Clustering of End Stage Renal Disease Patients by Dimensionality Reduction Algorithms According to Lymphocyte Senescence Markers - Lioulios, Georgios; Fylaktou, Asimina; Xochelli, Aliki; Sampani, Erasmia; Tsouchnikas, Ioannis; Giamalis, Panagiotis; Daikidou, Dimitra-Vasilia; Nikolaidou, Vasiliki; Papagianni, Aikaterini; Theodorou, Ioannis; Stangou, Maria (Frontiers in Immunology, 2022)
End stage renal disease (ESRD) engenders detrimental effects in the Immune system, manifested as quantitative alterations of lymphocyte subpopulations, akin, albeit not identical to those observed during the ageing process. We performed dimensionality reduction of an extended lymphocyte phenotype panel of senescent and exhaustion related markers in ESRD patients and controls with Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP). The plane defined by the first two principal components of PCA showed two fuzzy clusters, for patients and controls, respectively, with loadings of non-senescent markers pointing towards the controls' centroid. Naive lymphocytes were reduced in ESRD patients compared to controls (CD4+CD45RA+CCR7+ 200(150-328) vs. 426(260-585cells/μl respectively, P = 0.001, CD19+IgD+CD27- 54(26-85) vs. 130(83-262)cells/μl respectively, P < 0.001). PCA projections of the multidimensional ESRD immune phenotype suggested a more senescent phenotype in hemodialysis compared to hemodiafiltration treated patients. Lastly, clustering based on UMAP revealed three distinct patient groups, exhibiting gradual changes for naive, senescent, and exhausted lymphocyte markers. Machine learning algorithms can distinguish ESRD patients from controls, based on their immune-phenotypes and also, unveil distinct immunological groups within patients’ cohort, determined possibly by dialysis prescription.