Anti-KIF20B autoantibodies are associated with cranial neuropathy in systemic lupus erythematosus.

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.

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.

Identification of novel clusters of co-expressing cytokines in a diagnostic cytokine multiplex test.

Introduction: Cytokines are mediators of the immune system that are essential for the maintenance, development and resolution of immune responses. Beneficial immune responses depend on complex, interdependent networks of signaling and regulatory events in which individual cytokines influence the production and release of others. Since disruptions in these signaling networks are associated with a wide spectrum of diseases, cytokines have gained considerable interest as diagnostic, prognostic and precision therapy-relevant biomarkers. However, currently individual cytokines testing has limited value because the wider immune response context is often overlooked. The aim of this study was to identify specific cytokine signaling patterns associated with different diseases. Methods: Unbiased clustering analyses were performed on a clinical cytokine multiplex test using a cohort of human plasma specimens drawn from individuals with known or suspected diseases for which cytokine profiling was considered clinically indicated by the attending physician. Results and discussion: Seven clusters of co-expressing cytokines were identified, representing common patterns of immune activation. Common expression profiles of the cytokine clusters and preliminary associations of these profiles with specific diseases or disease categories were also identified. These findings increase our understanding of the immune environments underlying the clinical presentations of patients of inflammatory, autoimmune and neoplastic diseases, which could then improve diagnoses and the identification of evidence-based treatment targets.

Machine learning identifies clusters of longitudinal autoantibody profiles predictive of systemic lupus erythematosus disease outcomes.

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.

Anti-Valosin-Containing Protein (VCP/p97) Autoantibodies in Inclusion Body Myositis and Other Inflammatory Myopathies.

OBJECTIVE: The rationale for this study was based on reports that valosin-containing protein (VCP) mutations are found in hereditary inclusion body myositis (IBM) and VCP was detected in rimmed vacuoles of sporadic IBM (sIBM) muscle biopsies. Autoantibodies to VCP have not been reported in sIBM or other inflammatory myopathies (IIMs). The aim of this study was to determine the frequency and clinical significance of anti-VCP antibodies in sIBM and other IIMs. METHODS: Sera were collected from 73 patients with sIBM and 383 comparators or controls, including patients with IIM (n = 69), those with juvenile dermatomyositis (JDM) (n = 67), those with juvenile idiopathic arthritis (JIA) (n = 47), those with primary biliary cholangitis (PBC) (n = 105), controls that were age matched to patients with sIBM (similarly aged controls [SACs]) (n = 63), and healthy controls (HCs) (n = 32). Immunoglobulin G antibodies to VCP were detected by addressable laser bead immunoassay using a full-length recombinant human protein. RESULTS: Among patients with sIBM, 26.0% (19/73) were positive for anti-VCP. The frequency in disease controls was 15.0% (48/320). Among SACs, the frequency was 1.6% (1/63), and in HCs 0% (0/32). Frequencies were 17.5% (11/63) for IIM, 25.7% (27/105) for PBC, 3.0% (2/67) for JDM, and 17.0% (8/47) for JIA. The sensitivity, specificity, positive predictive value, and negative predictive value of anti-VCP for sIBM were 26.0%, 87.2%, 28.4%, and 85.9%, respectively. Of patients with sIBM, 15.1% (11/73) were positive for both anti-VCP and anti-cytosolic 5'-nucleotidase 1A (NT5c1A). Eleven percent of patients (8/73) were positive for anti-VCP, but negative for anti-NT5c1A. CONCLUSION: Anti-VCP has low sensitivity and moderate specificity for sIBM but may help fill the seronegative gap in sIBM. Further studies are needed to determine whether anti-VCP is a biomarker for a clinical phenotype that may have clinical value.

Circulating Calprotectin as a Biomarker of COVID-19 Severity.

INTRODUCTION: Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Although demographic and clinical parameters such as sex, age, comorbidities, genetic background and various biomarkers have been identified as risk factors, there is an unmet need to predict the risk and onset of severe inflammatory disease leading to poor clinical outcomes. In addition, very few mechanistic biomarkers are available to inform targeted treatment of severe (auto)-inflammatory conditions associated with COVID-19. Calprotectin, also known as S100A8/S100A9, MRP8/14 (Myeloid-Related Protein) or L1, is a heterodimer involved in neutrophil-related inflammatory processes. In COVID-19 patients, calprotectin levels were reported to be associated with poor clinical outcomes such as significantly reduced survival time, especially in patients with severe pulmonary disease. AREAS COVERED: Pubmed was searched using the following keywords: Calprotectin + COVID19, S100A8/A9 + COVID19, S100A8 + COVID-19, S100A9 + COVID-19, MRP8/14 + COVID19; L1 + COVID-19 between May 2020 and 8 March 2021. The results summarized in this review provide supporting evidence and propose future directions that define calprotectin as an important biomarker in COVID-19. EXPERT OPINION: Calprotectin represents a promising serological biomarker for the risk assessment of COVID-19 patients.