Explore the vast range of titles available.

Systemic lupus erythematosus (SLE) is a prototypic autoimmune disease characterized by antinuclear antibodies (ANAs) that form immune complexes that mediate pathogenesis by tissue deposition or cytokine induction. Some ANAs bind DNA or associated nucleosome proteins, whereas other ANAs bind protein components of complexes of RNA and RNA-binding proteins (RBPs). Levels of anti-DNA antibodies can fluctuate widely, unlike those of anti-RBP antibodies, which tend to be stable. Because anti-DNA antibody levels can reflect disease activity, repeat testing is common; by contrast, a single anti-RBP antibody determination is thought to suffice for clinical purposes. Experience from clinical trials of novel therapies has provided a new perspective on ANA expression during disease, as many patients with SLE are ANA negative at screening despite previously testing positive. Because trial results suggest that patients who are ANA negative might not respond to certain agents, screening strategies now involve ANA and anti-DNA antibody testing to identify patients with so-called ‘active, autoantibody-positive SLE’. Evidence suggests that ANA responses can decrease over time because of the natural history of disease or the effects of therapy. Together, these findings suggest that, during established disease, more regular serological testing could illuminate changes relevant to pathogenesis and disease status.Antinuclear antibodies (ANAs), characteristic features of systemic lupus erythematosus (SLE), are a requirement for disease classification and trial enrolment. In this Review, the authors re-examine the role of ANAs in SLE and discuss changing attitudes towards using ANAs as biomarkers.

ObjectivesTo analyse the influence of geolocation and ethnicity on the clinical presentation of primary Sjögren's syndrome (SjS) at diagnosis.MethodsThe Big Data Sjögren Project Consortium is an international, multicentre registry designed in 2014. By January 2016, 20 centres from five continents were participating. Multivariable logistic regression analyses were performed.ResultsWe included 7748 women (93%) and 562 men (7%), with a mean age at diagnosis of primary SjS of 53 years. Ethnicity data were available for 7884 patients (95%): 6174 patients (78%) were white, 1066 patients (14%) were Asian, 393 patients (5%) were Hispanic, 104 patients (1%) were black/African-American and 147 patients (2%) were of other ethnicities. SjS was diagnosed a mean of 7 years earlier in black/African-American compared with white patients; the female-to-male ratio was highest in Asian patients (27:1) and lowest in black/African-American patients (7:1); the prevalence of sicca symptoms was lowest in Asian patients; a higher frequency of positive salivary biopsy was found in Hispanic and white patients. A north-south gradient was found with respect to a lower frequency of ocular involvement in northern countries for dry eyes and abnormal ocular tests in Europe (OR 0.46 and 0.44, respectively) and Asia (OR 0.18 and 0.49, respectively) compared with southern countries. Higher frequencies of antinuclear antibodies (ANAs) were reported in northern countries in America (OR=1.48) and Asia (OR=3.80) while, in Europe, northern countries had lowest frequencies of ANAs (OR=0.67) and Ro/La (OR=0.69).ConclusionsThis study provides the first evidence of a strong influence of geolocation and ethnicity on the phenotype of primary SjS at diagnosis.

COVID-19 is associated with a wide range of clinical manifestations, including autoimmune features and autoantibody production. Here we develop three protein arrays to measure IgG autoantibodies associated with connective tissue diseases, anti-cytokine antibodies, and anti-viral antibody responses in serum from 147 hospitalized COVID-19 patients. Autoantibodies are identified in approximately 50% of patients but in less than 15% of healthy controls. When present, autoantibodies largely target autoantigens associated with rare disorders such as myositis, systemic sclerosis and overlap syndromes. A subset of autoantibodies targeting traditional autoantigens or cytokines develop de novo following SARS-CoV-2 infection. Autoantibodies track with longitudinal development of IgG antibodies recognizing SARS-CoV-2 structural proteins and a subset of non-structural proteins, but not proteins from influenza, seasonal coronaviruses or other pathogenic viruses. We conclude that SARS-CoV-2 causes development of new-onset IgG autoantibodies in a significant proportion of hospitalized COVID-19 patients and are positively correlated with immune responses to SARS-CoV-2 proteins. Infection with SARS-CoV2 and the development of Coronavirus disease 2019 (COVID-19) has been linked to induction of autoimmunity and autoantibody production. Here the authors characterise the new-onset IgG autoantibody response in hospitalised patients with COVID-19 which they correlate to the magnitude of the SARS-CoV2 response.

BackgroundThe relationship between serum antinuclear antibody (ANA) and rheumatoid arthritis (RA) remains unknown. Therefore, we aimed to evaluate whether serum ANA was associated with an increased risk of RA in a case–control study.MethodsPatients with rheumatoid arthritis hospitalized at Shandong Provincial Hospital from January 2018 to December 2022 were recruited as the case group, and patients with other types of arthritis and healthy people at the same time were taken as the control group. Antinuclear antibody (ANA) was detected by indirect immunofluorescence assays. Propensity score matching was employed to construct a cohort of patients exhibiting comparable baseline characteristics. The relationship between serum ANA and the risk of rheumatoid arthritis was analyzed by logistic regression analysis.ResultsA total of 1,175 patients with RA and 1,662 control subjects were included in this study. After adjusting for potential confounding factors in the propensity-score matched cohort, the risk of RA gradually increased with rising of ANA titers. When ANA titers were divided into three groups (1:100, 1:320, and 1:1,000), the OR (95% CI) for ANA titers from low to high was 3.95 (3.01, 5.18), 16.63 (9.44, 29.30), and 17.34 (9.53, 31.54), respectively, compared to those when ANA was negative. The ANA patterns closely related to the occurrence of RA include nuclear homogeneous, nuclear speckled, and cytoplasmic speckled. Among them, the positive rate of nuclear homogeneous was the highest, which accounted for 42.64%. The OR (95% CI) of ANA patterns including nuclear homogeneous, nuclear speckled, and cytoplasmic speckled was 16.81 (11.46, 24.65), 3.40 (2.49, 4.63), and 3.09 (1.77, 5.40), respectively.ConclusionThere was a curve relation between ANA titer and RA, and the higher the ANA titer, the higher the probability of RA. However, there was no statistical difference in probability of RA for 1:320 versus 1:1,000 ANA titers. The most important kind of ANA pattern in the blood of RA patients was nuclear homogeneous. These findings suggest that ANA may be a novel risk marker for RA.

Results of the anti-nuclear antibodies-indirect immunofluorescence assay (anti-cell antibodies test) on HEp-2 cell substrates should be communicated to clinicians in a standardized way, adding value to laboratory findings and helping with critical clinical decisions. This paper proposes a test report based on the practices informed by 118 laboratories in 68 countries, with recommendations from the International Consensus on ANA Patterns (ICAP) group. Major focus is placed on the report format containing endpoint titers, immunofluorescence patterns together with anti-cell (AC) nomenclature, remarks on follow-up or reflex testing, and possible other autoantibody associations. ISO 15,189 directives were integrated into the test report. Special situations addressed include serum screening dilutions and endpoint titers, relevance of immunofluorescence patterns with special attention to cytoplasmic patterns, mixed and compound patterns, and how to report different titers corresponding to multiple patterns or autoantibodies in the same sample. This paper suggests a subtitle for the HEp-2-IIFA, namely anti-cell antibodies test, which could gradually substitute the original outdated ANA nomenclature. This ICAP pro forma report represents a further step in harmonizing the way relevant clinical information could be provided by laboratories.

The indirect immunofluorescence assay (IIFA) on HEp-2 cells is widely used for detection of antinuclear antibodies (ANA). The dichotomous outcome, negative or positive, is integrated in diagnostic and classification criteria for several systemic autoimmune diseases. However, the HEp-2 IIFA test has much more to offer: besides the titre or fluorescence intensity, it also provides fluorescence pattern(s). The latter include the nucleus and the cytoplasm of interphase cells as well as patterns associated with mitotic cells. The International Consensus on ANA Patterns (ICAP) initiative has previously reached consensus on the nomenclature and definitions of HEp-2 IIFA patterns. In the current paper, the ICAP consensus is presented on the clinical relevance of the 29 distinct HEp-2 IIFA patterns. This clinical relevance is primarily defined within the context of the suspected disease and includes recommendations for follow-up testing. The discussion includes how this information may benefit the clinicians in daily practice and how the knowledge can be used to further improve diagnostic and classification criteria.

ObjectivesA 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.MethodsDemographic, 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.ResultsCluster 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.ConclusionFour discrete SLE patient longitudinal autoantibody clusters were predictive of long-term disease activity, organ involvement, treatment requirements and mortality risk.

ObjectivesDiagnostic reasoning in systemic lupus erythematosus (SLE) is a complex process reflecting the probability of disease at a given timepoint against competing diagnoses. We applied machine learning in well-characterised patient data sets to develop an algorithm that can aid SLE diagnosis.MethodsFrom a discovery cohort of randomly selected 802 adults with SLE or control rheumatologic diseases, clinically selected panels of deconvoluted classification criteria and non-criteria features were analysed. Feature selection and model construction were done with Random Forests and Least Absolute Shrinkage and Selection Operator-logistic regression (LASSO-LR). The best model in 10-fold cross-validation was tested in a validation cohort (512 SLE, 143 disease controls).ResultsA novel LASSO-LR model had the best performance and included 14 variably weighed features with thrombocytopenia/haemolytic anaemia, malar/maculopapular rash, proteinuria, low C3 and C4, antinuclear antibodies (ANA) and immunologic disorder being the strongest SLE predictors. Our model produced SLE risk probabilities (depending on the combination of features) correlating positively with disease severity and organ damage, and allowing the unbiased classification of a validation cohort into diagnostic certainty levels (unlikely, possible, likely, definitive SLE) based on the likelihood of SLE against other diagnoses. Operating the model as binary (lupus/not-lupus), we noted excellent accuracy (94.8%) for identifying SLE, and high sensitivity for early disease (93.8%), nephritis (97.9%), neuropsychiatric (91.8%) and severe lupus requiring immunosuppressives/biologics (96.4%). This was converted into a scoring system, whereby a score >7 has 94.2% accuracy.ConclusionsWe have developed and validated an accurate, clinician-friendly algorithm based on classical disease features for early SLE diagnosis and treatment to improve patient outcomes.

Background/objectivesThe European League Against Rheumatism (EULAR)/American College of Rheumatology (ACR) 2019 classification criteria for systemic lupus erythematosus system showed high specificity, while attaining also high sensitivity. We hereby analysed the performance of the individual criteria items and their contribution to the overall performance of the criteria.MethodsWe combined the EULAR/ACR derivation and validation cohorts for a total of 1197 systemic lupus erythematosus (SLE) and n=1074 non-SLE patients with a variety of conditions mimicking SLE, such as other autoimmune diseases, and calculated the sensitivity and specificity for antinuclear antibodies (ANA) and the 23 specific criteria items. We also tested performance omitting the EULAR/ACR criteria attribution rule, which defines that items are only counted if not more likely explained by a cause other than SLE.ResultsPositive ANA, the new entry criterion, was 99.5% sensitive, but only 19.4% specific, against a non-SLE population that included other inflammatory rheumatic, infectious, malignant and metabolic diseases. The specific criteria items were highly variable in sensitivity (from 0.42% for delirium and 1.84% for psychosis to 75.6% for antibodies to double-stranded DNA), but their specificity was uniformly high, with low C3 or C4 (83.0%) and leucopenia <4.000/mm³ (83.8%) at the lowest end. Unexplained fever was 95.3% specific in this cohort. Applying the attribution rule improved specificity, particularly for joint involvement.ConclusionsChanging the position of the highly sensitive, non-specific ANA to an entry criterion and the attribution rule resulted in a specificity of >80% for all items, explaining the higher overall specificity of the criteria set.