2023
Prenatal per- and polyfluoroalkyl substances (PFAS) exposure in relation to preterm birth subtypes and size-for-gestational age in the LIFECODES cohort 2006–2008
Siwakoti R, Cathey A, Ferguson K, Hao W, Cantonwine D, Mukherjee B, McElrath T, Meeker J. Prenatal per- and polyfluoroalkyl substances (PFAS) exposure in relation to preterm birth subtypes and size-for-gestational age in the LIFECODES cohort 2006–2008. Environmental Research 2023, 237: 116967. PMID: 37634691, PMCID: PMC10913455, DOI: 10.1016/j.envres.2023.116967.Peer-Reviewed Original ResearchConceptsLarge-for-gestational agePreterm birth subtypesBayesian kernel machine regressionSize-for-gestational ageSmall-for-gestational agePreterm birthFetal sexPregnancy outcomesSex-specific estimatesIncreased risk of adverse pregnancy outcomesInterquartile range increaseRisk of adverse pregnancy outcomesBayesian kernel machine regression analysisEarly pregnancy samplesAdverse pregnancy outcomesCase-control studyPrenatal PFAS exposureAssociations of polyfluoroalkyl substancesBW z-scoreEffects of polyfluoroalkyl substancesKernel machine regressionEffect modificationEffects of prenatal exposureRange increaseStratified analysis
2022
Exposure to heavy metals and hormone levels in midlife women: The Study of Women's Health Across the Nation (SWAN)
Wang X, Ding N, Harlow S, Randolph J, Mukherjee B, Gold E, Park S. Exposure to heavy metals and hormone levels in midlife women: The Study of Women's Health Across the Nation (SWAN). Environmental Pollution 2022, 317: 120740. PMID: 36436662, PMCID: PMC9897061, DOI: 10.1016/j.envpol.2022.120740.Peer-Reviewed Original ResearchConceptsUrinary metal concentrationsExposure to heavy metalsHeavy metalsMetal concentrationsStudy of Women's HealthAssociation of heavy metalsEnvironmental heavy metal exposureHeavy metal exposureSex hormone-binding globulinFollicle-stimulating hormoneWomen's HealthBayesian kernel machine regressionAssociated with E<sub>2</sub>, TMidlife womenKernel machine regressionMetal exposureSerum hormone levelsMetal mixturesHealth-related factorsNation Multi-Pollutant StudyCalculate percent changesCadmiumHormone levelsProspective cohort studyLinear mixed effects modelsRace-specific associations of urinary phenols and parabens with adipokines in midlife women: The Study of Women's Health Across the Nation (SWAN)
Lee S, Karvonen-Gutierrez C, Mukherjee B, Herman W, Park S. Race-specific associations of urinary phenols and parabens with adipokines in midlife women: The Study of Women's Health Across the Nation (SWAN). Environmental Pollution 2022, 303: 119164. PMID: 35306088, PMCID: PMC9883839, DOI: 10.1016/j.envpol.2022.119164.Peer-Reviewed Original ResearchConceptsStudy of Women's HealthBayesian kernel machine regressionWomen's HealthLeptin levelsBlack womenAssociated with lower leptinAssociated with favorable profilesAsian womenSoluble leptin receptorRacial differencesRace-specific associationsUrinary phenolCross-sectional associationsNo significant associationObesity-related metabolic diseasesLog-transformed levelsSOB-RKernel machine regressionSerum adipokinesMetabolic disease burdenEffect modificationNation Multi-Pollutant StudyLow leptinLeptin receptorLinear regression models
2021
Associations of perfluoroalkyl and polyfluoroalkyl substances (PFAS) and PFAS mixtures with adipokines in midlife women
Ding N, Karvonen-Gutierrez C, Herman W, Calafat A, Mukherjee B, Park S. Associations of perfluoroalkyl and polyfluoroalkyl substances (PFAS) and PFAS mixtures with adipokines in midlife women. International Journal Of Hygiene And Environmental Health 2021, 235: 113777. PMID: 34090141, PMCID: PMC8207532, DOI: 10.1016/j.ijheh.2021.113777.Peer-Reviewed Original ResearchConceptsBayesian kernel machine regressionNormal weightStudy of Women's HealthCirculating levels of leptinHMW adiponectinBayesian kernel machine regression analysisSoluble leptin receptorFree leptin indexSOB-R concentrationsBaseline serum samplesLevels of leptinMultivariate linear regressionPhysical activityAssociated with obesityStatistically significant associationWomen's HealthPolyfluoroalkyl substancesWaist circumferenceKernel machine regressionWomen's backgroundInfluence obesityMidlife womenSmoking statusSOB-RMenopausal statusIndividual and joint effects of phthalate metabolites on biomarkers of oxidative stress among pregnant women in Puerto Rico
Cathey A, Eaton J, Ashrap P, Watkins D, Rosario Z, Vega C, Alshawabkeh A, Cordero J, Mukherjee B, Meeker J. Individual and joint effects of phthalate metabolites on biomarkers of oxidative stress among pregnant women in Puerto Rico. Environment International 2021, 154: 106565. PMID: 33882432, PMCID: PMC9923976, DOI: 10.1016/j.envint.2021.106565.Peer-Reviewed Original ResearchConceptsBayesian kernel machine regressionPhthalate metabolitesEnvironmental risk scoreBiomarkers of oxidative stressPregnant womenPhthalate mixtureBayesian kernel machine regression analysisEffects of phthalate metabolitesOxidative stressIndividual phthalate metabolitesLipid oxidative stressUrinary biomarker measurementsKernel machine regressionAdverse birth outcomesPuerto Rico TestsiteOxidative stress biomarkersChemical fractionationContamination threatLongitudinal birth cohortPhthalate compoundsLinear mixed effects modelsBirth outcomesOxidative stress mechanismsStudy visitsPhthalate exposureCross-Sectional Estimation of Endogenous Biomarker Associations with Prenatal Phenols, Phthalates, Metals, and Polycyclic Aromatic Hydrocarbons in Single-Pollutant and Mixtures Analysis Approaches
Aung M, Yu Y, Ferguson K, Cantonwine D, Zeng L, McElrath T, Pennathur S, Mukherjee B, Meeker J. Cross-Sectional Estimation of Endogenous Biomarker Associations with Prenatal Phenols, Phthalates, Metals, and Polycyclic Aromatic Hydrocarbons in Single-Pollutant and Mixtures Analysis Approaches. Environmental Health Perspectives 2021, 129: 037007. PMID: 33761273, PMCID: PMC7990518, DOI: 10.1289/ehp7396.Peer-Reviewed Original ResearchConceptsExposure analytesToxicity classesMixtures of toxicantsSingle-pollutant modelsPolycyclic aromatic hydrocarbonsHierarchical Bayesian kernel machine regressionBayesian kernel machine regressionKernel machine regressionPrenatal toxicant exposureAdaptive elastic net regressionClasses of toxicantsSingle-pollutantTrace metalsAromatic hydrocarbonsToxic mixturePair-wise associationsAdaptive elastic netToxicant exposurePhthalateMachine regressionEndogenous biomarkersBiomarkers indicativeMultiple linear regressionMetalToxicity
2020
Maternal blood metal and metalloid concentrations in association with birth outcomes in Northern Puerto Rico
Ashrap P, Watkins D, Mukherjee B, Boss J, Richards M, Rosario Z, Vélez-Vega C, Alshawabkeh A, Cordero J, Meeker J. Maternal blood metal and metalloid concentrations in association with birth outcomes in Northern Puerto Rico. Environment International 2020, 138: 105606. PMID: 32179314, PMCID: PMC7198231, DOI: 10.1016/j.envint.2020.105606.Peer-Reviewed Original ResearchConceptsBayesian kernel machine regressionShorter gestational agePreterm birthGestational ageEnvironmental risk scoreBirth outcomesMetal concentrationsNon-essential metal(loid)sHigher risk of preterm birthRisk of preterm birthLow-level prenatal lead exposureInterquartile rangeOdds of preterm birthAssociated with higher risk of preterm birthRisk scoreEffects of metalsPredictors of birth outcomesAssociated with birth outcomesBayesian kernel machine regression modelsBirthweight z-scoreAssociated with adverse birth outcomesAssociated with higher riskAdverse birth outcomesKernel machine regressionPuerto Rico Testsite
2019
Manganese is associated with increased plasma interleukin-1β during pregnancy, within a mixtures analysis framework of urinary trace metals
Aung M, Meeker J, Boss J, Bakulski K, Mukherjee B, Cantonwine D, McElrath T, Ferguson K. Manganese is associated with increased plasma interleukin-1β during pregnancy, within a mixtures analysis framework of urinary trace metals. Reproductive Toxicology 2019, 93: 43-53. PMID: 31881266, PMCID: PMC7138746, DOI: 10.1016/j.reprotox.2019.12.004.Peer-Reviewed Original ResearchConceptsIL-1BBayesian kernel machine regressionKernel machine regressionInterleukin-1bInterquartile range differenceCross-sectional studyAssociated with biomarkersReproductive health outcomesImmune signaling moleculesHealth outcomesLIFECODES birth cohortPregnant womenImmune perturbationsImmune biomarkersBirth cohortUrinary manganeseMachine regressionLinear regressionExposure analytesAssociationSignaling moleculesRegressionPair-wise associationsTrimesterPregnancy
2017
Construction of environmental risk score beyond standard linear models using machine learning methods: application to metal mixtures, oxidative stress and cardiovascular disease in NHANES
Park S, Zhao Z, Mukherjee B. Construction of environmental risk score beyond standard linear models using machine learning methods: application to metal mixtures, oxidative stress and cardiovascular disease in NHANES. Environmental Health 2017, 16: 102. PMID: 28950902, PMCID: PMC5615812, DOI: 10.1186/s12940-017-0310-9.Peer-Reviewed Original ResearchConceptsEnvironmental risk scoreBayesian kernel machine regressionNational Health and Nutrition Examination SurveyHealth and Nutrition Examination SurveyRisk scoreAssociated with odds ratiosNutrition Examination SurveyAssociated with systolicExamination SurveyMulti-pollutant approachKernel machine regressionPollutant mixturesSD increaseEpidemiological researchDiastolic blood pressureMortality outcomesOdds ratioBayesian additive regression treesDisease endpointsHealth endpointsCumulative riskPositive associationEnvironmental exposuresIntermediate markersCardiovascular disease