Lupus nephritis pathology prediction with clinical indices

Development and validation of non-invasive prediction models for assessing kidney histopathological activity index in lupus nephritis

OBJECTIVE

To develop and validate prediction models for estimating the activity index (AI) of kidney histopathology in lupus nephritis (LN) using clinical and laboratory data.

METHODS

This study used single-center data from patients with kidney biopsy-confirmed LN between January 2012 and March 2022. The development and validation datasets were temporally cut. We discriminated AI > 10 and ≤ 10 as high and mild/moderate activity status, respectively. We constructed four models for AI: Model 1 included demographic information; Model 2 additionally incorporated data of systemic conditions; Model 3 further included kidney-specific conditions; and Model 4 included all the aforementioned predictors. Logistic regression was employed in Models 1 to 3, while Model 4 utilized least absolute shrinkage and selection operator for predictor selection and model building. Internal validation was performed using 1000 bootstrap resampling, while external validation was performed in the temporal validation dataset. Both calibration and discrimination metrics were evaluated.

RESULTS

There were 160 patients in the development dataset and 70 patients in the validation dataset. In the temporal validation, all the models achieved acceptable calibration and excellent discrimination. Model 2 which contained relatively fewer predictors achieved the highest area under the receiver operator characteristic curve of 0.86 (95% confidence interval 0.76 to 0.94).

CONCLUSION

Our Model 2 incorporating demographic and systemic indicators exhibited good performance in estimating the AI of LN. We thus provide a simple yet effective algorithm to predict AI in patients with LN, potentially aiding clinicians in non-invasively assessing disease activity and guiding treatment decisions. Key Points • We developed a prediction model (Model 2) incorporating demographic and systemic indicators to predict AI in patients with LN. • The prediction model can aid clinicians in noninvasively assessing disease activity and guiding treatment decisions.

Machine learning-based identification of novel hub genes associated with oxidative stress in lupus nephritis: implications for diagnosis and therapeutic targets

BACKGROUND

Lupus nephritis (LN) is a complication of SLE characterised by immune dysfunction and oxidative stress (OS). Limited options exist for LN. We aimed to identify LN-related OS, highlighting the need for non-invasive diagnostic and therapeutic approaches.

METHODS

LN-differentially expressed genes (DEGs) were extracted from Gene Expression Omnibus datasets (GSE32591, GSE112943 and GSE104948) and Molecular Signatures Database for OS-associated DEGs (OSEGs). Functional enrichment analysis was performed for OSEGs related to LN. Weighted gene co-expression network analysis identified hub genes related to OS-LN. These hub OSEGs were refined as biomarker candidates via least absolute shrinkage and selection operator. The predictive value was validated using receiver operating characteristic (ROC) curves and nomogram for LN prognosis. We evaluated LN immune cell infiltration using single-sample gene set enrichment analysis and CIBERSORT. Additionally, gene set enrichment analysis explored the functional enrichment of hub OSEGs in LN.

RESULTS

The study identified four hub genes, namely STAT1, PRODH, TXN2 and SETX, associated with OS related to LN. These genes were validated for their diagnostic potential, and their involvement in LN pathogenesis was elucidated through ROC and nomogram. Additionally, alterations in immune cell composition in LN correlated with hub OSEG expression were observed. Immunohistochemical analysis reveals that the hub gene is most correlated with activated B cells and CD8 T cells. Finally, we uncovered that the enriched pathways of OSEGs were mainly involved in the PI3K-Akt pathway and the Janus kinase-signal transducer and activator of transcription pathway.

CONCLUSION

These findings contribute to advancing our understanding of the complex interplay between OS, immune dysregulation and molecular pathways in LN, laying a foundation for the identification of potential diagnostic biomarkers and therapeutic targets.

Systemic lupus in the era of machine learning medicine

Artificial intelligence and machine learning applications are emerging as transformative technologies in medicine. With greater access to a diverse range of big datasets, researchers are turning to these powerful techniques for data analysis. Machine learning can reveal patterns and interactions between variables in large and complex datasets more accurately and efficiently than traditional statistical methods. Machine learning approaches open new possibilities for studying SLE, a multifactorial, highly heterogeneous and complex disease. Here, we discuss how machine learning methods are rapidly being integrated into the field of SLE research. Recent reports have focused on building prediction models and/or identifying novel biomarkers using both supervised and unsupervised techniques for understanding disease pathogenesis, early diagnosis and prognosis of disease. In this review, we will provide an overview of machine learning techniques to discuss current gaps, challenges and opportunities for SLE studies. External validation of most prediction models is still needed before clinical adoption. Utilisation of deep learning models, access to alternative sources of health data and increased awareness of the ethics, governance and regulations surrounding the use of artificial intelligence in medicine will help propel this exciting field forward.

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.

Predict, diagnose, and treat chronic kidney disease with machine learning: a systematic literature review

OBJECTIVES

In this systematic review we aimed at assessing how artificial intelligence (AI), including machine learning (ML) techniques have been deployed to predict, diagnose, and treat chronic kidney disease (CKD). We systematically reviewed the available evidence on these innovative techniques to improve CKD diagnosis and patient management.

METHODS

We included English language studies retrieved from PubMed. The review is therefore to be classified as a "rapid review", since it includes one database only, and has language restrictions; the novelty and importance of the issue make missing relevant papers unlikely. We extracted 16 variables, including: main aim, studied population, data source, sample size, problem type (regression, classification), predictors used, and performance metrics. We followed the Preferred Reporting Items for Systematic Reviews (PRISMA) approach; all main steps were done in duplicate.

RESULTS

From a total of 648 studies initially retrieved, 68 articles met the inclusion criteria. Models, as reported by authors, performed well, but the reported metrics were not homogeneous across articles and therefore direct comparison was not feasible. The most common aim was prediction of prognosis, followed by diagnosis of CKD. Algorithm generalizability, and testing on diverse populations was rarely taken into account. Furthermore, the clinical evaluation and validation of the models/algorithms was perused; only a fraction of the included studies, 6 out of 68, were performed in a clinical context.

CONCLUSIONS

Machine learning is a promising tool for the prediction of risk, diagnosis, and therapy management for CKD patients. Nonetheless, future work is needed to address the interpretability, generalizability, and fairness of the models to ensure the safe application of such technologies in routine clinical practice.

Current state and completeness of reporting clinical prediction models using machine learning in systemic lupus erythematosus: A systematic review

OBJECTIVE

We carried out a systematic review (SR) of adherence in diagnostic and prognostic applications of ML in SLE using the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Statement.

METHODS

A SR employing five databases was conducted from its inception until December 2021. We identified articles that evaluated the utilization of ML for prognostic and/or diagnostic purposes. This SR was reported based on the PRISMA guidelines. The TRIPOD statement assessed adherence to reporting standards. Assessment for risk of bias was done using PROBAST tool.

RESULTS

We included 45 studies: 29 (64.4%) diagnostic and 16 (35.5%) prognostic prediction- model studies. Overall, articles adhered by between 17% and 67% (median 43%, IQR 37-49%) to TRIPOD items. Only few articles reported the model's predictive performance (2.3%, 95% CI 0.06-12.0), testing of interaction terms (2.3%, 95% CI 0.06-12.0), flow of participants (50%, 95% CI; 34.6-65.4), blinding of predictors (2.3%, 95% CI 0.06-12.0), handling of missing data (36.4%, 95% CI 22.4-52.2), and appropriate title (20.5%, 95% CI 9.8-35.3). Some items were almost completely reported: the source of data (88.6%, 95% CI 75.4-96.2), eligibility criteria (86.4%, 95% CI 76.2-96.5), and interpretation of findings (88.6%, 95% CI 75.4-96.2). In addition, most of model studies had high risk of bias.

CONCLUSIONS

The reporting adherence of ML-based model developed for SLE, is currently inadequate. Several items deemed crucial for transparent reporting were not fully reported in studies on ML-based prediction models.

REVIEW REGISTRATION

PROSPERO ID# CRD42021284881. (Amended to limit the scope).

Artificial neural network - an effective tool for predicting the lupus nephritis outcome

Background

Lupus nephropathy (LN) occurs in approximately 50% of patients with systemic lupus erythematosus (SLE), and 20% of them will eventually progress into end-stage renal disease (ESRD). A clinical tool predicting remission of proteinuria might be of utmost importance. In our work, we focused on predicting the chance of complete remission achievement in LN patients, using artificial intelligence models, especially an artificial neural network, called the multi-layer perceptron.

Methods

It was a single centre retrospective study, including 58 individuals, with diagnosed systemic lupus erythematous and biopsy proven lupus nephritis. Patients were assigned into the study cohort, between 1st January 2010 and 31st December 2020, and eventually randomly allocated either to the training set (N = 46) or testing set (N = 12). The end point was remission achievement. We have selected an array of variables, subsequently reduced to the optimal minimum set, providing the best performance.

Results

We have obtained satisfactory results creating predictive models allowing to assess, with accuracy of 91.67%, a chance of achieving a complete remission, with a high discriminant ability (AUROC 0.9375).

Conclusion

Our solution allows an accurate assessment of complete remission achievement and monitoring of patients from the group with a lower probability of complete remission. The obtained models are scalable and can be improved by introducing new patient records.

Renal Fibrosis in Lupus Nephritis

Fibrosis can be defined as a pathological process in which deposition of connective tissue replaces normal parenchyma. The kidney, like any organ or tissue, can be impacted by this maladaptive reaction, resulting in persistent inflammation or long-lasting injury. While glomerular injury has traditionally been regarded as the primary focus for classification and prognosis of lupus nephritis (LN), increasing attention has been placed on interstitial fibrosis and tubular atrophy as markers of injury severity, predictors of therapeutic response, and prognostic factors of renal outcome in recent years. This review will discuss the fibrogenesis in LN and known mechanisms of renal fibrosis. The importance of the chronicity index, which was recently added to the histological categorization of LN, and its role in predicting treatment response and renal prognosis for patients with LN, will be explored. A better understanding of cellular and molecular pathways involved in fibrosis in LN could enable the identification of individuals at higher risk of progression to chronic kidney disease and end-stage renal disease, and the development of new therapeutic strategies for lupus patients.

Silent Lupus Nephritis: Renal Histopathological Profile and Early Detection with Urinary Monocyte Chemotactic Protein 1

Objective

Lupus nephritis (LN) affects almost half of all individuals with systemic lupus erythematosus (SLE). Overt LN (OLN) symptoms might vary from asymptomatic microscopic hematuria to renal failure. However, when there are no clinical or laboratory indicators of renal involvement, some people with silent LN (SLN) may have pathological evidence of renal involvement identified by renal biopsy. Monocyte Chemotactic Protein 1 (MCP-1) is a chemotactic factor that promotes leukocyte migration to the kidney. MCP-1 urine levels (uMCP-1) have been demonstrated to be high in individuals with active LN. The purpose of this study was to discover the occurrence of SLN, as well as the possible variations between overt LN (OLN) and SLN across SLE patients based on the histopathological assessment, as well as the role of uMCP-1 in the early detection of SLN.

Methods

An overall of 144 patients with SLE were included in the current research. Patients were subsequently divided into two groups: individuals who did not have clinical evidence of LN (84 patients) and those with OLN (60 patients). All the patients were subjected to the following investigations: uMCP-1, erythrocyte sedimentation rate (ESR), complement C3 (C3), complement C4 (C4), creatinine, albumin/creatinine ratio (uACR), creatinine clearance, quantitative assessment of proteinuria by 24-hour urine proteinuria (24hr UP) and percutaneous renal biopsy.

Results

Sixty patients from group I (71.4%) showed glomerular lesions on renal biopsy (SLN), and class II was the predominant class. uMCP-1 had a sensitivity of 95.2% and a specificity of 98% in the detection of SLN, and uMCP-1 values were markedly higher in patients with OLN in comparison to SLN.

Conclusion

The actual frequency of SLN may be higher than expected. High levels of uMCP-1 may have warranted the early activity of LN. uMCP-1 can be used as a non-invasive, useful tool for the prediction of LN.

Lupus nephritis diagnosis using enhanced moth flame algorithm with support vector machines

Systemic lupus erythematosus is a chronic autoimmune disease that affects the kidney in most patients. Lupus nephritis (LN) is divided into six categories by the International Society of Nephrology/Renal Pathology Society (ISN/RPS). The purpose of this research is to build a framework for discriminating between ISN/RPS pure class V(MLN) and classes III ± V or IV ± V (PLN) using real clinical data. The framework is developed by merging a hybrid stochastic optimizer, moth-flame algorithm (HMFO), with a support vector machine (SVM), dubbed HMFO-SVM. The HMFO is constructed by enhancing the original moth-flame algorithm (MFO) with a bee-foraging learning operator, which guarantees that the algorithm speeds convergence and departs from the local optimum. The HMFO is used to optimize parameters and select features simultaneously for SVM on clinical SLE data. On 23 benchmark tests, the suggested HMFO method is validated. Finally, clinical data from LN patients are analyzed to determine the efficacy of HMFO-SVM over other SVM rivals. The statistical findings indicate that all measures have predictive capabilities and that the suggested HMFO-SVM is more stable for analyzing systemic LN. HMFO-SVM may be used to analyze LN as a feasible computer-assisted technique.

Systemic sclerosis complicated with renal thrombotic microangiopathy: a case report and literature review

Background

Systemic sclerosis (SSc) may overlap with other connective tissue diseases, which is named overlap syndrome. Scleroderma renal crisis (SRC) is a rare but severe complication of SSc. SSc related thrombotic microangiopathy (SSc-TMA) is an infrequent pathology type of SRC, while SSc-TMA accompanied by overlap syndrome is very rare.

Case presentation

This study reported a case of acute kidney injury (AKI) accompanied with overlap syndrome of SSc, systemic lupus erythematosus (SLE) and polymyositis (PM). The renal pathology supported the diagnosis of SSc-TMA but not SLE or PM-related renal injury, characterized by renal arteriolar thrombosis, endothelial cells edema, little cast in tubules and mild immune complex deposition. The primary TMA related factors (ADAMTS13 and complement H factor) were normal. Thus, this case was diagnosed as secondary TMA associated with SSc. The patient was treated with renin angiotensin system inhibitors, sildenafil, supportive plasma exchange/dialysis, and rituximab combined with glucocorticoids. After 2 months of peritoneal dialysis treatment, her renal function recovered and dialysis was stopped.

Conclusion

This study presented a case of SSc-TMA with overlap syndrome. Rituximab can be used as a treatment option in patients with high SRC risk or already manifesting SRC.

Assessment of glomerular morphological patterns by deep learning algorithms

Background

Compilation of different morphological lesion signatures is characteristic of renal pathology. Previous studies have documented the potential value of artificial intelligence (AI) in recognizing relatively clear-cut glomerular structures and patterns, such as segmental or global sclerosis or mesangial hypercellularity. This study aimed to test the capacity of deep learning algorithms to recognize complex glomerular structural changes that reflect common diagnostic dilemmas in nephropathology.

Methods

For this purpose, we defined nine classes of glomerular morphological patterns and trained twelve convolutional neuronal network (CNN) models on these. The two-step training process was done on a first dataset defined by an expert nephropathologist (12,253 images) and a second consensus dataset (11,142 images) defined by three experts in the field.

Results

The efficacy of CNN training was evaluated using another set with 180 consensus images, showing convincingly good classification results (kappa-values 0.838–0.938).

Furthermore, we elucidated the image areas decisive for CNN-based decision making by class activation maps. Finally, we demonstrated that the algorithm could decipher glomerular disease patterns coinciding in a single glomerulus (e.g. necrosis along with mesangial and endocapillary hypercellularity).

Conclusions

In summary, our model, focusing on glomerular lesions detectable by conventional microscopy, is the first sui generis to deploy deep learning as a reliable and promising tool in recognition of even discrete and/or overlapping morphological changes. Our results provide a stimulus for ongoing projects that integrate further input levels next to morphology (such as immunohistochemistry, electron microscopy, and clinical information) to develop a novel tool applicable for routine diagnostic nephropathology.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40620-021-01221-9.

An introduction to machine learning and analysis of its use in rheumatic diseases

Machine learning (ML) is a computerized analytical technique that is being increasingly employed in biomedicine. ML often provides an advantage over explicitly programmed strategies in the analysis of multidimensional information by recognizing relationships in the data that were not previously appreciated. As such, the use of ML in rheumatology is increasing, and numerous studies have employed ML to classify patients with rheumatic autoimmune inflammatory diseases (RAIDs) from medical records and imaging, biometric or gene expression data. However, these studies are limited by sample size, the accuracy of sample labelling, and absence of datasets for external validation. In addition, there is potential for ML models to overfit or underfit the data and, thereby, these models might produce results that cannot be replicated in an unrelated dataset. In this Review, we introduce the basic principles of ML and discuss its current strengths and weaknesses in the classification of patients with RAIDs. Moreover, we highlight the successful analysis of the same type of input data (for example, medical records) with different algorithms, illustrating the potential plasticity of this analytical approach. Altogether, a better understanding of ML and the future application of advanced analytical techniques based on this approach, coupled with the increasing availability of biomedical data, may facilitate the development of meaningful precision medicine for patients with RAIDs.

Development of a lupus nephritis suboptimal response prediction tool using renal histopathological and clinical laboratory variables at the time of diagnosis

Objective

Lupus nephritis (LN) is an immune complex-mediated glomerular and tubulointerstitial disease in patients with SLE. Prediction of outcomes at the onset of LN diagnosis can guide decisions regarding intensity of monitoring and therapy for treatment success. Currently, no machine learning model of outcomes exists. Several outcomes modelling works have used univariate or linear modelling but were limited by the disease heterogeneity. We hypothesised that a combination of renal pathology results and routine clinical laboratory data could be used to develop and to cross-validate a clinically meaningful machine learning early decision support tool that predicts LN outcomes at approximately 1 year.

Methods

To address this hypothesis, patients with LN from a prospective longitudinal registry at the Medical University of South Carolina enrolled between 2003 and 2017 were identified if they had renal biopsies with International Society of Nephrology/Renal Pathology Society pathological classification. Clinical laboratory values at the time of diagnosis and outcome variables at approximately 1 year were recorded. Machine learning models were developed and cross-validated to predict suboptimal response.

Results

Five machine learning models predicted suboptimal response status in 10 times cross-validation with receiver operating characteristics area under the curve values >0.78. The most predictive variables were interstitial inflammation, interstitial fibrosis, activity score and chronicity score from renal pathology and urine protein-to-creatinine ratio, white blood cell count and haemoglobin from the clinical laboratories. A web-based tool was created for clinicians to enter these baseline clinical laboratory and histopathology variables to produce a probability score of suboptimal response.

Conclusion

Given the heterogeneity of disease presentation in LN, it is important that risk prediction models incorporate several data elements. This report provides for the first time a clinical proof-of-concept tool that uses the five most predictive models and simplifies understanding of them through a web-based application.

Evolving fuzzy k-nearest neighbors using an enhanced sine cosine algorithm: Case study of lupus nephritis

Because of its simplicity and effectiveness, fuzzy K-nearest neighbors (FKNN) is widely used in literature. The parameters have an essential impact on the performance of FKNN. Hence, the parameters need to be attuned to suit different problems. Also, choosing more representative features can enhance the performance of FKNN. This research proposes an improved optimization technique based on the sine cosine algorithm (LSCA), which introduces a linear population size reduction mechanism for enhancing the original algorithm's performance. Moreover, we developed an FKNN model based on the LSCA, it simultaneously performs feature selection and parameter optimization. Firstly, the search performance of LSCA is verified on the IEEE CEC2017 benchmark test function compared to the classical and improved algorithms. Secondly, the validity of the LSCA-FKNN model is verified on three medical datasets. Finally, we used the proposed LSCA-FKNN to predict lupus nephritis classes, and the model showed competitive results. The paper will be supported by an online web service for any question at https://aliasgharheidari.com.

Association of Lupus Nephritis Histopathologic Classification With Venous Thromboembolism-Modification by Age at Biopsy

INTRODUCTION

Lupus nephritis (LN) is an independent risk factor for venous thromboembolism (VTE). The risk of VTE has not been analyzed by International Society of Nephrology/Renal Pathology Society or World Health Organization LN class. Study goals were to measure VTE incidence in an LN patient cohort, to evaluate associations between VTE and LN class, and to investigate factors modifying associations between VTE and LN class.

METHODS

A retrospective analysis was performed using Glomerular Disease Collaborative Network data. Image-confirmed VTE was compared between patients with any LN class V lesion and patients with only LN class III or IV. Logistic regression was used to calculate odds ratios and 95% confidence intervals. Effect modification was assessed between main effect and covariates.

RESULTS

Our cohort consisted of 534 LN patients, 310 (58%) with class III/IV and 224 (42%) with class V with or without class III/IV, including 106 with class V alone. The VTE incidence was 62 of 534 (11.6%). The odds of VTE were not significantly different between patients with class III/IV and class V in adjusted analyses (odds ratio [OR] = 0.82, 95% confidence interval [CI] = 0.45-1.48). An age interaction was observed (P = 0.009), with increased odds of VTE with class III/IV diagnosed at a younger age (2.75, 0.90-8.41 estimated at age 16 years) and decreased odds with class III/IV diagnosed at an older age (0.23, 0.07-0.72 estimated at age 46 years), compared to class V.

CONCLUSIONS

The VTE incidence was similar among patients with LN classes III/IV and V, suggesting that VTE risk is not limited to class V-related nephrotic syndrome and that age may modulate LN class-specific VTE risk.

Interleukin-12 exacerbates symptoms in an MRL/MpJ-Faslpr mouse model of systemic lupus erythematosus

Interleukin (IL)-12 modulates the generation and function of a variety of immune cells and serves an important role in the pathogenesis of autoimmune diseases. However, the precise role of IL-12 in the pathogenesis of systemic lupus erythematosus (SLE) remains to be elucidated. In the present study, the serum levels of IL-12 in patients with SLE were determined using an ELISA. The association between serum levels of IL-12 and clinical and laboratory indices, specifically, disease activity and complement 3, were analyzed. Recombinant IL-12 or an anti-IL-12 antibody was used to treat the MRL/MpJ-Fas^lpr mouse model of systemic lupus erythematosus. The glomerulonephritis and inflammatory cell infiltration was examined to evaluate histological changes using hematoxylin and eosin and Periodic acid-Schiff staining. Serum creatinine and proteinuria were used to determine renal function. The levels of anti-double stranded DNA and anti-nuclear autoantibodies were assessed. The results demonstrated that serum levels of IL-12 were markedly increased in patients with SLE compared with controls and in lupus model mice in comparison with control mice. The serum levels of IL-12 increased with disease severity in patients with SLE. SLE-like symptoms were exacerbated in lupus model mice treated with exogenous IL-12. However, SLE-like symptoms were ameliorated in lupus model mice treated with an anti-IL-12 antibody. The present results demonstrated that IL-12 aggravated SLE and anti-IL12 antibodies ameliorated SLE. The present data suggest that blocking IL-12 may be a beneficial therapeutic strategy to halt the progression of lupus nephritis.

Stellenwert der Nierenbiopsie bei Lupusnephritis

Bei systemischem Lupus erythematodes (SLE) findet sich häufig eine renale Mitbeteiligung, der verschiedene pathogenetische Mechanismen zugrunde liegen. Die Nierenbeteiligung hat einen negativen Einfluss auf die Prognose des SLE, insbesondere bei progredienter Niereninsuffizienz. Eine Nierenbiopsie ist aufgrund der Heterogenität der Nierenbeteiligung und der damit verbundenen therapeutischen Konsequenzen unabdingbar. Sie kann durch nicht-invasive Untersuchungen wie die Urindiagnostik oder Serologie nicht ersetzt werden, da das Ausmaß der Proteinurie oder der Mikrohämaturie keine sicheren Rückschlüsse auf den Schweregrad, die Pathogenese und die Prognose der Nierenbeteiligung erlauben. Die Nierenbiopsie gibt neben der korrekten Klassifikation der Lupusnephritis (LN) Informationen über die Mitbeteiligung des Niereninterstitium, der intrarenalen Gefäße und der Aktivität sowie Chronizität der Nephritis. Auch kann der Pathologe die Frage beantworten, inwieweit mit einer Besserung der Nierenfunktion unter Therapie gerechnet werden kann. Der folgende Beitrag gibt einen Überblick über den Stellenwert der Nierenbiopsie bei SLE, der revidierten Klassifikation der LN von 2018 einschließlich Sonderformen der LN und über die Implikationen des Biopsie-Ergebnisses für die Therapie.

Analysis of clinical and laboratory characteristics and pathology of lupus nephritis-based on 710 renal biopsies in China

OBJECTIVES

This study aimed to analyse the clinical and laboratory characteristics of different pathologic classifications of lupus nephritis (LN) patients in terms of age at systemic lupus erythematosus (SLE) diagnosis and nephritis onset.

METHOD

Clinical, laboratory, and pathological data of 710 LN patients diagnosed by renal biopsy at our institution between 2000 and 2018 were retrospectively analysed. Patients were divided into the different pathological classification groups; childhood-, adult- and elderly-onset SLE groups and early- and late-onset LN groups.

RESULTS

Class IV occurred most frequently and had the lowest complement C3 level. There was an obvious increase in active index in class IV and class V + IV. Patients with class VI showed some clinical characteristics similar to end-stage renal disease. Patients with proliferative nephritis were younger at SLE diagnosis and had higher blood pressure, higher frequency of proteinuria and urinary erythrocyte and lower haemoglobin and complement C3. Pathologic classification between childhood-, adult- and elderly-onset SLE patients or between early- and late-onset LN patients was not significantly different. Elderly-onset SLE patients had the highest chronic index (CI), IgA, IgG and Sjögren's syndrome A antibodies and Sjögren's syndrome B antibodies rates, whereas late-onset LN patients showed significantly higher CI, haemoglobin, complement C3 and C4 but lower uric acid, IgM and IgG.

CONCLUSIONS

LN patients present with different clinical and laboratory characteristics according to pathological classification, age at SLE diagnosis and nephritis onset. These results might be valuable for estimating the pathology and guiding treatment and prognosis. Key Points • Patients with proliferative nephritis have more severe immune disorders, worse renal function and stronger inflammatory state. • The elderly-onset SLE patients showed a poorer condition. • The late-onset LN patients might have a more stable status.

A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases

Autoimmune diseases are chronic, multifactorial conditions. Through machine learning (ML), a branch of the wider field of artificial intelligence, it is possible to extract patterns within patient data, and exploit these patterns to predict patient outcomes for improved clinical management. Here, we surveyed the use of ML methods to address clinical problems in autoimmune disease. A systematic review was conducted using MEDLINE, embase and computers and applied sciences complete databases. Relevant papers included "machine learning" or "artificial intelligence" and the autoimmune diseases search term(s) in their title, abstract or key words. Exclusion criteria: studies not written in English, no real human patient data included, publication prior to 2001, studies that were not peer reviewed, non-autoimmune disease comorbidity research and review papers. 169 (of 702) studies met the criteria for inclusion. Support vector machines and random forests were the most popular ML methods used. ML models using data on multiple sclerosis, rheumatoid arthritis and inflammatory bowel disease were most common. A small proportion of studies (7.7% or 13/169) combined different data types in the modelling process. Cross-validation, combined with a separate testing set for more robust model evaluation occurred in 8.3% of papers (14/169). The field may benefit from adopting a best practice of validation, cross-validation and independent testing of ML models. Many models achieved good predictive results in simple scenarios (e.g. classification of cases and controls). Progression to more complex predictive models may be achievable in future through integration of multiple data types.

Downregulated miR-203 attenuates IL-β, IL-6, and TNF-α activation in TRAF6-treated human renal mesangial and tubular epithelial cells

Circulating microRNAs (miRNAs) are attracting major interest as novel non-invasive biomarkers for human autoimmune diseases including lupus nephritis (LN). A previous study showed that altered miR-203 expression may provide highly diagnostic for systemic lupus erythematosus. However, whether miR-203 is a diagnostic biomarker for LN is still unknown. In the present research, serum samples from 35 cases of active LN patients, 58 cases of inactive LN patients, and 74 cases of healthy volunteers were collected to analyze the expression profiles of miR-203 by qRT-PCR. The serum concentration of complement component 3 (C3) and complement component 4 (C4) was detected using nephelometry method. The expression of inflammatory cytokines, including interleukin 1 beta (IL-1β), interleukin 6 (IL-6), and tumor necrosis factor α (TNF-α), were analyzed using enzyme-linked immunosorbent assay (ELISA). The effect of miR-203 overexpression on the TNF receptor associated factor 6 (TRAF6)-induced inflammation of human renal mesangial cells (HRMCs) and human renal tubular epithelial cell line (HK-2) were evaluated. Results showed that miR-203 in serum of active LN patients was significantly down-regulated when compared with serum from inactive LN patients and healthy volunteers. Receiver operating curve (ROC) showed that decreased circulating miR-203 was a significant diagnostic biomarker for active LN patients, with an area under curve (AUC) of 0.974; sensitivity was 85.79%, and specificity was 89.40%. Significant downregulation of C3 and C4, and obvious upregulation of IL-β, IL-6, and TNF-α, was observed in serum of active LN patients. Furthermore, circulating miR-203 expression was positively correlated with the serum concentrations of C3 and C4, and negatively correlated with the serum expression of IL-1β, IL-6, and TNF-α in active LN patients. In addition, transfection of HRMCs and HK-2 cells with miR-203 mimics could suppress TRAF6-induced IL-β, IL-6, or TNF-α expression compared to cells treated with the mimics control group. In summary, decreased circulating miR-203 might be a candidate diagnostic biomarker for human active LN, and it attenuated IL-β, IL-6, and TNF-α activation in TRAF6-treated HRMCs and HK-2 cells.

Association of Lupus Nephritis With Coronary Artery Disease by ISN/RPS Classification: Results From a Large Real-world Lupus Population

Objective

Patients with systemic lupus erythematosus (SLE) are at an increased risk for developing coronary artery disease (CAD). Several studies suggest that the presence of lupus nephritis (LN) is independently associated with CAD. The purpose of our study was to assess whether the presence of LN is independently associated with CAD in our patient population and whether this association varies by class of LN.

Methods

A retrospective cross‐sectional analysis was performed using medical records of patients 18 years and older with SLE at University of North Carolina Hospitals from April 4, 2014, to December 31, 2017. Subjects were identified using International Classification of Diseases, Ninth Revision (ICD‐9) and International Classification of Diseases, 10th Revision (ICD‐10) codes specific for SLE. LN class was defined by International Society of Nephrology/Renal Pathology Society (ISN/RPS) classification. CAD was the outcome of interest. Logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs).

Results

Our sample consisted of 3732 patients with SLE, of whom 598 (16%) had LN and 537 (14%) had CAD. When adjusting for demographics and factors associated with CAD and LN, the odds of having CAD were significantly higher for patients with SLE and LN compared with patients without LN (OR 1.47; 95% CI 1.07‐2.02; P = 0.017). Controlling for these factors, class III LN (OR 1.98; 95% CI 0.95‐4.12; P = 0.069) and class III/V LN (OR 2.23; 95% CI 1.09‐4.62; P = 0.028) were very strongly associated with CAD in subjects with LN compared with subjects without LN.

Conclusion

We confirm the observations of previous studies that LN is significantly associated with CAD. Our study is the first to show the association between CAD and specific classes of LN.

The long-term outcomes and histological transformation in class II lupus nephritis

Objectives:

To examined the short and long-term outcome of class II lupus nephritis (LN).

Methods:

This retrospective study included patients with class II LN at their first renal biopsy between January 1996 and December 2016 in King Khaled University Hospital, Riyadh, Saudi Arabia. The rate of complete remission, worsening renal function, and histological transformation in the second biopsy were examined.

Results:

The study included 32 female patients with class II LN. The most frequent presentation (62.5% of patients) was hematuria with subnephrotic range proteinuria. The clinical presentation included acute kidney injury in 22% of patients, and 9.4% had nephrotic range proteinuria. Management with steroid monotherapy in 25 patients resulted in complete remission for 92% of these patients at 6 months. After a median follow up of 8 years, 2 patients had a doubling of their serum creatinine. During the follow up 17 patients (53%) needed a second biopsy, which revealed transformation to other classes (65%).

Conclusions:

Daily steroid monotherapy may be an appropriate first-line treatment for class II LN that presents with subnephrotic range proteinuria and normal kidney function. Patients with acute kidney injury and/or nephrotic range proteinuria may warrant more aggressive immunosuppressive regimens.