2024
Fasting-mimicking diet causes hepatic and blood markers changes indicating reduced biological age and disease risk
Brandhorst S, Levine M, Wei M, Shelehchi M, Morgan T, Nayak K, Dorff T, Hong K, Crimmins E, Cohen P, Longo V. Fasting-mimicking diet causes hepatic and blood markers changes indicating reduced biological age and disease risk. Nature Communications 2024, 15: 1309. PMID: 38378685, PMCID: PMC10879164, DOI: 10.1038/s41467-024-45260-9.Peer-Reviewed Original ResearchConceptsMultiple cardiometabolic risk factorsAssociated with reduced insulin resistanceCardiometabolic risk factorsFasting-mimicking dietImmune system agingRandomized clinical trialsAnalysis of blood samplesAutoimmune cellsBiological ageClinical trialsReduce inflammationMarker changesRisk factorsHepatic fatInsulin resistanceAdult study participantsBlood samplesNormal cellsWeight lossReducing biological ageBiomarker of biological agingDamaged cellsStudy participantsDisease riskAge
2022
Life course traumas and cardiovascular disease—the mediating role of accelerated aging
Cao X, Zhang J, Ma C, Li X, Kuo C, Levine ME, Hu G, Allore H, Chen X, Wu X, Liu Z. Life course traumas and cardiovascular disease—the mediating role of accelerated aging. Annals Of The New York Academy Of Sciences 2022, 1515: 208-218. PMID: 35725988, PMCID: PMC10145586, DOI: 10.1111/nyas.14843.Peer-Reviewed Original Research
2021
The Socioeconomic Gradient in Epigenetic Ageing Clocks: Evidence from the Multi-Ethnic Study of Atherosclerosis and the Health and Retirement Study
Schmitz LL, Zhao W, Ratliff SM, Goodwin J, Miao J, Lu Q, Guo X, Taylor KD, Ding J, Liu Y, Levine M, Smith JA. The Socioeconomic Gradient in Epigenetic Ageing Clocks: Evidence from the Multi-Ethnic Study of Atherosclerosis and the Health and Retirement Study. Epigenetics 2021, 17: 589-611. PMID: 34227900, PMCID: PMC9235889, DOI: 10.1080/15592294.2021.1939479.Peer-Reviewed Original ResearchConceptsMulti-Ethnic StudySocioeconomic statusSocioeconomic gradientFaster biological agingEpigenetic agingBiological agingRetirement StudyAlcohol consumptionHealth behaviorsSignificant associationDisease riskSES gradientOlder adultsGenetic riskPolygenic riskEpigenetic clocksAtherosclerosisSES measuresAssociationInconsistent resultsRobust associationRiskMultiple tissues
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
Genetic architecture of epigenetic and neuronal ageing rates in human brain regions
Lu AT, Hannon E, Levine ME, Crimmins EM, Lunnon K, Mill J, Geschwind DH, Horvath S. Genetic architecture of epigenetic and neuronal ageing rates in human brain regions. Nature Communications 2017, 8: 15353. PMID: 28516910, PMCID: PMC5454371, DOI: 10.1038/ncomms15353.Peer-Reviewed Original ResearchMeSH KeywordsAdolescentAdultAgedAged, 80 and overAgingBrainBrain MappingCalcium-Binding ProteinsChildChild, PreschoolCognitive DysfunctionDNA MethylationEpigenesis, GeneticFemaleGenome, HumanGenome-Wide Association StudyHumansInfantMaleMiddle AgedNerve Tissue ProteinsNeurodegenerative DiseasesNeuronsQuantitative Trait LociConceptsGenome-wide association studiesCis-expression quantitative trait lociGenome-wide significant lociProportion of neuronsQuantitative trait lociEpigenetic aging ratesDNA methylation-based biomarkersEpigenetic agingMethylation-based biomarkersGenetic architectureTrait lociSignificant lociAssociation studiesBrain regionsAge-related macular degenerationType 2 diabetesAging rateGenesLociHuman brain regionsUlcerative colitisWaist circumferenceMacular degenerationParkinson's diseaseBrain samples
2016
Menopause accelerates biological aging
Levine ME, Lu AT, Chen BH, Hernandez DG, Singleton AB, Ferrucci L, Bandinelli S, Salfati E, Manson JE, Quach A, Kusters CD, Kuh D, Wong A, Teschendorff AE, Widschwendter M, Ritz BR, Absher D, Assimes TL, Horvath S. Menopause accelerates biological aging. Proceedings Of The National Academy Of Sciences Of The United States Of America 2016, 113: 9327-9332. PMID: 27457926, PMCID: PMC4995944, DOI: 10.1073/pnas.1604558113.Peer-Reviewed Original ResearchConceptsEpigenetic age accelerationBilateral oophorectomyBuccal epitheliumAge accelerationHealth initiativesMedical Research Council National SurveyMenopausal hormone therapyWomen's Health InitiativeEpigenetic clock analysisHigher epigenetic ageMendelian randomization approachMendelian randomization analysisEpigenetic ageHormone therapyEarly menopauseParkinson's diseaseMenopauseReproductive agingSignificant associationLarger studyBloodRandomization analysisDiseaseAgeEpithelium
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 biomarkersTherapyEarly-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 ResearchConceptsBiological 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
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 IIIEvidence 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
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 variablesHealthModeling 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 ResearchThe 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