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Using Machine Learning to Identify Factors Affecting Antibody Production and Adverse Reactions After COVID-19

Nahomi Miyamoto1, Tohru Yamaguchi2, Yoshinori Tamada2

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Vaccines
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Summary

COVID-19 vaccine responses vary by individual factors. Female sex, younger age, and specific health markers influence adverse reactions and antibody production, with green tea potentially boosting immunity.

Keywords:
Bayesian networkCOVID-19 vaccineIwaki Health Promotion ProjectSARS-CoV-2 viruscarrier moleculesmachine learning

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Area of Science:

  • Immunology
  • Vaccinology
  • Biostatistics

Background:

  • COVID-19 vaccines utilize mRNA technology encapsulated in lipid nanoparticles.
  • Intramuscular injection is the standard delivery method for these vaccines.
  • Understanding factors influencing vaccine response is crucial for public health.

Purpose of the Study:

  • To investigate factors affecting antibody production after COVID-19 vaccination.
  • To identify variables associated with adverse reactions post-vaccination.
  • To explore causal relationships between health data and vaccine response using multi-omics.

Main Methods:

  • Survey of 211 participants from the Iwaki Health Promotion Project (IHPP) on antibody titers and adverse reactions.
  • Application of machine learning algorithms (ridge regression, elastic-net, light gradient boosting, neural network) for variable extraction.
  • Bayesian network analysis to determine causal links between health data, multi-omics, and vaccine outcomes.

Main Results:

  • Females with lower free testosterone and younger individuals exhibited more adverse reactions and higher antibody production.
  • Spikevax vaccination associated with fever, higher antibody production; green tea consumption linked to increased antibody levels.
  • Factors influencing side effect risk included natural killer cell count, muscle quality, plasma metabolome, gut microbiota, and folate intake.

Conclusions:

  • Identified factors influencing COVID-19 vaccine response, including sex, age, lifestyle, and specific biomarkers.
  • Findings suggest potential for personalized vaccine strategies and lifestyle interventions to enhance vaccine efficacy.
  • Further research can inform new vaccine development and optimize public health recommendations.