Blood immune cell profiling in adults with longstanding type 1 diabetes is associated with macrovascular complications

• Published in: Frontiers in immunology Vol. 15; p. 1401542
• Main Authors: He, Xuehui, Wang, Xinhui, van Heck, Julia, van Cranenbroek, Bram, van Rijssen, Esther, Stienstra, Rinke, Netea, Mihai G, Joosten, Irma, Tack, Cees J, Koenen, Hans J P M
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 01.07.2024
• Subjects: Adult; Case-Control Studies; Diabetes Mellitus, Type 1 - blood; Diabetes Mellitus, Type 1 - immunology; Diabetic Angiopathies - blood; Diabetic Angiopathies - immunology; Female; Flow Cytometry; heterogeneity; hierarchical classification; Humans; immune cell profile; Immunology; Immunophenotyping; Machine Learning; Male; Middle Aged; multiparameter flow cytometry; T-Lymphocytes, Regulatory - immunology; type 1 diabetes mellitus; multiparameter flow cytometry; type 1 diabetes mellitus; immune cell profile; machine learning; heterogeneity; hierarchical classification
• ISSN: 1664-3224; 1664-3224
• DOI: 10.3389/fimmu.2024.1401542

There is increasing evidence for heterogeneity in type 1 diabetes mellitus (T1D): not only the age of onset and disease progression rate differ, but also the risk of complications varies markedly. Consequently, the presence of different disease endotypes has been suggested. Impaired T and B cell responses have been established in newly diagnosed diabetes patients. We hypothesized that deciphering the immune cell profile in peripheral blood of adults with longstanding T1D may help to understand disease heterogeneity. Adult patients with longstanding T1D and healthy controls (HC) were recruited, and their blood immune cell profile was determined using multicolour flow cytometry followed by a machine-learning based elastic-net (EN) classification model. Hierarchical clustering was performed to identify patient-specific immune cell profiles. Results were compared to those obtained in matched healthy control subjects. Hierarchical clustering analysis of flow cytometry data revealed three immune cell composition-based distinct subgroups of individuals: HCs, T1D-group-A and T1D-group-B. In general, T1D patients, as compared to healthy controls, showed a more active immune profile as demonstrated by a higher percentage and absolute number of neutrophils, monocytes, total B cells and activated CD4+CD25+ T cells, while the abundance of regulatory T cells (Treg) was reduced. Patients belonging to T1D-group-A, as compared to T1D-group-B, revealed a more proinflammatory phenotype characterized by a lower percentage of FOXP3+ Treg, higher proportions of CCR4 expressing CD4 and CD8 T cell subsets, monocyte subsets, a lower Treg/conventional Tcell (Tconv) ratio, an increased proinflammatory cytokine (TNFα, IFNγ) and a decreased anti-inflammatory (IL-10) producing potential. Clinically, patients in T1D-group-A had more frequent diabetes-related macrovascular complications. Machine-learning based classification of multiparameter flow cytometry data revealed two distinct immunological profiles in adults with longstanding type 1 diabetes; T1D-group-A and T1D-group-B. T1D-group-A is characterized by a stronger pro-inflammatory profile and is associated with a higher rate of diabetes-related (macro)vascular complications.