of fruitCause mortalityCohort studyPoultry intakeChronic conditionsFish intake
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 carcinogensEarly-Life Intelligence Predicts Midlife Biological Age
Schaefer JD, Caspi A, Belsky DW, Harrington H, Houts R, Israel S, Levine ME, Sugden K, Williams B, Poulton R, Moffitt TE. Early-Life Intelligence Predicts Midlife Biological Age. The Journals Of Gerontology Series B 2015, 71: 968-977. PMID: 26014827, PMCID: PMC5067943, DOI: 10.1093/geronb/gbv035.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgingBiomarkersChildChild, PreschoolCohort StudiesFemaleHuman DevelopmentHumansIntelligenceMaleNew ZealandTelomereConceptsBiological ageEarly-life intelligencePopulation-representative birth cohortNutrition Examination SurveyRates of morbidityMost age-related diseasesAdvanced biological ageHeart ageExamination SurveyAge-related diseasesNational HealthChildhood healthBirth cohortParental socioeconomic statusStudy membersDunedin StudySocioeconomic statusMultiple causesTelomere lengthSignificant predictorsAgeEarly childhoodMortalityMidlifeChildhood
2012
Be Well
Kogan AC, Gonzalez J, Hart B, Halloran S, Thomason B, Levine M, Enguidanos S. Be Well. Journal Of Applied Gerontology 2012, 32: 889-901. PMID: 25474803, PMCID: PMC5923899, DOI: 10.1177/0733464812440043.Peer-Reviewed Original ResearchConceptsCommunity-based senior centersMore emergency department visitsOlder adultsCommunity-dwelling older adultsEmergency department visitsLow-impact exerciseMultiple chronic conditionsNutritional education interventionDepartment visitsHospital admissionNutritional counselingChronic conditionsWeekly exerciseWeight managementCohort designPhysical activityHigh riskEducation interventionWeight lossGood nutritionFitness programPerson interviewsSenior centersDepressionFitness tests