2024
Residential exposure associations with ALS risk, survival, and phenotype: a Michigan-based case-control study
Goutman S, Boss J, Jang D, Piecuch C, Farid H, Batra M, Mukherjee B, Feldman E, Batterman S. Residential exposure associations with ALS risk, survival, and phenotype: a Michigan-based case-control study. Amyotrophic Lateral Sclerosis And Frontotemporal Degeneration 2024, 25: 543-553. PMID: 38557405, PMCID: PMC11269018, DOI: 10.1080/21678421.2024.2336110.Peer-Reviewed Original ResearchAmyotrophic lateral sclerosis riskAssociated with ALS riskALS riskCase-only analysisResidential settingsControl participantsCox proportional hazards modelsLogistic regression modelsCase-control studyMultinomial logistic regressionMultiple testing correctionProportional hazards modelLatent profile analysisResidential exposureExposure variablesPolytomous outcomesExposure associationsDecrease disease burdenALS susceptibilityLogistic regressionDisease burdenTesting correctionHazards modelRisk factorsRegression models
2023
Cardiometabolic disease and obesity patterns differentially predict acute kidney injury after total joint replacement: a retrospective analysis
Leis A, Mathis M, Kheterpal S, Zawistowski M, Mukherjee B, Pace N, O'Reilly-Shah V, Smith J, Karvonen-Gutierrez C. Cardiometabolic disease and obesity patterns differentially predict acute kidney injury after total joint replacement: a retrospective analysis. British Journal Of Anaesthesia 2023, 131: 37-46. PMID: 37188560, PMCID: PMC10308436, DOI: 10.1016/j.bja.2023.04.001.Peer-Reviewed Original ResearchConceptsOdds of acute kidney injuryAcute kidney injuryCardiometabolic diseasesNon-Hispanic blacksGroup of hospitalsKidney injuryCardiometabolic patternTotal joint arthroplasty complicationsObesity statusObesity patternsIncreased oddsDisease co-occurrenceIncreased odds of AKIRetrospective analysisRisk of postoperative acute kidney injuryLatent class analysisLatent classesPostoperative acute kidney injuryRisk of acute kidney injuryRisk factorsDifferential riskTotal joint replacementMetabolic syndromeObesityPostoperative AKI risk
2022
ExPRSweb: An online repository with polygenic risk scores for common health-related exposures
Ma Y, Patil S, Zhou X, Mukherjee B, Fritsche L. ExPRSweb: An online repository with polygenic risk scores for common health-related exposures. American Journal Of Human Genetics 2022, 109: 1742-1760. PMID: 36152628, PMCID: PMC9606385, DOI: 10.1016/j.ajhg.2022.09.001.Peer-Reviewed Original ResearchConceptsPolygenic risk scoresChronic conditionsPhenome-wide association studyMichigan Genomics InitiativeRisk scoreAssociation studiesHealth-related exposuresGenome-wide association studiesUK BiobankGenetic risk factorsPRS methodsFollow-up studyRisk factorsComplex traitsGenome InitiativeGenetic modifiersBiobankInfluence of exposureEnvironmental variablesScoresLipid levelsExpRLifestyleSmokingOnline repositoryDNA Methylation Mediates the Association Between Individual and Neighborhood Social Disadvantage and Cardiovascular Risk Factors
Wang Y, Zhao W, Ammous F, Song Y, Du J, Shang L, Ratliff S, Moore K, Kelly K, Needham B, Roux A, Liu Y, Butler K, Kardia S, Mukherjee B, Zhou X, Smith J. DNA Methylation Mediates the Association Between Individual and Neighborhood Social Disadvantage and Cardiovascular Risk Factors. Frontiers In Cardiovascular Medicine 2022, 9: 848768. PMID: 35665255, PMCID: PMC9162507, DOI: 10.3389/fcvm.2022.848768.Peer-Reviewed Original ResearchBody mass indexCardiovascular risk factorsNeighborhood socioeconomic disadvantageMulti-Ethnic Study of AtherosclerosisNeighborhood-level disadvantagePoor cardiovascular healthSocioeconomic statusSocioeconomic disadvantageRisk factorsCardiovascular healthAssociated with poor cardiovascular healthSocial disadvantageAdult socioeconomic statusBody mass index adjustmentAssociations of individual-HDL-CPrincipal components of ancestryGenetic principal componentsMeasures of obesityLow socioeconomic statusPopulation-based cohortMulti-Ethnic StudyStudy of AtherosclerosisHDL-C associationHigh-density lipoprotein cholesterolIntegrating 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
On cross-ancestry cancer polygenic risk scores
Fritsche L, Ma Y, Zhang D, Salvatore M, Lee S, Zhou X, Mukherjee B. On cross-ancestry cancer polygenic risk scores. PLOS Genetics 2021, 17: e1009670. PMID: 34529658, PMCID: PMC8445431, DOI: 10.1371/journal.pgen.1009670.Peer-Reviewed Original ResearchConceptsPolygenic risk scoresGenome-wide association studiesProstate cancer polygenic risk scoresPolygenic risk score distributionRecruitment of diverse participantsAncestry groupsPolygenic risk score methodsRisk scoreNon-genetic risk factorsElectronic health recordsBreast cancer casesHealth recordsUK BiobankGWAS effortsDisease risk assessmentCancer casesAssociation studiesGenetic dataEuropean ancestryPersonalized risk stratificationSummary statisticsRisk factorsAncestryDiverse participantsField of cancerChanges in COVID-19-related outcomes, potential risk factors and disparities over time
Yu Y, Gu T, Valley T, Mukherjee B, Fritsche L. Changes in COVID-19-related outcomes, potential risk factors and disparities over time. Epidemiology And Infection 2021, 149: e192. PMCID: PMC8376857, DOI: 10.1017/s0950268821001898.Peer-Reviewed Original ResearchIntensive care unit admission ratePotential risk factorsHospitalisation ratesAdmission ratesIntensive care unitCOVID-19-positive cohortCOVID-19-related hospitalisationMichigan MedicineResidential-level socioeconomic characteristicsOdds ratioRisk factorsTime-stratified analysisT1 to T3Impact of potential risk factorsInvestigate temporal trendsCOVID-19-related outcomesRetrospective cohort studySocioeconomic statusRacial disparitiesCalendar timeCohort studySocioeconomic characteristicsHospitalisationBlack patientsComorbid conditions
2020
An efficient and computationally robust statistical method for analyzing case-control mother–offspring pair genetic association studies
Zhang H, Mukherjee B, Arthur V, Hu G, Hochner H, Chen J. An efficient and computationally robust statistical method for analyzing case-control mother–offspring pair genetic association studies. The Annals Of Applied Statistics 2020, 14: 560-584. DOI: 10.1214/19-aoas1298.Peer-Reviewed Original ResearchEnvironmental risk factorsRisk factorsMaternal environmental risk factorsOffspring genetic effectsPerinatal environmental risk factorsGenetic association studiesFinite sample performancePregnancy healthGenetic risk factorsAssessment of pre-Extensive simulation studyGestational diabetes mellitusIncreased statistical efficiencyLogistic regressionAssociation studiesMaternal genotypeSample performanceMendelian transmissionProfile likelihoodRegression modelsOffspring genotypesEarly-lifeInference proceduresLagrange multiplier methodLikelihood methodEstrogen Plus Progestin Hormone Therapy and Ovarian Cancer: A Complicated Relationship Explored.
Lee A, Wu A, Wiensch A, Mukherjee B, Terry K, Harris H, Carney M, Jensen A, Cramer D, Berchuck A, Doherty J, Modugno F, Goodman M, Alimujiang A, Rossing M, Cushing-Haugen K, Bandera E, Thompson P, Kjaer S, Hogdall E, Webb P, Huntsman D, Moysich K, Lurie G, Ness R, Stram D, Roman L, Pike M, Pearce C. Estrogen Plus Progestin Hormone Therapy and Ovarian Cancer: A Complicated Relationship Explored. Epidemiology 2020, 31: 402-408. PMID: 32028322, PMCID: PMC7584395, DOI: 10.1097/ede.0000000000001175.Peer-Reviewed Original ResearchConceptsRisk of ovarian cancerEstrogen-progestin combined therapyEstrogen-alone therapyAssociated with increased riskOvarian cancerCombination therapyRisk of ovarian cancer overallAssociated with increased risk of ovarian cancerOvarian cancer risk factorsPopulation-based case-control studyOvarian Cancer Association ConsortiumMenopausal hormone therapy useIncreased risk of endometrial cancerOvarian cancer overallRisk of endometrial cancerCancer risk factorsHistotypes of ovarian cancerRisk factorsProgestin hormone therapyMucinous ovarian cancerOvarian cancer casesIn-person interviewsHormone therapy useOvarian cancer histotypesCase-control study
2019
Does Information on Blood Heavy Metals Improve Cardiovascular Mortality Prediction?
Wang X, Mukherjee B, Park S. Does Information on Blood Heavy Metals Improve Cardiovascular Mortality Prediction? Journal Of The American Heart Association 2019, 8: e013571. PMID: 31631727, PMCID: PMC6898859, DOI: 10.1161/jaha.119.013571.Peer-Reviewed Original ResearchConceptsCardiovascular diseaseNational Health and Nutrition Examination SurveyHealth and Nutrition Examination SurveyRisk factorsStudy sampleCardiovascular disease risk factorsCardiovascular disease mortalityCardiovascular disease risk assessmentImprove CVD risk predictionC-statisticNutrition Examination SurveyCardiovascular mortality predictionCVD risk predictionCox modelBlood markersExamination SurveyPrecision healthRisk scorePairwise interaction termsBlood metalsIntegrated discrimination improvementRisk predictionReclassification improvementMortality predictionInteraction termsA comprehensive gene–environment interaction analysis in Ovarian Cancer using genome‐wide significant common variants
Kim S, Wang M, Tyrer J, Jensen A, Wiensch A, Liu G, Lee A, Ness R, Salvatore M, Tworoger S, Whittemore A, Anton‐Culver H, Sieh W, Olson S, Berchuck A, Goode E, Goodman M, Doherty J, Chenevix‐Trench G, Rossing M, Webb P, Giles G, Terry K, Ziogas A, Fortner R, Menon U, Gayther S, Wu A, Song H, Brooks‐Wilson A, Bandera E, Cook L, Cramer D, Milne R, Winham S, Kjaer S, Modugno F, Thompson P, Chang‐Claude J, Harris H, Schildkraut J, Le N, Wentzensen N, Trabert B, Høgdall E, Huntsman D, Pike M, Pharoah P, Pearce C, Mukherjee B. A comprehensive gene–environment interaction analysis in Ovarian Cancer using genome‐wide significant common variants. International Journal Of Cancer 2019, 144: 2192-2205. PMID: 30499236, PMCID: PMC6399057, DOI: 10.1002/ijc.32029.Peer-Reviewed Original ResearchConceptsOral contraceptive pill useExcess risk due to additive interactionOvarian cancer risk factorsOral contraceptive pillsGene-environment interaction analysisCancer risk factorsGene-environment analysisOvarian cancer casesOCP useCase-control studyGenome-wide association analysisAdditive scaleCancer casesOvarian cancerOdds ratioCommon variantsDuration of OCP useRisk allelesRisk factorsGenetic variantsAdditive interactionAssociation analysisWomenFollow-upC allele
2018
Foetal ultrasound measurement imputations based on growth curves versus multiple imputation chained equation (MICE)
Ferguson K, Yu Y, Cantonwine D, McElrath T, Meeker J, Mukherjee B. Foetal ultrasound measurement imputations based on growth curves versus multiple imputation chained equation (MICE). Paediatric And Perinatal Epidemiology 2018, 32: 469-473. PMID: 30016545, PMCID: PMC6939297, DOI: 10.1111/ppe.12486.Peer-Reviewed Original ResearchConceptsLinear mixed modelsComplete-case analysisMultiple imputationEpidemiological studies of risk factorsImputed datasetsComplete-caseDemographic factorsStudy of risk factorsLIFECODES birth cohortUltrasound measurementsCalculate associationsBirth cohortCross-sectionEpidemiological studiesRisk factorsStudy visitsLongitudinal analysisParametric linear mixed modelImputationMissing dataMixed modelsLongitudinal measurementsSample sizeCovariate dataGrowth restrictionRisk Factors During Pregnancy and Early Childhood in Rural West Bengal, India: A Feasibility Study Implemented via Trained Community Health Workers Using Mobile Data Collection Devices
Wagner A, Xia L, Pandey P, Datta S, Chattopadhyay S, Mazumder T, Santra S, Nandi U, Pal J, Joshi S, Mukherjee B. Risk Factors During Pregnancy and Early Childhood in Rural West Bengal, India: A Feasibility Study Implemented via Trained Community Health Workers Using Mobile Data Collection Devices. Maternal And Child Health Journal 2018, 22: 1286-1296. PMID: 29500782, DOI: 10.1007/s10995-018-2509-y.Peer-Reviewed Original ResearchMeSH KeywordsAdultChildChild, PreschoolCommunity Health WorkersCross-Sectional StudiesFeasibility StudiesFemaleHealth Knowledge, Attitudes, PracticeHealth Services AccessibilityHumansIndiaInfantPregnancyPregnant WomenReferral and ConsultationRural Health ServicesRural PopulationSmartphoneYoung AdultConceptsCommunity health workersHealth workersFeasibility of community health workersRisk factorsPregnant womenTrained community health workersPrevalence of risk factorsRural communitiesHealth care servicesCross-sectional studyCare servicesBlood pressureAbnormal blood pressureAnthropometric measurementsYoung childrenAbnormal anthropometric measurementsVulnerable populationsRural West BengalStudy implementationHealth concernLower scoresStages QuestionnaireLower blood pressureWomenHealth
2017
Explaining Disparities in Ovarian Cancer Incidence Rates between Women of African and European Ancestry: The Role of Genetic Factors
Mullins M, Mukherjee B, Wu A, Pike M, Pharoah P, Berchuck A, Pearce C. Explaining Disparities in Ovarian Cancer Incidence Rates between Women of African and European Ancestry: The Role of Genetic Factors. Cancer Epidemiology Biomarkers & Prevention 2017, 26: 433-434. DOI: 10.1158/1055-9965.epi-17-0030.Peer-Reviewed Original ResearchGenetic risk scoreOvarian cancer incidence ratesNon-Hispanic White (NHWPopulation attributable risk percentCollaborative Oncological Gene-environment StudyCancer incidence ratesNon-genetic risk factorsIncidence rateAfrican AmericansAssociated with ovarian cancer riskOvarian Cancer Association ConsortiumOophorectomy ratesRisk of ovarian cancerAncestry groupsOvarian cancer riskAttributable risk percentNon-genetic riskGene-environment studiesOvarian cancerSingle nucleotide polymorphismsRisk factorsConditional logistic regressionGRS quintileRisk percentLowest quintile
2016
Association of Environmental Toxins With Amyotrophic Lateral Sclerosis
Su F, Goutman S, Chernyak S, Mukherjee B, Callaghan B, Batterman S, Feldman E. Association of Environmental Toxins With Amyotrophic Lateral Sclerosis. JAMA Neurology 2016, 73: 803-11. PMID: 27159543, PMCID: PMC5032145, DOI: 10.1001/jamaneurol.2016.0594.Peer-Reviewed Original ResearchMeSH KeywordsAgedAmyotrophic Lateral SclerosisCase-Control StudiesEnvironmental ExposureEnvironmental PollutantsFemaleGas Chromatography-Mass SpectrometryHumansMaleMichiganMiddle AgedMultivariate AnalysisOccupational ExposureOdds RatioOutcome Assessment, Health CareRetrospective StudiesRisk FactorsSurveys and QuestionnairesConceptsBrominated flame retardantsPersistent environmental pollutantsResidential exposureOrganochlorine pesticidesPolychlorinated biphenylsOdds of ALSAssociation of occupational exposureDisease risk factorsExposure time windowsEnvironmental pollutionRisk factorsModifiable risk factorsMultivariate modelOccupational exposureLogistic regression modelsCase-control studySurvey dataFamily history of amyotrophic lateral sclerosisExposure windowsIncreased oddsFamily historyAssociated with amyotrophic lateral sclerosisHistory of amyotrophic lateral sclerosisRegression modelsMilitary serviceTests for Gene-Environment Interactions and Joint Effects With Exposure Misclassification
Boonstra P, Mukherjee B, Gruber S, Ahn J, Schmit S, Chatterjee N. Tests for Gene-Environment Interactions and Joint Effects With Exposure Misclassification. American Journal Of Epidemiology 2016, 183: 237-247. PMID: 26755675, PMCID: PMC4724093, DOI: 10.1093/aje/kwv198.Peer-Reviewed Original ResearchConceptsG-E interactionsPresence of exposure misclassificationExposure misclassificationImpact of exposure misclassificationGene-environment (G-EGene-environment interactionsGenome-wide levelGenome-wide searchGenome-wide testingGenetic susceptibility lociJoint testDisease-gene relationshipsGene-environmentGenetic risk factorsType I error rateFamily-wise type I error rateSusceptibility lociG-EGenetic associationRisk factorsStatistical powerJoint effectsSimulation studyMisclassificationPublished simulation studies
2012
Environmental Cadmium and Lead Exposures and Hearing Loss in U.S. Adults: The National Health and Nutrition Examination Survey, 1999 to 2004
Choi Y, Hu H, Mukherjee B, Miller J, Park S. Environmental Cadmium and Lead Exposures and Hearing Loss in U.S. Adults: The National Health and Nutrition Examination Survey, 1999 to 2004. Environmental Health Perspectives 2012, 120: 1544-1550. PMID: 22851306, PMCID: PMC3556613, DOI: 10.1289/ehp.1104863.Peer-Reviewed Original ResearchConceptsNational Health and Nutrition Examination SurveyPure-tone averageHealth and Nutrition Examination SurveyHearing lossNutrition Examination SurveyExamination SurveyU.S. adultsRisk factorsGeneral populationU.S. general populationBlood cadmiumHearing thresholdsNonoccupational noiseHearing abilityLow-level exposure to cadmiumLead exposureYears of ageClinical risk factorsU.S. populationExamination componentsEnvironmental cadmiumEpidemiological studiesHearingAnalyzed dataAdultsEfficient designs of gene–environment interaction studies: implications of Hardy–Weinberg equilibrium and gene–environment independence
Chen J, Kang G, VanderWeele T, Zhang C, Mukherjee B. Efficient designs of gene–environment interaction studies: implications of Hardy–Weinberg equilibrium and gene–environment independence. Statistics In Medicine 2012, 31: 2516-2530. PMID: 22362617, PMCID: PMC3448495, DOI: 10.1002/sim.4460.Peer-Reviewed Original ResearchConceptsPresence of G-E interactionsG-E interactionsSubsample of casesGene-environmentHardy-Weinberg equilibriumG-E independenceGene-environment interaction studiesGene-environment independenceRandom subsampleGenetic susceptibility variantsCase-control sampleEnvironmental risk factorsSusceptibility variantsExternal control dataRisk factorsGenetic effectsWald statisticInteraction studiesSubsampleVariable EControl dataEnvironmental effectsIndependenceDataWaldWhere science meets policy: comparing longitudinal and cross-sectional designs to address diarrhoeal disease burden in the developing world
Markovitz A, Goldstick J, Levy K, Cevallos W, Mukherjee B, Trostle J, Eisenberg J. Where science meets policy: comparing longitudinal and cross-sectional designs to address diarrhoeal disease burden in the developing world. International Journal Of Epidemiology 2012, 41: 504-513. PMID: 22253314, PMCID: PMC3324455, DOI: 10.1093/ije/dyr194.Peer-Reviewed Original ResearchConceptsCross-sectional studyCross-sectional designEffect estimatesLongitudinal studyRisk factorsDisease risk factorsRisk factor distributionInforming public health policyPublic health policiesPublic health communityRisk factor effectsHousehold risk factorsDiarrhoeal disease burdenFactor effect estimatesHealth policyDiarrhoeal disease surveillanceEcuadorian villageNational policy decisionsHealth communityDisease burdenCross-sectionDisease surveillanceFactor distributionRiskGeographic regions
2007
Analysis of matched case–control data with multiple ordered disease states: possible choices and comparisons
Mukherjee B, Liu I, Sinha S. Analysis of matched case–control data with multiple ordered disease states: possible choices and comparisons. Statistics In Medicine 2007, 26: 3240-3257. PMID: 17206600, DOI: 10.1002/sim.2790.Peer-Reviewed Original ResearchConceptsConditional logistic regressionStratum-specific nuisance parametersCase-control dataAdjacent-category logit modelCase-control studyOrdered categorical dataConditional-likelihood approachLikelihood-based approachNuisance parametersProportional-odds modelCumulative logitsSimulation studyAnalyse such dataMantel-Haenszel approachCumulative logit modelNatural orderPotential risk factorsStages of cancerReference categoryCategorical dataLogistic regressionOrdinal natureEffect of potential risk factorsLow birthweightRisk factors