Developing a multivariate prediction model of antibody features associated with protection of malaria-infected pregnant women from placental malaria

Research output: Contribution to journal › Journal article › Research › peer-review

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Developing a multivariate prediction model of antibody features associated with protection of malaria-infected pregnant women from placental malaria. / Aitken, Elizabeth H; Damelang, Timon; Ortega-Pajares, Amaya; Alemu, Agersew; Hasang, Wina; Dini, Saber; Unger, Holger W; Ome-Kaius, Maria; Nielsen, Morten A; Salanti, Ali; Smith, Joe; Kent, Stephen; Hogarth, P Mark; Wines, Bruce D; Simpson, Julie A; Chung, Amy; Rogerson, Stephen J.

In: eLife, Vol. 10, e65776, 2021.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Aitken, EH, Damelang, T, Ortega-Pajares, A, Alemu, A, Hasang, W, Dini, S, Unger, HW, Ome-Kaius, M, Nielsen, MA, Salanti, A, Smith, J, Kent, S, Hogarth, PM, Wines, BD, Simpson, JA, Chung, A & Rogerson, SJ 2021, 'Developing a multivariate prediction model of antibody features associated with protection of malaria-infected pregnant women from placental malaria', eLife, vol. 10, e65776. https://doi.org/10.7554/eLife.65776

APA

Aitken, E. H., Damelang, T., Ortega-Pajares, A., Alemu, A., Hasang, W., Dini, S., Unger, H. W., Ome-Kaius, M., Nielsen, M. A., Salanti, A., Smith, J., Kent, S., Hogarth, P. M., Wines, B. D., Simpson, J. A., Chung, A., & Rogerson, S. J. (2021). Developing a multivariate prediction model of antibody features associated with protection of malaria-infected pregnant women from placental malaria. eLife, 10, [e65776]. https://doi.org/10.7554/eLife.65776

Vancouver

Aitken EH, Damelang T, Ortega-Pajares A, Alemu A, Hasang W, Dini S et al. Developing a multivariate prediction model of antibody features associated with protection of malaria-infected pregnant women from placental malaria. eLife. 2021;10. e65776. https://doi.org/10.7554/eLife.65776

Author

Aitken, Elizabeth H ; Damelang, Timon ; Ortega-Pajares, Amaya ; Alemu, Agersew ; Hasang, Wina ; Dini, Saber ; Unger, Holger W ; Ome-Kaius, Maria ; Nielsen, Morten A ; Salanti, Ali ; Smith, Joe ; Kent, Stephen ; Hogarth, P Mark ; Wines, Bruce D ; Simpson, Julie A ; Chung, Amy ; Rogerson, Stephen J. / Developing a multivariate prediction model of antibody features associated with protection of malaria-infected pregnant women from placental malaria. In: eLife. 2021 ; Vol. 10.

Bibtex

@article{32027e173b9c4145989f6e94407cfc47,
title = "Developing a multivariate prediction model of antibody features associated with protection of malaria-infected pregnant women from placental malaria",
abstract = "Background: Plasmodium falciparum causes placental malaria, which results in adverse outcomes for mother and child. P. falciparum-infected erythrocytes that express the parasite protein VAR2CSA on their surface can bind to placental chondroitin sulfate A. It has been hypothesized that naturally acquired antibodies towards VAR2CSA protect against placental infection, but it has proven difficult to identify robust antibody correlates of protection from disease. The objective of this study was to develop a prediction model using antibody features that could identify women protected from placental malaria.Methods: We used a systems serology approach with elastic net-regularized logistic regression, partial least squares discriminant analysis, and a case-control study design to identify naturally acquired antibody features mid-pregnancy that were associated with protection from placental malaria at delivery in a cohort of 77 pregnant women from Madang, Papua New Guinea.Results: The machine learning techniques selected 6 out of 169 measured antibody features towards VAR2CSA that could predict (with 86% accuracy) whether a woman would subsequently have active placental malaria infection at delivery. Selected features included previously described associations with inhibition of placental binding and/or opsonic phagocytosis of infected erythrocytes, and network analysis indicated that there are not one but multiple pathways to protection from placental malaria.Conclusions: We have identified candidate antibody features that could accurately identify malaria-infected women as protected from placental infection. It is likely that there are multiple pathways to protection against placental malaria.Funding: This study was supported by the National Health and Medical Research Council (Nos. APP1143946, GNT1145303, APP1092789, APP1140509, and APP1104975).",
author = "Aitken, {Elizabeth H} and Timon Damelang and Amaya Ortega-Pajares and Agersew Alemu and Wina Hasang and Saber Dini and Unger, {Holger W} and Maria Ome-Kaius and Nielsen, {Morten A} and Ali Salanti and Joe Smith and Stephen Kent and Hogarth, {P Mark} and Wines, {Bruce D} and Simpson, {Julie A} and Amy Chung and Rogerson, {Stephen J}",
note = "{\textcopyright} 2021, Aitken et al.",
year = "2021",
doi = "10.7554/eLife.65776",
language = "English",
volume = "10",
journal = "eLife",
issn = "2050-084X",
publisher = "eLife Sciences Publications Ltd.",

}

RIS

TY - JOUR

T1 - Developing a multivariate prediction model of antibody features associated with protection of malaria-infected pregnant women from placental malaria

AU - Aitken, Elizabeth H

AU - Damelang, Timon

AU - Ortega-Pajares, Amaya

AU - Alemu, Agersew

AU - Hasang, Wina

AU - Dini, Saber

AU - Unger, Holger W

AU - Ome-Kaius, Maria

AU - Nielsen, Morten A

AU - Salanti, Ali

AU - Smith, Joe

AU - Kent, Stephen

AU - Hogarth, P Mark

AU - Wines, Bruce D

AU - Simpson, Julie A

AU - Chung, Amy

AU - Rogerson, Stephen J

N1 - © 2021, Aitken et al.

PY - 2021

Y1 - 2021

N2 - Background: Plasmodium falciparum causes placental malaria, which results in adverse outcomes for mother and child. P. falciparum-infected erythrocytes that express the parasite protein VAR2CSA on their surface can bind to placental chondroitin sulfate A. It has been hypothesized that naturally acquired antibodies towards VAR2CSA protect against placental infection, but it has proven difficult to identify robust antibody correlates of protection from disease. The objective of this study was to develop a prediction model using antibody features that could identify women protected from placental malaria.Methods: We used a systems serology approach with elastic net-regularized logistic regression, partial least squares discriminant analysis, and a case-control study design to identify naturally acquired antibody features mid-pregnancy that were associated with protection from placental malaria at delivery in a cohort of 77 pregnant women from Madang, Papua New Guinea.Results: The machine learning techniques selected 6 out of 169 measured antibody features towards VAR2CSA that could predict (with 86% accuracy) whether a woman would subsequently have active placental malaria infection at delivery. Selected features included previously described associations with inhibition of placental binding and/or opsonic phagocytosis of infected erythrocytes, and network analysis indicated that there are not one but multiple pathways to protection from placental malaria.Conclusions: We have identified candidate antibody features that could accurately identify malaria-infected women as protected from placental infection. It is likely that there are multiple pathways to protection against placental malaria.Funding: This study was supported by the National Health and Medical Research Council (Nos. APP1143946, GNT1145303, APP1092789, APP1140509, and APP1104975).

AB - Background: Plasmodium falciparum causes placental malaria, which results in adverse outcomes for mother and child. P. falciparum-infected erythrocytes that express the parasite protein VAR2CSA on their surface can bind to placental chondroitin sulfate A. It has been hypothesized that naturally acquired antibodies towards VAR2CSA protect against placental infection, but it has proven difficult to identify robust antibody correlates of protection from disease. The objective of this study was to develop a prediction model using antibody features that could identify women protected from placental malaria.Methods: We used a systems serology approach with elastic net-regularized logistic regression, partial least squares discriminant analysis, and a case-control study design to identify naturally acquired antibody features mid-pregnancy that were associated with protection from placental malaria at delivery in a cohort of 77 pregnant women from Madang, Papua New Guinea.Results: The machine learning techniques selected 6 out of 169 measured antibody features towards VAR2CSA that could predict (with 86% accuracy) whether a woman would subsequently have active placental malaria infection at delivery. Selected features included previously described associations with inhibition of placental binding and/or opsonic phagocytosis of infected erythrocytes, and network analysis indicated that there are not one but multiple pathways to protection from placental malaria.Conclusions: We have identified candidate antibody features that could accurately identify malaria-infected women as protected from placental infection. It is likely that there are multiple pathways to protection against placental malaria.Funding: This study was supported by the National Health and Medical Research Council (Nos. APP1143946, GNT1145303, APP1092789, APP1140509, and APP1104975).

U2 - 10.7554/eLife.65776

DO - 10.7554/eLife.65776

M3 - Journal article

C2 - 34181872

VL - 10

JO - eLife

JF - eLife

SN - 2050-084X

M1 - e65776

ER -

ID: 273127300