Antiretroviral therapy suppressed participants with low CD4+ T-cell counts segregate according to opposite immunological phenotypes.

backgroundThe failure to increase CD4 T-cell counts in some antiretroviral therapy suppressed participants (immunodiscordance) has been related to perturbed CD4 T-cell homeostasis and impacts clinical evolution.

methodsWe evaluated different definitions of immunodiscordance based on CD4 T-cell counts (cutoff) or CD4 T-cell increases from nadir value (ΔCD4) using supervised random forest classification of 74 immunological and clinical variables from 196 antiretroviral therapy suppressed individuals. Unsupervised clustering was performed using relevant variables identified in the supervised approach from 191 individuals.

resultsCutoff definition of CD4 cell count 400 cells/μl performed better than any other definition in segregating immunoconcordant and immunodiscordant individuals (85% accuracy), using markers of activation, nadir and death of CD4 T cells. Unsupervised clustering of relevant variables using this definition revealed large heterogeneity between immunodiscordant individuals and segregated participants into three distinct subgroups with distinct production, programmed cell-death protein-1 (PD-1) expression, activation and death of T cells. Surprisingly, a nonnegligible number of immunodiscordant participants (22%) showed high frequency of recent thymic emigrants and low CD4 T-cell activation and death, very similar to immunoconcordant participants. Notably, human leukocyte antigen - antigen D related (HLA-DR) PD-1 and CD45RA expression in CD4 T cells allowed reproducing subgroup segregation (81.4% accuracy). Despite sharp immunological differences, similar and persistently low CD4 values were maintained in these participants over time.

conclusionA cutoff value of CD4 T-cell count 400 cells/μl classified better immunodiscordant and immunoconcordant individuals than any ΔCD4 classification. Immunodiscordance may present several, even opposite, immunological patterns that are identified by a simple immunological follow-up. Subgroup classification may help clinicians to delineate diverse approaches that may be needed to boost CD4 T-cell recovery.