Evaluation of systemic lupus erythematosus disease activity using anti-α-enolase antibody and RDW

Analysis of hsa_circ_0136256 as a biomarker for fibrosis in systemic sclerosis

BACKGROUND

Exploration of whether circRNAs in the skin of systemic sclerosis (SSc) model mice interact with 4E-BP1 protein to mediate the mTOR signaling pathway to regulate SSc fibrosis is crucial to identify homologous human circRNAs as markers to guide the diagnosis and treatment of SSc.

METHODS

C57BL/6 mice aged 6-8 weeks and weighing approximately 20 g were subcutaneously injected with bleomycin (BLM) to establish an SSc model. High-throughput sequencing was used to screen the differentially expressed circRNA in the skin of SSc model mice and control mice. RNA immunoprecipitation and RNA pulldown confirmed the interaction between circRNA and 4E-BP1 protein. SSc model mice were treated with empty plasmid (OE-NC), overexpression plasmid of mmu_circ_0005372 (OE-circ_0005372), interference plasmid of mmu_circ_0005372 (sh-circ5372), mutant plasmid of mmu_circ_0005372 (circ5372-MT), mTOR activator (MHY1485), mTOR inhibitor (omipalisib), or JAK1/2 inhibitor (ruxolitinib). Sections of mouse skin tissue were stained with Hematoxylin and eosin and Masson's stain. The collagen volume fraction (CVF) was calculated as CVF = area of blue collagen/total area with ImageJ. The correlation between homologous human circRNAs and clinical data was analyzed.

RESULTS

Compared to the control group, 21,839 circRNAs were upregulated and 27, 946 circRNAs were downregulated in the skin tissue of mice in the SSc model group. Among them was mmu_circ_0005372, which is derived from the FZD3 gene, is closely related to fibrosis, and is involved in the mTOR signaling pathway. Hsa_circ_0136256 was identified as the homologous human circRNA of mmu_circ_0005372. RT-qPCR confirmed that the expression of mmu_circ_0005372 was significantly reduced in the skin tissue of SSc mice, and the expression of hsa_circ_0136256 was significantly reduced in the peripheral blood mononuclear cells of patients with SSc. The interaction between mmu_circ_0005372 and 4E-BP1 protein was inhibited in the skin tissue of SSc model mice. The results showed that the CVF of OE-circ_0005372 group was significantly lower than that of the sh-circ5372, circ5372-MT, and MHY1485 groups, indicating that OE-circ5372 significantly improved skin fibrosis in the SSc mice. ROC curve analysis was performed on hsa_circ_0136256 (AUC = 0.719, P = 0.035). The expression of hsa_circ_0136256 was negatively correlated with COL IV, RDW-SD, and RDW-CV, and positively correlated with VC, PLT, and PCT. The results suggested that hsa_circ_0136256 may have important roles in the clinical diagnosis of SSc.

CONCLUSION

Mmu_circ_0005372 and homologous human hsa_circ_0136256 may be biomarkers and therapeutic targets for SSc fibrosis.

Evaluation of disease activity in systemic lupus erythematosus using standard deviation of lymphocyte volume combined with red blood cell count and lymphocyte percentage

Systemic lupus erythematosus (SLE) commonly damages the blood system and often manifests as blood cell abnormalities. The performance of biomarkers for predicting SLE activity still requires further improvement. This study aimed to analyze blood cell parameters to identify key indicators for a SLE activity prediction model. Clinical data of 138 patients with SLE (high activity, n = 40; moderate activity, n = 44; mild activity, n = 37; low activity, n = 17) and 100 healthy controls (HCs) were retrospectively analyzed. Data from 89 paired admission-discharge patients with SLE were collected. Differences and associations between blood cell parameters and disease indicators, as well as the relationship between the these parameters and organ damage, were examined. Machine-learning methods were employed to develop a prediction model for disease activity evaluation. Most blood cell parameters (22/26, 84.62%) differed significantly between patients with SLE and HCs. Analysis of 89 paired patients with SLE revealed significant changes in most blood cell parameters at discharge. The standard deviation of lymphocyte volume (SD-V-LY), red blood cell (RBC) count, lymphocyte percentage (LY%), hemoglobin(HGB), hematocrit(HCT), and neutrophil percentage(NE%) correlated with disease activity. By employing machine learning, an optimal model was established to predict active SLE using SD-V-LY, RBC count, and LY% (area under the curve [AUC] = 0.908, sensitivity = 0.811). External validation indicated impressive performance (AUC = 0.940, sensitivity = 0.833). Correlation analysis revealed that SD-V-LY was positively correlated with ESR, IgG, IgA, and IgM but was negatively correlated with C3 and C4. The RBC count was linked to renal and hematopoietic system impairments, whereas LY% was associated with joint/muscle involvement. In conclusion, SD-V-LY is associated with SLE disease activity. SD-V-LY combined with RBC count and LY% contributes to a prediction model, which can be utilized as an effective tool for assessing SLE activity.

A systematic review and meta-analysis of the association between the D-dimer and rheumatic diseases

INTRODUCTION

There is good evidence that specific autoimmune rheumatic diseases (RDs), for example, rheumatoid arthritis and systemic lupus erythematosus (SLE), are associated with a state of hypercoagulability and an increased risk of venous thromboembolism (VTE). However, limited information regarding this association is available for other autoimmune or autoinflammatory RDs. We sought to address this issue by conducting a systematic review and meta-analysis of the association between the d-dimer, an established marker of hypercoagulability and VTE, and RDs and the possible clinical and demographic factors mediating this association.

METHODS

We searched the electronic databases PubMed, Web of Science, and Scopus from inception to January 31, 2024. The risk of bias and the certainty of evidence were assessed using the Joanna Briggs Institute Critical Appraisal Checklist and GRADE, respectively.

RESULTS

In 31 studies selected for analysis (2724 RD patients and 3437 healthy controls), RD patients had overall significantly higher d-dimer concentrations when compared to controls (standard mean difference = 0.93, 95% CI 0.76-1.10, p < .001; I2 = 86.1%, p < .001; moderate certainty of evidence). The results were stable in a sensitivity analysis. Significant associations were observed between the effect size of the between-group differences in d-dimer concentration and age, specific RD and RD category, RD duration, fibrinogen, plasminogen activator inhibitor, C-reactive protein, and erythrocyte sedimentation rate.

CONCLUSIONS

Overall, patients with RDs have significantly higher d-dimer concentrations when compared with healthy controls, indicating a state of hypercoagulability. The alterations in d-dimer concentrations are mediated by age, specific RD and RD category, RD duration, and markers of anticoagulation and inflammation. Further research is warranted to investigate d-dimer concentrations across the spectrum of RDs and their utility in predicting and managing VTE in these patients (PROSPERO registration number: CRD42024517712).

Promising applications of red cell distribution width in diagnosis and prognosis of diseases with or without disordered iron metabolism

Many indicators, including red cell distribution width (RDW) and iron metabolism, are sensitive to a variety of risk factors, and are associated with the pathological alterations and disease onset. RDW reflects the degree of heterogeneous volumes of peripheral red blood cells (RBCs). It has been well-known that increased RDW indicates iron deficiency anemia, hemolytic anemia, ineffective erythropoiesis, and shorten lifespan of RBCs. Increased RDW is also prevalent in various non-anemic pathological conditions and diseases. We here review the factors affecting RDW, particularly disordered iron metabolism, chronic inflammation, and oxidative stress, and recapitulate the interplays among these factors. Furthermore, we review the application of increased RDW together with disordered iron homeostasis and the deregulations of hepcidin expression and ferritin levels in the diagnoses and prognosis of anemic and nonanemic diseases. RDW is inexpensive and readily available and may be valuable in adding to the diagnosis and monitoring of many pathological conditions. RDW combined with other indicators, for example, hepcidin and ferritin levels, should be utilized more frequently in clinical practice.

Expression profile of PU.1 in CD4+T cells from patients with systemic lupus erythematosus

Systemic lupus erythematosus (SLE) is a multisystem autoimmune disease with complex genetic predisposing factors involved. PU.1 is an important member of the ETS transcription factors family which has diverse functions such as regulating the proliferation, differentiation of immune cells and multiple inflammatory cytokines. Previous studies preliminary explored the relation between PU.1 and SLE. To further explain the potential role of PU.1 in the pathogenesis of SLE, 40 SLE patients and 20 age-sex matched healthy controls (HC) were recruited in this study. Flow cytometry was used to test the percentages of CD4+PU.1+T cells in peripheral blood mononuclear cells (PBMCs) from patients with SLE and HC. Expression levels of PU.1 mRNA in CD4+T cells from SLE patients and HC were analyzed by real-time transcription-polymerase chain reaction. Expression levels of plasma IL-1β, IL-9, IL-18, IL-6, IFN-α, TNF-α, IL-10 and TGF-β1 were measured by enzyme-linked immunosorbent assay. The percentage of CD4+PU.1+T cells in PBMCs from patients with SLE was significantly higher than that from HC (P < 0.001). In addition, the PU.1 mRNA expression in CD4+T cells from SLE patients was increased than that from HC (P = 0.002). In SLE patients, no significant correlation was found between the percentage of CD4+PU.1+T cells and the expression of PU.1 mRNA in CD4+T cells (P > 0.05). Associations of PU.1 mRNA expression in CD4+T cells with major clinical and laboratory parameters of SLE patients were also analyzed, but no significant correlations were found. Consistent with previous studies, SLE patients had increased IL-1β, IL-18, IL-6, IFN-α, TNF-α and IL-10 plasma concentrations than HC (P < 0.01). The expression level of plasma TGF-β1 was significantly decreased in SLE patients than in HC (P < 0.001). In SLE patients, the expression level of IL-1β was positive correlated with PU.1 mRNA expression in CD4+T cells (P = 0.001). Our study first time evaluated the expression profile of PU.1 in CD4+T cells from SLE patients confirming that PU.1 may participate in the pathogenesis of SLE.

A Nomogram Model that Predicts the Risk of Diabetic Nephropathy in Type 2 Diabetes Mellitus Patients: A Retrospective Study

OBJECTIVE

To construct a novel nomogram model that predicts the risk of diabetic nephropathy (DN) incidence in Chinese patients with type 2 diabetes mellitus (T2DM).

METHODS

Questionnaire surveys, physical examinations, routine blood tests, and biochemical index evaluations were conducted on 1095 patients with T2DM from Guilin. A least absolute contraction selection operator (LASSO) regression and multivariable logistic regression analysis were used to screen out DN risk factors. A logistic regression analysis incorporating the screened risk factors was used to establish a predictive nomogram model. The performance of the nomogram model was evaluated using the C-index, an area under the receiver operating characteristic curve (AUC), calibration plots, and a decision curve analysis. Bootstrapping was applied for internal validation.

RESULTS

Independent predictors for DN incidence risk included gender, age, hypertension, medicine use, duration of diabetes, body mass index, blood urea nitrogen level, serum creatinine level, neutrophil to lymphocyte ratio, and red blood cell distribution width. The nomogram model exhibited moderate prediction ability with a C-index of 0.819 (95% confidence interval (CI): 0.783-0.853) and an AUC of 0.813 (95%CI: 0.778-0.848). The C-index from internal validation reached 0.796 (95%CI: 0.763-0.829). The decision curve analysis displayed that the DN risk nomogram was clinically applicable when the risk threshold was between 1 and 83%.

CONCLUSION

Our novel and simple nomogram containing 10 factors may be useful in predicting DN incidence risk in T2DM patients.