Evaluation of determinants of the serological response to the quadrivalent split‐inactivated influenza vaccine
Abstract The seasonal influenza vaccine is only effective in half of the vaccinated population. To identify determinants of vaccine efficacy, we used data from > 1,300 vaccination events to predict the response to vaccination measured as seroconversion as well as hemagglutination inhibition (HAI)...
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Format: | Article |
Language: | English |
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Springer Nature
2022-05-01
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Series: | Molecular Systems Biology |
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Online Access: | https://doi.org/10.15252/msb.202110724 |
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author | Shaohuan Wu Ted M Ross Michael A Carlock Elodie Ghedin Hyungwon Choi Christine Vogel |
author_facet | Shaohuan Wu Ted M Ross Michael A Carlock Elodie Ghedin Hyungwon Choi Christine Vogel |
author_sort | Shaohuan Wu |
collection | DOAJ |
description | Abstract The seasonal influenza vaccine is only effective in half of the vaccinated population. To identify determinants of vaccine efficacy, we used data from > 1,300 vaccination events to predict the response to vaccination measured as seroconversion as well as hemagglutination inhibition (HAI) titer levels one year after. We evaluated the predictive capabilities of age, body mass index (BMI), sex, race, comorbidities, vaccination history, and baseline HAI titers, as well as vaccination month and vaccine dose in multiple linear regression models. The models predicted the categorical response for > 75% of the cases in all subsets with one exception. Prior vaccination, baseline titer level, and age were the major determinants of seroconversion, all of which had negative effects. Further, we identified a gender effect in older participants and an effect of vaccination month. BMI had a surprisingly small effect, likely due to its correlation with age. Comorbidities, vaccine dose, and race had negligible effects. Our models can generate a new seroconversion score that is corrected for the impact of these factors which can facilitate future biomarker identification. |
first_indexed | 2024-03-07T16:44:36Z |
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institution | Directory Open Access Journal |
issn | 1744-4292 |
language | English |
last_indexed | 2024-03-07T16:44:36Z |
publishDate | 2022-05-01 |
publisher | Springer Nature |
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series | Molecular Systems Biology |
spelling | doaj.art-afaaae3aace147a5afe40c6741b340322024-03-03T07:07:55ZengSpringer NatureMolecular Systems Biology1744-42922022-05-01185n/an/a10.15252/msb.202110724Evaluation of determinants of the serological response to the quadrivalent split‐inactivated influenza vaccineShaohuan Wu0Ted M Ross1Michael A Carlock2Elodie Ghedin3Hyungwon Choi4Christine Vogel5Center for Genomics and Systems Biology New York University NY USADepartment of Infectious Diseases College of Veterinary Medicine University of Georgia Athens GA USADepartment of Infectious Diseases College of Veterinary Medicine University of Georgia Athens GA USACenter for Genomics and Systems Biology New York University NY USADepartment of Medicine Yong Loo Lin School of Medicine National University of Singapore Singapore City SingaporeCenter for Genomics and Systems Biology New York University NY USAAbstract The seasonal influenza vaccine is only effective in half of the vaccinated population. To identify determinants of vaccine efficacy, we used data from > 1,300 vaccination events to predict the response to vaccination measured as seroconversion as well as hemagglutination inhibition (HAI) titer levels one year after. We evaluated the predictive capabilities of age, body mass index (BMI), sex, race, comorbidities, vaccination history, and baseline HAI titers, as well as vaccination month and vaccine dose in multiple linear regression models. The models predicted the categorical response for > 75% of the cases in all subsets with one exception. Prior vaccination, baseline titer level, and age were the major determinants of seroconversion, all of which had negative effects. Further, we identified a gender effect in older participants and an effect of vaccination month. BMI had a surprisingly small effect, likely due to its correlation with age. Comorbidities, vaccine dose, and race had negligible effects. Our models can generate a new seroconversion score that is corrected for the impact of these factors which can facilitate future biomarker identification.https://doi.org/10.15252/msb.202110724human cohortimmune responseinfluenzasplit‐inactivated influenza vaccinestatistical modeling |
spellingShingle | Shaohuan Wu Ted M Ross Michael A Carlock Elodie Ghedin Hyungwon Choi Christine Vogel Evaluation of determinants of the serological response to the quadrivalent split‐inactivated influenza vaccine Molecular Systems Biology human cohort immune response influenza split‐inactivated influenza vaccine statistical modeling |
title | Evaluation of determinants of the serological response to the quadrivalent split‐inactivated influenza vaccine |
title_full | Evaluation of determinants of the serological response to the quadrivalent split‐inactivated influenza vaccine |
title_fullStr | Evaluation of determinants of the serological response to the quadrivalent split‐inactivated influenza vaccine |
title_full_unstemmed | Evaluation of determinants of the serological response to the quadrivalent split‐inactivated influenza vaccine |
title_short | Evaluation of determinants of the serological response to the quadrivalent split‐inactivated influenza vaccine |
title_sort | evaluation of determinants of the serological response to the quadrivalent split inactivated influenza vaccine |
topic | human cohort immune response influenza split‐inactivated influenza vaccine statistical modeling |
url | https://doi.org/10.15252/msb.202110724 |
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