2021
Associations of Age, Sex, Race/Ethnicity, and Education With 13 Epigenetic Clocks in a Nationally Representative U.S. Sample: The Health and Retirement Study
Crimmins EM, Thyagarajan B, Levine ME, Weir DR, Faul J. Associations of Age, Sex, Race/Ethnicity, and Education With 13 Epigenetic Clocks in a Nationally Representative U.S. Sample: The Health and Retirement Study. The Journals Of Gerontology Series A 2021, 76: 1117-1123. PMID: 33453106, PMCID: PMC8140049, DOI: 10.1093/gerona/glab016.Peer-Reviewed Original Research
2018
Is 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
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 carcinogensA Genetic Network Associated With Stress Resistance, Longevity, and Cancer in Humans
Levine ME, Crimmins EM. A Genetic Network Associated With Stress Resistance, Longevity, and Cancer in Humans. The Journals Of Gerontology Series A 2015, 71: 703-712. PMID: 26355015, PMCID: PMC4888382, DOI: 10.1093/gerona/glv141.Peer-Reviewed Original ResearchConceptsFunctional interaction networkSingle nucleotide polymorphismsStress resistanceNucleotide polymorphismsInteraction networksGenome-wide association studiesPathway analysisAssociation studiesPolygenic risk scoresNetworks AssociatedBiological networksHuman longevityUnique phenotypeHuman agingGenesPolymorphismLongevityPhenotypeInnate resilienceRisk scoreAge 52Threefold increasePathwayWeighted polygenic risk scoreResistanceChildhood and later life stressors and increased inflammatory gene expression at older ages
Levine ME, Cole SW, Weir DR, Crimmins EM. Childhood and later life stressors and increased inflammatory gene expression at older ages. Social Science & Medicine 2015, 130: 16-22. PMID: 25658624, PMCID: PMC4394113, DOI: 10.1016/j.socscimed.2015.01.030.Peer-Reviewed Original ResearchConceptsChildhood traumaLow SESPro-inflammatory gene expressionProinflammatory gene expression levelsEarly lifeLater lifeLife health outcomesInflammatory gene expressionLater-life health outcomesHigh riskInflammatory genesAdulthood adversityChildhood healthHealth outcomesAdverse experiencesAdult traumaOlder ageTraumaLate-life stressorsGene expressionElevated levelsFuture healthComposite scoreLife stressorsChildhood
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