Evidence map›Paper›PMID 40110889›Full record

ArticleInternational immunology2025

Effect of prevaccination blood and T-cell phenotypes on antibody responses to a COVID-19 mRNA vaccine.

Yu Hidaka, Norihide Jo, Osamu Kikuchi, Masaru Fukahori, Takeshi Sawada, Yutaka Shimazu, Masaki Yamamoto, Kohei Kometani, Miki Nagao, Takako E Nakajima and 3 more

Abstract read
In one paragraph

Article in International immunology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Yu HidakaDepartment of Biomedical Statistics and Bioinformatics, Graduate School of Medicine, Kyoto University, Kyoto, Japan.ORCID 0000-0003-3637-4429
Norihide JoDepartment of Life Science Frontiers, Center for iPS Cell Research and Application (CiRA), Kyoto University, Kyoto, Japan.
Osamu KikuchiDepartment of Medical Oncology, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
Masaru FukahoriDepartment of Early Clinical Development, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
Takeshi SawadaDepartment of Early Clinical Development, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
Yutaka ShimazuDepartment of Early Clinical Development, Graduate School of Medicine, Kyoto University, Kyoto, Japan.ORCID 0000-0002-1604-7220
Masaki YamamotoDepartment of Clinical Laboratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
Kohei KometaniDepartment of Life Science Frontiers, Center for iPS Cell Research and Application (CiRA), Kyoto University, Kyoto, Japan.
Miki NagaoDepartment of Clinical Laboratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
Takako E NakajimaDepartment of Early Clinical Development, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
Manabu MutoDepartment of Medical Oncology, Graduate School of Medicine, Kyoto University, Kyoto, Japan.ORCID 0000-0002-3127-8203
Satoshi MoritaDepartment of Biomedical Statistics and Bioinformatics, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
Yoko HamazakiDepartment of Life Science Frontiers, Center for iPS Cell Research and Application (CiRA), Kyoto University, Kyoto, Japan.ORCID 0000-0002-3911-5708

Funding

CiRACOVID-19 Private Fund to the Shinya Yamanaka LaboratoryiPS Cell Research FundJapan Agency for Medical Research and Development JP17gm5010005Japan Agency for Medical Research and Development JP20fk0108265Japan Agency for Medical Research and Development JP20fk0108454Japan Agency for Medical Research and Development JP223fa627009Japan Agency for Medical Research and Development JP24gm1810012Japanese Ministry of Education, Culture, Sports, Science and Technology (MEXT)Japan Society for the Promotion of Science 21K15467Kansai Economic FederationKyoto UniversitySumitomo-Mitsui Trust Bank Fund for COVID-19 ResearchTakeda Science Foundation
6 · The paper itself

Abstract

Despite the high effectiveness of the coronavirus disease 2019 (COVID-19) mRNA vaccines, both immunogenicity and reactogenicity show substantial interindividual variability. One key challenge is predicting high and low responders using easily measurable parameters. In this study, we performed multivariate linear regression analysis, which allows adjustment for confounding, to explore independent predictive factors for antibody responses. Using data from 216 healthy vaccinated donors aged 23-81 years, we evaluated baseline characteristics, prevaccination blood and T-cell phenotypes, and post-vaccination T-cell responses as variables, with anti-receptor-binding domain (RBD) immunoglobulin G (IgG) titers following two doses of BNT162b2 vaccination as the primary outcome. Consistent with previous reports, higher age, a history of allergic disease, and autoimmune disease were associated with lower peak IgG titers. Additionally, the frequencies of interferon-γ+ spike-specific CD4+ T cells (T-cell response) following the first vaccination strongly correlated with higher IgG responses, while those of pre-existing spike-reactive T cells showed no association with peak IgG titers. Furthermore, we identified lower percentages of naïve CD8+ T cells, lower hemoglobin levels, lower lymphocyte counts, and higher mean corpuscular volume as independent pre-vaccination predictors of lower peak IgG levels. Notably, the frequency of naïve CD8+ T cells showed a positive correlation with the peak IgG levels even in univariate analysis. These findings contribute to the individualized prediction of mRNA vaccine efficacy and may provide insights into the mechanisms underlying individual heterogeneity in immune responses.

Indexed as

Antibodies, ViralCD4-Positive T-LymphocytesCD8-Positive T-LymphocytesCOVID-19COVID-19 VaccinesSARS-CoV-2AdultAgedAged, 80 and overAntibody FormationBNT162 VaccineFemaleHumansImmunoglobulin GMaleMiddle AgedAntibodies, ViralBNT162 VaccineCOVID-19 VaccinesImmunoglobulin GmRNA VaccinesSpike Glycoprotein, Coronavirusimmunogenicitymultivariate linear regression analysisprediction of vaccine responseSARS-CoV-2vaccine efficacy

Identifiers

PMID40110889
PMCPMC12190804

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.