A meta-analysis of clinical manifestations in asian systemic lupus erythematous: The effects of ancestry, ethnicity and gender

Performance of the Systemic Lupus Erythematosus Risk Probability Index (SLERPI) in a cohort of Colombian population

OBJECTIVE

To evaluate the performance of the Systemic Lupus Erythematosus Risk Probability Index (SLERPI) in Colombian patients with systemic lupus erythematosus (SLE).

METHODS

The Colombian cohort included 435 SLE patients and 430 controls with other autoimmune diseases (ADs). Clinical and serological data were collected, and SLE was indicated by SLERPI scores > 7. The American College of Rheumatology (ACR)-1997, Systemic Lupus International Collaborating Clinics (SLICC)-2012, and European League Against Rheumatism (EULAR)/ACR-2019 criteria were used as reference standards. The impact of overt polyautoimmunity (PolyA) on SLERPI performance was assessed. Additionally, multivariate lineal regression analysis was performed to evaluate the contribution of SLERPI features to the overall SLERPI score.

RESULTS

SLE patients had higher SLERPI scores (P < 0.0001), with almost 90% meeting "definite" lupus criteria. Main factors influencing SLERPI included immunological disorder (β:44.75, P < 0.0001), malar/maculopapular rash (β:18.43, P < 0.0001), and anti-nuclear antibody positivity (β:15.65, P < 0.0001). In contrast, subacute cutaneous lupus erythematosus/discoid lupus erythematosus (β:2.40, P > 0.05) and interstitial lung disease (β:-21.58, P > 0.05) were not significant factors to the overall SLERPI score. SLERPI demonstrated high sensitivity for SLE, both for the overall SLE group and for those without overt PolyA (95.4% and 94.6%, respectively), but had relatively low specificity (92.8% and 93.7%, respectively). The model showed high sensitivity for hematological lupus (98.8%) and lupus nephritis (96.0%), but low sensitivity for neuropsychiatric lupus (93.2%). Compared to the ACR-1997, SLICC-2012 and EULAR/ACR-2019 criteria, SLERPI yielded the highest sensitivity and lowest specificity.

CONCLUSION

SLERPI efficiently identified SLE patients in a Colombian cohort, showing high sensitivity but low specificity. The model effectively distinguishes SLE patients, even in the presence of concurrent overt PolyA. Key Points •SLERPI has a high sensitivity, but low specificity compared to ACR-1997, SLICC-2012 and EULAR/ACR-2019 criteria in the Colombian population. •Within the SLERPI score, immunological disorder, malar/maculopapular rash, and anti-nuclear antibody positivity are the strongest predictors of SLE. •SLERPI model can efficiently distinguish patients with SLE, regardless of concomitant overt PolyA. •SLERPI demonstrates high sensitivity in identifying hematological and nephritic subphenotypes of SLE.

Cell-Specific Transposable Element and Gene Expression Analysis Across Systemic Lupus Erythematosus Phenotypes

OBJECTIVE

There is an established yet unexplained link between interferon (IFN) and systemic lupus erythematosus (SLE). The expression of sequences derived from transposable elements (TEs) may contribute to SLE phenotypes, specifically production of type I IFNs and generation of autoantibodies.

METHODS

We profiled cell-sorted RNA-sequencing data (CD4+ T cells, CD14+ monocytes, CD19+ B cells, and natural killer cells) from peripheral blood mononuclear cells of 120 patients with SLE and quantified TE expression identifying 27,135 TEs. We tested for differential TE expression across 10 SLE phenotypes, including autoantibody production and disease activity.

RESULTS

We found 731 differentially expressed (DE) TEs across all SLE phenotypes that were mostly cell specific and phenotype specific. DE TEs were enriched for specific families and open reading frames of viral genes encoded in TE sequences. Increased expression of DE TEs was associated with genes involved in antiviral activity, such as LY6E, ISG15, and TRIM22, and pathways such as IFN signaling.

CONCLUSION

These findings suggest that expression of TEs contributes to activation of SLE-related mechanisms in a cell-specific manner, which can impact disease diagnostics and therapeutics.

Latent Class Analysis Identifies Distinct Phenotypes of Systemic Lupus Erythematosus Predictive of Flares after mRNA COVID-19 Vaccination: Results from the Coronavirus National Vaccine Registry for ImmuNe Diseases SINGapore (CONVIN-SING)

We recently reported that messenger ribonucleic acid (mRNA) coronavirus disease 2019 (COVID-19) vaccination was associated with flares in 9% of patients with systemic lupus erythematosus (SLE). Herein, we focused our analysis on patients from a multi-ethnic Southeast Asian lupus cohort with the intention of identifying distinct phenotypes associated with increased flares after mRNA COVID-19 vaccination.

METHODS

Six hundred and thirty-three SLE patients from eight public healthcare institutions were divided into test and validation cohorts based on healthcare clusters. Latent class analysis was performed based on age, ethnicity, gender, vaccine type, past COVID-19 infection, interruption of immunomodulatory/immunosuppressive treatment for vaccination, disease activity and background immunomodulatory/immunosuppressive treatment as input variables. Data from both cohorts were then combined for mixed effect Cox regression to determine which phenotypic cluster had a higher risk for time to first SLE flare, adjusted for the number of vaccine doses.

RESULTS

Two clusters were identified in the test (C1 vs. C2), validation (C1' vs. C2') and combined (C1″ vs. C2″) cohorts, with corresponding clusters sharing similar characteristics. Of 633 SLE patients, 88.6% were female and there was multi-ethnic representation with 74.9% Chinese, 14.2% Malay and 4.6% Indian. The second cluster (C2, C2' and C2″) was smaller compared to the first. SLE patients in the second cluster (C2 and C2') were more likely to be male, non-Chinese and younger, with higher baseline disease activity. The second cluster (C2″) had more incident flares (hazard ratio = 1.4, 95% confidence interval 1.1-1.9, p = 0.014) after vaccination. A higher proportion of patients in C2″ had immunomodulatory/immunosuppressive treatment interruption for vaccination as compared to patients in C1″ (6.6% vs. 0.2%) (p < 0.001).

CONCLUSION

We identified two distinct phenotypic clusters of SLE with different patterns of flares following mRNA COVID-19 vaccination. Caution has to be exercised in monitoring for post-vaccination flares in patients with risk factors for flares such as non-Chinese ethnicity, young age, male gender and suboptimal disease control at the time of vaccination.

Cell-Specific Transposable Element Gene Expression Analysis Identifies Associations with Systemic Lupus Erythematosus Phenotypes

There is an established yet unexplained link between interferon (IFN) and systemic lupus erythematosus (SLE). The expression of sequences derived from transposable elements (TEs) may contribute to production of type I IFNs and generation of autoantibodies. We profiled cell-sorted RNA-seq data (CD4+ T cells, CD14+ monocytes, CD19+ B cells, and NK cells) from PBMCs of 120 SLE patients and quantified TE expression identifying 27,135 TEs. We tested for differential TE expression across 10 SLE phenotypes including autoantibody production and disease activity and discovered 731 differentially expressed (DE) TEs whose effects were mostly cell-specific and phenotype-specific. DE TEs were enriched for specific families and viral genes encoded in TE sequences. Increased expression of DE TEs was associated with genes involved in antiviral activity such as LY6E, ISG15, TRIM22 and pathways such as interferon signaling. These findings suggest that expression of TEs contributes to activation of SLE-related mechanisms in a cell-specific manner, which can impact disease diagnostics and therapeutics.

Epidemiology of the idiopathic inflammatory myopathies

The idiopathic inflammatory myopathies (IIMs) are a heterogeneous group of systemic autoimmune diseases that affect the skeletal muscles and can also involve the skin, joints, lungs and heart. The epidemiology of IIM is obscured by changing classification criteria and the inherent shortcomings of case identification using healthcare record diagnostic coding. The incidence of IIM is estimated to range from 0.2 to 2 per 100,000 person-years, with prevalence from 2 to 25 per 100,000 people. Although the effects of age and gender on incidence are known, there is only sparse understanding of ethnic differences, particularly in indigenous populations. The incidence of IIM has reportedly increased in the twenty-first century, but whether this is a genuine increase is not yet known. Understanding of the genetic risk factors for different IIM subtypes has advanced considerably. Infections, medications, malignancy and geography are also commonly identified risk factors. Potentially, the COVID-19 pandemic has altered IIM incidence, although evidence of this occurrence is limited to case reports and small case series. Consideration of the current understanding of the epidemiology of IIM can highlight important areas of interest for future research into these rare diseases.

Analysis of risk factors and development of a nomogram prediction model for lupus nephritis in systemic lupus erythematosus patients

BACKGROUND

This study aimed to explore risk factors for lupus nephritis (LN) in systemic lupus erythematosus (SLE) patients and establish a Nomogram prediction model based on LASSO-logistic regression.

METHODS

The clinical and laboratory data of SLE patients in Meishan People's Hospital from July 2012 to December 2021 were analyzed retrospectively. All SLE patients were divided into two groups with or without LN. Risk factors were screened based on LASSO-logistic regression analysis, and a Nomogram prediction model was established. The receiver operating characteristic curve, calibration curves, and decision curve analysis were adopted to evaluate the performance of the Nomogram model.

RESULTS

A total of 555 SLE patients were enrolled, including 303 SLE patients with LN and 252 SLE patients without LN. LASSO regression and multivariate logistic regression analyses showed that ESR, mucosal ulcer, proteinuria, and hematuria were independent risk factors for LN in SLE patients. The four clinical features were incorporated into the Nomogram prediction model. Results showed that calibration curve was basically close to the diagonal dotted line with slope 1 (ideal prediction case), which proved that the prediction ability of the model was acceptable. In addition, the decision curve analysis showed that the Nomogram prediction model could bring net clinical benefits to patients when the threshold probability was 0.12-0.54.

CONCLUSION

Four clinical indicators of ESR, mucosal ulcer, proteinuria, and hematuria were independent risk factors for LN in SLE patients. The predictive power of the Nomogram model based on LASSO-logistic regression was acceptable and could be used to guide clinical work.

Glomerular Diseases Across Lifespan: Key Differences in Diagnostic and Therapeutic Approaches

Glomerular diseases are common causes of chronic kidney disease in childhood, adolescence, and adulthood. The epidemiology of glomerular diseases differs between different age groups, with minimal change disease being the leading cause of nephrotic syndrome in childhood, while membranous nephropathy and focal segmental glomerulosclerosis are more common in adulthood. IgA vasculitis is also more common in childhood. Moreover, there is a difference in disease severity with more children presenting with a relapsing form of nephrotic syndrome and a more acute presentation of antineutrophil cytoplasmic antibody-associated vasculitis and concomitant glomerulonephritis, as highlighted by the higher percentage of cellular crescents on kidney biopsy specimens in comparison with older patients. There is also a female preponderance in antineutrophil cytoplasmic antibody-associated vasculitis and more children present with tracheobroncholaryngeal disease. This article aims to summarize differences in the presentation of different glomerular diseases that are encountered commonly by pediatric and adult nephrologists and potential differences in the management.

Utility of the ACR-1997, SLICC-2012 and EULAR/ACR-2019 classification criteria for systemic lupus erythematosus: a single-centre retrospective study

Background and aims

Several different versions of classification criteria, including the American College of Rheumatology (ACR)-1997, Systemic Lupus International Collaborating Clinics (SLICC)-2012 and European Alliance of Associations for Rheumatology (EULAR)/ACR-2019 classification criteria, have been launched in the past decades. The current study aimed to investigate the performance of these three classification criteria for diagnosing patients with SLE in a Chinese cohort.

Methods

352 patients with SLE and 385 controls with other diseases who had the detection results of ANA were enrolled into the study. Various clinical parameters were estimated, such as demographics variables, clinical characteristics and other variables related to three criteria.

Results

The current study demonstrated great diagnostic ability of the three criteria; and the receiver operating characteristic curve and the area under curve (AUC) were used to evaluate the diagnostic ability of three criteria: ACR-1997 (AUC=0.972), SLICC-2012 (AUC=0.986) and EULAR/ACR-2019 (AUC=0.983). Despite lower specificity of the SLICC-2012 and EULAR/ACR-2019 classification criteria, their sensitivity is significantly improved than ACR-1997. Of note, we also compared the median time interval between the appearance of the earliest item and fulfilment of the three sets of criteria, suggesting the SLICC-2012 and EULAR/ACR-2019 could achieve earlier diagnosis. Adjusting the thresholds of the EULAR/ACR-2019 criteria from 10 to 12, the specificity and accuracy significantly increased.

Conclusion

The SLICC-2012 and EULAR/ACR-2019 performed well in Chinese patients with SLE and showed better early diagnosis ability. In addition, by adjusting the classification threshold, the accuracy of the EULAR/ACR-2019 classification criteria was improved.