Systemic Lupus Erythematosus Risk: The Role of Environmental Factors

Methyl supplemented diet reduces liver pathology in lupus-prone MRL/lpr mice

Genetic predisposition is a necessary but not sufficient requirement for the development of a pathological immune response in systemic lupus erythematosus (SLE). Various environmental factors and nutrition in particular also play a key role in the pathogenesis of SLE. In the present study, we evaluated the effect of a specific diet containing additional amounts of methyl donors on the development of pathological changes in the liver of lupus-prone MRL/lpr mice. The results showed that long-term administration of the diet reduced the level of inflammation and the number of inflammatory cells in the liver of the treated mice compared to the control group. These data point to methyl-containing micronutrients as a potential immunomodulatory tool and suggest the application of a methyl-supplemented diet as a novel approach to manipulate the course of liver disease in SLE.

Visceral fat, cardiovascular risk factors and quality of life in lupus activity categorised via complement C3

BACKGROUND

Patients with lupus face increased cardiovascular risk linked to their autoimmune status. This study assesses the relationships between cardiovascular risk factors, lifestyle and health-related quality of life (HRQoL) concerning SLE activity categorised by complement C3.

METHODS

74 patients with SLE were recruited and stratified as active (C3 <90 mg/dL) or inactive (C3 >90 mg/dL), alongside 74 controls with obesity-related low-grade inflammation, at Hospital Universitario Puerta de Hierro Majadahonda. Anthropometric measurements, clinical and demographic data were recorded, and participants completed validated questionnaires on physical activity, dietary intake and HRQoL. Fasting blood samples were collected for metabolic determinations. Comparative analyses between SLE groups and controls, along with regression models adjusted for variables associated with disease activity, were performed.

RESULTS

The inactive SLE group exhibited a less healthy adiposity profile compared with the active group (36.7% vs 33.2% total fat mass; 8.5 AU vs 6.5 AU visceral fat mass) and showed a higher prevalence of cardiovascular risk factors, including markers of obesity, hypertension, dyslipidaemia and increased waist circumference, along with worse HRQoL outcomes. Notably, age, body mass index and insulin resistance were associated with SLE inactivity, while fibrinogen correlated with disease activity as assessed by complement C3 levels. Interestingly, household composition as a sociodemographic variable (alone, couple/children/elderly or other) also showed an independent association with SLE activity.

CONCLUSIONS

Inactive patients with SLE exhibited more adverse cardiovascular risk markers compared with active patients categorised by complement C3, even when glucocorticoid administration was accounted for. Additionally, this research highlights the potential influence of fibrinogen as well as metabolic and sociodemographic factors on disease activity. These findings emphasise the need for personalised precision management strategies such as measurement of fibrinogen levels and insulin resistance and sociodemographic considerations that address both cardiovascular risk and overall lifestyle plus exposome in patients with SLE and may partly explain SLE activity evolution.

Characterization of autoantibody profiles in clusters of systemic lupus erythematosus using a novel autoantigen discovery technology

Systemic lupus erythematosus (SLE) is a complex systemic autoimmune disease characterized by a wide range of clinical and immunologic manifestations, most prominently, the production of autoantibodies to nuclear components (ANAs). A previous study delineated four SLE patient clusters based on autoantibody expression to common antigens. To further assess autoantibody diversity within these clusters, we surveyed serum autoantibody expression using a novel autoantigen discovery technology, the Antigenome Platform. This phage-based system assesses serum antibody interactions with large protein fragments (up to 250 amino acids) spanning approximately 90% of the human genome. Bound autoantibody targets were identified through next-generation sequencing and robust bioinformatics and statistical analysis. Our study revealed 88, 49, 10, and 24 autoantibodies that expand the characterization of four SLE clusters, including 24 autoantibodies that characterize a cluster of patients lacking common autoantibodies by conventional assays. Further, some autoantibodies identified have potential links to patient disease features. Although SLE is characterized by antinuclear antibody expression, a significant proportion of autoantigens (ranging from 28% to 54%) in each cluster localized to the cytoplasm, which suggests extensive autoreactivity beyond targets in the cell nucleus that formed the original basis of clustering. This study identifies new markers to aid in the clustering and understanding of SLE disease subtypes and provides a rationale for elucidating autoantibody expression in SLE beyond antinuclear antibodies.

The intersection of systemic lupus erythematosus with social and occupational environments among black adults: A qualitative study

BACKGROUND

Black people have disproportionately high morbidity and mortality due to systemic lupus erythematosus (SLE). Features of social and occupational environments can protect against poor health outcomes.

OBJECTIVE

We aimed to characterize how SLE intersects with interpersonal relationships and employment among Black adults to inform tailored care approaches and public policies that could alleviate racial inequities in SLE.

METHODS

We used a qualitative, interpretive description approach that involved semi-structured interviews with Black adults who had SLE. The interview guide included questions about SLE diagnosis, impacts on day-to-day life, and symptom management. We analyzed the de-identified transcripts using inductive, thematic analysis with input from representatives of the study population. Our theme development focused on how SLE (i.e., symptoms, severity, management) intersected with interpersonal relationships and employment.

RESULTS

This study included 30 interviews (97 % female, age range = 18-65 years). Among participants, 30 % were not working due to unemployment or disability and 23 % reported a lack of emotional support. Our analysis resulted in three main themes: 1) The bi-directional relationship between employment and SLE; 2) The positive and negative effects of connecting with other people who have SLE; and 3) The moderating role of relationships with family and friends, and the impact on self-identity.

CONCLUSIONS

This study describes features of social and occupational environments that influence SLE management and wellbeing. Our results highlight directions for further study that could identify and address how systemic racism affects race-based inequities in SLE.

Just Autoimmunity? The Role of the Innate Immune Response in Lupus

ABSTRACT

Systemic lupus erythematosus is considered a prototype of human autoimmune disease based on the appearance of multiple autoantibodies, some of which can have a direct pathogenic effect on tissues. Most therapeutic modalities aim to check the enhanced humoral responses by targeting T and B cells with conventional or biologic drugs. However, in some cases, the clinical response is limited and frequently takes a high toll of toxicity in patients. The last 2 decades have brought up novel discoveries showing profound disturbances of innate immune cell function in systemic lupus erythematosus, including dysregulated NETosis, increased apoptosis, type 1 interferon, and granulopoiesis signatures that are grounded in basic cell biology abnormalities, including response to excessive oxidative stress, mitochondrial dysfunction, and upregulation of the cGAS-STING pathway. Whether the prominent autoimmunity component of lupus patients is sufficient to drive this chronic disease or follows a breakdown of innate immune homeostasis in response to the environmental factors triggering disease is the subject of this revision.

Gut microbiota and autoimmune diseases: Insights from Mendelian randomization

In recent years, the scientific community has shown interest in the role of gut microbiota in the development of autoimmune diseases (AID). Although observational studies have revealed significant associations between gut microbiota and AID like rheumatoid arthritis, systemic lupus erythematosus, and multiple sclerosis, these connections do not necessarily imply causality. Mendelian randomization (MR) approach has been extensively employed to investigate the causal relationship. Relevant MR study findings indicate that a reduction in beneficial microbial populations, particularly Bifidobacterium and Lactobacillus, and an increase in potential pathogenic microbes, is correlated with an elevated AID risk. Given the innovative potential of MR in unraveling the etiopathogenesis of AIDs, this article offers an overview of this methodological approach and its recent applications in AID research.

Systemic lupus erythematosus with high disease activity identification based on machine learning

OBJECTIVE

Clinical evaluation of systemic lupus erythematosus (SLE) disease activity is limited and inconsistent, and high disease activity significantly, seriously impacts on SLE patients. This study aims to generate a machine learning model to identify SLE patients with high disease activity.

METHOD

A total of 1014 SLE patients with low disease activity and 453 SLE patients with high disease activity were included. A total of 94 clinical, laboratory data and 17 meteorological indicators were collected. After data preprocessing, we use mutual information and multisurf to evaluate and select the importance of features. The selected features are used for machine learning modeling. Performance of the model is evaluated and verified by a series of binary classification indicators.

RESULTS

We screened out hematuria, proteinuria, pyuria, low complement, precipitation, sunlight and other features for model construction by integrated feature selection. After hyperparameter optimization, the LGB has the best performance (ROC: AUC = 0.930; PRC: AUC = 0.911, APS = 0.913; balance accuracy: 0.856), and the worst is the naive bayes (ROC: AUC = 0.849; PRC: AUC = 0.719, APS = 0.714; balance accuracy: 0.705). Finally, the selection of features has good consistency in the composite feature importance bar plot.

CONCLUSION

We identify SLE patients with high disease activity by a simple machine learning pipeline, especially the LGB model based on the characteristics of proteinuria, hematuria, pyuria and other feathers screened out by collective feature selection.