Predicting humoral responses to primary and booster SARS-CoV-2 mRNA vaccination in people living with HIV: a machine learning approach

Background SARS-CoV-2 mRNA vaccines are highly immunogenic in people living with HIV (PLWH) on effective antiretroviral therapy (ART). However, whether viro-immunologic parameters or other factors affect immune responses to vaccination is debated. This study aimed to develop a machine learning-based...

Full description

Saved in:
Bibliographic Details
Published in:Journal of translational medicine Vol. 22; no. 1; pp. 432 - 9
Main Authors: Montesi, Giorgio, Augello, Matteo, Polvere, Jacopo, Marchetti, Giulia, Medaglini, Donata, Ciabattini, Annalisa
Format: Journal Article
Language:English
Published: London BioMed Central 07.05.2024
BioMed Central Ltd
BMC
Subjects:
Adult
Algorithms
Analysis
Antibodies, Viral - blood
Antibodies, Viral - immunology
Biomedical and Life Sciences
Biomedicine
Care and treatment
COVID-19 - immunology
COVID-19 - prevention & control
COVID-19 Vaccines - administration & dosage
COVID-19 Vaccines - immunology
Female
Health aspects
HIV
HIV Infections - immunology
HIV patients
Humans
Immune response
Immunity, Humoral
Immunization, Secondary
ImmunoVirology and ImmunoOncology
Longitudinal Studies
Machine Learning
Male
Medicine/Public Health
Methods
Middle Aged
mRNA
mRNA Vaccines
RNA, Messenger - genetics
RNA, Messenger - metabolism
SARS-CoV-2
SARS-CoV-2 - immunology
Statistical modeling
Vaccination
Vaccines
Italy
SARS-CoV-2
Statistical modeling
Immune response
HIV
ImmunoVirology
Machine learning
Antibodies
mRNA
Vaccines
ISSN:1479-5876, 1479-5876
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Background SARS-CoV-2 mRNA vaccines are highly immunogenic in people living with HIV (PLWH) on effective antiretroviral therapy (ART). However, whether viro-immunologic parameters or other factors affect immune responses to vaccination is debated. This study aimed to develop a machine learning-based model able to predict the humoral response to mRNA vaccines in PLWH and to assess the impact of demographic and clinical variables on antibody production over time. Methods Different machine learning algorithms have been compared in the setting of a longitudinal observational study involving 497 PLWH, after primary and booster SARS-CoV-2 mRNA vaccination. Both Generalized Linear Models and non-linear Models (Tree Regression and Random Forest) were trained and tested. Results Non-linear algorithms showed better ability to predict vaccine-elicited humoral responses. The best-performing Random Forest model identified a few variables as more influential, within 39 clinical, demographic, and immunological factors. In particular, previous SARS-CoV-2 infection, BMI, CD4 T-cell count and CD4/CD8 ratio were positively associated with the primary cycle immunogenicity, yet their predictive value diminished with the administration of booster doses. Conclusions In the present work we have built a non-linear Random Forest model capable of accurately predicting humoral responses to SARS-CoV-2 mRNA vaccination, and identifying relevant factors that influence the vaccine response in PLWH. In clinical contexts, the application of this model provides promising opportunities for predicting individual vaccine responses, thus facilitating the development of vaccination strategies tailored for PLWH.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ObjectType-Undefined-3
ISSN:1479-5876
1479-5876
DOI:10.1186/s12967-024-05147-1