Department of Laboratory Medicine - Medical Immunology, Radboud University Medical Center, Nijmegen, Netherlands.
Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg.
Front Immunol. 2024 Jul 1;15:1401542. doi: 10.3389/fimmu.2024.1401542. eCollection 2024.
AIMS/HYPOTHESIS: 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.
目的/假设：1 型糖尿病（T1D）存在明显的异质性：不仅发病年龄和疾病进展速度不同，而且并发症风险也有显著差异。因此，有人提出了不同的疾病终末类型。新诊断的糖尿病患者存在 T 细胞和 B 细胞反应受损。我们假设，解析长期 T1D 成人外周血免疫细胞谱有助于了解疾病异质性。
招募了长期 T1D 成年患者和健康对照（HC），并使用多色流式细胞术测定其血液免疫细胞谱，然后使用基于机器学习的弹性网（EN）分类模型进行分析。进行层次聚类以确定患者特异性免疫细胞谱。将结果与匹配的健康对照者进行比较。
流式细胞术数据分析的层次聚类分析显示，个体存在三种基于免疫细胞组成的不同亚群：HC、T1D 组-A 和 T1D 组-B。一般来说，与健康对照组相比，T1D 患者表现出更活跃的免疫表型，表现为中性粒细胞、单核细胞、总 B 细胞和活化 CD4+CD25+T 细胞的比例和绝对数更高，而调节性 T 细胞（Treg）的丰度降低。与 T1D 组-B 相比，T1D 组-A 患者表现出更具促炎表型的特征，表现为 FOXP3+Treg 的比例较低、CCR4 表达的 CD4 和 CD8 T 细胞亚群、单核细胞亚群的比例较高、Treg/常规 T 细胞（Tconv）比值较低、促炎细胞因子（TNFα、IFNγ）增加和抗炎（IL-10）产生潜力降低。临床方面，T1D 组-A 患者糖尿病相关大血管并发症更频繁。
基于机器学习的多参数流式细胞术数据分析显示，长期 1 型糖尿病成人存在两种不同的免疫表型；T1D 组-A 和 T1D 组-B。T1D 组-A 的特点是更强的促炎表型，与糖尿病相关（大）血管并发症的发生率较高有关。
