Prediction of early Toxoplasma gondii infection during pregnancy combining host biomarkers and machine learning

Abstract

Toxoplasma gondii is a major cause of reproductive failure and severe gestational complications in both humans and small ruminants and early diagnosis is essential to prevent adverse outcomes. In sheep, congenital toxoplasmosis can trigger early fetal death (EFD) during the acute phase of the infection (days 7-14 post-infection) when conventional diagnosis methods are ineffective. Therefore, this study aimed to identify early infection biomarkers using a machine learning approach to support EFD diagnosis and providing a translational foundation for early diagnostic strategies in human toxoplasmosis. For that, 27 serum biomarkers (cytokines, acute-phase proteins, oxidative stress and hemostatic parameters) were evaluated during the early stage of infection in 22 pregnant sheep experimentally challenged with T. gondii and 6 uninfected controls. To assess diagnostic specificity, models were tested using sera from animals experimentally infected with other abortifacient agents (Neospora caninum and Chlamydia abortus) and validated with samples from 19 natural abortion outbreaks. A four-biomarkers panel, encompassing CXCL9, CXCL10, IFN-{gamma}, and serum amyloid A, achieved over 74% accuracy in the specific detection of early T. gondii infection and 100% of sensitivity when studying natural infection cases. This approach might support the development of rapid diagnostic strategies, improving early intervention and disease management in livestock production systems. Its translation to human medicine could also aid in addressing current diagnostic gaps and facilitate timely interventions to prevent congenital toxoplasmosis and pregnancy loss in women.