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

Josué Pérez-Santiago, Dan Ouchi, Victor Urrea, Jorge Carrillo, Cecilia Cabrera, Jordi Villà-Freixa, Jordi Puig, Roger Paredes, Eugènia Negredo, Bonaventura Clotet, Marta Massanella, Julià Blanco

The failure to increase CD4 T-cell counts in some ART-suppressed subjects (immunodiscordance) has been related to perturbed CD4 T-cell homeostasis and impacts clinical evolution. We 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 ART-suppressed individuals. Unsupervised clustering was performed using relevant variables identified in the supervised approach from 191 individuals. Cutoff definition of 400 CD4 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 subjects into three distinct subgroups with distinct production, PD-1 expression, activation and death of T-cells. Surprisingly, a non-negligible number of immunodiscordant subjects (22%) showed high frequency of recent thymic emigrants and low CD4 T-cell activation and death, very similar to immunoconcordant subjects. Notably, 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 subjects overtime. A cutoff value of 400 CD4 T 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.