ID
44954
Description
dbGaP Study Accession: phs001297.v1.p1 NCT01607216 Approximately 550,000 babies born prematurely each year in the United States suffer from birth at a time in development when the respiratory tract and immune system would normally be protected and maintained in a naïve state. This project is a component of the NIH Prematurity and Respiratory Outcomes Program (PROP) whose goals are the identification of disease mechanisms and biomarkers to stratify premature infants, at the time of discharge, for their risk of subsequent pulmonary morbidity. This Clinical Research Center (CRC) project will investigate prematurity-dependent alterations in cellular innate and adaptive immune systems resulting in increased susceptibility to respiratory infections and environmental irritants, and leading to respiratory morbidity in the first year of life. Prior studies have established developmental (maturity) and disease-related changes in circulating and pulmonary lymphocyte populations, but a comprehensive assessment of their relationship to disease risk/outcome has not been undertaken. We hypothesize that cellular and molecular immuno-maturity is altered due to intrinsic and extrinsic factors presented by premature birth in such a way as to reduce resistance to viral infections and to promote cytotoxic damage to the lung. We will evaluate immunologic maturity by comprehensively phenotyping lymphocyte populations in peripheral blood sampled at premature delivery, at the time of discharge from the hospital and at twelve months corrected age. The lymphocytic phenotype will be analyzed particularly in the context of gestational age and maternal-fetal stressors capable of modulating oxidative stress (oxygen exposure, infection and environmental tobacco smoke exposure). Additionally, we will assess changes in the molecular phenotype of isolated CD8 lymphocytes, a cell type preferentially recruited to the lungs of premature infants and capable of contributing to disease pathogenesis, by genome-wide expression profiling, in order to uncover novel disease pathways and define a gene expression signature associated with disease risk. We propose to build a statistical model, using cellular and molecular phenotypes and additional clinical variables, for stratifying risk of lung morbidity within the first year of life. Finally, we will assess the development of the gut microbiome in the preterm subjects to correlate with the observation of development of the adaptive immune system. Study Design: Prospective Longitudinal Cohort Study Type: Observational Longitudinal Prospective Cohort Total number of consented subjects: 277 Acknowledgement Statement: Please cite/reference the use of dbGaP data by including the dbGaP accession phs001297.v1.p1. Additionally, use the following statement to acknowledge the submitter(s) of this study: The datasets used for the analyses described in this manuscript were obtained from dbGaP ( https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001297.v1.p1 ) through dbGaP study accession numbers phs001297.v1.p1 . The data for the study Functional and Lymphocytic Markers of Respiratory Morbidity in Hyperoxic Preemies were provided by Drs. Gloria Pryhuber, Tom Mariani, Steven Gill, Rita Ryan, and Anne Marie Reynolds, University of Rochester and University at Buffalo Center for the Prematurity and Respiratory Outcomes Program (PROP). This study was supported by the U.S. National Institutes of Health (NIH), National Heart, Lung, and Blood Institute (NHLBI) grants U01 HL101813 and U01 HL101794, NHLBI and National Institute of Child Health and Human Development (NICHD) supplement grant R01 HL101794-01S1, National Institute of Allergy and Infectious Diseases (NIAID) contract HHSN272201200005C, and National Center for Advancing Translational Sciences (NCATS) grant UL1 TR000042. Dr. Pryhuber and her collaborators request that publications resulting from these data cite their original publications: Misra RS, Bhattacharya S, Huyck HL, Wang J-C E, Slaunwhite CG, Slaunwhite SL, Wightman TR, Secor- Soch S, Misra SK, Bushnell TP, Reynolds A-M, Ryan RM, Quataert SA, Pryhuber GS, Mariani TJ. Flow-Based Sorting of Neonatal Lymphocyte Populations for Transcriptomics Analysis. J Immunol Methods 2016; 437: 13-20. PMID: 27438473. PMCID in process. (And original microbiome publication to follow, after submission).
Link
https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001297.v1.p1
Keywords
Versions (10)
- 4/14/22 4/14/22 - Dr. Christian Niklas
- 4/19/22 4/19/22 - Dr. Christian Niklas
- 4/19/22 4/19/22 - Dr. Christian Niklas
- 4/22/22 4/22/22 - Dr. Christian Niklas
- 4/22/22 4/22/22 - Dr. Christian Niklas
- 5/12/22 5/12/22 - Martin Dugas
- 5/13/22 5/13/22 - Dr. Christian Niklas
- 10/12/22 10/12/22 - Adrian Schulz
- 5/23/23 5/23/23 - Dr. Christian Niklas
- 6/26/23 6/26/23 - Dr. Christian Niklas
Copyright Holder
Drs. Gloria Pryhuber, Tom Mariani, Steven Gill, Rita Ryan, and Anne Marie Reynolds
Uploaded on
May 13, 2022
DOI
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License
Creative Commons BY-NC 4.0
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dbGaP phs001297 PROP Functional and Lymphocytic Markers of Respiratory Morbidity in Hyperoxic Preemies
Dataset pht006202: This sample attributes table includes body site where sample was collected, analyte type, tumor status, histological type, postmenstrual age at time of rectal swab, and day of life at time of rectal swab, Z score of body weight at rectal swab, microbiome phase, read count, medications taken in the past week (n=6 variables; corticosteroid, motility, proton pump inhibitors, H2 receptor antagonists, diurectics, and antimicrobials), and nutrients consumed in the past week (n=11 variables; protein, lipid, total enteral calories, total calories, total carbohydrates, total lipids, total proteins, enteral calories, enteral carbohydrates, enteral lipids, and enteral proteins).
- StudyEvent: Prematurity and Respiratory Outcome Program
- Dataset pht006199: The subject consent data table contains subject IDs, consent group information, and subject aliases.
- Dataset pht006200: This subject sample mapping data table includes a mapping of study subject IDs to sample IDs. Samples are the final preps submitted for genotyping, sequencing, and/or expression data. For example, if one patient (subject ID) gave one sample, and that sample was processed differently to generate 2 sequencing runs, there would be two rows, both using the same subject ID, but having 2 unique sample IDs. The data table also includes sample aliases and sample use.
- Dataset pht006201: This subject phenotype data table includes the subject's sex, race, ethnicity, cohort assignment based on full term or pre-term at gestational birth, gestational age, birth weight and mode, day of life and diagnosis of NEC.
- Dataset pht006202: This sample attributes table includes body site where sample was collected, analyte type, tumor status, histological type, postmenstrual age at time of rectal swab, and day of life at time of rectal swab, Z score of body weight at rectal swab, microbiome phase, read count, medications taken in the past week (n=6 variables; corticosteroid, motility, proton pump inhibitors, H2 receptor antagonists, diurectics, and antimicrobials), and nutrients consumed in the past week (n=11 variables; protein, lipid, total enteral calories, total calories, total carbohydrates, total lipids, total proteins, enteral calories, enteral carbohydrates, enteral lipids, and enteral proteins).
Similar models
Dataset pht006202: This sample attributes table includes body site where sample was collected, analyte type, tumor status, histological type, postmenstrual age at time of rectal swab, and day of life at time of rectal swab, Z score of body weight at rectal swab, microbiome phase, read count, medications taken in the past week (n=6 variables; corticosteroid, motility, proton pump inhibitors, H2 receptor antagonists, diurectics, and antimicrobials), and nutrients consumed in the past week (n=11 variables; protein, lipid, total enteral calories, total calories, total carbohydrates, total lipids, total proteins, enteral calories, enteral carbohydrates, enteral lipids, and enteral proteins).
- StudyEvent: Prematurity and Respiratory Outcome Program
- Dataset pht006199: The subject consent data table contains subject IDs, consent group information, and subject aliases.
- Dataset pht006200: This subject sample mapping data table includes a mapping of study subject IDs to sample IDs. Samples are the final preps submitted for genotyping, sequencing, and/or expression data. For example, if one patient (subject ID) gave one sample, and that sample was processed differently to generate 2 sequencing runs, there would be two rows, both using the same subject ID, but having 2 unique sample IDs. The data table also includes sample aliases and sample use.
- Dataset pht006201: This subject phenotype data table includes the subject's sex, race, ethnicity, cohort assignment based on full term or pre-term at gestational birth, gestational age, birth weight and mode, day of life and diagnosis of NEC.
- Dataset pht006202: This sample attributes table includes body site where sample was collected, analyte type, tumor status, histological type, postmenstrual age at time of rectal swab, and day of life at time of rectal swab, Z score of body weight at rectal swab, microbiome phase, read count, medications taken in the past week (n=6 variables; corticosteroid, motility, proton pump inhibitors, H2 receptor antagonists, diurectics, and antimicrobials), and nutrients consumed in the past week (n=11 variables; protein, lipid, total enteral calories, total calories, total carbohydrates, total lipids, total proteins, enteral calories, enteral carbohydrates, enteral lipids, and enteral proteins).
phv00282757 (dbGaP ID)
C0444215 (UMLS CUI [1,2])
phv00282758 (dbGaP ID)
C0439228 (UMLS CUI [1,2])
C0444215 (UMLS CUI [1,3])
phv00282759 (dbGaP ID)
C0205390 (UMLS CUI [1,2])
phv00282760 (dbGaP ID)
C5236470 (UMLS CUI [1,2])
phv00282761 (dbGaP ID)
C0871421 (UMLS CUI [1,2])
C0444215 (UMLS CUI [2,1])
phv00282762 (dbGaP ID)
C2987125 (UMLS CUI [1,2])
phv00282763 (dbGaP ID)
C1268865 (UMLS CUI [1,2])
phv00282764 (dbGaP ID)
C0358591 (UMLS CUI [1,2])
phv00282755 (dbGaP ID)
C0019593 (UMLS CUI [1,2])
phv00282766 (dbGaP ID)
C0012798 (UMLS CUI [1,2])
phv00282767 (dbGaP ID)
C0443071 (UMLS CUI [1,2])
phv00282768 (dbGaP ID)
C5437053 (UMLS CUI [1,2])
C1556156 (UMLS CUI [1,3])
C2987125 (UMLS CUI [1,4])
phv00282769 (dbGaP ID)
C0556196 (UMLS CUI [1,2])
C1556156 (UMLS CUI [1,3])
C2987125 (UMLS CUI [1,4])
phv00282770 (dbGaP ID)
C0086225 (UMLS CUI [1,2])
C1556156 (UMLS CUI [1,3])
C2987125 (UMLS CUI [1,4])
phv00282771 (dbGaP ID)
C2987125 (UMLS CUI [1,2])
phv00282772 (dbGaP ID)
C2987125 (UMLS CUI [1,2])
phv00282773 (dbGaP ID)
C2987125 (UMLS CUI [1,2])
phv00282774 (dbGaP ID)
C2987125 (UMLS CUI [1,2])
phv00282775 (dbGaP ID)
C1556156 (UMLS CUI [1,2])
C2987125 (UMLS CUI [1,3])
phv00282776 (dbGaP ID)
C0012170 (UMLS CUI [1,2])
C2987125 (UMLS CUI [1,3])
phv00282777 (dbGaP ID)
C0556196 (UMLS CUI [1,2])
C2987125 (UMLS CUI [1,3])
phv00282778 (dbGaP ID)
C5437053 (UMLS CUI [1,2])
C2987125 (UMLS CUI [1,3])
phv00282779 (dbGaP ID)
C0200345 (UMLS CUI [1,2])
phv00282780 (dbGaP ID)
C0332307 (UMLS CUI [1,2])
phv00282781 (dbGaP ID)
phv00282782 (dbGaP ID)
C0370003 (UMLS CUI [1,2])
C0040300 (UMLS CUI [2,1])
C0370003 (UMLS CUI [2,2])
C0040300 (UMLS CUI [3,1])
C0449560 (UMLS CUI [3,2])
C0370003 (UMLS CUI [3,3])
phv00282783 (dbGaP ID)