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For: Park SK, 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. Environ Health 2017;16:102. [PMID: 28950902 DOI: 10.1186/s12940-017-0310-9] [Cited by in Crossref: 37] [Cited by in F6Publishing: 36] [Article Influence: 7.4] [Reference Citation Analysis]
Number Citing Articles
1 Lamas GA, Ujueta F, Navas-Acien A. Lead and Cadmium as Cardiovascular Risk Factors: The Burden of Proof Has Been Met. J Am Heart Assoc 2021;10:e018692. [PMID: 33942628 DOI: 10.1161/JAHA.120.018692] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
2 Cathey AL, Watkins DJ, Rosario ZY, Vélez C, Mukherjee B, Alshawabkeh AN, Cordero JF, Meeker JD. Biomarkers of Exposure to Phthalate Mixtures and Adverse Birth Outcomes in a Puerto Rico Birth Cohort. Environ Health Perspect 2022;130:37009. [PMID: 35333099 DOI: 10.1289/EHP8990] [Reference Citation Analysis]
3 Wang F, Preininger A. AI in Health: State of the Art, Challenges, and Future Directions. Yearb Med Inform 2019;28:16-26. [PMID: 31419814 DOI: 10.1055/s-0039-1677908] [Cited by in Crossref: 44] [Cited by in F6Publishing: 17] [Article Influence: 14.7] [Reference Citation Analysis]
4 Yim G, Wang Y, Howe CG, Romano ME. Exposure to Metal Mixtures in Association with Cardiovascular Risk Factors and Outcomes: A Scoping Review. Toxics 2022;10:116. [DOI: 10.3390/toxics10030116] [Reference Citation Analysis]
5 Paolocci G, Bauleo L, Folletti I, Murgia N, Muzi G, Ancona C. Industrial Air Pollution and Respiratory Health Status among Residents in an Industrial Area in Central Italy. Int J Environ Res Public Health 2020;17:E3795. [PMID: 32471097 DOI: 10.3390/ijerph17113795] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
6 Huang H, Wei L, Chen X, Zhang R, Su L, Rahman M, Golam Mostofa M, Qamruzzaman Q, Zhao Y, Yu H, Wei Y, Christiani DC, Chen F. Cord serum elementomics profiling of 56 elements depicts risk of preterm birth: Evidence from a prospective birth cohort in rural Bangladesh. Environ Int 2021;156:106731. [PMID: 34197971 DOI: 10.1016/j.envint.2021.106731] [Reference Citation Analysis]
7 Wang X, Mukherjee B, Park SK. Associations of cumulative exposure to heavy metal mixtures with obesity and its comorbidities among U.S. adults in NHANES 2003-2014. Environ Int 2018;121:683-94. [PMID: 30316184 DOI: 10.1016/j.envint.2018.09.035] [Cited by in Crossref: 65] [Cited by in F6Publishing: 63] [Article Influence: 16.3] [Reference Citation Analysis]
8 Ju MJ, Kim J, Park SK, Kim DH, Choi YH. Long-term exposure to ambient air pollutants and age-related macular degeneration in middle-aged and older adults. Environ Res 2022;204:111953. [PMID: 34454934 DOI: 10.1016/j.envres.2021.111953] [Reference Citation Analysis]
9 Zheng Y, Zhang C, Weisskopf MG, Williams PL, Claus Henn B, Parsons PJ, Palmer CD, Buck Louis GM, James-Todd T. Evaluating associations between early pregnancy trace elements mixture and 2nd trimester gestational glucose levels: A comparison of three statistical approaches. Int J Hyg Environ Health 2020;224:113446. [PMID: 31978739 DOI: 10.1016/j.ijheh.2019.113446] [Cited by in Crossref: 7] [Cited by in F6Publishing: 6] [Article Influence: 2.3] [Reference Citation Analysis]
10 Wang X, Karvonen-Gutierrez CA, Mukherjee B, Herman WH, Park SK. Urinary metals and adipokines in midlife women: The Study of Women's Health Across the nation (SWAN). Environ Res 2021;196:110426. [PMID: 33157106 DOI: 10.1016/j.envres.2020.110426] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
11 Kim KN, Lee MR, Choi YH, Lee BE, Hong YC. Association between phthalate exposure and lower lung function in an urban elderly population: A repeated-measures longitudinal study. Environ Int 2018;113:177-83. [PMID: 29427879 DOI: 10.1016/j.envint.2018.02.004] [Cited by in Crossref: 13] [Cited by in F6Publishing: 13] [Article Influence: 3.3] [Reference Citation Analysis]
12 Nadler DW. Decision support: using machine learning through MATLAB to analyze environmental data. J Environ Stud Sci 2019;9:419-28. [DOI: 10.1007/s13412-019-00558-9] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
13 Martins AC, Almeida Lopes ACB, Urbano MR, Carvalho MFH, Silva AMR, Tinkov AA, Aschner M, Mesas AE, Silbergeld EK, Paoliello MMB. An updated systematic review on the association between Cd exposure, blood pressure and hypertension. Ecotoxicol Environ Saf 2021;208:111636. [PMID: 33396156 DOI: 10.1016/j.ecoenv.2020.111636] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
14 Aung MT, Meeker JD, Boss J, Bakulski KM, Mukherjee B, Cantonwine DE, McElrath TF, Ferguson KK. Manganese is associated with increased plasma interleukin-1β during pregnancy, within a mixtures analysis framework of urinary trace metals. Reprod Toxicol 2020;93:43-53. [PMID: 31881266 DOI: 10.1016/j.reprotox.2019.12.004] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 1.3] [Reference Citation Analysis]
15 Duan W, Xu C, Liu Q, Xu J, Weng Z, Zhang X, Basnet TB, Dahal M, Gu A. Levels of a mixture of heavy metals in blood and urine and all-cause, cardiovascular disease and cancer mortality: A population-based cohort study. Environ Pollut 2020;263:114630. [PMID: 33618481 DOI: 10.1016/j.envpol.2020.114630] [Cited by in Crossref: 9] [Cited by in F6Publishing: 6] [Article Influence: 4.5] [Reference Citation Analysis]
16 [DOI: 10.1101/2020.05.30.20117655] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
17 Eggers S, Gennings C, Malecki KMC, Safdar N, Arora M. Exposure to environmental chemical mixtures is associated with nasal colonization by Staphylococcus aureus: NHANES 2001-2004. Environ Res 2020;190:109994. [PMID: 32771801 DOI: 10.1016/j.envres.2020.109994] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
18 Wang X, Mukherjee B, Batterman S, Harlow SD, Park SK. Urinary metals and metal mixtures in midlife women: The Study of Women's Health Across the Nation (SWAN). Int J Hyg Environ Health 2019;222:778-89. [PMID: 31103473 DOI: 10.1016/j.ijheh.2019.05.002] [Cited by in Crossref: 16] [Cited by in F6Publishing: 15] [Article Influence: 5.3] [Reference Citation Analysis]
19 Yao X, Steven Xu X, Yang Y, Zhu Z, Zhu Z, Tao F, Yuan M. Stratification of population in NHANES 2009-2014 based on exposure pattern of lead, cadmium, mercury, and arsenic and their association with cardiovascular, renal and respiratory outcomes. Environ Int 2021;149:106410. [PMID: 33548850 DOI: 10.1016/j.envint.2021.106410] [Cited by in Crossref: 5] [Cited by in F6Publishing: 1] [Article Influence: 5.0] [Reference Citation Analysis]
20 Everson TM, Niedzwiecki MM, Toth D, Tellez-Plaza M, Liu H, Barr DB, Gribble MO. Metal biomarker mixtures and blood pressure in the United States: cross-sectional findings from the 1999-2006 National Health and Nutrition Examination Survey (NHANES). Environ Health 2021;20:15. [PMID: 33583418 DOI: 10.1186/s12940-021-00695-1] [Reference Citation Analysis]
21 Aung MT, Song Y, Ferguson KK, Cantonwine DE, Zeng L, McElrath TF, Pennathur S, Meeker JD, Mukherjee B. Application of an analytical framework for multivariate mediation analysis of environmental data. Nat Commun 2020;11:5624. [PMID: 33159049 DOI: 10.1038/s41467-020-19335-2] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
22 Carwile JL, Seshasayee SM, Ahrens KA, Hauser R, Chavarro JE, Fleisch AF. Dietary correlates of urinary phthalate metabolite concentrations in 6-19 Year old children and adolescents. Environ Res 2021;204:112083. [PMID: 34582800 DOI: 10.1016/j.envres.2021.112083] [Reference Citation Analysis]
23 Ashrap P, Watkins DJ, Mukherjee B, Boss J, Richards MJ, Rosario Z, Vélez-Vega CM, Alshawabkeh A, Cordero JF, Meeker JD. Maternal blood metal and metalloid concentrations in association with birth outcomes in Northern Puerto Rico. Environ Int 2020;138:105606. [PMID: 32179314 DOI: 10.1016/j.envint.2020.105606] [Cited by in Crossref: 21] [Cited by in F6Publishing: 19] [Article Influence: 10.5] [Reference Citation Analysis]
24 Cathey AL, Eaton JL, Ashrap P, Watkins DJ, Rosario ZY, Vélez Vega C, Alshawabkeh AN, Cordero JF, Mukherjee B, Meeker JD. Individual and joint effects of phthalate metabolites on biomarkers of oxidative stress among pregnant women in Puerto Rico. Environ Int 2021;154:106565. [PMID: 33882432 DOI: 10.1016/j.envint.2021.106565] [Reference Citation Analysis]
25 Liu L, Li X, Wu M, Yu M, Wang L, Hu L, Li Y, Song L, Wang Y, Mei S. Individual and joint effects of metal exposure on metabolic syndrome among Chinese adults. Chemosphere 2022;287:132295. [PMID: 34563779 DOI: 10.1016/j.chemosphere.2021.132295] [Reference Citation Analysis]
26 Park SK, Wang X, Ding N, Karvonen-gutierrez CA, Calafat AM, Herman WH, Mukherjee B, Harlow SD. Per- and polyfluoroalkyl substances and incident diabetes in midlife women: the Study of Women’s Health Across the Nation (SWAN). Diabetologia. [DOI: 10.1007/s00125-022-05695-5] [Reference Citation Analysis]
27 Xu C, Su X, Xu Y, Ma S, Duan W, Mo X. Exploring the associations of serum concentrations of PCBs, PCDDs, and PCDFs with walking speed in the U.S. general population: Beyond standard linear models. Environmental Research 2019;178:108666. [DOI: 10.1016/j.envres.2019.108666] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
28 Wang X, Mukherjee B, Park SK. Does Information on Blood Heavy Metals Improve Cardiovascular Mortality Prediction? J Am Heart Assoc 2019;8:e013571. [PMID: 31631727 DOI: 10.1161/JAHA.119.013571] [Cited by in Crossref: 15] [Cited by in F6Publishing: 13] [Article Influence: 5.0] [Reference Citation Analysis]
29 Liu SH, Bobb JF, Claus Henn B, Gennings C, Schnaas L, Tellez-Rojo M, Bellinger D, Arora M, Wright RO, Coull BA. Bayesian varying coefficient kernel machine regression to assess neurodevelopmental trajectories associated with exposure to complex mixtures. Stat Med 2018;37:4680-94. [PMID: 30277584 DOI: 10.1002/sim.7947] [Cited by in Crossref: 17] [Cited by in F6Publishing: 19] [Article Influence: 4.3] [Reference Citation Analysis]
30 Chiu YH, Bellavia A, James-Todd T, Correia KF, Valeri L, Messerlian C, Ford JB, Mínguez-Alarcón L, Calafat AM, Hauser R, Williams PL; EARTH Study Team. Evaluating effects of prenatal exposure to phthalate mixtures on birth weight: A comparison of three statistical approaches. Environ Int 2018;113:231-9. [PMID: 29453090 DOI: 10.1016/j.envint.2018.02.005] [Cited by in Crossref: 41] [Cited by in F6Publishing: 41] [Article Influence: 10.3] [Reference Citation Analysis]
31 Gibson EA, Nunez Y, Abuawad A, Zota AR, Renzetti S, Devick KL, Gennings C, Goldsmith J, Coull BA, Kioumourtzoglou MA. An overview of methods to address distinct research questions on environmental mixtures: an application to persistent organic pollutants and leukocyte telomere length. Environ Health 2019;18:76. [PMID: 31462251 DOI: 10.1186/s12940-019-0515-1] [Cited by in Crossref: 26] [Cited by in F6Publishing: 23] [Article Influence: 8.7] [Reference Citation Analysis]
32 Wang X, Mukherjee B, Karvonen-Gutierrez CA, Herman WH, Batterman S, Harlow SD, Park SK. Urinary metal mixtures and longitudinal changes in glucose homeostasis: The Study of Women's Health Across the Nation (SWAN). Environ Int 2020;145:106109. [PMID: 32927284 DOI: 10.1016/j.envint.2020.106109] [Cited by in Crossref: 12] [Cited by in F6Publishing: 10] [Article Influence: 6.0] [Reference Citation Analysis]
33 Wang X, Karvonen-Gutierrez CA, Herman WH, Mukherjee B, Park SK. Metals and risk of incident metabolic syndrome in a prospective cohort of midlife women in the United States. Environ Res 2022;210:112976. [PMID: 35202625 DOI: 10.1016/j.envres.2022.112976] [Reference Citation Analysis]
34 Zhao Y, Naumova EN, Bobb JF, Claus Henn B, Singh GM. Joint Associations of Multiple Dietary Components With Cardiovascular Disease Risk: A Machine-Learning Approach. Am J Epidemiol 2021;190:1353-65. [PMID: 33521815 DOI: 10.1093/aje/kwab004] [Reference Citation Analysis]
35 Shih YH, Howe CG, Scannell Bryan M, Shahriar M, Kibriya MG, Jasmine F, Sarwar G, Graziano JH, Persky VW, Jackson B, Ahsan H, Farzan SF, Argos M. Exposure to metal mixtures in relation to blood pressure among children 5-7 years old: An observational study in Bangladesh. Environ Epidemiol 2021;5:e135. [PMID: 33778363 DOI: 10.1097/EE9.0000000000000135] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
36 Merced-Nieves FM, Arora M, Wright RO, Curtin P. Metal mixtures and neurodevelopment: recent findings and emerging principles. Curr Opin Toxicol 2021;26:28-32. [PMID: 34017930 DOI: 10.1016/j.cotox.2021.03.005] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
37 Boss J, Zhai J, Aung MT, Ferguson KK, Johns LE, McElrath TF, Meeker JD, Mukherjee B. Associations between mixtures of urinary phthalate metabolites with gestational age at delivery: a time to event analysis using summative phthalate risk scores. Environ Health 2018;17:56. [PMID: 29925380 DOI: 10.1186/s12940-018-0400-3] [Cited by in Crossref: 13] [Cited by in F6Publishing: 15] [Article Influence: 3.3] [Reference Citation Analysis]
38 Lee S, Karvonen-Gutierrez C, Mukherjee B, Herman WH, Harlow SD, Park SK. Urinary concentrations of phenols and parabens and incident diabetes in midlife women: The Study of Women's Health Across the Nation. Environ Epidemiol 2021;5:e171. [PMID: 34934892 DOI: 10.1097/EE9.0000000000000171] [Reference Citation Analysis]
39 Salvatore M, Beesley LJ, Fritsche LG, Hanauer D, Shi X, Mondul AM, Pearce CL, Mukherjee B. Phenotype risk scores (PheRS) for pancreatic cancer using time-stamped electronic health record data: Discovery and validation in two large biobanks. J Biomed Inform 2021;113:103652. [PMID: 33279681 DOI: 10.1016/j.jbi.2020.103652] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
40 Ashrap P, Watkins DJ, Mukherjee B, Rosario-Pabón Z, Vélez-Vega CM, Alshawabkeh A, Cordero JF, Meeker JD. Performance of urine, blood, and integrated metal biomarkers in relation to birth outcomes in a mixture setting. Environ Res 2021;200:111435. [PMID: 34097892 DOI: 10.1016/j.envres.2021.111435] [Reference Citation Analysis]
41 Pries LK, Lage-Castellanos A, Delespaul P, Kenis G, Luykx JJ, Lin BD, Richards AL, Akdede B, Binbay T, Altinyazar V, Yalinçetin B, Gümüş-Akay G, Cihan B, Soygür H, Ulaş H, Cankurtaran EŞ, Kaymak SU, Mihaljevic MM, Petrovic SA, Mirjanic T, Bernardo M, Cabrera B, Bobes J, Saiz PA, García-Portilla MP, Sanjuan J, Aguilar EJ, Santos JL, Jiménez-López E, Arrojo M, Carracedo A, López G, González-Peñas J, Parellada M, Maric NP, Atbaşoğlu C, Ucok A, Alptekin K, Saka MC, Arango C, O'Donovan M, Rutten BPF, van Os J, Guloksuz S; Genetic Risk and Outcome of Psychosis (GROUP) investigators. Estimating Exposome Score for Schizophrenia Using Predictive Modeling Approach in Two Independent Samples: The Results From the EUGEI Study. Schizophr Bull 2019;45:960-5. [PMID: 31508804 DOI: 10.1093/schbul/sbz054] [Cited by in Crossref: 17] [Cited by in F6Publishing: 15] [Article Influence: 8.5] [Reference Citation Analysis]
42 Mork D, Wilson A. Estimating perinatal critical windows of susceptibility to environmental mixtures via structured Bayesian regression tree pairs. Biometrics 2021. [PMID: 34562017 DOI: 10.1111/biom.13568] [Reference Citation Analysis]
43 Fu Y, Liu Y, Liu Y, Wang Y, Zhu M, Lin W, Li M, Liu Y, He M, Yu L, Wang J. Relationship between cumulative exposure to metal mixtures and heart rate among Chinese preschoolers. Chemosphere 2022;:134548. [PMID: 35413364 DOI: 10.1016/j.chemosphere.2022.134548] [Reference Citation Analysis]
44 Wang X, Ding N, Harlow SD, Randolph JF Jr, Mukherjee B, Gold EB, Park SK. Urinary metals and metal mixtures and timing of natural menopause in midlife women: The Study of Women's Health Across the Nation. Environ Int 2021;157:106781. [PMID: 34311223 DOI: 10.1016/j.envint.2021.106781] [Reference Citation Analysis]
45 Comess S, Akbay A, Vasiliou M, Hines RN, Joppa L, Vasiliou V, Kleinstreuer N. Bringing Big Data to Bear in Environmental Public Health: Challenges and Recommendations. Front Artif Intell 2020;3:31. [PMID: 33184612 DOI: 10.3389/frai.2020.00031] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
46 Wong J, Manderson T, Abrahamowicz M, Buckeridge DL, Tamblyn R. Can Hyperparameter Tuning Improve the Performance of a Super Learner?: A Case Study. Epidemiology 2019;30:521-31. [PMID: 30985529 DOI: 10.1097/EDE.0000000000001027] [Cited by in Crossref: 9] [Cited by in F6Publishing: 4] [Article Influence: 4.5] [Reference Citation Analysis]