The effect of gut microbiome and plasma metabolome on systemic sclerosis: a bidirectional two-sample Mendelian randomization study.

Background: Cellular and molecular biology, combined with research on the human microbiome and metabolome, have provided new insights into the pathogenesis of systemic sclerosis (SSc). However, most studies on gut microbiota (GM) and metabolome in SSc are observational studies. The impact of confounding factors and reverse causation leads to different insights. To shed light on this matter, we utilized Mendelian randomization (MR) to determine the causal effect of GM/metabolites on SSc. Methods: Based on summary-level data from genome-wide association studies, bidirectional Two-sample MR was conducted involving 196 GM, 1400 plasma metabolism, and 9,095 SSc. Inverse Variance Weighting (IVW) was mainly used for effect estimation. Results: Forward MR analysis found that three GM and two plasma metabolites are causally related to SSc. IVW results showed Victivallaceae (family) (OR, 1.469; 95%CI, 1.099-1.963; p = 0.009) and LachnospiraceaeUCG004 (genus) (OR, 1.548; 95%CI, 1.020-2.349; p = 0.04) were risk factor of SSc. Conversely, Prevotella7 (genus) (OR, 0.759; 95%CI, 0.578-0.997; p = 0.048)was a protective factor of SSc. The results on plasma metabolites indicated that Pregnenediol disulfate (C21H34O8S2) levels (OR, 1.164; 95%CI, 1.006-1.347; p = 0.041)was a risk factor of SSc, while Sphingomyelin (d18:1/19:0, d19:1/18:0) levels (OR, 0.821; 95%CI, 0.677-0.996; p = 0.045)was a protective factor of SSc. Reverse MR analysis did not find causally relationship between SSc and the above GM/plasma metabolites. Conclusion: Our results revealed the causally effect between GM/plasma metabolites and SSc. These findings provided new insights into the mechanism of SSc. In particular, we demonstrated Prevotella7 was a protective factor of SSc despite its controversial role in SSc in previous researches.

A new proposal for phenotypic classification and outcome assessment of dermatomyositis based on clinical manifestations and serological testing.

BACKGROUND: Dermatomyositis (DM) is an infrequent disease subgroup of idiopathic inflammatory myopathies characterized by distinct skin lesions. However, high heterogeneity makes clinical diagnosis and treatment of DM very challenging. OBJECTIVES: Unsupervised classification in DM patients and analysis of key factors related to clinical outcomes. METHODS: This retrospective study was conducted between 2017 and 2022 at the Department of Rheumatology, Xiangya Hospital, Central South University. 162 DM patients were enrolled for unsupervised hierarchical cluster analysis. In addition, we divided the clinical outcomes of DM patients into four subgroups: withdrawal, stabilization, aggravation, and death, and compared the clinical profiles amongst the subgroups. RESULTS: Out of 162 DM patients, three clusters were defined. Cluster 1 (n = 40) was mainly grouped by patients with prominent muscular involvement and mild Interstitial Lung Disease (ILD). Cluster 2 (n = 72) grouped patients with skin rash, anti-Melanoma Differentiation Associated protein 5 positive (anti-MDA5+), and Rapid Progressive Interstitial Lung Disease (RP-ILD). Cluster 3 (n = 50) grouped patients with the mildest symptoms. The proportion of death increased across the three clusters (cluster 3 < cluster 1 < cluster 2). STUDY LIMITATIONS: The number of cases was limited for the subsequent construction and validation of predictive models. We did not review all skin symptoms or pathological changes in detail. CONCLUSIONS: We reclassified DM into three clusters with different risks for poor outcome based on diverse clinical profiles. Clinical serological testing and cluster analysis are necessary to help clinicians evaluate patients during follow-up and conduct phenotype-based personalized care in DM.

A new proposal for phenotypic classification and outcome assessment of dermatomyositis based on clinical manifestations and serological testing

Abstract Background Dermatomyositis (DM) is an infrequent disease subgroup of idiopathic inflammatory myopathies characterized by distinct skin lesions. However, high heterogeneity makes clinical diagnosis and treatment of DM very challenging. Objectives Unsupervised classification in DM patients and analysis of key factors related to clinical outcomes. Methods This retrospective study was conducted between 2017 and 2022 at the Department of Rheumatology, Xiangya Hospital, Central South University. 162 DM patients were enrolled for unsupervised hierarchical cluster analysis. In addition, we divided the clinical outcomes of DM patients into four subgroups: withdrawal, stabilization, aggravation, and death, and compared the clinical profiles amongst the subgroups. Results Out of 162 DM patients, three clusters were defined. Cluster 1 (n = 40) was mainly grouped by patients with prominent muscular involvement and mild Interstitial Lung Disease (ILD). Cluster 2 (n = 72) grouped patients with skin rash, anti-Melanoma Differentiation Associated protein 5 positive (anti-MDA5+), and Rapid Progressive Interstitial Lung Disease (RP-ILD). Cluster 3 (n = 50) grouped patients with the mildest symptoms. The proportion of death increased across the three clusters (cluster 3 < cluster 1 < cluster 2). Study limitations The number of cases was limited for the subsequent construction and validation of predictive models. We did not review all skin symptoms or pathological changes in detail. Conclusions We reclassified DM into three clusters with different risks for poor outcome based on diverse clinical profiles. Clinical serological testing and cluster analysis are necessary to help clinicians evaluate patients during follow-up and conduct phenotype-based personalized care in DM.

The Expression of Cytokine Profiles and Related Receptors in Idiopathic Inflammatory Myopathies.

Background: Cytokines play a vital role in the pathogenesis of idiopathic inflammatory myopathies (IIMs). Here, we investigated the expression of serum cytokine profiles in untreated IIMs and their correlations with clinical indicators, and further studied the expression of related cytokines receptors in IIMs. Methods: The Human 48-Plex Luminex assay for cytokines was performed in the serum of IIMs, including 93 untreated and 18 follow-up (39 samples) patients, and 32 healthy controls (HC). Mann-Whitney U test with bonferroni adjusted was used to identify the differentially expressed cytokines among groups. Celltalker software was used to identify the receptors of differentially expressed cytokines. The expression of receptors was further validated by published GEO datasets (muscle, blood and skin), RT-qPCR, western blot and flow cytometry. Results: The serum levels of Eotaxin, IL7, IL18, IP10, MCP1, MCSF, MIG and SCGFß were elevated in the 93 untreated patients. Except for IL7, all other cytokines were decreased after treatment and their levels were positively correlated with clinical indices such as LDH, ESR, CRP, ALT, IgA, AST and IgG while negatively correlated with albumin and MMT8. According to the serum myositis-specific antibodies (MSAs), patients were classified into three groups: anti-ARS (Jo-1, OJ, EJ, PL7, PL12), anti-MDA5 positive, and anti-TIF1γ positive. Compared with HC, the levels of IP10 and MIG were increased in three groups. Moreover, IL18 and MSCF were increased in anti-ARS patients, and CTACK, Eotaxin, IL1Rα, IL7, IL18, MCP1, MCP3, MCSF and SCGFß were elevated in anti-MDA5 patients. Twenty receptors of the 8 differentially expressed cytokines were matched by celltalker software, among them, IL18R1 and CCR1 were up-regulated in blood, muscle and skin of IIMs from the analysis of GEO published datasets. RT-qPCR and western blot further validated IL18R1 was upregulated in the muscle tissues of dermatomyositis. The number of IL18R1+CD4+ cells was increased while IL18R1+CD8+ cells was decreased in peripheral blood of anti-MDA5 patients. Conclusion: This study showed that cytokine profiles were significantly changed in IIMs, and different MSA groups had unique cytokine expression patterns. The levels of some cytokine were correlated with clinical indices. The IL18 receptor IL18R1 might play important roles in IIMs.