Statistical prediction of immunity to placental malaria based on multi-assay antibody data for malarial antigens

Malaria Journal

Research

John J. Chen¹  Chathura Siriwardhana¹  Rui Fang¹  Ali Salanti²  Diane Wallace Taylor³  Naveen Bobbili³  Rose G. F. Leke⁴ 

[1] Biostatistics Core, Department of Complementary and Integrative Medicine, John A. Burns School of Medicine, University of Hawaii at Manoa, 96813, Honolulu, HI, USA;Centre for Medical Parasitology at Department of Immunology and Microbiology, University of Copenhagen and Department of Infectious Diseases, Copenhagen University Hospital, Copenhagen, Denmark;Department of Tropical Medicine, Medical Microbiology and Pharmacology, John A. Burns School of Medicine, University of Hawaii at Manoa, 96813, Honolulu, HI, USA;The Biotechnology Center, Faculty of Medicine and Biomedical Research, University of Yaoundé I, Yaoundé, Cameroon;

关键词: Predictive models;    Placental malaria;    Multiplex assays;    VAR2CSA;   

DOI  :  10.1186/s12936-017-2041-3

 received in 2017-06-07, accepted in 2017-09-21,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundPlasmodium falciparum infections are especially severe in pregnant women because infected erythrocytes (IE) express VAR2CSA, a ligand that binds to placental trophoblasts, causing IE to accumulate in the placenta. Resulting inflammation and pathology increases a woman’s risk of anemia, miscarriage, premature deliveries, and having low birthweight (LBW) babies. Antibodies (Ab) to VAR2CSA reduce placental parasitaemia and improve pregnancy outcomes. Currently, no single assay is able to predict if a woman has adequate immunity to prevent placental malaria (PM). This study measured Ab levels to 28 malarial antigens and used the data to develop statistical models for predicting if a woman has sufficient immunity to prevent PM.MethodsArchival plasma samples from 1377 women were screened in a bead-based multiplex assay for Ab to 17 VAR2CSA-associated antigens (full length VAR2CSA (FV2), DBL 1-6 of the FCR3, 3D7 and 7G8 lines, ID1-ID2a (FCR3 and 3D7) and 11 antigens that have been reported to be associated with immunity to P. falciparum (AMA-1, CSP, EBA-175, LSA1, MSP1, MSP2, MSP3, MSP11, Pf41, Pf70 and RESA)). Ab levels along with clinical variables (age, gravidity) were used in the following seven statistical approaches: logistic regression full model, logistic regression reduced model, recursive partitioning, random forests, linear discriminant analysis, quadratic discriminant analysis, and support vector machine.ResultsThe best and simplest model proved to be the logistic regression reduced model. AMA-1, MSP2, EBA-175, Pf41, and MSP11 were found to be the top five most important predictors for the PM status based on overall prediction performance.ConclusionsNot surprising, significant differences were observed between PM positive (PM+) and PM negative (PM−) groups for Ab levels to the majority of malaria antigens. Individually though, these malarial antigens did not achieve reasonably high performances in terms of predicting the PM status. Utilizing multiple antigens in predictive models considerably improved discrimination power compared to individual assays. Among seven different classifiers considered, the reduced logistic regression model produces the best overall predictive performance.

【 授权许可】

CC BY   
© The Author(s) 2017