MO004: Hidden Patterns of T Cell and B Cell Phenotypes seen Through Machine Learning Algorithms in End-Stage Renal Disease

Abstract

Abstract BACKGROUND AND AIMS End-stage renal disease (ESRD) is essentially a chronic inflammatory state and, consequently, it engenders detrimental effects on the immune system. Due to the specific uremic environment, multiple phenotypic lymphocyte alterations are described, similar but not identical to ageing. We aimed to evaluate these changes through simple unsupervised dimensionality reduction algorithms in order to reveal unique phenotypical lymphocyte patterns in ESRD patients. METHOD A wide panel of senescent and exhaustion-related lymphocyte markers, including CD45RA, CCR7, CD31, CD28, CD57, and PD1 on T cells and CD27 and IgD on B cells, was assessed by flow cytometry in 30 ESRD patients and 20 healthy controls of similar age, sex and ethnicity. The resulting immunological multidimensional phenotype was projected at lower dimensions using two algorithms: principal component analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP). RESULTS The plane defined by the two first eigenvectors of PCA showed two clusters, patients and controls, with PCA variable loadings of non-senescent markers pointing towards the controls' centroid. Indeed, ‘naïve’ lymphocytes were reduced in ESRD patients compared with controls [CD4+CD45RA+CCR7+200(150–328) versus 426(260–585) cells/μL, respectively; P = .001] and [CD19+IgD+CD27–54(26–85) versus 130(83–262) cells/μL, respectively; P < .0001]. Also, PCA projections of the multidimensional ESRD immune phenotype suggested a more senescent phenotype in haemodialysis compared with haemodiafiltration treated patients. Finally, clustering based on UMAP projections revealed three distinct patients groups (UMAP 1–3), exhibiting gradual changes for naive, senescent and exhausted lymphocyte markers. Out of these, the UMAP 1 group was characterized by a predominance of CD8 senescent T cell subsets, in contrast, to groups UMAP 2 and 3, which showed a gradual decrease of all T cell subsets, in comparison to healthy controls. These groups were found to differ in the type of dialyzer used, with polysulfone or derivatives alone more often prescribed in UMAP 2–3 patients (13 out of 17) compared with 4 out of 13 in UMAP 1, P = .012. CONCLUSION Simple machine learning algorithms may help to unravel hidden lymphocyte markers and define patterns characteristic of ESRD. Moreover, significant connections of immune changes with dialysis methods and dialyzers were revealed and described.