Antibody Responses in HIV-Infected Patients With Advanced Immunosuppression and Asymptomatic Cryptococcal Antigenemia. (11th December 2018)

• Record Type: Journal Article
• Authors: Hlupeni, Admire; Nakouzi, Antonio; Wang, Tao; Boyd, Kathryn F; Makadzange, Tariro A; Ndhlovu, Chiratidzo E; Pirofski, Liise-anne
• Is Part Of: Open forum infectious diseases. Volume 6:Number 1(2019)
• Publication Date: 2018-12-11
• Subjects: antibody -- cryptococccal antigenenmia -- glucuronoxylomannan -- HIV -- IgG -- IgM -- immunoglobulin -- Laminarin -- Sub-Saharan Africa -- Zimbabwe; Communicable diseases -- Periodicals; Medical microbiology -- Periodicals; Infection -- Periodicals; 616.9
• DOI: 10.1093/ofid/ofy333
• Languages: English
• ISSNs: 2328-8957

Abstract

Abstract: Background: There are no host biomarkers of risk for HIV-associated cryptococcal meningitis (CM) except CD4+ T-cell deficiency. At present, serum cryptococcal antigen (CrAg) screening of those with CD4 <100 cells/µL is used to identify persons at risk for HIV-associated CM. We determined if plasma antibody profiles could discriminate CrAg+ from CrAg- patients. Methods: We performed serological analyses of 237 HIV-infected asymptomatic Zimbabwean patients with CD4 <100 cells/µL; 125 CrAg- and CrAg+ but cerebrospinal fluid CrAg- by CrAg lateral flow assay. We measured plasma immunoglobulin M (IgM), immunoglobulin G (IgG) 1, and IgG2 concentrations by Luminex, and titers of Cryptococcus neoformans (Cn) glucuronoxylomannan (GXM) polysaccharide and naturally occurring Laminarin (natural Lam, a β-(1–3)-glucan linked polysaccharide)-binding IgM and IgG by enzyme-linked immunosorbent assay. Results: GXM-IgG, -IgM, and -IgG2 levels were significantly higher in CrAg+ patients, whereas natural Lam-IgM and Lam-IgG were higher in CrAg- patients before and after adjustment for age, sex, and CD4 T-cell count, despite overlap of values. To address this variability and better discriminate the groups, we used Akaike Information Criteria to select variables that independently predicted CrAg+ status and included them in a receiver operating characteristic curve to predict CrAg status. By inclusion of CD4, GXM-IgG, GXM-IgM, and Lam-IgG, -IgG2, and -IgM, this model had an 80.4% probability (95% confidence interval, 0.75–0.86) of predicting CrAg+ status. Conclusions: Statistical models that include multiple serological variables may improve the identification of patients at risk for CM and inform new directions in research on the complex role that antibodies may play in resistance and susceptibility to CM.