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

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  1. Capture and Detection of Circulating Glioma Cells Using the Recombinant VAR2CSA Malaria Protein

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  2. A proof-of-concept study for the design of a VLP-based combinatorial HPV and placental malaria vaccine

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  3. Fighting Cancer Using an Oncofetal Glycosaminoglycan-Binding Protein from Malaria Parasites

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  4. Association of Antibodies to VAR2CSA and Merozoite Antigens with Pregnancy Outcomes in Women Living in Yaoundé, Cameroon

    Research output: Contribution to journalJournal articleResearchpeer-review

  • Chathura Siriwardhana
  • Rui Fang
  • Ali Salanti
  • Rose G F Leke
  • Naveen Bobbili
  • Diane Wallace Taylor
  • John J Chen
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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.

Original languageEnglish
JournalMalaria Journal
Issue number1
Pages (from-to)e391
Publication statusPublished - 29 Sep 2017

    Research areas

  • Journal Article

ID: 52821653