Current Status of the Evaluation and Management of Lupus Patients and Future Prospects

Serum Uric Acid Combined with Homocysteine as a Predictive Biomarker of Lupus Nephritis

Serum uric acid (UA) and homocysteine (Hcy) are potential biomarkers of systemic lupus erythematosus (SLE). In this study, the expressions of UA and Hcy in SLE patients and the predictive value of these two parameters for lupus nephritis (LN) were studied. A total of 476 SLE patients were recruited to this case-control study, of which 176 SLE patients diagnosed with LN and 300 without LN. Serum UA and Hcy levels were analyzed. Multivariate logistic regression analysis was used to evaluate the relationship between serum UA and Hcy and LN. The receiver operating characteristic (ROC) curves were used to predict the role of combination of serum UA and Hcy in LN. We found that serum UA and Hcy levels in SLE patients with LN were significantly higher than those in controls (p<0.05). Multivariate logistic regressions showed that serum UA (OR+=+1.003, 95+% CI: 1.001-1.006, p+=+0.003), apolipoprotein B (Apo B) (OR+=+21.361, 95+% CI: 2.312-195.373, p+=+0.007) and Hcy (OR+=+1.042, 95+% CI: 1.011-1.080, p+=+0.014) were independent markers of LN. Combined serum UA and Hcy revealed a better result (AUC+=+0.718, 95+% CI: 0.670-0.676, p<0.001) in prediction of LN compared to that of the serum UA (AUC+=+0.710) and Hcy (AUC+=+0.657) independently. In conclusion, serum UA and Hcy could be predictive biomarkers of LN, and joint detection of serum UA and Hcy might be useful in the clinical setting.

Systemic Lupus Erythematosus: How Machine Learning Can Help Distinguish between Infections and Flares

Systemic Lupus Erythematosus (SLE) is a multifaceted autoimmune ailment that impacts multiple bodily systems and manifests with varied clinical manifestations. Early detection is considered the most effective way to save patients' lives, but detecting severe SLE activity in its early stages is proving to be a formidable challenge. Consequently, this work advocates the use of Machine Learning (ML) algorithms for the diagnosis of SLE flares in the context of infections. In the pursuit of this research, the Random Forest (RF) method has been employed due to its performance attributes. With RF, our objective is to uncover patterns within the patient data. Multiple ML techniques have been scrutinized within this investigation. The proposed system exhibited around a 7.49% enhancement in accuracy when compared to k-Nearest Neighbors (KNN) algorithm. In contrast, the Support Vector Machine (SVM), Binary Linear Discriminant Analysis (BLDA), Decision Trees (DT) and Linear Regression (LR) methods demonstrated inferior performance, with respective values around 81%, 78%, 84% and 69%. It is noteworthy that the proposed method displayed a superior area under the curve (AUC) and balanced accuracy (both around 94%) in comparison to other ML approaches. These outcomes underscore the feasibility of crafting an automated diagnostic support method for SLE patients grounded in ML systems.

Uncertainty and its related coping strategies in systemic lupus erythematosus. Life in the fog

BACKGROUND

One of the most prevalent problems of patients with systemic lupus erythematosus (SLE) is the uncertainty over an indefinite future. Uncertainty has significant effects on quality of life. The aim of this study was to explore uncertainty and personal strategies to cope with it among patients with SLE.

MATERIALS AND METHODS

This qualitative study was conducted in 2020-2021 using conventional content analysis. Participants were 21 patients with SLE who were purposefully selected from rheumatology clinics in Kashan, Kerman, and Bandar Abbass in Iran. Data collection was performed using face-to-face, in-depth, semi-structured interviews and was continued up to data saturation. Data were analyzed concurrently with data collection through conventional content analysis approach proposed by Graneheim and Lundman.

RESULTS

Two main themes, namely, "life in the fog" and "attempt to find peace" emerged from patients' experiences of illness uncertainty of SLE. Life in the fog included three main categories of "perception of threat to health", "challenge of doubt and certainty," and "indefinite future." Attempt to find peace included three main categories of "spirituality," "reflection," and "attempt to acquire SLE-related knowledge.

CONCLUSIONS

Uncertainty is a major psychological stress for patients with SLE. Healthcare providers should therefore consider the challenges and concerns faced by patients and, through utilizing appropriate training and communicational practices, plan interventions and strategies to empower patients for coping with uncertainty.

TMT-based quantitative proteomics analysis and potential serum protein biomarkers for systemic lupus erythematosus

Systemic lupus erythematosus (SLE) was not only a typical systemic autoimmune disease, but also one of the most challenging heterogeneous diseases for physicians. Currently, the pathogenesis of SLE was unclear, and there were no accurate, universal or easy-to-use diagnostic criteria for assessing SLE activity and predicting SLE severity. Proteins were direct effectors of biological mechanisms, and were closer to clinical phenotypes than the other discovered biomarkers. Moreover, proteins were widely used as biomarkers for clinical diagnosis and mechanism research of many diseases. Herein, we compared the proteins profiles of healthy individuals (HCs) and SLE patients to reveal the pathogenesis and provide evidence for diagnosis and management of persons with SLE. Serum samples were collected from 28 SLE patients and 30 HCs. Tandem mass tag (TMT)-based quantitative proteomics method was used to identify, screen and detect differentially expressed proteins (DEPs) in the collected serum samples. A total of 744 proteins were identified, and 84 of them were considered as DEPs with 71 upregulated and 13 downregulated. Bioinformatics analysis suggested that these DEPs were mainly involved in many biological processes, including immune response, signal transduction, inflammatory response, proteolysis, innate immune response and apoptosis, which were closely related to the pathogenesis of SLE. After comprehensive analysis, serum amyloid A1 (SAA1) and endothelin (CD248) were identified as specific biomarkers for the diagnosis of SLE, and were confirmed by subsequent enzyme-linked immunosorbent assays (ELISA), indicating a high reliability of TMT-based quantitative proteomics results. Areas under the ROC curve (AUC) results confirmed that SAA1 and CD248 combination as early immune diagnosis biomarkers of SLE presented excellent sensitivity and specificity.

Conceptual framework for defining disease modification in systemic lupus erythematosus: a call for formal criteria

Disease modification has become a well-established concept in several therapeutic areas; however, no widely accepted definition of disease modification exists for SLE.

We reviewed established definitions of disease modification in other conditions and identified a meaningful effect on ‘disease manifestations’ (ie, signs, symptoms and patient-reported outcomes) and on ‘disease outcomes’ (eg, long-term remission or progression of damage) as the key principles of disease modification, indicating a positive effect on the natural course of the disease. Based on these findings and the treatment goals and outcome measures for SLE, including lupus nephritis, we suggest a definition of disease modification based on disease activity indices and organ damage outcomes, with the latter as a key anchor. A set of evaluation criteria is also suggested.

Establishing a definition of disease modification in SLE will clarify which treatments can be considered disease modifying, provide an opportunity to harmonise future clinical trial outcomes and enable comparison between therapies, all of which could ultimately help to improve patient outcomes. This publication seeks to catalyse further discussion and provide a framework to develop an accepted definition of disease modification in SLE.