Dynamic immune markers predict HIV acquisition and augment associations with sociobehavioral factors for HIV exposure

Rachel A Bender Ignacio; Sayan Dasgupta; Rogelio Valdez; Urvashi Pandey; Siavash Pasalar; Ricardo Alfaro; Florian Hladik; Germán Gornalusse; Javier R Lama; Ann Duerr

doi:10.1016/j.isci.2022.105632

Dynamic immune markers predict HIV acquisition and augment associations with sociobehavioral factors for HIV exposure

iScience. 2022 Nov 19;25(12):105632. doi: 10.1016/j.isci.2022.105632. eCollection 2022 Dec 22.

Authors

Rachel A Bender Ignacio^{1

2

3}, Sayan Dasgupta², Rogelio Valdez⁴, Urvashi Pandey⁵, Siavash Pasalar², Ricardo Alfaro⁶, Florian Hladik^{2

5}, Germán Gornalusse⁵, Javier R Lama^{7

3}, Ann Duerr^{2

3}

Affiliations

¹ Division of Allergy and Infectious Diseases, Department of Medicine, University of Washington, Seattle, WA 98104, USA.
² Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA.
³ Department of Global Health, University of Washington, Seattle, WA 98195, USA.
⁴ Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA.
⁵ Department of Obstetrics and Gynecology, University of Washington, Seattle, WA 98195, USA.
⁶ Centro de Investigaciones Tecnológicas Biomédicas y Medioambientales, Universidad Nacional Mayor de San Marcos, Bellavista, Lima 07006, Peru.
⁷ Asociación Civil Impacta Salud y Educación, Lima 15063, Peru.

Abstract

Prior studies attempting to link biomarkers of immune activation with risk of acquiring HIV have relied on cross sectional samples, most without proximity to HIV acquisition. We created a nested case-control study within the Sabes study in Peru, and assessed a panel of plasma immune biomarkers at enrollment and longitudinally, including within a month of diagnosis of primary HIV or matched timepoint in controls. We used machine learning to select biomarkers and sociobehavioral covariates predictive of HIV acquisition. Most biomarkers were indistinguishable between cases and controls one month before HIV diagnosis. However, levels differed between cases and controls at study entry, months to years earlier. Dynamic changes in IL-2, IL-7, IL-10, IP-10 and IL-12, rather than absolute levels, jointly predicted HIV risk when added to traditional risk factors, and there was modest effect modification of biomarkers on association between sociobehavioral risk factors and HIV acquisition.

Keywords: Health sciences; Machine learning; Virology.

Abstract

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