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
2019
The role of epigenetic aging in education and racial/ethnic mortality disparities among older U.S. Women
Liu Z, Chen BH, Assimes TL, Ferrucci L, Horvath S, Levine ME. The role of epigenetic aging in education and racial/ethnic mortality disparities among older U.S. Women. Psychoneuroendocrinology 2019, 104: 18-24. PMID: 30784901, PMCID: PMC6555423, DOI: 10.1016/j.psyneuen.2019.01.028.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
2017
A Weighted SNP Correlation Network Method for Estimating Polygenic Risk Scores
Levine ME, Langfelder P, Horvath S. A Weighted SNP Correlation Network Method for Estimating Polygenic Risk Scores. Methods In Molecular Biology 2017, 1613: 277-290. PMID: 28849564, PMCID: PMC5998804, DOI: 10.1007/978-1-4939-7027-8_10.Peer-Reviewed Original ResearchConceptsComplex traitsHuman heightCorrelation network analysisPolygenic score methodsPolygenic scoresSNP networkGWAS dataHub genesBiological insightsGenetic markersAllele countsGenetic pleiotropyTraitsGene scoreBiologyNetwork analysisPolygenic risk scoresNetwork-based methodPleiotropyGenesEnvironmental effectsSNPsPathwayLarge proportionDeeper understandingBiological 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 modelsContemporaneous Social Environment and the Architecture of Late-Life Gene Expression Profiles
Levine ME, Crimmins EM, Weir DR, Cole SW. Contemporaneous Social Environment and the Architecture of Late-Life Gene Expression Profiles. American Journal Of Epidemiology 2017, 186: 503-509. PMID: 28911009, PMCID: PMC5860329, DOI: 10.1093/aje/kwx147.Peer-Reviewed Original ResearchConceptsTranscriptional responseGene expression programsGene regulation pathwaysBioinformatics-based approachGene expression profilesTranscription factor activationGene expression levelsExpression programsLow socioeconomic statusRegulation pathwaysExpression profilesStress response systemNeuroendocrine signalingFactor activationAntiviral responseExpression levelsSocioeconomic statusPhysiological changesDistinct patternsChronic activationLong-term healthActivationGenesSignalingBiology
2016
An epigenetic clock analysis of race/ethnicity, sex, and coronary heart disease
Horvath S, Gurven M, Levine ME, Trumble BC, Kaplan H, Allayee H, Ritz BR, Chen B, Lu AT, Rickabaugh TM, Jamieson BD, Sun D, Li S, Chen W, Quintana-Murci L, Fagny M, Kobor MS, Tsao PS, Reiner AP, Edlefsen KL, Absher D, Assimes TL. An epigenetic clock analysis of race/ethnicity, sex, and coronary heart disease. Genome Biology 2016, 17: 171. PMID: 27511193, PMCID: PMC4980791, DOI: 10.1186/s13059-016-1030-0.Peer-Reviewed Original ResearchConceptsMortality rateRace/ethnicityRisk factorsTraditional cardio-metabolic risk factorsCardio-metabolic risk factorsCHD risk factorsCoronary heart diseaseCardio-metabolic diseasesEpigenetic clock analysisIntrinsic epigenetic age accelerationAfrican AmericansEpigenetic aging ratesLonger life expectancyCHD outcomesOlder African AmericansHeart diseaseHigh burdenEpigenetic age accelerationLower mortalityDifferent mortality ratesBrain samplesEthnic groupsBrain tissueBloodSocioeconomic differencesEpigenetic Aging and Immune Senescence in Women With Insomnia Symptoms: Findings From the Women’s Health Initiative Study
Carroll JE, Irwin MR, Levine M, Seeman TE, Absher D, Assimes T, Horvath S. Epigenetic Aging and Immune Senescence in Women With Insomnia Symptoms: Findings From the Women’s Health Initiative Study. Biological Psychiatry 2016, 81: 136-144. PMID: 27702440, PMCID: PMC5536960, DOI: 10.1016/j.biopsych.2016.07.008.Peer-Reviewed Original ResearchConceptsNaive T cellsWomen's Health Initiative studyDifferentiated T cellsT cellsInsomnia symptomsAge-related morbidityShort sleepSleep durationInitiative studyLarge population-based studyEpigenetic agePopulation-based studyLong sleep durationSymptoms of insomniaImmune senescenceImmune cellsLong sleepAdvanced epigenetic ageCell countSymptomsInfluence riskSleepCross-sectional dataAgeAge acceleration
2015
A 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 scoreResistance
2014
A comparison of methods for assessing mortality risk
Levine ME, Crimmins EM. A comparison of methods for assessing mortality risk. American Journal Of Human Biology 2014, 26: 768-776. PMID: 25088793, PMCID: PMC4286244, DOI: 10.1002/ajhb.22595.Peer-Reviewed Original ResearchConceptsFramingham risk scoreDisease-specific mortalityRisk scoreAllostatic loadBiological ageNutrition Examination Survey IIICox proportional hazards modelStratified age groupsStrong associationExamination Survey IIIProportional hazards modelParticipants ages 50CVD mortalityPerson yearsCancer mortalityNational HealthStudy populationMortality riskAge 50Hazards modelAge 30Age groupsMortalityAge rangeSurvey IIINot 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
2012
The role of physiological markers of health in the association between demographic factors and periodontal disease
Levine ME, Kim JK, Crimmins EM. The role of physiological markers of health in the association between demographic factors and periodontal disease. Journal Of Periodontal Research 2012, 48: 367-372. PMID: 23231345, PMCID: PMC3609891, DOI: 10.1111/jre.12016.Peer-Reviewed Original ResearchConceptsC-reactive proteinPeriodontal diseaseRace/ethnicitySocio-economic statusDemographic factorsOral health statusMultiple physiological systemsSystemic healthOral diseasesMeasures of SESHigh prevalencePhysiological measuresHealth statusSocio-demographic variablesLogistic regressionDiseaseCytomegalovirusElevated levelsAssociationEarly agingLow-income individualsPhysiological markersBiomarkersDemographic variablesHealthThe Impact of Insulin Resistance and Inflammation on the Association Between Sarcopenic Obesity and Physical Functioning
Levine ME, Crimmins EM. The Impact of Insulin Resistance and Inflammation on the Association Between Sarcopenic Obesity and Physical Functioning. Obesity 2012, 20: 2101-2106. PMID: 22310233, PMCID: PMC3527629, DOI: 10.1038/oby.2012.20.Peer-Reviewed Original ResearchConceptsDual-energy X-ray absorptiometryC-reactive proteinInsulin resistancePhysical functioningSarcopenic obesityPhysical functioning problemsMuscle massDifferent body composition phenotypesSerum C-reactive proteinAppendicular skeletal muscle massHomeostasis model assessmentNutrition Examination SurveyCross-sectional studyX-ray absorptiometryBody composition categoriesSkeletal muscle massBody composition phenotypesBody composition measurementsPoisson regression modelsRace/ethnicityNonsarcopenic obesitySarcopenic nonobeseSarcopenic nonobesitySarcopenic obeseSystemic inflammation