2023
Uncovering associations between pre-existing conditions and COVID-19 Severity: A polygenic risk score approach across three large biobanks
Fritsche L, Nam K, Du J, Kundu R, Salvatore M, Shi X, Lee S, Burgess S, Mukherjee B. Uncovering associations between pre-existing conditions and COVID-19 Severity: A polygenic risk score approach across three large biobanks. PLOS Genetics 2023, 19: e1010907. PMID: 38113267, PMCID: PMC10763941, DOI: 10.1371/journal.pgen.1010907.Peer-Reviewed Original ResearchConceptsPolygenic risk scoresMichigan Genomics InitiativeUK BiobankPre-existing conditionsPhenome-wide association studyAssociation studiesCohort-specific analysesPolygenic risk score approachUK Biobank cohortMeta-analysisIncreased risk of hospitalizationGenome-wide association studiesBody mass indexRisk of hospitalizationIdentified novel associationsRisk score approachCOVID-19 outcome dataCOVID-19 hospitalizationCOVID-19Mass indexRisk scoreBiobankCardiovascular conditionsCOVID-19 severityIncreased risk
2022
Body mass index associates with amyotrophic lateral sclerosis survival and metabolomic profiles
Goutman S, Boss J, Iyer G, Habra H, Savelieff M, Karnovsky A, Mukherjee B, Feldman E. Body mass index associates with amyotrophic lateral sclerosis survival and metabolomic profiles. Muscle & Nerve 2022, 67: 208-216. PMID: 36321729, PMCID: PMC9957813, DOI: 10.1002/mus.27744.Peer-Reviewed Original ResearchConceptsBody mass indexBody mass index trajectoriesMetabolomic profilesAmyotrophic lateral sclerosis survivalAmyotrophic lateral sclerosisAmyotrophic lateral sclerosis participantsMass indexBody mass index lossBody mass index trajectory groupsSelf-reported body heightParticipants lost weightShorter survivalProspective cohortSurvival associationsStudy entryMetabolomic networksGeneralized estimating equationsSymptom onsetBMI trendsWeight lossSurvivalBile acidsBody heightTrajectory groupsPotential mechanisms
2021
Per- and Polyfluoroalkyl Substances and Hormone Levels During the Menopausal Transition
Harlow S, Hood M, Ding N, Mukherjee B, Calafat A, Randolph J, Gold E, Park S. Per- and Polyfluoroalkyl Substances and Hormone Levels During the Menopausal Transition. The Journal Of Clinical Endocrinology & Metabolism 2021, 106: e4427-e4437. PMID: 34181018, PMCID: PMC8677593, DOI: 10.1210/clinem/dgab476.Peer-Reviewed Original ResearchConceptsMidlife womenSex hormone-binding globulinFollicle-stimulating hormoneInverse associationStudy of Women's HealthPositive associationMenopausal transitionBody mass indexReproductive ageWomen's HealthSignificant linear trendNo significant associationNulliparous womenLinear mixed modelsSmoking statusYears of ageMass indexHormone-binding globulinMenopausal statusLongitudinal serum concentrationsSignificant associationSerum PFAS concentrationsMidlifeWomenPolyfluoroalkyl substancesPerformance of urine, blood, and integrated metal biomarkers in relation to birth outcomes in a mixture setting
Ashrap P, Watkins D, Mukherjee B, Rosario-Pabón Z, Vélez-Vega C, Alshawabkeh A, Cordero J, Meeker J. Performance of urine, blood, and integrated metal biomarkers in relation to birth outcomes in a mixture setting. Environmental Research 2021, 200: 111435. PMID: 34097892, PMCID: PMC8403638, DOI: 10.1016/j.envres.2021.111435.Peer-Reviewed Original ResearchConceptsEnvironmental risk scoreIntraclass correlation coefficientBirth outcomesBody mass indexWeighted quantile sumOdds ratio of preterm birthSecond-hand smoke exposurePre-pregnancy body mass indexOdds of preterm birthAssociated with birth outcomesIncreased odds of preterm birthPractice study designHealth effectsPreterm birthMaternal educationIncreased oddsOdds ratioSmoke exposureStudy designMaternal ageMass indexArea under the curveRisk scoreLogistic regressionConfidence intervals
2020
Patterns of repeated diagnostic testing for COVID‐19 in relation to patient characteristics and outcomes
Salerno S, Zhao Z, Sankar S, Salvatore M, Gu T, Fritsche L, Lee S, Lisabeth L, Valley T, Mukherjee B. Patterns of repeated diagnostic testing for COVID‐19 in relation to patient characteristics and outcomes. Journal Of Internal Medicine 2020, 289: 726-737. PMID: 33253457, PMCID: PMC7753604, DOI: 10.1111/joim.13213.Peer-Reviewed Original ResearchConceptsAssociated with repeat testingFalse-negative rateNeighborhood poverty levelPre-existing type 2 diabetesSevere COVID-19-related outcomesDiagnostic testsPatient characteristicsPre-existing kidney diseaseBody mass indexICU-level careHealth outcomesCOVID-19-related outcomesCharacteristics of patientsCOVID-19 diagnostic testsType 2 diabetesMichigan MedicineMass indexCOVID-19Pain symptomsPatient agePoverty levelDownstream outcomesKidney diseaseRepeated testingLiver disease
2019
Longitudinal trends in perfluoroalkyl and polyfluoroalkyl substances among multiethnic midlife women from 1999 to 2011: The Study of Women′s Health Across the Nation
Ding N, Harlow S, Batterman S, Mukherjee B, Park S. Longitudinal trends in perfluoroalkyl and polyfluoroalkyl substances among multiethnic midlife women from 1999 to 2011: The Study of Women′s Health Across the Nation. Environment International 2019, 135: 105381. PMID: 31841808, PMCID: PMC7374929, DOI: 10.1016/j.envint.2019.105381.Peer-Reviewed Original ResearchConceptsMidlife womenWomen's HealthBlack womenStudy of Women's HealthChinese womenLongitudinal trendsTemporal trendsBody mass indexSerum concentrationsLinear mixed modelsParous womenFollow-up visitPFAS homologuesPFNA concentrationsRace/ethnicityMass indexSerum PFAS concentrationsMidlifePatterns of exposurePolyfluoroalkyl substances concentrationsLongitudinal declineWomenAssociated with lower concentrationsHealthMixed models
2017
Meta‐analysis of gene‐environment interaction exploiting gene‐environment independence across multiple case‐control studies
Estes J, Rice J, Li S, Stringham H, Boehnke M, Mukherjee B. Meta‐analysis of gene‐environment interaction exploiting gene‐environment independence across multiple case‐control studies. Statistics In Medicine 2017, 36: 3895-3909. PMID: 28744888, PMCID: PMC5624850, DOI: 10.1002/sim.7398.Peer-Reviewed Original ResearchMeSH KeywordsAge FactorsAlpha-Ketoglutarate-Dependent Dioxygenase FTOBayes TheoremBiasBiometryBody Mass IndexCase-Control StudiesComputer SimulationDiabetes Mellitus, Type 2Gene-Environment InteractionHumansLogistic ModelsMeta-Analysis as TopicModels, GeneticModels, StatisticalPolymorphism, Single NucleotideRetrospective StudiesConceptsGene-environment independenceGene-environmentEmpirical Bayes estimatorsGene-environment interactionsCase-control studyMeta-analysis settingBayes estimatorsRetrospective likelihood frameworkShrinkage estimatorsMeta-analysisTesting gene-environment interactionsCombination of estimatesFactors body mass indexSimulation studyBody mass indexUnconstrained modelLikelihood frameworkInverse varianceMeta-analysis frameworkFTO geneMass indexGenetic markersEstimationStandard alternativeChatterjee
2014
Environmental Risk Score as a New Tool to Examine Multi-Pollutants in Epidemiologic Research: An Example from the NHANES Study Using Serum Lipid Levels
Park S, Tao Y, Meeker J, Harlow S, Mukherjee B. Environmental Risk Score as a New Tool to Examine Multi-Pollutants in Epidemiologic Research: An Example from the NHANES Study Using Serum Lipid Levels. PLOS ONE 2014, 9: e98632. PMID: 24901996, PMCID: PMC4047033, DOI: 10.1371/journal.pone.0098632.Peer-Reviewed Original ResearchConceptsEnvironmental risk scoreLipid outcomesEpidemiological researchNational Health and Nutrition Examination SurveyHealth and Nutrition Examination SurveyRisk scoreNutrition Examination SurveyAdverse health responsesSocio-demographic factorsMulti-pollutant exposuresDevelopment of chronic diseasesBody mass indexExamination SurveySerum nutrient levelsMulti-pollutant approachSociodemographic factorsHealth responseChronic diseasesSingle-pollutantDisease riskMass indexEpidemiological studiesNHANES studyRisk predictionMulti-pollutantsThe Role of Environmental Heterogeneity in Meta‐Analysis of Gene–Environment Interactions With Quantitative Traits
Li S, Mukherjee B, Taylor J, Rice K, Wen X, Rice J, Stringham H, Boehnke M. The Role of Environmental Heterogeneity in Meta‐Analysis of Gene–Environment Interactions With Quantitative Traits. Genetic Epidemiology 2014, 38: 416-429. PMID: 24801060, PMCID: PMC4108593, DOI: 10.1002/gepi.21810.Peer-Reviewed Original ResearchMeSH KeywordsAlpha-Ketoglutarate-Dependent Dioxygenase FTOBiasBody Mass IndexCase-Control StudiesCholesterol, HDLCohort StudiesDiabetes Mellitus, Type 2Gene FrequencyGene-Environment InteractionGenetic Predisposition to DiseaseHumansMeta-Analysis as TopicModels, GeneticPhenotypePolymorphism, Single NucleotideProteinsQuantitative Trait, HeritableConceptsIndividual level dataMeta-analysisInverse-variance weighted meta-analysisEnvironmental heterogeneityGene-environment interaction studiesInverse-variance weighted estimatorMeta-analysis of interactionsStudy of type 2 diabetesGene-environment interactionsBody mass indexMeta-regression approachSingle nucleotide polymorphismsAdaptive weighted estimatorFTO geneType 2 diabetesMass indexMeta-regressionQuantitative traitsSummary statisticsCholesterol dataNucleotide polymorphismsLevel dataUnivariate summary statisticsData harmonizationEnvironmental covariates