2025
Periods of susceptibility for associations between phthalate exposure and preterm birth: Results from a pooled analysis of 16 US cohorts
Friedman A, Welch B, Keil A, Bloom M, Braun J, Buckley J, Dabelea D, Factor-Litvak P, Meeker J, Michels K, Padmanabhan V, Starling A, Weinberg C, Aalborg J, Alshawabkeh A, Barrett E, Binder A, Bradman A, Bush N, Calafat A, Cantonwine D, Christenbury K, Cordero J, Engel S, Eskenazi B, Harley K, Hauser R, Herbstman J, Holland N, James-Todd T, Jukic A, Lanphear B, McElrath T, Messerlian C, Newman R, Nguyen R, O'Brien K, Rauh V, Redmon J, Rich D, Rosen E, Sathyanarayana S, Schmidt R, Sparks A, Swan S, Wang C, Watkins D, Weinberger B, Wenzel A, Wilcox A, Yolton K, Zhang Y, Zota A, Ferguson K. Periods of susceptibility for associations between phthalate exposure and preterm birth: Results from a pooled analysis of 16 US cohorts. Environment International 2025, 198: 109392. PMID: 40132438, PMCID: PMC12021553, DOI: 10.1016/j.envint.2025.109392.Peer-Reviewed Original ResearchUrinary phthalate metabolite concentrationsPreterm birthPhthalate metabolite concentrationsGeneralized estimating equationsPhthalate metabolitesOdds ratioMono (2-ethylhexyl) phthalateUS cohortAssociation of preterm birthPhthalate exposureAssociated with preterm birthAssociated with odds ratiosInterquartile range increaseMetabolite concentrationsIndividual-level dataLogistic regression modelsThird trimesterTrimester-specific associationsMechanism of actionPretermPeriod of susceptibilityTime of exposurePooled analysisTrimesterTrimester exposureObservational and Genetic Analyses of Traumatic Experiences and Endometriosis
Koller D, Løkhammer S, Goroshchuk O, Denner V, Stiltner B, Mitjans M, He J, Taylor H, Lawn R, Koenen K, Polimanti R. Observational and Genetic Analyses of Traumatic Experiences and Endometriosis. JAMA Psychiatry 2025, 82: 386-394. PMID: 39908042, PMCID: PMC11800128, DOI: 10.1001/jamapsychiatry.2024.4694.Peer-Reviewed Original ResearchPolygenic risk scoresLatent class analysisAssociated with increased oddsTraumatic experiencesImpact of childhoodIndividual-level dataCase-control studyStressful eventsGenetic predispositionTrauma-related outcomesUnaffected womenMain OutcomesFinnGen cohortRisk scoreAssociation of endometriosisPosttraumatic stress disorderTrauma eventsMeta-analysisSexual traumaAssociated with endometriosisContact traumaPotential associationStress disorderPsychological traumaPhenotypic associationsAssociations of Genetically Predicted NPR3 and NPR2 Perturbation and Preeclampsia Risk: A Two‐Sample Mendelian Randomization Analysis
de La Harpe R, Rogne T, Nyberg M, Cronjé H, Burgess S, Karhunen V, Gill D. Associations of Genetically Predicted NPR3 and NPR2 Perturbation and Preeclampsia Risk: A Two‐Sample Mendelian Randomization Analysis. International Journal Of Hypertension 2025, 2025: 9972031. PMID: 40406480, PMCID: PMC12097871, DOI: 10.1155/ijhy/9972031.Peer-Reviewed Original ResearchGenome-wide association studiesMendelian randomizationTwo-sample Mendelian randomization analysisRisk of preeclampsiaTwo-sample MR analysisGenetic association estimatesGenetic instrumental variablesPreeclampsia riskMendelian randomization analysisFemale participantsC-type natriuretic peptideIndividual-level dataEffects of C-type natriuretic peptideGenetic instrumentsMR analysisUK BiobankRandomization analysisAssociation estimatesMR paradigmAssociation studiesGenetic variantsPregnancy complicationsProtective effects of C-type natriuretic peptideTwo-sampleInstrumental variables
2024
Using Phosphatidylethanol as an Adjunct to Self-Reported Alcohol Use Improves Identification of Liver Fibrosis Risk
Murnane P, Afshar M, Chamie G, Cook R, Ferguson T, Haque L, Jacobson K, Justice A, Kim T, Khalili M, Krupitsky E, McGinnis K, Molina P, Muyindike W, Myers B, Richards V, So-Armah K, Stewart S, Sulkowski M, Tien P, Hahn J. Using Phosphatidylethanol as an Adjunct to Self-Reported Alcohol Use Improves Identification of Liver Fibrosis Risk. The American Journal Of Gastroenterology 2024, 120: 1567-1575. PMID: 39480054, PMCID: PMC12041314, DOI: 10.14309/ajg.0000000000003178.Peer-Reviewed Original ResearchLiver fibrosis riskAUDIT-CFIB-4Self-reported alcohol useOdds ratioAlcohol useFibrosis riskPooled individual-level dataLiver fibrosis preventionBody mass indexAssessment of alcohol useManagement of liver diseaseOne-unit differenceIndividual-level dataMedian ageAUDIT-C.Mass indexSuppression statusLiver diseaseFibrosis preventionBlood concentrationsAdjusted modelsLogistic regressionCategorical variablesHIVImproving prediction of linear regression models by integrating external information from heterogeneous populations: James–Stein estimators
Han P, Li H, Park S, Mukherjee B, Taylor J. Improving prediction of linear regression models by integrating external information from heterogeneous populations: James–Stein estimators. Biometrics 2024, 80: ujae072. PMID: 39101548, PMCID: PMC11299067, DOI: 10.1093/biomtc/ujae072.Peer-Reviewed Original ResearchConceptsJames-Stein estimatorLinear regression modelsIndividual-level dataComprehensive simulation studyRegression modelsNumerical performanceSimulation studyShrinkage methodCoefficient estimatesPredictive meanReduced modelStudy population heterogeneityInternal modelEstimationStudy populationBlood lead levelsInternational studiesCovariatesPatella bonePublished literatureLead levelsExternal studiesSummary informationPopulationSubsetsBayesian network-based Mendelian randomization for variant prioritization and phenotypic causal inference
Sun J, Zhou J, Gong Y, Pang C, Ma Y, Zhao J, Yu Z, Zhang Y. Bayesian network-based Mendelian randomization for variant prioritization and phenotypic causal inference. Human Genetics 2024, 143: 1081-1094. PMID: 38381161, DOI: 10.1007/s00439-024-02640-x.Peer-Reviewed Original ResearchMendelian randomizationGenetic instrumental variablesGenomic dataIndividual-level dataInstrumental variablesUK BiobankFalse-positive discoveriesGenetic structureVariant prioritizationEffect estimatesMR methodsGene interactionsGenetic variantsCausal inferencePsychiatric disordersStatistical powerBlood pressureBayesian frameworkInference frameworkInferenceBiobankEstimationCausal relationshipsPleiotropyGenesAPOE ε4 and Intracerebral Hemorrhage in Patients With Brain Arteriovenous Malformation
Renedo D, Rivier C, Koo A, Sujijantarat N, Clocchiatti-Tuozzo S, Wu K, Torres-Lopez V, Huo S, Gunel M, de Havenon A, Sheth K, Matouk C, Falcone G. APOE ε4 and Intracerebral Hemorrhage in Patients With Brain Arteriovenous Malformation. JAMA Network Open 2024, 7: e2355368. PMID: 38363572, PMCID: PMC10873768, DOI: 10.1001/jamanetworkopen.2023.55368.Peer-Reviewed Original ResearchConceptsApolipoprotein E e4Participants of European ancestryRisk of intracerebral hemorrhageHigh risk of intracerebral hemorrhageCross-sectional studyUK BiobankEuropean ancestryHigh riskUs Research ProgramUK Biobank participantsInternational Classification of DiseasesAssociated with higher risk of ICHCross-sectional study of patientsAPOE e4 statusClassification of DiseasesApolipoprotein ENinth Revision and Tenth RevisionAssociated with higher riskIndividual-level dataMultivariate logistic regressionIntracerebral hemorrhage riskBrain arteriovenous malformationsIntracerebral hemorrhageBiobank participantsTenth Revision
2023
Ethnic Differences in the Association Between Age at Natural Menopause and Risk of Type 2 Diabetes Among Postmenopausal Women: A Pooled Analysis of Individual Data From 13 Cohort Studies.
Chung H, Dobson A, Hayashi K, Hardy R, Kuh D, Anderson D, van der Schouw Y, Greenwood D, Cade J, Demakakos P, Brunner E, Eastwood S, Sandin S, Weiderpass E, Mishra G. Ethnic Differences in the Association Between Age at Natural Menopause and Risk of Type 2 Diabetes Among Postmenopausal Women: A Pooled Analysis of Individual Data From 13 Cohort Studies. Diabetes Care 2023, 46: 2024-2034. PMID: 37747341, PMCID: PMC10696407, DOI: 10.2337/dc23-1209.Peer-Reviewed Original ResearchConceptsIncreased risk of T2DPremature ovarian insufficiencyRisk of T2DType 2 diabetesPooled individual-level dataCohort studyHazard ratioIncident type 2 diabetesRisk of type 2 diabetesEthnic groupsNatural menopauseChinese womenEarly menopauseIncreased riskYears of follow-upIndividual-level dataBirth yearEducation levelInvestigate associationsRisk estimatesOvarian insufficiencyPostmenopausal womenEthnic differencesRisk factorsPooled analysisSociodemographic Differences in COVID-19 Pandemic Experiences Among Families in the United States
LeWinn K, Trasande L, Law A, Blackwell C, Bekelman T, Arizaga J, Sullivan A, Bastain T, Breton C, Karagas M, Elliott A, Karr C, Carroll K, Dunlop A, Croen L, Margolis A, Alshawabkeh A, Cordero J, Singh A, Seroogy C, Jackson D, Wood R, Hartert T, Kim Y, Duarte C, Schweitzer J, Lester B, McEvoy C, O’Connor T, Oken E, Bornkamp N, Brown E, Porucznik C, Ferrara A, Camargo C, Zhao Q, Ganiban J, Jacobson L, Smith P, Newby K, Jacobson L, Parker C, Gershon R, Cella D, Teitelbaum S, Stroustrup A, Lampland A, Hudak M, Washburn L, Canino G, Pastyrnak S, Neal C, Carter B, Helderman J, Simhan H, Kerver J, Barone C, Paneth N, Elliott M, Schantz S, Silver R, Wright R, Bosquet-Enlow M, Mason A, Tylavsky F, Zhao Q, Sathyanarayana S, Fussman C, Farzan S, Habre R, Tepper R, Gern J, Miller R, Nguyen R, Aschner J, Merhar S, Moore P, Pryhuber G, Smith L, Barrett E, Reynolds A, Gatzke-Kopp L, Swingler M, Mansbach J, Spergel J, Zoratti E, Bendixsen C, Bacharier L, O’Connor G, Kattan M, Rivera-Spoljaric K, Johnson C, Hertz-Picciotto I, Koinis Mitchell D, D’Sa V, Dabelea D, Deoni S, Hipwell A, Leve L, Weiss S, Lyall K, Volk H, Dager S, Schultz R, Obeid R, Rollins C, Msall M, O'Shea M, Vaidya R, Meeker J, Laham F, Wu S, Celedón J, Puls H, Teach S, Porter S, Waynik I, Iyer S, Samuels-Kalow M, Thompson A, Stevenson M, Bauer C, Inhofe N, Boos M, Macias C, Monk C, Posner J, Hershey G, Keenan K, Neiderhiser J, Litonjua A, Zeiger R, Bacharier L, Landa R, Ozonoff S, Schmidt R, Piven J, Bear K, Lenski M, Singh R, Frazier J, Gogcu S, Montgomery A, Kuban K, Douglass L, Jara H, Joseph R, Ruden D, Herbstman J, Woodruff T, Giardino A, Stanford J, Innocenti M, Conradt E, Huddleston K, Swan S. Sociodemographic Differences in COVID-19 Pandemic Experiences Among Families in the United States. JAMA Network Open 2023, 6: e2330495. PMID: 37610749, PMCID: PMC10448300, DOI: 10.1001/jamanetworkopen.2023.30495.Peer-Reviewed Original ResearchConceptsChild health outcomesCaregiver education levelLow socioeconomic statusPopulation-based studyChild's life stageCross-sectional analysisHigh school educationIndividual-level dataPandemic-related experiencesLogistic regression modelsRural caregiversCOVID-19 surveyUrban caregiversUS census divisionsHealth outcomesCOVID-19 pandemic experiencePublic health crisisHealth careSociodemographic differencesSocioeconomic statusMain OutcomesCaregiversYoung childrenUS populationEducation levelHeat-mortality relationship in North Carolina: Comparison using different exposure methods
Choi H, Bell M. Heat-mortality relationship in North Carolina: Comparison using different exposure methods. Journal Of Exposure Science & Environmental Epidemiology 2023, 33: 637-645. PMID: 37029251, PMCID: PMC10403356, DOI: 10.1038/s41370-023-00544-y.Peer-Reviewed Original ResearchConceptsDifferent exposure methodsMortality riskHeat-mortality relationshipLower odds ratioCase-crossover analysisBackgroundMany studiesOdds ratioExposure methodHealth policyMinimum mortality temperatureRiskExposureIndividual-level dataComparability of resultsIndividual deathNorth CarolinaHeat-mortality associationsMethodsWeA Synthetic Data Integration Framework to Leverage External Summary-Level Information from Heterogeneous Populations
Gu T, Taylor J, Mukherjee B. A Synthetic Data Integration Framework to Leverage External Summary-Level Information from Heterogeneous Populations. Biometrics 2023, 79: 3831-3845. PMID: 36876883, PMCID: PMC10480346, DOI: 10.1111/biom.13852.Peer-Reviewed Original ResearchConceptsCovariate effectsStatistical inferenceHeterogeneity of covariate effectsRegression coefficient estimatesSummary-level informationImprove statistical inferenceInternational studiesOutcome YCovariate informationData integration frameworkStatistical efficiencyCoefficient estimatesPartial informationExternal populationGeneral frameworkIndividual-level dataRisk prediction modelExternal modelPrediction problemInternational study populationMultiple imputationShort-term association between ambient air pollution and cardio-respiratory mortality in Rio de Janeiro, Brazil
Cortes T, Silveira I, de Oliveira B, Bell M, Junger W. Short-term association between ambient air pollution and cardio-respiratory mortality in Rio de Janeiro, Brazil. PLOS ONE 2023, 18: e0281499. PMID: 36795640, PMCID: PMC9934392, DOI: 10.1371/journal.pone.0281499.Peer-Reviewed Original ResearchConceptsAmbient air pollutionCardio-respiratory mortalityShort-term associationsAir pollutionRespiratory mortalityExposure to particulate matter <Individual-level mortality dataOdds ratioIndividual exposure to air pollutionExposure to air pollutionCase-crossover study designHealth risk estimatesConditional logistic regression modelsConfidence intervalsEvaluation of public healthParticulate matter <Inverse distance weighting methodExposure assessment methodsIndividual-level dataDistributed lag non-linear modelLogistic regression modelsDistance weighting methodPM10 exposurePollution exposureRio de Janeiro
2022
Rural-Urban Differences in Diabetes Care and Control in 42 Low- and Middle-Income Countries: A Cross-sectional Study of Nationally Representative Individual-Level Data.
Flood D, Geldsetzer P, Agoudavi K, Aryal K, Brant L, Brian G, Dorobantu M, Farzadfar F, Gheorghe-Fronea O, Gurung M, Guwatudde D, Houehanou C, Jorgensen J, Kondal D, Labadarios D, Marcus M, Mayige M, Moghimi M, Norov B, Perman G, Quesnel-Crooks S, Rashidi M, Moghaddam S, Seiglie J, Bahendeka S, Steinbrook E, Theilmann M, Ware L, Vollmer S, Atun R, Davies J, Ali M, Rohloff P, Manne-Goehler J. Rural-Urban Differences in Diabetes Care and Control in 42 Low- and Middle-Income Countries: A Cross-sectional Study of Nationally Representative Individual-Level Data. Diabetes Care 2022, 45: 1961-1970. PMID: 35771765, PMCID: PMC9472489, DOI: 10.2337/dc21-2342.Peer-Reviewed Original ResearchConceptsLower relative riskRelative riskDiabetes careMiddle-income countriesRural-urban differencesBlood pressure controlCardiovascular risk factorsControl of diabetesAge-adjusted differenceCross-sectional studyDiabetes performance measuresRepresentative health surveyRural populationPoisson regression modelsGlycemic controlCholesterol controlDiabetes prevalenceRisk factorsHealth SurveyEffective careProportion of individualsIndividual-level dataDiabetes diagnosisDiabetesPressure controlIntegrating information from existing risk prediction models with no model details
Han P, Taylor J, Mukherjee B. Integrating information from existing risk prediction models with no model details. Canadian Journal Of Statistics 2022, 51: 355-374. PMID: 37346757, PMCID: PMC10281716, DOI: 10.1002/cjs.11701.Peer-Reviewed Original Research
2021
A meta-inference framework to integrate multiple external models into a current study.
Gu T, Taylor J, Mukherjee B. A meta-inference framework to integrate multiple external models into a current study. Biostatistics 2021, 24: 406-424. PMID: 34269371, PMCID: PMC10102901, DOI: 10.1093/biostatistics/kxab017.Peer-Reviewed Original ResearchConceptsAccuracy of statistical inferenceEmpirical Bayes estimatorsSummary-level informationBias-variance trade-offRelevant external informationBayes estimatorsStatistical inferenceExternal informationExternal estimatesNaive analysisNaive combinationInternational dataWeight estimationExternal modelMeta-analysis frameworkIndividual-level dataEfficiency gainsEstimationInfluence of informationTrade-offsInformationFramework
2020
Global Dietary Intake in Relation to the EAT Lancet Commission’s Scientific Targets; Results from the Global Dietary Database 2015
Reedy J, Cudhea F, Miller V, Zhang J, Shi P, Puklin L, Coates J, Micha R, Mozaffarian D. Global Dietary Intake in Relation to the EAT Lancet Commission’s Scientific Targets; Results from the Global Dietary Database 2015. Current Developments In Nutrition 2020, 4: nzaa053_100. PMCID: PMC7258514, DOI: 10.1093/cdn/nzaa053_100.Peer-Reviewed Original ResearchGlobal Dietary DatabaseDietary intakeEAT-LancetPopulation subgroupsWhole grainsDietary databaseNon-starchy vegetablesDietary intake dataActual dietary intakeGlobal dietary patternsIndividual-level dataCountry-level covariatesRural adultsUrban/rural residenceEAT-Lancet CommissionDietary targetsStarchy vegetablesHealthy dietDietary patternsIntake dataDietary factorsEducation levelStratum-specificRed meatPlanetary healthGlobal Intake of Major Beverages in Adults by Country Wealth and Sociodemographic Characteristics: Analysis of the Global Dietary Database 2015
Castor L, Cudhea F, Shi P, Zhang J, Miller V, Reedy J, Puklin L, Karageorgou D, Webb P, Mozaffarian D, Micha R. Global Intake of Major Beverages in Adults by Country Wealth and Sociodemographic Characteristics: Analysis of the Global Dietary Database 2015. Current Developments In Nutrition 2020, 4: nzaa053_063. PMCID: PMC7258132, DOI: 10.1093/cdn/nzaa053_063.Peer-Reviewed Original ResearchSugar-sweetened beveragesIntake of sugar-sweetened beveragesLower-middle income countriesHigh-income countriesUrban-rural residenceLow-income countriesGlobal Dietary DatabaseHigher education levelEducation levelUrban residentsDietary databaseIndividual-level intakesUrban-rural differencesIndividual-level dataBeverage intakeSociodemographic characteristicsIntake dataCoffee intakeSubnational surveysTea intakeMean intakeIncome countriesAge groupsWealth categoriesResidents
2019
Natural History of Autosomal Recessive Stargardt Disease in Untreated Eyes A Systematic Review and Meta-analysis of Study- and Individual-Level Data
Shen LL, Sun M, Grossetta Nardini HK, Del Priore LV. Natural History of Autosomal Recessive Stargardt Disease in Untreated Eyes A Systematic Review and Meta-analysis of Study- and Individual-Level Data. Ophthalmology 2019, 126: 1288-1296. PMID: 31227323, DOI: 10.1016/j.ophtha.2019.05.015.Peer-Reviewed Original ResearchConceptsBaseline lesion sizeHorizontal translation factorAutosomal recessive Stargardt diseaseRecessive Stargardt diseaseLog-transformed areaLesion sizeUntreated eyesAtrophic lesionsStargardt diseaseSystematic reviewNatural historyOnset of symptomsNewcastle-Ottawa ScaleRisk of biasReliable outcome measuresIndividual-level dataAtrophy progressionFundus autofluorescenceInclusion criteriaOutcome measuresMeta-AnalysisLesion onsetProgression patternsLiterature databasesLesionsCost-effectiveness and Budgetary Impact of Hepatitis C Virus Testing, Treatment, and Linkage to Care in US Prisons
Assoumou S, Tasillo A, Vellozzi C, Yazdi G, Wang J, Nolen S, Hagan L, Thompson W, Randall L, Strick L, Salomon J, Linas B. Cost-effectiveness and Budgetary Impact of Hepatitis C Virus Testing, Treatment, and Linkage to Care in US Prisons. Clinical Infectious Diseases 2019, 70: 1388-1396. PMID: 31095676, PMCID: PMC7318776, DOI: 10.1093/cid/ciz383.Peer-Reviewed Original ResearchConceptsDepartment of CorrectionsQuality-adjusted life yearsHepatitis C virus testingHepatitis C virusIndividual-level dataCirrhosis casesDrug pricesUnited StatesCurrent drug pricesSustained virological responseUS prisonsPrison cohortIncremental cost-effectiveness ratioPrison entrantsBudgetary impactPrisonCost-effectiveness ratioVirological responseClinical outcomesC virusNo treatmentCost-effectiveAnnual additional costTreatment uptakeDrug costs
2018
Associations of obesity and circulating insulin and glucose with breast cancer risk: a Mendelian randomization analysis
Shu X, Wu L, Khankari N, Shu X, Wang T, Michailidou K, Bolla M, Wang Q, Dennis J, Milne R, Schmidt M, Pharoah P, Andrulis I, Hunter D, Simard J, Easton D, Zheng W, Alicia B, Anton-Culver H, Antonenkova N, Arndt V, Aronson K, Auer P, Barrdahl M, Baynes C, Freeman L, Beckmann M, Behrens S, Benitez J, Bermisheva M, Blomqvist C, Bogdanova N, Bojesen S, Brauch H, Brenner H, Brinton L, Broberg P, Brucker S, Brüning T, Burwinkel B, Cai Q, Caldés T, Canzian F, Carter B, Castelao J, Chang-Claude J, Chenevix-Trench G, Cheng T, Clarke C, Conroy D, Couch F, Cox D, Cox A, Cross S, Cunningham J, Czene K, Daly M, Doheny K, Dörk T, dos-Santos-Silva I, Dumont M, Dunning A, Dwek M, Earp H, Eccles D, Eliassen A, Engel C, Eriksson M, Evans D, Fachal L, Fasching P, Figueroa J, Fletcher O, Flyger H, Fritschi L, Gabrielson M, Gago-Dominguez M, Gapstur S, García-Closas M, Gaudet M, Ghoussaini M, Giles G, Goldberg M, Goldgar D, González-Neira A, Guénel P, Hahnen E, Haiman C, Håkansson N, Hall P, Hallberg E, Hamann U, Harrington P, He W, Hein A, Hicks B, Hillemanns P, Hogervorst F, Hollestelle A, Hoover R, Hopper J, Howell A, Huang G, Jakubowska A, Janni W, John E, Johnson N, Jones K, Jung A, Kaaks R, Kabisch M, Kerin M, Khusnutdinova E, Kitahara C, Kosma V, Koutros S, Kraft P, Kristensen V, Lambrechts D, Le Marchand L, Lindström S, Linet M, Lissowska J, Loibl S, Lubinski J, Luccarini C, Lux M, Maishman T, Kostovska I, Mannermaa A, Manoukian S, Manson J, Margolin S, Mavroudis D, Meijers-Heijboer H, Meindl A, Menon U, Meyer J, Mulligan A, Neuhausen S, Nevanlinna H, Neven P, Newman W, Nielsen S, Nordestgaard B, Olopade O, Olshan A, Olson J, Olsson H, Olswold C, Orr N, Perou C, Peto J, Plaseska-Karanfilska D, Prentice R, Presneau N, Pylkäs K, Rack B, Radice P, Rahman N, Rennert G, Rennert H, Romero A, Romm J, Saloustros E, Sandler D, Sawyer E, Schmutzler R, Schneeweiss A, Scott R, Scott C, Seal S, Seynaeve C, Smeets A, Southey M, Spinelli J, Stone J, Surowy H, Swerdlow A, Tamimi R, Tapper W, Taylor J, Terry M, Tessier D, Thöne K, Tollenaar R, Torres D, Troester M, Truong T, Untch M, Vachon C, Van Den Berg D, van den Ouweland A, van Veen E, Vincent D, Waisfisz Q, Weinberg C, Wendt C, Whittemore A, Wildiers H, Winqvist R, Wolk A, Xia L, Yang X, Ziogas A, Ziv E. Associations of obesity and circulating insulin and glucose with breast cancer risk: a Mendelian randomization analysis. International Journal Of Epidemiology 2018, 48: 795-806. PMID: 30277539, PMCID: PMC6734940, DOI: 10.1093/ije/dyy201.Peer-Reviewed Original ResearchConceptsBreast cancer riskAssociated with breast cancer riskBody mass indexMendelian randomization analysisCancer riskRandomization analysisAssociation of breast cancer riskInverse associationFamily history of breast cancerGenome-wide association study consortiaAssociated with risk of type 2 diabetesBreast Cancer Association ConsortiumHistory of breast cancerFasting insulinAetiology of breast cancerType 2 diabetes riskControls of European descentRisk of type 2 diabetesWaist-hip ratioAssociation of obesityCirculating fasting insulinAssociated with riskBreast cancerIndividual-level dataGenetic instruments
This site is protected by hCaptcha and its Privacy Policy and Terms of Service apply