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

## TL;DR

This study uses machine learning to find factors affecting antibody production and adverse reactions after receiving a COVID-19 vaccine.

## Contribution

The novel use of machine learning and Bayesian network analysis to identify health and lifestyle factors influencing vaccine outcomes.

## Key findings

- Females with lower free testosterone levels experience more adverse reactions than males.
- Younger individuals have more active immune systems, leading to higher antibody production and adverse reactions.
- Drinking 2–3 cups of green tea daily is associated with increased antibody production.

## Abstract

Background: Coronavirus disease 2019 (COVID-19) vaccines deliver mRNA packaged in lipid nanoparticles via intramuscular injection. This study investigated several factors influencing antibody production patterns and adverse reactions after vaccination with COVID-19 vaccines. Methods: Among the participants of the Iwaki Health Promotion Project (IHPP), 211 individuals who consented to this study were surveyed regarding antibody titers and adverse reaction symptoms following vaccination. A machine learning approaches such as ridge regression, elastic-net, light gradient boosting, and neural network were applied to extract the variables, and Bayesian network analysis was applied to explore causal relationships between health data and the multi-omics dataset obtained from the IHPP health checkups. Results: Females with lower levels of free testosterone experienced more adverse reactions than males. Moreover, the immune system is more active in younger individuals, causing adverse reactions and higher antibody production. The Spikevax vaccine induced adverse reaction symptoms with higher antibody production in cases of fever. Meanwhile, drinking 2–3 cups of green tea daily seemed to be effective in increasing antibody production. Factors increasing side effect risk include blood natural killer cell count and muscle quality in the vaccinated arm. Plasma metabolome metabolite concentrations, tongue coating bacterial colonization, and folate intake were also identified as factors influencing side effect risk. Furthermore, characteristics of participants at risk for fever symptoms included longer telomere length, higher antibody production patterns, and higher CD4-positive T cell counts. Conclusions: Further investigation of these identified influencing factors is expected to clarify the rationale for new vaccine development and identify lifestyle and dietary habits that enhance vaccine efficacy.