Applied immuno-epidemiological research: an approach for integrating existing knowledge into the statistical analysis of multiple immune markers
Autor(a) principal: | |
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Data de Publicação: | 2016 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFBA |
Texto Completo: | http://repositorio.ufba.br/ri/handle/ri/22752 |
Resumo: | Background: Immunologists often measure several correlated immunological markers, such as concentrations of different cytokines produced by different immune cells and/or measured under different conditions, to draw insights from complex immunological mechanisms. Although there have been recent methodological efforts to improve the statistical analysis of immunological data, a framework is still needed for the simultaneous analysis of multiple, often correlated, immune markers. This framework would allow the immunologists’ hypotheses about the underlying biological mechanisms to be integrated. Results: We present an analytical approach for statistical analys is of correlated immune markers, such as those commonly collected in modern immuno-epidemiological studies. We demonstrate i) how to deal with interdependencies among multiple measurements of the same immune marker, ii) how to analyse association patterns among different markers, iii) how to aggregate different measures and/or markers to immunological summary scores, iv) how to model the inter-relationships among these scores, and v) how to use these scores in epidemiological association analyses. We illustrate the application of our approach to multiple cytokine measurements from 818 children enrolled in a large immuno-epidemiological study (SCAALA Salvador), which aimed to quantify the major immunological mechanisms underlying atopic diseases or asthma. We demonstrate how to aggregate systematically the information captured in multiple cytokine measurements to immunological summary scores aimed at reflecting the presumed underlying immunological mechanisms (Th1/Th2 balance and immune regulatory network). We show how these aggregated immune scores can be used as predictors in regression models with outcomes of immunological studies (e.g. specific IgE) and compare the results to those obtained by a traditional multivariate regression approach. Conclusion: The proposed analytical approach may be especially useful to quantify complex immune responses in immuno-epidemiological studies, where investigators examine the relationship among epidemiological patterns, immune response, and disease outcomes. |
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Genser, BerndFischer, Joachim E.Figueiredo, Camila A.Alcântara-Neves, Neuza MariaBarreto, Mauricio L.Philip J. Cooper, Philip J. Cooper, et al.Genser, BerndFischer, Joachim E.Figueiredo, Camila A.Alcântara-Neves, Neuza MariaBarreto, Mauricio L.Philip J. Cooper, Philip J. Cooper, et al.2017-06-05T12:30:49Z2017-06-05T12:30:49Z20161471-2172http://repositorio.ufba.br/ri/handle/ri/22752v.17, n.1, p.1-14, 2016Background: Immunologists often measure several correlated immunological markers, such as concentrations of different cytokines produced by different immune cells and/or measured under different conditions, to draw insights from complex immunological mechanisms. Although there have been recent methodological efforts to improve the statistical analysis of immunological data, a framework is still needed for the simultaneous analysis of multiple, often correlated, immune markers. This framework would allow the immunologists’ hypotheses about the underlying biological mechanisms to be integrated. Results: We present an analytical approach for statistical analys is of correlated immune markers, such as those commonly collected in modern immuno-epidemiological studies. We demonstrate i) how to deal with interdependencies among multiple measurements of the same immune marker, ii) how to analyse association patterns among different markers, iii) how to aggregate different measures and/or markers to immunological summary scores, iv) how to model the inter-relationships among these scores, and v) how to use these scores in epidemiological association analyses. We illustrate the application of our approach to multiple cytokine measurements from 818 children enrolled in a large immuno-epidemiological study (SCAALA Salvador), which aimed to quantify the major immunological mechanisms underlying atopic diseases or asthma. We demonstrate how to aggregate systematically the information captured in multiple cytokine measurements to immunological summary scores aimed at reflecting the presumed underlying immunological mechanisms (Th1/Th2 balance and immune regulatory network). We show how these aggregated immune scores can be used as predictors in regression models with outcomes of immunological studies (e.g. specific IgE) and compare the results to those obtained by a traditional multivariate regression approach. Conclusion: The proposed analytical approach may be especially useful to quantify complex immune responses in immuno-epidemiological studies, where investigators examine the relationship among epidemiological patterns, immune response, and disease outcomes.Submitted by Maria Creuza Silva (mariakreuza@yahoo.com.br) on 2017-06-05T12:30:49Z No. of bitstreams: 1 Mauricio Barreto. Applied...2016.pdf: 1478041 bytes, checksum: f331a7cf1475e0df9da3df21f0826725 (MD5)Made available in DSpace on 2017-06-05T12:30:49Z (GMT). No. of bitstreams: 1 Mauricio Barreto. Applied...2016.pdf: 1478041 bytes, checksum: f331a7cf1475e0df9da3df21f0826725 (MD5)Londonhttps://www.ncbi.nlm.nih.gov/pubmed/27206492reponame:Repositório Institucional da UFBAinstname:Universidade Federal da Bahia (UFBA)instacron:UFBAImmuno-epidemiologyCorrelated immune markersCytokinesStatistical analysisConceptual frameworksApplied immuno-epidemiological research: an approach for integrating existing knowledge into the statistical analysis of multiple immune markersBMC Immunol.info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleBrasilinfo:eu-repo/semantics/openAccessengORIGINALMauricio Barreto. Applied...2016.pdfMauricio Barreto. 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dc.title.pt_BR.fl_str_mv |
Applied immuno-epidemiological research: an approach for integrating existing knowledge into the statistical analysis of multiple immune markers |
dc.title.alternative.pt_BR.fl_str_mv |
BMC Immunol. |
title |
Applied immuno-epidemiological research: an approach for integrating existing knowledge into the statistical analysis of multiple immune markers |
spellingShingle |
Applied immuno-epidemiological research: an approach for integrating existing knowledge into the statistical analysis of multiple immune markers Genser, Bernd Immuno-epidemiology Correlated immune markers Cytokines Statistical analysis Conceptual frameworks |
title_short |
Applied immuno-epidemiological research: an approach for integrating existing knowledge into the statistical analysis of multiple immune markers |
title_full |
Applied immuno-epidemiological research: an approach for integrating existing knowledge into the statistical analysis of multiple immune markers |
title_fullStr |
Applied immuno-epidemiological research: an approach for integrating existing knowledge into the statistical analysis of multiple immune markers |
title_full_unstemmed |
Applied immuno-epidemiological research: an approach for integrating existing knowledge into the statistical analysis of multiple immune markers |
title_sort |
Applied immuno-epidemiological research: an approach for integrating existing knowledge into the statistical analysis of multiple immune markers |
author |
Genser, Bernd |
author_facet |
Genser, Bernd Fischer, Joachim E. Figueiredo, Camila A. Alcântara-Neves, Neuza Maria Barreto, Mauricio L. Philip J. Cooper, Philip J. Cooper, et al. |
author_role |
author |
author2 |
Fischer, Joachim E. Figueiredo, Camila A. Alcântara-Neves, Neuza Maria Barreto, Mauricio L. Philip J. Cooper, Philip J. Cooper, et al. |
author2_role |
author author author author author |
dc.contributor.author.fl_str_mv |
Genser, Bernd Fischer, Joachim E. Figueiredo, Camila A. Alcântara-Neves, Neuza Maria Barreto, Mauricio L. Philip J. Cooper, Philip J. Cooper, et al. Genser, Bernd Fischer, Joachim E. Figueiredo, Camila A. Alcântara-Neves, Neuza Maria Barreto, Mauricio L. Philip J. Cooper, Philip J. Cooper, et al. |
dc.subject.por.fl_str_mv |
Immuno-epidemiology Correlated immune markers Cytokines Statistical analysis Conceptual frameworks |
topic |
Immuno-epidemiology Correlated immune markers Cytokines Statistical analysis Conceptual frameworks |
description |
Background: Immunologists often measure several correlated immunological markers, such as concentrations of different cytokines produced by different immune cells and/or measured under different conditions, to draw insights from complex immunological mechanisms. Although there have been recent methodological efforts to improve the statistical analysis of immunological data, a framework is still needed for the simultaneous analysis of multiple, often correlated, immune markers. This framework would allow the immunologists’ hypotheses about the underlying biological mechanisms to be integrated. Results: We present an analytical approach for statistical analys is of correlated immune markers, such as those commonly collected in modern immuno-epidemiological studies. We demonstrate i) how to deal with interdependencies among multiple measurements of the same immune marker, ii) how to analyse association patterns among different markers, iii) how to aggregate different measures and/or markers to immunological summary scores, iv) how to model the inter-relationships among these scores, and v) how to use these scores in epidemiological association analyses. We illustrate the application of our approach to multiple cytokine measurements from 818 children enrolled in a large immuno-epidemiological study (SCAALA Salvador), which aimed to quantify the major immunological mechanisms underlying atopic diseases or asthma. We demonstrate how to aggregate systematically the information captured in multiple cytokine measurements to immunological summary scores aimed at reflecting the presumed underlying immunological mechanisms (Th1/Th2 balance and immune regulatory network). We show how these aggregated immune scores can be used as predictors in regression models with outcomes of immunological studies (e.g. specific IgE) and compare the results to those obtained by a traditional multivariate regression approach. Conclusion: The proposed analytical approach may be especially useful to quantify complex immune responses in immuno-epidemiological studies, where investigators examine the relationship among epidemiological patterns, immune response, and disease outcomes. |
publishDate |
2016 |
dc.date.issued.fl_str_mv |
2016 |
dc.date.accessioned.fl_str_mv |
2017-06-05T12:30:49Z |
dc.date.available.fl_str_mv |
2017-06-05T12:30:49Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://repositorio.ufba.br/ri/handle/ri/22752 |
dc.identifier.issn.none.fl_str_mv |
1471-2172 |
dc.identifier.number.pt_BR.fl_str_mv |
v.17, n.1, p.1-14, 2016 |
identifier_str_mv |
1471-2172 v.17, n.1, p.1-14, 2016 |
url |
http://repositorio.ufba.br/ri/handle/ri/22752 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.publisher.country.fl_str_mv |
Brasil |
dc.source.pt_BR.fl_str_mv |
https://www.ncbi.nlm.nih.gov/pubmed/27206492 |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da UFBA instname:Universidade Federal da Bahia (UFBA) instacron:UFBA |
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