Clinical correlates of lifetime and current comorbidity patterns in autoimmune and inflammatory diseases.

BACKGROUND: Autoimmune and inflammatory diseases (AIDs) are a heterogeneous group of disorders with diverse etiopathogenic mechanisms. This study explores the potential utility of family history, together with present and past comorbidities, in identifying distinct etiopathogenic subgroups. This approach may facilitate more accurate diagnosis, prognosis and personalized therapy. METHODS: We performed a multiple correspondence analysis on patients' comorbidities, followed by hierarchical principal component clustering of clinical data from 48 healthy volunteers and 327 patients with at least one of 19 selected AIDs included in the TRANSIMMUNOM cross-sectional study. RESULTS: We identified three distinct clusters characterized by: 1) the absence of comorbidities, 2) polyautoimmunity, and 3) polyinflammation. These clusters were further distinguished by specific comorbidities and biological parameters. Autoantibodies, allergies, and viral infections characterized the polyautoimmunity cluster, while older age, BMI, depression, cancer, hypertension, periodontal disease, and dyslipidemia characterized the polyinflammation cluster. Rheumatoid arthritis patients were distributed across all three clusters. They had higher DAS28 and prevalence of extra-articular manifestations when belonging to the polyinflammation and polyautoimmunity clusters, and also lower ACPA and RF seropositivity and higher pain scores within the polyinflammation cluster. We developed a model allowing to classify AID patients into comorbidity clusters. CONCLUSIONS: In this study, we have uncovered three distinct comorbidity profiles among AID patients. These profiles suggest the presence of distinct etiopathogenic mechanisms underlying these subgroups. Validation, longitudinal stability assessment, and exploration of their impact on therapy efficacy are needed for a comprehensive understanding of their potential role in personalized medicine.

The role of obesity and adipose tissue dysfunction in osteoarthritis pain.

Obesity has a pivotal and multifaceted role in pain associated with osteoarthritis (OA), extending beyond the mechanistic influence of BMI. It exerts its effects both directly and indirectly through various modifiable risk factors associated with OA-related pain. Adipose tissue dysfunction is highly involved in OA-related pain through local and systemic inflammation, immune dysfunction, and the production of pro-inflammatory cytokines and adipokines. Adipose tissue dysfunction is intricately connected with metabolic syndrome, which independently exerts specific effects on OA-related pain, distinct from its association with BMI. The interplay among obesity, adipose tissue dysfunction and metabolic syndrome influences OA-related pain through diverse pain mechanisms, including nociceptive pain, peripheral sensitization and central sensitization. These complex interactions contribute to the heightened pain experience observed in individuals with OA and obesity. In addition, pain management strategies are less efficient in individuals with obesity. Importantly, therapeutic interventions targeting obesity and metabolic syndrome hold promise in managing OA-related pain. A deeper understanding of the intricate relationship between obesity, metabolic syndrome and OA-related pain is crucial and could have important implications for improving pain management and developing innovative therapeutic options in OA.

Single-cell RNA-Seq analysis reveals cell subsets and gene signatures associated with rheumatoid arthritis disease activity.

Rheumatoid arthritis (RA) management leans toward achieving remission or low disease activity. In this study, we conducted single-cell RNA sequencing (scRNA-Seq) of peripheral blood mononuclear cells (PBMCs) from 36 individuals (18 patients with RA and 18 matched controls, accounting for age, sex, race, and ethnicity), to identify disease-relevant cell subsets and cell type-specific signatures associated with disease activity. Our analysis revealed 18 distinct PBMC subsets, including an IFN-induced transmembrane 3-overexpressing (IFITM3-overexpressing) IFN-activated monocyte subset. We observed an increase in CD4+ T effector memory cells in patients with moderate-high disease activity (DAS28-CRP ≥ 3.2) and a decrease in nonclassical monocytes in patients with low disease activity or remission (DAS28-CRP < 3.2). Pseudobulk analysis by cell type identified 168 differentially expressed genes between RA and matched controls, with a downregulation of proinflammatory genes in the γδ T cell subset, alteration of genes associated with RA predisposition in the IFN-activated subset, and nonclassical monocytes. Additionally, we identified a gene signature associated with moderate-high disease activity, characterized by upregulation of proinflammatory genes such as TNF, JUN, EGR1, IFIT2, MAFB, and G0S2 and downregulation of genes including HLA-DQB1, HLA-DRB5, and TNFSF13B. Notably, cell-cell communication analysis revealed an upregulation of signaling pathways, including VISTA, in both moderate-high and remission-low disease activity contexts. Our findings provide valuable insights into the systemic cellular and molecular mechanisms underlying RA disease activity.

Deep immunophenotyping reveals that autoimmune and autoinflammatory disorders are spread along two immunological axes capturing disease inflammation levels and types.

OBJECTIVES: Based on genetic associations, McGonagle and McDermott suggested a classification of autoimmune and autoinflammatory diseases as a continuum ranging from purely autoimmune to purely autoinflammatory diseases and comprising diseases with both components. We used deep immunophenotyping to identify immune cell populations and molecular targets characterising this continuum. METHODS: We collected blood from 443 patients with one of 15 autoimmune or autoinflammatory diseases and 71 healthy volunteers. Deep phenotyping was performed using 13 flow cytometry panels characterising over 600 innate and adaptive cell populations. Unsupervised and supervised analyses were conducted to identify disease clusters with their common and specific cell parameters. RESULTS: Unsupervised clustering categorised these diseases into five clusters. Principal component analysis deconvoluted this clustering into two immunological axes. The first axis was driven by the ratio of LAG3+ to ICOS+ in regulatory T lymphocytes (Tregs), and segregated diseases based on their inflammation levels. The second axis was driven by activated Tregs and type 3 innate lymphoid cells (ILC3s), and segregated diseases based on their types of affected tissues. We identified a signature of 23 cell populations that accurately characterised the five disease clusters. CONCLUSIONS: We have refined the monodimensional continuum of autoimmune and autoinflammatory diseases as a continuum characterised by both disease inflammation levels and targeted tissues. Such classification should be helpful for defining therapies. Our results call for further investigations into the role of the LAG3+/ICOS+ balance in Tregs and the contribution of ILC3s in autoimmune and autoinflammatory diseases. TRIAL REGISTRATION NUMBER: NCT02466217.

Identification of Symptom Phenotypes of Hand Osteoarthritis Using Hierarchical Clustering: Results From the DIGICOD Cohort.

OBJECTIVE: We aimed to delineate phenotypes in hand osteoarthritis (HOA) based on cardinal symptoms (pain, functional limitation, stiffness, and aesthetic discomfort). METHODS: With data from the Digital Cohort Design (DIGICOD), we performed a hierarchical agglomerative clustering analysis based on Australian/Canadian Osteoarthritis Hand Index (AUSCAN) subscores for pain, physical function, stiffness, and visual analog scale for aesthetic discomfort. Kruskal-Wallis and post hoc analyses were used to assess differences between clusters. RESULTS: Among 389 patients, we identified 5 clusters: cluster 1 (n = 88) and cluster 2 (n = 91) featured low and mild symptoms; cluster 3 (n = 80) featured isolated aesthetic discomfort; cluster 4 (n = 42) featured a high level of pain, stiffness, and functional limitation; and cluster 5 (n = 88) had the same features as cluster 4 but with high aesthetic discomfort. For clusters 4 and 5, AUSCAN pain score was >41 of 100, representing only one-third of our patients. Aesthetic discomfort (clusters 3 and 5) was significantly associated with erosive HOA and a higher number of nodes. The highly symptomatic cluster 5 was associated but not significantly with metabolic syndrome, and body mass index and C-reactive protein level did not differ among clusters. Symptom intensity was significantly associated with joint destruction as well as with physical and psychological burden. Patients' main expectations differed among clusters, and function improvement was the most frequent expectation overall. CONCLUSION: The identification of distinct clinical clusters based on HOA cardinal symptoms suggests previously undescribed subtypes of this condition, warranting further study of biological characteristics of such clusters, and opening a path toward phenotype-based personalized medicine in HOA.

Use of machine learning in osteoarthritis research: a systematic literature review.

OBJECTIVE: The aim of this systematic literature review was to provide a comprehensive and exhaustive overview of the use of machine learning (ML) in the clinical care of osteoarthritis (OA). METHODS: A systematic literature review was performed in July 2021 using MEDLINE PubMed with key words and MeSH terms. For each selected article, the number of patients, ML algorithms used, type of data analysed, validation methods and data availability were collected. RESULTS: From 1148 screened articles, 46 were selected and analysed; most were published after 2017. Twelve articles were related to diagnosis, 7 to prediction, 4 to phenotyping, 12 to severity and 11 to progression. The number of patients included ranged from 18 to 5749. Overall, 35% of the articles described the use of deep learning And 74% imaging analyses. A total of 85% of the articles involved knee OA and 15% hip OA. No study investigated hand OA. Most of the studies involved the same cohort, with data from the OA initiative described in 46% of the articles and the MOST and Cohort Hip and Cohort Knee cohorts in 11% and 7%. Data and source codes were described as publicly available respectively in 54% and 22% of the articles. External validation was provided in only 7% of the articles. CONCLUSION: This review proposes an up-to-date overview of ML approaches used in clinical OA research and will help to enhance its application in this field.