Melbourne School of Population and Global Health - Theses
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ItemQuantification of the dynamics of antibody response to malaria in pregnant women( 2020)Malaria remains a major public health threat with pregnant women and young children the most vulnerable populations. Approximately 125 million pregnant women are at risk of malaria infection every year. Immunity to malaria can significantly reduce the severity of malaria symptoms. This has long motivated efforts to better understand the immune response to malaria in order to develop an efficacious antimalarial vaccine. However, the complex behaviour of the immune system, particularly in pregnant women, has hindered identifying the underpinning mechanisms of the immune response to malaria infection. This project investigates antibody-mediated immunity in pregnant women who attended antenatal clinics located at the Thai-Myanmar border, where malaria transmission is low and P. falciparum and P. vivax (the most prevalent malaria species globally) coexist. Antibody responses during pregnancy to six parasite antigens were measured for 250 pregnant women in a median of 7 samples per woman (range 2 to 13) over the gestation period. A multivariate mixture linear mixed model was fitted to longitudinal antibody data of 250 pregnant women to characterise the highly dynamic antibody responses using a Bayesian approach. The posterior distribution of the parameters estimated via Markov chain Monte Carlo (MCMC) simulations were used to classify the antibody responses. The results show that the infectious status of a woman during follow-up is a key factor influencing the classification of the joint behaviour of the antibody responses. Hence, the two malaria exposure categories (exposed to infection during pregnancy defined as a case and those non-exposed defined as controls) were represented by the two antibody profile clusters. Using a manually developed code, entropy values were computed for each antibody, with which, the contribution made by each antibody on the classification was assessed. The antibodies PfMSP3 and PvAMA1 which maintained less dynamic antibody profiles significantly influenced the classification. However, these antibodies identified controls exceptionally well but did not perform well for the cases. For sero-surveillance, antibodies which best identify the cases are required, hence, the study was extended in performing classification based on all possible univariate and multivariate combinations of the six antibodies. The bivariate combination PfAMA1 and PfVAR2CSA resulted in identifying the majority of cases by contributing towards identification of potential biomarker(s) for sero-surveillance of recent exposure to malaria during pregnancy. Therefore, the bivariate combination of antibodies, PfAMA1 and PfVAR2CSA should be used in the field, particularly for accurate malaria surveillance of pregnant women living in low malaria transmission settings. This could lead to the early detection and treatment of malaria infections in pregnant women, reducing transmission and thereby progressing towards elimination of malaria.