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APPLIED IMMUNO-EPIDEMIOLOGICAL RESEARCH: AN APPROACH FOR INTEGRATING EXISTING KNOWLEDGE INTO THE STATISTICAL ANALYSIS OF MULTIPLE IMMUNE MARKERS
Marcadores imunológicos correlacionados
Citocinas
Análise estatística
Estruturas conceituais
Correlated immune markers
Cytokines
Statistical analysis
Conceptual frameworks
Author
Affilliation
Universidade Federal da Bahia. Instituto de Saúde Coletiva. Salvador, BA, Brasil / University of Heidelberg. Mannheim Institute of Public Health, Social and Preventive Medicine. Heidelberg, Germany
University of Heidelberg. Mannheim Institute of Public Health, Social and Preventive Medicine. Heidelberg, Germany
Universidade Federal da Bahia. Instituto de Ciências da Saúde. Salvador, BA, Brasil
Universidade Federal da Bahia. Instituto de Ciências da Saúde. Salvador, BA, Brasil
Fundação Oswaldo Cruz. Centro de Pesquisas Gonçalo Moniz. Salvador, BA, Brasil / Universidade Federal da Bahia. Instituto de Saúde Coletiva. Salvador, BA, Brasil
St George’s University of London. Institute of Infection and Immunity. London, UK / Pontificia Universidad Católica del Ecuador. Centro de Investigación en Enfermedades Infecciosas y Crónicas. Quito, Ecuador
Universidade Federal da Bahia. Instituto de Saúde Coletiva. Salvador, BA, Brasil / Universidade Federal da Bahia. Instituto de Matemática. Salvador, BA, Brasil
Medical University of Vienna. Clinical Division of Nephrology. Internal Medicine III. Vienna, Austria
Medical University of Vienna. Institute of Medical Genetics. Vienna, Austria
London School of Hygiene & Tropical Medicine. London, UK
University of Heidelberg. Mannheim Institute of Public Health, Social and Preventive Medicine. Heidelberg, Germany
Universidade Federal da Bahia. Instituto de Ciências da Saúde. Salvador, BA, Brasil
Universidade Federal da Bahia. Instituto de Ciências da Saúde. Salvador, BA, Brasil
Fundação Oswaldo Cruz. Centro de Pesquisas Gonçalo Moniz. Salvador, BA, Brasil / Universidade Federal da Bahia. Instituto de Saúde Coletiva. Salvador, BA, Brasil
St George’s University of London. Institute of Infection and Immunity. London, UK / Pontificia Universidad Católica del Ecuador. Centro de Investigación en Enfermedades Infecciosas y Crónicas. Quito, Ecuador
Universidade Federal da Bahia. Instituto de Saúde Coletiva. Salvador, BA, Brasil / Universidade Federal da Bahia. Instituto de Matemática. Salvador, BA, Brasil
Medical University of Vienna. Clinical Division of Nephrology. Internal Medicine III. Vienna, Austria
Medical University of Vienna. Institute of Medical Genetics. Vienna, Austria
London School of Hygiene & Tropical Medicine. London, UK
Abstract
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 analysis 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.
Keywords in Portuguese
Imuno-epidemiologiaMarcadores imunológicos correlacionados
Citocinas
Análise estatística
Estruturas conceituais
Keywords
Immuno-epidemiologyCorrelated immune markers
Cytokines
Statistical analysis
Conceptual frameworks
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