Abstract:Prior vaccination can alternately enhance or attenuate influenza vaccine immunogenicity and effectiveness. Analogously, we found that vaccine immunogenicity was enhanced by prior A(H3N2) virus infection among participants of the Ha Nam Cohort, Viet Nam, but was attenuated by prior vaccination among Australian Health Care Workers (HCWs) vaccinated in the same year. Here, we combined these studies to directly compare antibody titers against 35 A(H3N2) viruses spanning 1968–2018. Participants received licensed in… Show more
“…96 For influenza B, however, estimates were comparable with their main findings when the analysis was restricted to those patients with no documented influenza B infection in the previous season. 96 41 (49%) of 83 studies were eligible for inclusion in the meta-analysis (appendix pp [10][11][12]. These studies reported a total of 85 type-specific or subtype-specific allage estimates: 19 (23%) for influenza A(H1N1)pdm09, 30 (36%) for influenza A(H3N2), 22 (26%) for influenza B (lineage not specified), five (4%) for influenza B/ Victoria, and nine (11%) for influenza B/Yamagata (figures 2-4 and table 2).…”
Section: Resultsmentioning
confidence: 99%
“… 5 A 1999 review of ensuing immunological studies identified that roughly half of published serological studies reported reduced post-vaccination antibody titres against A(H3N2) in people who had received multiple influenza vaccinations compared with those who had received a single influenza vaccination. 6 Several subsequent studies have shown diminishing post-vaccination antibody responses 7 , 8 , 9 , 10 , 11 and diminishing vaccine effectiveness 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 as the number of previous vaccines an individual has been given increases.…”
“…96 For influenza B, however, estimates were comparable with their main findings when the analysis was restricted to those patients with no documented influenza B infection in the previous season. 96 41 (49%) of 83 studies were eligible for inclusion in the meta-analysis (appendix pp [10][11][12]. These studies reported a total of 85 type-specific or subtype-specific allage estimates: 19 (23%) for influenza A(H1N1)pdm09, 30 (36%) for influenza A(H3N2), 22 (26%) for influenza B (lineage not specified), five (4%) for influenza B/ Victoria, and nine (11%) for influenza B/Yamagata (figures 2-4 and table 2).…”
Section: Resultsmentioning
confidence: 99%
“… 5 A 1999 review of ensuing immunological studies identified that roughly half of published serological studies reported reduced post-vaccination antibody titres against A(H3N2) in people who had received multiple influenza vaccinations compared with those who had received a single influenza vaccination. 6 Several subsequent studies have shown diminishing post-vaccination antibody responses 7 , 8 , 9 , 10 , 11 and diminishing vaccine effectiveness 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 as the number of previous vaccines an individual has been given increases.…”
“…The Low Dimensionality of Antibody-Virus Interactions Empowers Matrix Completion Given the vast diversity of antibodies, it is easy to imagine that serum responses cannot inform one another. Indeed, many factors including age, geographic location, frequency/type of vaccinations, and infection history shape the antibody response and influence how it responds to a vaccine or a new viral threat (Kim et al, 2012;Fonville et al, 2014;Thompson et al, 2016;Gouma et al, 2020;Fox et al, 2022).…”
Section: Resultsmentioning
confidence: 99%
“…Using the age of each subject, these landscapes can quantify how immune imprinting throughout childhood shapes the subsequent antibody response (vinh et al, 2021). In addition, given the growing interest in universal influenza Vaccines capable of inhibiting diverse variants, these high-resolution responses will power studies examining the breadth of the antibody response not just forward in time against newly emerging variants, but also backwards in time to assess how rapidly immunity decays (Carter et al, 2016; Boyoglu-Barnum et al, 2021; Fox et al, 2022). We found that serum potency (the minimum HAI titer against a set of viruses) decreases for more distinct viruses [Figure 7B], as was shown for monoclonal antibodies (Creanga et al, 2021; Einav and Cleary, 2022).…”
A central challenge in every field of biology is to use existing measurements to predict the outcomes of future experiments. In this work, we consider the wealth of antibody inhibition data against variants of the influenza virus. Due to this virus's genetic diversity and evolvability, the variants examined in one study will often have little-to-no overlap with other studies, making it difficult to discern common patterns or unify datasets for further analysis. To that end, we develop a computational framework that predicts how an antibody or serum would inhibit any variant from any other study. We use this framework to greatly expand 7 influenza datasets utilizing hemagglutination inhibition, validating our method upon 200,000 existing measurements and predicting more than 2,000,000 new values along with their prediction uncertainties. This data-driven approach does not require any information beyond each virus's name and measurements, and even datasets with as few as 5 viruses can be expanded, making this approach widely applicable. Future influenza studies using hemagglutination inhibition can directly utilize our curated datasets to predict newly measured antibody responses against ≈80 H3N2 influenza viruses from 1968-2011, whereas immunological studies utilizing other viruses or a different assay only need to find a single partially-overlapping dataset to extend their work. In essence, this approach enables a shift in perspective when analyzing data from "what you see is what you get" into "what anyone sees is what everyone gets."
“…Whilst results may differ for a highly seronegative population, in our study, restricting analyses to those participants who had HI titres < 40 at baseline did not alter findings with similar results for children and adolescents with and without obesity. Prior vaccination or infection may potentially influence subsequent vaccine responses [ 31 ] and may provide cross-protective immunity against future novel influenza virus exposure [ 32 ]. Our study did not examine cross-protection against non-vaccine strains, however, we did compare seroprotection between participants who had ( n = 20) and had not ( n = 24) received a prior recent influenza vaccine and found similarly high levels of seroprotection.…”
Obesity can increase the severity of influenza infection. Data are limited regarding immune responses to influenza vaccination in obese children. We aimed to investigate the impact of obesity on quadrivalent influenza vaccine responses in children. Children with obesity (body mass index (BMI) ≥ 95th percentile for age and gender) and children without obesity (BMI < 95th percentile) were enrolled in the study. Blood samples were collected before, 1, and 6 months after influenza vaccination, to measure antibody responses by haemagglutination inhibition (HI) assay. Vaccine immunogenicity outcomes were compared between children with and without obesity. Forty-four children (mean age 13.3 ± 2.1 years, 18 males and 14 with obesity) completed the 6-month study. More than 90% of the participants with and without obesity had seroprotective antibody titres (HI ≥ 40) at both 1 and 6 months following vaccination for each of the four influenza strains (A/H3N2, A/H1N1, B/(Victoria) and B/(Yamagata)). Influenza-specific geometric mean titres at baseline, 1, and 6 months post-vaccination were similar between children with and without obesity for all influenza vaccine strains. Children with and without obesity have robust, sustained antibody responses over 6 months to the quadrivalent influenza vaccine.
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