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BACKGROUND: Cranial neuropathies (CN) are a rare neuropsychiatric SLE (NPSLE) manifestation. Previous studies reported that antibodies to the kinesin family member 20B (KIF20B) (anti-KIF20B) protein were associated with idiopathic ataxia and CN. We assessed anti-KIF20B as a potential biomarker for NPSLE in an international SLE inception cohort. METHODS: Individuals fulfilling the revised 1997 American College of Rheumatology (ACR) SLE classification criteria were enrolled from 31 centres from 1999 to 2011 and followed annually in the Systemic Lupus Erythematosus International Collaborating Clinics inception cohort. Anti-KIF20B testing was performed on baseline (within 15 months of diagnosis or first annual visit) samples using an addressable laser bead immunoassay. Logistic regression (penalised maximum likelihood and adjusting for confounding variables) examined the association between anti-KIF20B and NPSLE manifestations (1999 ACR case definitions), including CN, occurring over the first 5 years of follow-up. RESULTS: Of the 1827 enrolled cohort members, baseline serum and 5 years of follow-up data were available on 795 patients who were included in this study: 29.8% were anti-KIF20B-positive, 88.7% female, and 52.1% White. The frequency of anti-KIF20B positivity differed only for those with CN (n=10) versus without CN (n=785) (70.0% vs 29.3%; OR 5.2, 95% CI 1.4, 18.5). Compared with patients without CN, patients with CN were more likely to fulfil the ACR haematological (90.0% vs 66.1%; difference 23.9%, 95% CI 5.0%, 42.8%) and ANA (100% vs 95.7%; difference 4.3%, 95% CI 2.9%, 5.8%) criteria. In the multivariate analysis adjusting for age at baseline, female, White race and ethnicity, and ACR haematological and ANA criteria, anti-KIF20B positivity remained associated with CN (OR 5.2, 95% CI 1.4, 19.1). CONCLUSION: Anti-KIF20B is a potential biomarker for SLE-related CN. Further studies are needed to examine how autoantibodies against KIF20B, which is variably expressed in a variety of neurological cells, contribute to disease pathogenesis.

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.

OBJECTIVE: To phenotype SLE based on symptom burden (disease damage, system involvement and patient reported outcomes), with a specific focus on objective and subjective cognitive function. METHODS: SLE patients ages 18-65 years underwent objective cognitive assessment using the ACR Neuropsychological Battery (ACR-NB) and data were collected on demographic and clinical variables, disease burden/activity, health-related quality of life (HRQoL), depression, anxiety, fatigue and perceived cognitive deficits. Similarity network fusion (SNF) was used to identify patient subtypes. Differences between the subtypes were evaluated using Kruskal-Wallis and χ2 tests. RESULTS: Of the 238 patients, 90% were female, with a mean age of 41 years (s.d. 12) and a disease duration of 14 years (s.d. 10) at the study visit. The SNF analysis defined two subtypes (A and B) with distinct patterns in objective and subjective cognitive function, disease burden/damage, HRQoL, anxiety and depression. Subtype A performed worst on all significantly different tests of objective cognitive function (P

OBJECTIVES: A novel longitudinal clustering technique was applied to comprehensive autoantibody data from a large, well-characterised, multinational inception systemic lupus erythematosus (SLE) cohort to determine profiles predictive of clinical outcomes. METHODS: Demographic, clinical and serological data from 805 patients with SLE obtained within 15 months of diagnosis and at 3-year and 5-year follow-up were included. For each visit, sera were assessed for 29 antinuclear antibodies (ANA) immunofluorescence patterns and 20 autoantibodies. K-means clustering on principal component analysis-transformed longitudinal autoantibody profiles identified discrete phenotypic clusters. One-way analysis of variance compared cluster enrolment demographics and clinical outcomes at 10-year follow-up. Cox proportional hazards model estimated the HR for survival adjusting for age of disease onset. RESULTS: Cluster 1 (n=137, high frequency of anti-Smith, anti-U1RNP, AC-5 (large nuclear speckled pattern) and high ANA titres) had the highest cumulative disease activity and immunosuppressants/biologics use at year 10. Cluster 2 (n=376, low anti-double stranded DNA (dsDNA) and ANA titres) had the lowest disease activity, frequency of lupus nephritis and immunosuppressants/biologics use. Cluster 3 (n=80, highest frequency of all five antiphospholipid antibodies) had the highest frequency of seizures and hypocomplementaemia. Cluster 4 (n=212) also had high disease activity and was characterised by multiple autoantibody reactivity including to antihistone, anti-dsDNA, antiribosomal P, anti-Sjögren syndrome antigen A or Ro60, anti-Sjögren syndrome antigen B or La, anti-Ro52/Tripartite Motif Protein 21, antiproliferating cell nuclear antigen and anticentromere B). Clusters 1 (adjusted HR 2.60 (95% CI 1.12 to 6.05), p=0.03) and 3 (adjusted HR 2.87 (95% CI 1.22 to 6.74), p=0.02) had lower survival compared with cluster 2. CONCLUSION: Four discrete SLE patient longitudinal autoantibody clusters were predictive of long-term disease activity, organ involvement, treatment requirements and mortality risk.

ObjectivesA perception derived from cross-sectional studies of small systemic lupus erythematosus (SLE) cohorts is that there is a marked discrepancy between antinuclear antibody (ANA) assays, which impacts on clinicians' approach to diagnosis and follow-up. We compared three ANA assays in a longitudinal analysis of a large international incident SLE cohort retested regularly and followed for 5 years.MethodsDemographic, clinical and serological data was from 805 SLE patients at enrolment, year 3 and 5. Two HEp-2 indirect immunofluorescence assays (IFA1, IFA2), an ANA ELISA, and SLE-related autoantibodies were performed in one laboratory. Frequencies of positivity, titres or absorbance units (AU), and IFA patterns were compared using McNemar, Wilcoxon and kappa statistics, respectively.ResultsAt enrolment, ANA positivity (≥1:80) was 96.1% by IFA1 (median titre 1:1280 (IQR 1:640-1:5120)), 98.3% by IFA2 (1:2560 (IQR 1:640-1:5120)) and 96.6% by ELISA (176.3 AU (IQR 106.4 AU-203.5 AU)). At least one ANA assay was positive for 99.6% of patients at enrolment. At year 5, ANA positivity by IFAs (IFA1 95.2%; IFA2 98.9%) remained high, while there was a decrease in ELISA positivity (91.3%, p91% agreement in ANA positivity at all time points and ≥71% agreement in IFA patterns between IFA1 and IFA2.ConclusionIn recent-onset SLE, three ANA assays demonstrated commutability with a high proportion of positivity and titres or AU. However, over 5 years follow-up, there was modest variation in ANA assay performance. In clinical situations where the SLE diagnosis is being considered, a negative test by either the ELISA or HEp-2 IFA may require reflex testing.

ObjectiveWe previously demonstrated the utility of the Automated Neuropsychological Assessment Metrics (ANAM) for screening cognitive impairment (CI) in patients with systemic lupus erythematosus (SLE) and developed composite indices for interpreting ANAM results. Our objectives here were to provide further support for the ANAM's concurrent criterion validity against the American College of Rheumatology neuropsychological battery (ACR-NB), identify the most discriminatory subtests and scores of the ANAM for predicting CI, and provide a new approach to interpret ANAM results using Classification and Regression Tree (CART) analysis.Methods300 adult SLE patients completed an adapted ACR-NB and ANAM on the same day. As per objectives, six models were built using combinations of ANAM subtests and scores and submitted to CART analysis. Area under the curve (AUC) was calculated to evaluate the ANAM's criterion validity compared to the adapted ACR-NB; the most discriminatory ANAM subtests and scores in each model were selected, and performance of models with the highest AUCs were compared to our previous composite indices; decision trees were generated for models with the highest AUCs.ResultsTwo models had excellent AUCs of 86 and 89%. Eight most discriminatory ANAM subtests and scores were identified. Both models demonstrated higher AUCs against our previous composite indices. An adapted decision tree was created to simplify the interpretation of ANAM results.ConclusionWe provide further validity evidence for the ANAM as a valid CI screening tool in SLE. The decision tree improves interpretation of ANAM results, enhancing clinical utility.

ObjectiveThe spectrum of antinuclear antibodies (ANAs) is changing to include both nuclear staining as well as cytoplasmic and mitotic cell patterns (CMPs) and accordingly a change is occurring in terminology to anticellular antibodies. This study examined the prevalence of indirect immunofluorescence (IIF) anticellular antibody staining using the Systemic Lupus International Collaborating Clinics inception cohort.MethodsAnticellular antibodies were detected by IIF on HEp-2000 substrate using the baseline serum. Three serologic subsets were examined: ANA positive (presence of either nuclear or mixed nuclear/CMP staining), anticellular antibody negative (absence of any intracellular staining), and isolated CMP staining. The odds of being anticellular antibody negative versus ANA or isolated CMP positive was assessed by multivariable analysis.ResultsA total of 1,137 patients were included; 1,049 (92.3%) were ANA positive, 71 (6.2%) were anticellular antibody negative, and 17 (1.5%) had an isolated CMP. The isolated CMP-positive group did not differ from the ANA-positive or anticellular antibody-negative groups in clinical, demographic, or serologic features. Patients who were older (odds ratio [OR] 1.02 [95% confidence interval (95% CI) 1.00, 1.04]), of white race/ethnicity (OR 3.53 [95% CI 1.77, 7.03]), or receiving high-dose glucocorticoids at or prior to enrollment (OR 2.39 [95% CI 1.39, 4.12]) were more likely to be anticellular antibody negative. Patients on immunosuppressants (OR 0.35 [95% CI 0.19, 0.64]) or with anti-SSA/Ro 60 (OR 0.41 [95% CI 0.23, 0.74]) or anti-U1 RNP (OR 0.43 [95% CI 0.20, 0.93]) were less likely to be anticellular antibody negative.ConclusionIn newly diagnosed systemic lupus erythematosus, 6.2% of patients were anticellular antibody negative, and 1.5% had an isolated CMP. The prevalence of anticellular antibody-negative systemic lupus erythematosus will likely decrease as emerging nomenclature guidelines recommend that non-nuclear patterns should also be reported as a positive ANA.

Introduction: Despite progress in our understanding of disease pathogenesis for systemic autoimmune rheumatic diseases (SARD), these diseases are still associated with high morbidity, disability, and mortality. Much of the strongest evidence to date implicating environmental factors in the development of autoimmunity has been based on well-established, large, longitudinal prospective cohort studies. Methods: Herein, we review the current state of knowledge on known environmental factors associated with the development of SARD and potential areas for future research. Results: The risk attributable to any particular environmental factor ranges from 10-200%, but exposures are likely synergistic in altering the immune system in a complex interplay of epigenetics, hormonal factors, and the microbiome leading to systemic inflammation and eventual organ damage. To reduce or forestall the progression of autoimmunity, a better understanding of disease pathogenesis is still needed. Conclusion: Owing to the complexity and multifactorial nature of autoimmune disease, machine learning, a type of artificial intelligence, is increasingly utilized as an approach to analyzing large datasets. Future studies that identify patients who are at high risk of developing autoimmune diseases for prevention trials are needed. [ABSTRACT FROM AUTHOR]

This article discusses the use of machine learning techniques for the diagnosis, prognosis, and treatment of idiopathic inflammatory myopathies (IIMs), specifically focusing on anti-melanoma differentiation-associated protein 5 (MDA5) antibody-positive dermatomyositis (MDA5-DM). The authors highlight the heterogeneity of IIMs as a challenge and suggest that machine learning has the potential to address this issue by generating phenotypic clusters of patients. However, the authors also note that the studies reviewed lack clear and common definitions, leading to inconsistent results. They call for collaboration and integration of different cohorts, studies, data, and techniques to improve the usefulness of machine learning in clinical practice. [Extracted from the article]

Objective: Sleep deprivation has been associated with risk of autoimmune diseases. Using the Nurses' Health Study (NHS) (1986–2016) and NHSII (1989–2017) cohorts, we aimed in the present study to investigate whether sleep deprivation was associated with risk of developing systemic lupus erythematosus (SLE). Methods: Average sleep duration in a 24‐hour period was reported in the NHS (1986–2014) and NHSII (1989–2009). Lifestyle, exposure, and medical information was collected on biennial questionnaires. Adjusted Cox regression analyses modeled associations between cumulative average sleep duration (categorical variables) and incident SLE. Interactions between sleep duration and shiftwork, bodily pain (using the Short Form 36 [SF‐36] questionnaire), and depression were examined. Results: We included 186,072 women with 187 incident SLE cases during 4,246,094 person‐years of follow‐up. Chronic low sleep duration (≤5 hours/night versus reference >7–8 hours) was associated with increased SLE risk (adjusted hazard ratio [HRadj] 2.47 [95% confidence interval (95% CI) 1.29, 4.75]), which persisted after the analysis was lagged (4 years; HRadj 3.14 [95% CI 1.57, 6.29]) and adjusted for shiftwork, bodily pain, and depression (HRadj 2.13 [95% CI 1.11, 4.10]). We detected additive interactions between low sleep duration and high bodily pain (SF‐36 score <75) with an attributable proportion (AP) of 64% (95% CI 40%, 87%) and an HR for SLE of 2.97 (95% CI 1.86, 4.75) for those with both risk factors compared to those with neither. Similarly, there was an interaction between low sleep duration and depression, with an AP of 68% (95% CI 49%, 88%) and an HR for SLE of 2.82 (95% CI 1.64, 4.85). Conclusion: Chronic low sleep duration was associated with higher SLE risk, with stronger effects among those with bodily pain and depression, highlighting the potential role of adequate sleep in disease prevention. [ABSTRACT FROM AUTHOR]