Additional file 1. A heatmap that illustrates the observed antibody levels, along with clustering among antibodies (dendrogram in right) and 1377 patients (dendrogram on top). Below the top dendrogram, the panel with red and blue vertical bars represents malaria infected (in red) and none infected (in blue) subjects. Note that log-transformed antibody levels was used for the ease of visualization

Ali Salanti (40851)

Chathura Siriwardhana (4480816)

Diane Taylor (3407477)

John Chen (346835)

Naveen Bobbili (519515)

Rose Leke (3407480)

Rui Fang (138089)

Malaria Journal

Siriwardhana, Chathura

Fang, Rui

Salanti, Ali

Leke, Rose G. F.

Bobbili, Naveen

Taylor, Diane Wallace

Chen, John J.

Copenhagen University Research Information System

English

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

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MOESM1 of Statistical prediction of immunity to placental malaria based on multi-assay antibody data for malarial antigens

Abstract Background Plasmodium 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. Methods Archival 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. Results The 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. Conclusions Not 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

Chathura Siriwardhana

Rui Fang

Ali Salanti

Rose G. F. Leke

Naveen Bobbili

Diane Wallace Taylor

John J. Chen

Directory of Open Access Journals

https://curis.ku.dk/ws/files/184768448/s12936_017_2041_3.pdf

MOESM1 of Statistical prediction of immunity to placental malaria based on multi-assay antibody data for malarial antigens

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Copenhagen University Research Information System

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