This is a summary, written by members of the CITF Secretariat, of:

Cuperlovic-Culf M, Bennet S, Galipeau Y, McCluskie PS, Arnold C, Bagheri S, Cooper CL, Langlois M, Fritz J, Piccirillo C, Crawley AM. Multi-variate statistical and machine learning reveals the interplay between sex and age in antibody responses to de novo SARS-CoV-2 infection and vaccination. bioRxiv. 2023 December 5. doi:

The results and/or conclusions contained in the research do not necessarily reflect the views of all CITF members.

A CITF-funded study, published as a preprint and not yet peer-reviewed, found intriguing relationships — correlates of infection or protection — from all the COVID-19 data the researchers collected. Advanced machine learning, a form of artificial intelligence that allows computers to adapt and draw inferences from data without explicitly being programmed to do so, facilitated the analyses. Most notably, the team found compelling evidence of a link between an individual’s biological sex and the ability to generate and maintain antibodies. The research was led by Dr. Miroslava Cuperlovic-Culf (University of Ottawa) and Dr. Angela M. Crawley (The Ottawa Hospital Research Institute and University of Ottawa) in collaboration with Dr. Marc-André Langlois (University of Ottawa) and Dr. Ciriaco (Ciro) Piccirillo (McGill University).

For this analysis, serum was collected from 970 participants and separated into groups by:

  • Vaccination period (unvaccinated, dose 1, dose 2);
  • Age (<40, 40-60, >60);
  • Sex (male/female);
  • SARS-CoV-2 infection acquired immunity status (positive or negative for IgG against SARS-CoV-2 nucleoprotein).

For each of these groups, nine different Ab responses were measured: IgG, IgA, and IgM titers specific for SARS-CoV-2 receptor binding domain (R), spike (S), and nucleoprotein (N), as well as several neutralization measurements.

A multi-variate analysis was conducted on the acquired serology and neutralization assay data that could provide multiple possible groupings of the data and potential correlations between various data groups (both linear and non-linear). Various statistical functions were applied to identify significant relationships.

Key findings:

  • Age and sex weakly affect overall response to vaccination and infection;
  • Major differences in antibody responses between males and females are dependent on age and infection-acquired immunity;
  • The correlation analysis showed quantitative links between sex and antibody responses;
  • The correlation between antibodies and neutralization varies over time and differently in males and females.

Overall, the researchers introduced integrative and novel, multi-variate, machine learning, and network analysis-based tools that can assess multiple immunological parameters over time and across a diverse population. In the future, these may help to define correlates of protection and inform the design of age and sex precision-focused vaccines.