Maternal Plasma Metabolic Profile Demarcates a Role for Neuroinflammation in Non-Typical Development of Children.
Schmidt RJ, Liang D, Busgang SA, Curtin P, Giulivi C. Metabolites. 2021 Aug 18;11(8):545.
A method for the analysis of 121 multi-class environmental chemicals in urine by high-performance liquid chromatography-tandem mass spectrometry.
Zhu H, Chinthakindi S, Kannan K. J Chromatogr A. 2021 Jun 7;1646:462146.
Quality assurance and harmonization for targeted biomonitoring measurements of environmental organic chemicals across the Children's Health Exposure Analysis Resource laboratory network.
Kannan K, Stathis A, Mazzella MJ, Andra SS, Barr DB, Hecht SS, Merrill LS, Galusha AL, Parsons PJ. Int J Hyg Environ Health. 2021 May;234:113741.
Early pregnancy exposure to metal mixture and birth outcomes - A prospective study in Project Viva.
Rahman ML, Oken E, Hivert MF, Rifas-Shiman S, Lin PD, Colicino E, Wright RO, Amarasiriwardena C, Claus Henn BG, Gold DR, Coull BA, Cardenas A. Environ Int. 2021 Jun 17;156:106714. doi: 10.1016/j.envint.2021.106714. Online ahead of print.
Evaluating inter-study variability in phthalate and trace element analyses within the Children's Health Exposure Analysis Resource (CHEAR) using multivariate control charts.
Mazzella MJ, Barr DB, Kannan K, Amarasiriwardena C, Andra SS, Gennings C. J Expo Sci Environ Epidemiol. 2021 Mar;31(2):318-327.
Prenatal metal mixtures and birth weight for gestational age in a predominately lower income Hispanic pregnancy cohort in Los Angeles.
MJ, Meeker JD, Bastain TM, Breton CV. Environ Health Perspect. 2020 Nov;128(11).
Dysregulated lipid and fatty acid metabolism link perfluoroalkyl substances exposure and impaired glucose metabolism in young adults.
Chen Z, Yang T, Walker DI, Thomas DC, Qiu C, Chatzi L, Alderete TL, Kim JS, Conti DV, Breton CV, Liang D, Hauser ER, Jones DP, Gilliland FD. Environ Int. 2020 Sep 3;145.
Prospective Association Between Manganese in Early Pregnancy and the Risk of Preeclampsia.
Liu T , Hivert , M, Rifas-Shiman S, Rahman M ,Oken E , Andres Cardenas A , Mueller N. Epidemiology. 2020 Sep;31(5):677-680.
Prenatal Metal Mixtures and Fetal Size in Mid-Pregnancy in the MADRES Study.
Caitlin G Howe , Birgit Claus Henn , Shohreh F Farzan , Rima Habre , Sandrah P Eckel , Brendan H Grubbs, Thomas A Chavez, Dema Faham , Laila Al-Marayati, Deborah Lerner , Alyssa Quimby , Sara Twogood , Michael J Richards , John D Meeker , Theresa M Bastain , Carrie V Breton. Environ Research Oct 28, 2020.
Trimester-Specific Associations of Prenatal Lead Exposure With Infant Cord Blood DNA Methylation at Birth.
Rygiel C, Dolinoy D, Perng W, Jones T, Solano M, Hu H, Téllez-Rojo M,
Peterson K Goodrich J.
Epigenetic Insights. Jul 2020. Vol 13. 1-11.
Comparison of Liquid Chromatography Mass Spectrometry and Enzyme-Linked Immunosorbent Assay Methods to Measure Salivary Cotinine Levels in Ill Children.
Mahabee-Gittens EM, Mazzella MJ, Doucette JT, Merianos AL, Stone L, Wullenweber CA, A Busgang S, Matt GE. Int J Environ Res Public Health. 2020 Feb 12;17(4)
Quantitative methods for metabolomic analyses evaluated in the Children's Health Exposure Analysis Resource (CHEAR).
CHEAR Metabolomics Analysis Team, Mazzella M, Sumner SJ, Gao S, Su L, Diao N, Mostofa G, Qamruzzaman Q, Pathmasiri W, Christiani DC, Fennell T, Gennings C. J Expo Sci Environ Epidemiol. 2020 Jan;30(1):16-27.
Prenatal phenol and paraben exposures in relation to child neurodevelopment including autism spectrum disorders in the MARBLES study.
Barkoski JM, Busgang SA, Bixby M, Bennett D, Schmidt RJ, Barr DB, Panuwet P, Gennings C, Hertz-Picciotto
Environ Research. 2019 Dec;179(Pt A):108719.
Quality of Prenatal and Childhood Diet Predicts Neurodevelopmental Outcomes among Children in Mexico City.
Malin AJ, Busgang SA, Cantoral AJ, Svensson K, Orjuela MA, Pantic I, Schnaas L, Oken E, Baccarelli AA, Téllez-Rojo MM, Wright RO, Gennings C. Nutrients. 2018 Aug 15;10(8).
Lagged kernel machine regression for identifying time windows of susceptibility to exposures of complex mixtures.
Liu SH, Bobb JF, Lee KH, Gennings C, Claus Henn B, Bellinger D Austin C, Schnaas L, Tellez-Rojo MM, Hu H, Wright RO, Arora M, Coull BA. Biostatistics, 2018 Jul 1;19(3):325-341.
Prenatal exposure to PM2.5 and birth weight: A pooled analysis from three North American longitudinal pregnancy cohort studies.
Rosa, MJ, Pajak, A, Just, AC, Sheffield, PE, Kloog, I, Schwartz, J , Coull, B, Enlow, MB , Baccarelli, AA, Huddleston, K, Niederhuber, JE, Téllez-Rojo, MM, Wright, RO, Gennings, C, Wright, RJ Environmental International. (2017) 107:173-180.
Recurrence quantification analysis to characterize cyclical components of environmental elemental exposures during fetal and postnatal development.
Curtin, P, Curtin, A, Austin, C., Gennings, C, Arora, M PLOS ONE. (2017)
Extending the distributed lag model framework to handle chemical mixtures.
Bello GA, Arora M, Austin C, Horton MK, Wright RO, Gennings C. Environmental Research; 2017 156:253-264.
Toward Greater Implementation of the Exposome Research Paradigm within Environmental Epidemiology.
Stingone JA, Buck Louis GM, Nakayama SF, Vermeulen RC, Kwok RK, Cui Y, Balshaw DM, Teitelbaum SL. Annu Rev Public Health. 2017 Mar 20;38:315-327. Review.
Big and disparate data: considerations for pediatric consortia.
Stingone JA, Mervish N, Kovatch P, McGuinness DL, Gennings C, Teitelbaum SL.
Curr Opin Pediatr. 2017 Apr;29(2):231-239. Review.
R Package ‘gWQS’
Description: Fits Weighted Quantile Sum (WQS) regressions for continuous or binomial outcomes.
Usage: gwqs(formula, mix_name, data, q = 4, validation = 0.6, valid_var = NULL, b = 100, b1_pos = TRUE, family = "gaussian", seed = NULL, wqs2 = FALSE, plots = FALSE, tables = FALSE)
Maximizing reuse of existing environmental health data: Introducing the HHEAR Data Repository. A workshop to present the updated HHEAR ontology and harmonized dataset to the wider research community. Jeanette Stingone. ISEE Webinar Series. Dec. 1, 2020
Metabolomics involves the identification and measurement of small-molecule metabolites of endogenous and exogenous origin in a biospecimen. These metabolites represent a diverse group of low-molecular-weight structures, such as lipids, amino acids, peptides, nucleic acids, organic acids, vitamins, thiols, carbohydrates, environmental chemicals, and dietary compounds. Different approaches and analytical platforms are used to detect, characterize, and quantify metabolites and related metabolic pathways, including untargeted and targeted liquid chromatography-mass spectrometry (LC-MS), gas chromatography-MS (GC-MS), and nuclear magnetic resonance (NMR). In CHEAR, most metabolomics studies use a LC-MS platform to perform untargeted metabolomics. Therefore, the purpose of the tutorial is to provide a basic overview for non-experts of how LC-MS-based untargeted metabolomics datasets are generated, which should aid in data analysis and interpretation.
HHEAR Data Submission and Review Portal - User Manual for HHEAR PI
This User Manual outlines the Major Functions and Processes supported by the HHEAR Data Submission and Review Portal, and how to use them. This manual is intended for use by Primary Investigators (i.e., “PIs”) and their Co-Investigators. These users will be accessing the portal to upload their study results data, generate HHEAR Participant IDs (PIDs) and Specimen IDs (SIDs), map HHEAR SIDs to PIDs, retrieving lab result data, and other related activities.
HHEAR Data Submission and Review Portal - User Manual for HHEAR LH
This User Manual outlines the Laboratory Data Upload Processes supported by the HHEAR Data Submission and Review Portal. This manual is solely intended for use by HHEAR Lab Hub members.
HHEAR Targeted Data Template for LHs
File Hash Checker
If you are not sure how to verify the SHA-512 hash of the file that you downloaded or uploaded, you can use this in-browser file hasher. This will calculate the hash without uploading.
The Data Center is responsible for creating and maintaining the HHEAR Ontology—a common vocabulary for use in the HHEAR program. The Ontology is evolving with the program and will connect to best-in-class existing vocabularies, thus facilitating the integration of data from multiple studies.
Facilitating the mapping of variables from data dictionaries into terms consistent with the HHEAR Ontology
Incorporating the study's data into the HHEAR Ontology to support collaborative research across the HHEAR consortium, including pooled analyses from health research studies participating in CHEAR and HHEAR
Developing methods and services for comparing similar variables from different data dictionaries, starting with very basic mappings of equivalent terms and moving into more sophisticated analyses of relationships among variables
Providing tools and services to manage the HHEAR Ontology evolution