Predictive Factors for Neutralizing Antibody Levels Nine Months after Full Vaccination with BNT162b2: Results of a Machine Learning Analysis

Published on
Journal: Biomedicines
[저자] Dimitris Papadopoulos, Ioannis Ntanasis-Stathopoulos, Maria Gavriatopoulou, Zoi Evangelakou, Panagiotis Malandrakis, Maria S. Manola, Despoina D. Gianniou, Efstathios Kastritis, Ioannis P. Trougakos, Meletios A. Dimopoulos, Vangelis Karalis, Evangelos Terpos
[Category] COVID-19,
PMC URL    [DOI] 10.3390/biomedicines10020204    
[Article Type] Article
[Source] PMC

Vaccination against SARS-CoV-2 with BNT162b2 mRNA vaccine plays a critical role in COVID-19 prevention. Although BNT162b2 is highly effective against COVID-19, a time-dependent decrease in neutralizing antibodies (NAbs) is observed. The aim of this study was to identify the individual features that may predict NAbs levels after vaccination. Machine learning techniques were applied to data from 302 subjects. Principal component analysis (PCA), factor analysis of mixed data (FAMD), k-means clustering, and random forest were used. PCA and FAMD showed that younger subjects had higher levels of neutralizing antibodies than older subjects. The effect of age is strongest near the vaccination date and appears to decrease with time. Obesity was associated with lower antibody response. Gender had no effect on NAbs at nine months, but there was a modest association at earlier time points. Participants with autoimmune disease had lower inhibitory levels than participants without autoimmune disease. K-Means clustering showed the natural grouping of subjects into five categories in which the characteristics of some individuals predominated. Random forest allowed the characteristics to be ordered by importance. Older age, higher body mass index, and the presence of autoimmune diseases had negative effects on the development of NAbs against SARS-CoV-2, nine months after full vaccination.

All Keywords
【저자키워드】 COVID-19, SARS-CoV-2, Neutralizing antibodies, machine learning, principal component analysis, Random forest, K-means clustering, factor analysis of mixed data, 【초록키워드】 neutralizing antibody, vaccination, Antibody Response, BNT162b2, Autoimmune disease, body mass index, Characteristics, Clustering, age, Critical, predict, BNT162b2 mRNA vaccine, NAb, association, Analysis, PCA, Principal component, subject, individual, NAbs, participant, older subjects, machine, random, inhibitory, Effect, FIVE, feature, effective, decrease, identify, nine, applied, appear, were used, subjects, category, had no, younger subject, 【제목키워드】 Factor, Result, Level, Month,