PV217 / #472 Poster Topic:AS23 - SLE-Diagnosis, Manifestations, & Outcomes Individuals at risk of developing systemic lupus erythematosus (SLE) often go through a difficult diagnostic journey and may receive conflicting diagnoses from multiple medical providers over time. This study seeks to utilize a novel technology-based program employing virtual and digital care models to determine if the time taken for accurate SLE classification of at-risk individuals could be shortened from the current 5 to 7 years. Study participants were digitally recruited through a publicly available web portal designed for visitors to evaluate their risk of developing rheumatic connective tissue diseases with the Connective Tissue Disease Screening Questionnaire (CSQ). Individuals identified as “possible” (SLE-CSQ=3) or “probable” (SLE-CSQ≥4) risk of SLE were recruited to consent and participate in the study. Medical records (MRs) were obtained and reviewed for a stated SLE diagnosis and/or ICD-10 code M32.9 or related codes. Participants without an apparent diagnosis were eligible to move forward in the study in a sequential digital/virtual diagnostic protocol. They first completed a telehealth evaluation by a primary care physician (PCP) and mobile phlebotomy sample procurement for a predetermined panel of standard and lupus-associated laboratory tests. Laboratory test results, MRs, and PCP evaluation findings were made available to a community rheumatologist (CR) who completed a rheumatology-focused telehealth session. Finally, a tertiary care rheumatologist (TCR) specializing in SLE reviewed all study information and completed a telehealth session. The CR and TCR completed a classification form for each participant that included ACR 1997 or EULAR/ACR 2019 SLE classification criteria. The study target was 100 consented participants. In the first 60 days of the study, 108 participants that qualified by the CSQ and signed the informed consent were enrolled. The study population consisted of 95% females with mean age (SD) of 36 (6) years with 85% white, 7% black and 8% other races/ethnicities. MRs were requested for 102 that provided physician contacts; 81 were received. MRs review identified 67 with no previous SLE diagnosis and 14 with SLE diagnosis. Of the 67 who qualified, 39 completed the entire process and were evaluated by their PCP, CR, and TCR. Seven of 39 (18%) met SLE classification (ACR 1997 score range 4-6; EULAR/ACR 2019 score =13), 18 (46%) were classified as incomplete SLE, and 14 (36%) had no current indication of SLE. For those that met SLE classification, the time from date of consent to classification was mean (SD) 371 (43) days, with a range of 326 to 463 days. A major goal of this virtual/digital study program was to shorten time to accurately diagnose SLE classification from the typical 5 to 7 years. For the 18% who met classification, the mean time to accurate diagnosis was 1 year and 6 days. For the 46% with incomplete SLE and the 36% with no current indication of SLE, the program may have the potential to shorten time to accurate classification as these participants are prospectively followed. The digital and virtual care technologies applied in this study program combined with currently available laboratory tests demonstrate the potential to effectively classify SLE in a remote care model. Acknowledgments: This study was sponsored by Progentec and funding was provided by GSK (GSK 219884). GSK was provided with the opportunity to review a preliminary version of this abstract for factual accuracy, but the authors are solely responsible for final content and interpretation.

We report the design of a pilot study employing clinic visits and a synergized digital, health coaching, and biomarker platform (aiSLE® MGMT [Management]) aimed at improving patient self-efficacy and patient-physician interactions to mitigate heightened disease activity, curtail clinical flares, and reduce potential long-term organ damage in systemic lupus erythematosus (SLE). This 12-month study will utilize a longitudinal cross-over design. Adult participants with confirmed SLE will be recruited and appropriately consented at five community-based US rheumatology clinics. Board certified rheumatologists will perform an exam, documenting the SLE Physician Global assessment (PGA) at Baseline, 3-, 6- and 9-month visits. Blood samples will be collected for completion of a newly developed and validated Lupus Flare Risk Index (L-FRI) that predicts the risk of developing a clinical flare in the next 12 weeks. The digital component of the study will be initiated following the 3-month visit. Participants will be given a mobile study app interfaced with a smartwatch to record activity, heart rate, and sleep quality. The app will be used to administer patient-reported outcome (PRO) surveys and health coaching. In the first three months participants and their rheumatologists will be blinded to L-FRI results; all L-FRI and digital results will be available starting in month four. This prospective pilot study will assess the impact of a comprehensive disease management platform employing a novel blood biomarker test, a mobile app interfaced with a smartwatch to collect biometric data, and app-based video health coaching on self-efficacy, clinical decisions, and outcomes in SLE disease management.

Objectives Systemic lupus erythematosus (SLE) is a complex autoimmune disease. Significant morbidity and early mortality necessitate early intervention. This study harnessed SLE-associated immune dysregulation to create a Lupus Classification Risk Index (LCRII) and Lupus Disease Activity Immune Index (LDAII) that identified individuals at risk for SLE classification and disease activity.Methods The LCRII was developed from 84 military personnel who developed classified SLE (≥4 American College of Rheumatology criteria) versus matched healthy controls, which was confirmed in 56 lupus blood relatives who developed SLE versus 154 matched unaffected relatives and 77 unrelated controls. The LDAII was informed by SLE patient visits with low (n=132) or active (n=179) disease and 48 matched controls. Data from blood samples assessed for circulating SLE-associated autoantibody specificities and soluble immune mediators informed the LCRII and LDAII. Random forest modelling guided the selection of informative analytes.Results An LCRII informed by 32 or 17 log-transformed/standardised mediators, weighted by their correlation to SLE-associated autoantibodies, differentiated pre-SLE individuals before reaching disease classification (area under the curve (AUC) ≥0.79, p<0.0001; effect size ≥1.1), even before the appearance of clinical criteria (AUC ≥0.74, p<0.0001; effect size ≥0.9). The LCRII-32, LCRII-17 and select mediators, MCP-3/CCL7, TNFRII, stem cell factor (SCF), IL-1α, IP-10/CXCL10 and TGF-β differentiated renal and serositis classification criteria (p<0.05). An LDAII informed by 26 or 13 log-transformed/standardised mediators, weighted by their correlation to SLE-associated autoantibodies or disease activity (hybrid Systemic Lupus Erythematosus Disease Activity Index; hSLEDAI), differentiated SLE patients with low (hSLEDAI <4) or active (hSLEDAI ≥4) disease (AUC >0.6, p ≤0.002, effect size ≥0.4), including clinical/serologic active versus quiescent disease (AUC ≥0.7, p<0.0001, effect size ≥0.6). The LDAII-26, LDAII-13 and select mediators MCP-1/CCL2, TNFRII, SCF, IL-2Rα, IL-10 and TGF-β differentiated renal and serositis manifestations.Conclusions We have conceptualised two immune mediator-informed indexes, the LCRII that predicts SLE from months to years before clinical presentation, and the LDAII that analogously predicts active disease in SLE to distinguish patients who would benefit from early intervention.

PT002 / #417 Topic: AS09 - Emerging Approaches in SLE Management POSTER TOUR 01: CLINICAL OUTCOMES IN SLE 22-05-2025 10:00 AM - 10:40 AM Systemic lupus erythematosus (SLE) is driven by immune dysregulation, with increased risk for heightened clinical disease activity and flare that lead to permanent end-organ damage, morbidity, and early mortality. Capturing immune dysregulation as lab-based screening tests would help prioritize SLE patients for early intervention. This study assesses the utility of employing a Lupus Flare Risk Index (L-FRI) and Lupus Disease Activity Index (L-DAI) in parallel to assess simultaneous risk of future disease flare and concurrent disease activity to guide therapy. We assessed levels of 17 SLE-associated plasma mediators to calculate L-FRI and L-DAI scores in 80 preflare vs 76 prenonflare visits, as well as 49 flare vs 51 nonflare follow-up visits with available samples, from a unique cohort of prospectively followed SLE patients. Hybrid SLEDAI (hSLEDAI) scores, clinical features, medication usage, and the presence of SLE-associated autoantibody specificities, including dsDNA, chromatin, Ro/SSA, La/SSB, Sm, SmRNP, and RNP, were also compared. The L-FRI algorithm reflects the sum of 11 log-transformed, standardized immune mediators, weighted by the Spearman r correlation coefficient for each preflare (PF)/pre-nonflare (PNF) analyte vs subsequent hSLEDAI scores at the time of future flare/nonflare.[1,2] The L-DAI algorithm reflects the sum of 10 log-transformed, standardized immune mediators, weighted by the Spearman r correlation coefficient of each active (hSLEDAI ≥4)/low (hSLEDAI<4) disease activity analyte vs the composite of concurrent hSLEDAI scores and number of SLE-associated autoantibody specificities.[3] Forty of 80 (50%) preflare vs 24 of 76 (32%) pre-nonflare visits were associated with concurrent active disease (hSLEDAI≥4; p=0.0230). The L-FRI differentiated preflare vs pre-nonflare visits and subsequent flare vs nonflare visits, irrespective of disease activity state (Figure 1A), with severe flare visits present above the high-risk cut-off (decision curve analysis, [1,2]). The L-DAI differentiated concurrent active vs low (hSLEDAI<4) disease activity, irrespective of preflare/pre-nonflare or flare/nonflare status (Figure 1B), with renal manifestations present above the high-risk cut-off (decision curve analysis, [3]). All SLE groups had significantly higher L-FRI and L-DAI scores than demographically matched healthy Ctrl (n=71, p<0.0001, Figure 1A,B). Plasma levels of BLyS (L-FRI, L-DAI), as well as L-FRI informing mediators MCP-3, TNFRI, and TNFRII were highest in preflare visits with concurrent active disease (p<0.05), while IL-17A levels were highest in preflare visits with concurrent low disease activity (p<0.05), Figure 1C. IL-7 (L-FRI, L-DAI) levels were increased with both flare and disease activity risk (p<0.05), while L-DAI informing mediators IFN-α and IP-10 were highest in active disease, with preflare increased over pre-nonflare levels (p<0.05), Figure 1C. Of interest, although the L-FRI and L-DAI performed well at assessing flare and disease activity risk, respectively (AUC>0.9), parallel assessment of L-FRI and L-DAI performed better than either alone to identify simultaneous risk of concurrent active disease and imminent flare risk, Table 1. Figure 1. Using Lupus Flare Risk Index (L-FRI, A) and Lupus Disease Activity Index (L-DAI, B) to evaluate combination of impending flare and concurrent disease activity risk. Select L-FRI and L-DAI informing mediators reflect flare and/or disease activity rsk (C) PF-Preflare; PNF=PreNonflare; Active (hSLEDAI≥4); Low (hSLEDAI<4); *p<0.05; **p<0.01, ****p<0.0001 by Kruskal-Wallis test with Dunn’s multiple comparison. Table 1. Combination of L-FRI and L-DAI Tests Optimally Informs Future Flare and Concurrent Disease Activity Risk The L-FRI used with the L-DAI optimally identified risk of imminent lupus disease flare and concurrent active disease, including severe flare and renal manifestations. A subset of mediators consistently enhanced the L-FRI and L-DAI tests to identify SLE patients who may benefit from early intervention strategies. Such an approach would improve disease management and be advantageous in prospective clinical trials for study participant recruitment and assessment. References: [1.] Munroe M. Arthritis Rheumatol 2023;75:723-5. [2.] Munroe M. Annal Rheum Dis 2024;83:402-3. [3.] Munroe M. Annal Rheum Dis 2024;83:19-20.

OBJECTIVE:Lupus nephritis (LN) management remains challenging, and novel noninvasive biomarkers are needed. This study quantified serum soluble mediators in the Accelerating Medicines Partnership (AMP) LN cohort to identify biomarkers of histologic features and treatment response. METHODS:Patients with systemic lupus erythematosus (SLE) (n = 268) undergoing clinically indicated kidney biopsies (urine protein/creatinine ratio [UPCR] ≥ 0.5) were recruited through the AMP Rheumatoid Arthritis and SLE Network. Serum was collected at biopsy and 3-, 6-, and 12-month postbiopsy, alongside samples from 22 healthy controls. Concentrations of 66 immune mediators were quantified using xMAP multiplex assays, and (TACE) measured by enzyme-linked immunosorbent assay. Seven mediators with >95% values below detection limits were excluded from analyses. Bootstrapped least absolute shrinkage and selection operator (LASSO) regression identified proliferative LN (class III/IV ± V) predictors from baseline mediators. Associations with 12-month treatment response (complete/partial vs no response) were tested using three-month changes in LASSO-selected mediators and UPCR via logistic regression. Molecular clustering of mediator profiles was performed to identify LN subgroups. RESULTS:Proliferative patients with LN (class [III or IV] ± V; n = 160) displayed a distinct mediator profile compared with nonproliferative LN (class I, II, or V; n = 96). LASSO regression identified 20 mediators predictive of proliferative LN (areas under the curve, 0.82; 95% confidence interval [CI], 0.81-0.91), including elevated syndecan-1, tumor necrosis factor receptor type I, tumor necrosis factor receptor type II, and vascular cell adhesion molecule 1 (VCAM-1), as well as decreased CCL3//macrophage inflammatory protein 1α, CD40 ligand, and interleukin-5 levels. Among proliferative patients with LN, 3-month reductions in syndecan-1 and VCAM-1, mediators associated with intrarenal LN activity and/or chronicity, predicted the 12-month treatment response. A model incorporated these reductions and a decline in UPCR-predicted treatment response in proliferative LN (0.90; 95% CI, 0.82-0.98). Molecular clustering revealed four distinct LN subgroups with unique soluble mediator signatures and clinical features not captured by histology alone. CONCLUSION:Serum soluble mediators, particularly syndecan-1 and VCAM-1, reflect LN histologic activity, and early decreases predict treatment response, supporting their potential use as noninvasive longitudinal biomarkers. The substantial heterogeneity within LN highlights the potential for biomarker-guided reclassification to advance precision medicine approaches.

Background: SLE is marked by immune dysregulation linked to pathogenesis, clinical disease activity, and flare. Capturing immune dysregulation prior to clinical disease flare onset may provide a window for early intervention to decrease flare frequency and severity. Objectives: This study seeks to validate a recently refined Lupus Flare Risk Index (L-FRI)[1] that reflects altered immunity prior to clinical disease flare using a unique confirmatory cohort of SLE patients. Methods: The L-FRI is the sum of log-transformed, standardized immune mediators, weighted by the Spearman r correlation coefficient for each pre-flare/pre-nonflare analyte vs. subsequent flare/nonflare hSLEDAI[2] disease activity (flare is defined by the SELENA-SLEDAI Flare Index[3]). SLE-associated plasma mediators (n=11) were evaluated by microfluidic immunoassay in 52 pre-flare (105 ± 62 days prior to flare) and 52 pre-nonflare (105 ± 55 days prior to nonflare) samples from patients with classified SLE in this validation cohort. Hybrid SLEDAI (hSLEDAI) scores, clinical features, medication usage, and the presence of SLE-associated autoantibody (AutoAb) specificities, including dsDNA, chromatin, Ro/SSA, La/SSB, Sm, SmRNP, and RNP, were also compared at pre-flare vs. pre-nonflare time points. Data from this validation cohort were compared to development and combined development/validation cohorts. Results: This validation (Val) cohort is enriched for African American (AA) SLE patients (44% pre-flare, 48% pre-nonflare vs. 11% pre-flare, 15% pre-nonflare in the development [Dev] cohort), with an associated increase in pre-flare hSLEDAI scores (4.0±3.4 preflare, 1.9±2.2 pre-non-flare, p=0.0004 [Val] vs. 2.4±2.6 pre-flare, 2.8±3.8 pre-nonflare, p=0.8953 [Dev]). Otherwise, after adjusting for multiple comparison, we did not observe differences with respect to clinical features, medication usage, or number and type of SLE-associated AutoAbs in pre-flare vs. pre-nonflare samples. The L-FRI, informed by 11 mediators (Figure 1A), significantly (p<0.0001) differentiated pre-flare vs. pre-nonflare samples (Figure 1B), with low, moderate, and high flare risk cut-offs identified by decision curve analysis. The inclusion of 11 mediators in the L-FRI allowed for varied differences between pre-flare vs. pre-nonflare across cohorts (Figure 1C-E), yet with consistent differences in osteopontin, TNFRII, TNFRI, and TNF-α. The performance of the L-FRI in the Val cohort was similar to that of the Dev and combined (Com) cohorts, with a large (>0.8) Cohen's effect size, AUC>0.7 (p≤0.0003), and Spearman r ≥0.399 (p<0.0001) vs. hSLEDAI scores at disease flare/nonflare (Figure 2). Furthermore, the L-FRI differentiated SLE patients at risk of imminent severe (S) and mild-moderate (M/M) flares vs. nonflare (Figure 2), with increased L-FRI scores (p≤0.05), effect size (≥1.3), and AUC (≥0.843, p≤0.0002) in pre-severe flare samples across the Val, Dev, and Com cohorts (Figure 2). Conclusion: The L-FRI identified SLE patients at low, moderate, and high risk of imminent lupus disease flare across development and validation cohorts. Of particular interest is the ability of the L-FRI to differentiate future M/M vs. severe flare risk. A subset of 11 mediators spanning varied immune pathways consistently improved the L-FRI to identify SLE patients who may benefit from early intervention strategies. Such an approach would be advantageous in prospective clinical trials for study participant recruitment and assessment, as well as improved management of lupus. REFERENCES: [1] M. E. Munroe et al., A Flare Risk Index Informed by Select Immune Mediators in Systemic Lupus Erythematosus. Arthritis & rheumatology75, 723-735 (2023). [2] A. Thanou et al., Impact of heart rate variability, a marker for cardiac health, on lupus disease activity. Arthritis Res Ther18, 197 (2016). [3] J. P. Buyon et al., The effect of combined estrogen and progesterone hormone replacement therapy on disease activity in systemic lupus erythematosus: a randomized trial. Ann Intern Med142, 953-962 (2005). Acknowledgements: This study was supported by the National Institute of Arthritis and Musculoskeletal and Skin Diseases, the National Institute of Allergy and Infectious Diseases, the National Institute of General Medicinal Sciences, and the National Center for Research Resources of the National Institutes of Health under award numbers M01RR001070 (DLK), P50AR070591 (JPB), R01AR077518 (HZ), R44AI142967 (MEM), P30AR073750 (JAJ), UM1AI144292 (JAJ), and U54GM104938 (JAJ), as well as Oklahoma Center for the Advancement of Science and Technology under award numbers AR16-014 (MEM) and AR18-019 (EJ).The content of this publication is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Disclosure of Interests: Melissa E Munroe Progentec Diagnostics, Inc. (part-time employee), Progentec Diagnostics, Inc., Derek Blankenship Progentec Diagnostics, Inc., Daniele DeFreese Progentec Diagnostics, Inc., Adrian Holloway Progentec Diagnostics, Inc., Mohan Purushothaman Progentec Diagnostics, Inc., Wade DeJager: None declared, Susan Macwana: None declared, Joel M Guthridge: None declared, Stan Kamp: None declared, Nancy Redinger: None declared, Teresa Aberle: None declared, Eliza F Chakravary: None declared, Cristina Arriens AstraZeneca, Aurinia, AstraZeneca, Bristol-Meyers Squibb, Cabaletta, GSK, Kezar, UCB, AstraZeneca, Bristol-Myers Squibb, Yanfeng Li: None declared, Hu Zeng: None declared, Stephanie Dezzutti: None declared, Peter Izmirly: None declared, Uma Thanarajasingam: None declared, Diane L. Kamen: None declared, Jill P Buyon Bristol-Myers Squibb, GSK, Related Sciences, Judith A. James GSK, Novartis, Bristol-Myers Squibb, Progentec Diagnostics, Inc., Eldon Jupe Progentec Diagnostics, Inc.

Background: Systemic lupus erythematosus (SLE) is a chronic autoimmune disease underscored by complex immune dysregulation, including altered immune mediators and accumulation of autoantibody (AutoAb) specificities. Such immune dysregulation drives the pathogenesis of SLE; patients experience varied disease activity that must be managed to prevent end organ damage that leads to morbidity and early mortality. With the current shortage of rheumatologists and administrative burden of validated clinical disease activity measures, capturing such immune dysregulation associated with clinical disease activity as a lab-based screening test would help prioritize which SLE patients are in need of formal clinical evaluation and follow-up. Objectives: This study seeks to determine an optimal panel of analytes that distinguishes SLE patients with active disease and refine a Lupus Disease Activity Immune Index (L-DAI). Methods: We procured samples from patients with classified SLE on dates of low disease activity (<4, range 0-3, n=162; 141 met definition of LLDAS[1]; 21 had prednisone dose >7.5 mg/day) or active disease (≥4, range 4-30, n=162) defined by the hybrid SLEDAI (hSLEDAI)[2]. Race/sex/age-matched healthy control (Ctrl) samples (n=81) were also evaluated. Plasma immune mediators (n=33) were evaluated by microfluidic immunoassay and serum AutoAb specificities, including dsDNA, chromatin, Ro/SSA, La/SSB, Sm, SmRNP, and RNP, were assessed by xMAP assay. The L-DAI is the sum of log-transformed, standardized immune mediators, weighted by the Spearman r correlation coefficient of mediator levels vs. a composite, averaged weighting of hSLEDAI scores and number of AutoAb specificities associated with clinical disease activity. Log-transformed mediator levels were further evaluated using random forest applied machine learning modeling to determine an optimal subset of analytes to inform the L-DAI. Results: As expected, SLE patients with active disease demonstrated differences in clinical and serologic features, as well as increased use of steroids. Random forest modeling of immune mediators informed a variable importance ranking of 10 mediators that best informed the L-DAI (Figure 1A, mean ± SD) by differentiating low vs. active disease, including clinically and/or serologically active vs. quiescent disease, and Ctrls (Figure 1B). The L-DAI differentiated SLE patients with low, active, and clinically/serologically active vs. quiescent disease (vs. Ctrls, Figure 1C-D), with moderate (0.616) to large (1.11) effect size and AUC of 0.673 (95% CI 0.615-0.731, p<0.0001) differentiating low vs. active disease and 0.795 (0.727-0.862, p<0.0001) distinguishing clinically/serologically active vs. quiescent disease. Of interest, the L-DAI differentiated SLE patients with serositis (Seros) vs. non-serositis (NS) features and Ctrls (Figure 2A), as well as SLE patients with renal vs. non-renal (NR) active disease vs. low disease activity (Figure 2B). IFN-α, IL-10, and Osteopontin (OPN) differentiated SLE patients with both serositis and renal features, while BLyS and IL-7 distinguished serositis and renal features, respectively. Conclusion: We have refined the L-DAI to characterize SLE patients with active clinical and/or serological disease vs. those with low/quiescent disease. Treat-to-target approaches using a sensitive and objective biomarker surrogate for clinical disease activity has the potential to help improve clinical disease management and prevent organ damage in SLE, as well as characterize SLE patients enrolled in clinical trials and their response to treatment. REFERENCES: [1] K. Franklyn et al., Definition and initial validation of a Lupus Low Disease Activity State (LLDAS). Ann Rheum Dis, (2015). [2] A. Thanou et al., Impact of heart rate variability, a marker for cardiac health, on lupus disease activity. Arthritis Res Ther18, 197 (2016). Acknowledgements: This study was supported by the National Institute of Arthritis and Musculoskeletal and Skin Diseases, the National Institute of Allergy and Infectious Diseases, the National Institute of General Medicinal Sciences, and the National Center for Research Resources of the National Institutes of Health under award numbers M01RR001070 (DLK), P50AR070591 (JPB), R01AR077518 (HZ), R44AI142967 (MEM), P30AR073750 (JAJ), UM1AI144292 (JAJ), and U54GM104938 (JAJ), as well as Oklahoma Center for the Advancement of Science and Technology under award numbers AR16-014 (MEM) and AR18-019 (EJ).The content of this publication is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Disclosure of Interests: Melissa E Munroe Progentec Diagnostics, Inc. (part time employee), Progentec Diagnostics, Inc., Derek Blankenship Progentec Diagnostics, Inc., Daniele DeFreese Progentec Diagnostics, Inc., Adrian Holloway Progentec Diagnostics, Inc., Mohan Purushothaman Progentec Diagnostics, Inc., Wade DeJager: None declared, Susan Macwana: None declared, Joel M Guthridge: None declared, Stan Kamp: None declared, Nancy Redinger: None declared, Teresa Aberle: None declared, Eliza F Chakravary: None declared, Cristina Arriens AstraZeneca, Aurinia, AstraZeneca, Bristol-Myers Squibb, Cabaletta, GSK, Kezar, UCB, AstraZeneca, Bristol-Myers Squibb, Yanfeng Li: None declared, Hu Zeng: None declared, Stephanie Dezzutti: None declared, Peter Izmirly: None declared, Uma Thanarajasingam: None declared, Diane L. Kamen: None declared, Jill P Buyon Bristol-Myers Squibb, GSK, Related Sciences, Judith A. James GSK, Novartis, Bristol-Myers Squibb, Progentec Diagnostics, Inc., Eldon Jupe Progentec Diagnostics, Inc.

ObjectiveSystemic lupus erythematosus (SLE) is marked by immune dysregulation linked to varied clinical disease activity. Using a unique longitudinal cohort of SLE patients, this study sought to identify optimal immune mediators informing an empirically refined flare risk index (FRI) reflecting altered immunity prior to clinical disease flare.MethodsThirty‐seven SLE‐associated plasma mediators were evaluated by microfluidic immunoassay in 46 samples obtained in SLE patients with an imminent clinical disease flare (preflare) and 53 samples obtained in SLE patients without a flare over a corresponding period (pre‐nonflare). SLE patients were selected from a unique longitudinal cohort of 106 patients with classified SLE (meeting the American College of Rheumatology 1997 revised criteria for SLE or the Systemic Lupus International Collaborating Clinics 2012 revised criteria for SLE). Autoantibody specificities, hybrid SLE Disease Activity Index (hSLEDAI) scores, clinical features, and medication usage were also compared at preflare (mean ± SD 111 ± 47 days prior to flare) versus pre‐nonflare (99 ± 21 days prior to nonflare) time points. Variable importance was determined by random forest analysis with logistic regression subsequently applied to determine the optimal number and type of analytes informing a refined FRI.ResultsPreflare versus pre‐nonflare differences were not associated with demographics, autoantibody specificities, hSLEDAI scores, clinical features, nor medication usage. Forward selection and backward elimination of mediators ranked by variable importance resulted in 17 plasma mediator candidates differentiating preflare from pre‐nonflare visits. A final combination of 11 mediators best informed a newly refined FRI, which achieved a maximum sensitivity of 97% and maximum specificity of 98% after applying decision curve analysis to define low, medium, and high FRI scores.ConclusionWe verified altered immune mediators associated with imminent disease flare, and a subset of these mediators improved the FRI to identify SLE patients at risk of imminent flare. This molecularly informed, proactive management approach could be critical in prospective clinical trials and the clinical management of lupus.

(1) Objective: Systemic lupus erythematosus (SLE) is a complex disease involving immune dysregulation, episodic flares, and poor quality of life (QOL). For a decentralized digital study of SLE patients, machine learning was used to assess patient-reported outcomes (PROs), QOL, and biometric data for predicting possible disease flares. (2) Methods: Participants were recruited from the LupusCorner online community. Adults self-reporting an SLE diagnosis were consented and given a mobile application to record patient profile (PP), PRO, and QOL metrics, and enlisted participants received smartwatches for digital biometric monitoring. The resulting data were profiled using feature selection and classification algorithms. (3) Results: 550 participants completed digital surveys, 144 (26%) agreed to wear smartwatches, and medical records (MRs) were obtained for 68. Mining of PP, PRO, QOL, and biometric data yielded a 26-feature model for classifying participants according to MR-identified disease flare risk. ROC curves significantly distinguished true from false positives (ten-fold cross-validation: p < 0.00023; five-fold: p < 0.00022). A 25-feature Bayesian model enabled time-variant prediction of participant-reported possible flares (P(true) > 0.85, p < 0.001; P(nonflare) > 0.83, p < 0.0001). (4) Conclusions: Regular profiling of patient well-being and biometric activity may support proactive screening for circumstances warranting clinical assessment.

Gram-positive bacterial infections are a major cause of organ failure and mortality in sepsis. Cell wall peptidoglycan (PGN) is shed during bacterial replication, and Bacillus anthracis PGN promotes a sepsis-like pathology in baboons. Herein, we determined the ability of polymeric Bacillus anthracis PGN free from TLR ligands to shape human dendritic cell (DC) responses that are important for the initiation of T cell immunity. Monocyte-derived DCs from healthy donors were incubated with PGN polymers isolated from Bacillus anthracis and Staphylococcus aureus. PGN activated the human DCs, as judged by the increased expression of surface HLA-DR, CD83, the T cell costimulatory molecules CD40 and CD86, and the chemokine receptor CCR7. PGN elicited the DC production of IL-23, IL-6, and IL-1β but not IL-12p70. The PGN-stimulated DCs induced the differentiation of naïve allogeneic CD4+ T cells into T helper (TH) cells producing IL-17 and IL-21. Notably, the DCs from a subset of donors did not produce significant levels of IL-23 and IL-1β upon PGN stimulation, suggesting that common polymorphisms in immune response genes regulate the PGN response. In sum, purified PGN is a highly stimulatory cell wall component that activates human DCs to secrete proinflammatory cytokines and promote the differentiation of TH17 cells that are important for neutrophil recruitment in extracellular bacterial infections.

Systemic lupus erythematosus (SLE) is propelled by pathogenic autoantibody (AutoAb) and immune pathway dysregulation. Identifying populations at risk of reaching classified SLE is essential to curtail inflammatory damage. Lupus blood relatives (Rel) have an increased risk of developing SLE. We tested factors to identify Rel at risk of developing incomplete lupus (ILE) or classified SLE vs. clinically unaffected Rel and healthy controls (HC), drawing from two unique, well characterized lupus cohorts, the lupus autoimmunity in relatives (LAUREL) follow-up cohort, consisting of Rel meeting <4 ACR criteria at baseline, and the Lupus Family Registry and Repository (LFRR), made up of SLE patients, lupus Rel, and HC. Medical record review determined ACR SLE classification criteria; study participants completed the SLE portion of the connective tissue disease questionnaire (SLE-CSQ), type 2 symptom questions, and provided samples for assessment of serum SLE-associated AutoAb specificities and 52 plasma immune mediators. Elevated SLE-CSQ scores were associated with type 2 symptoms, ACR scores, and serology in both cohorts. Fatigue at BL was associated with transition to classified SLE in the LAUREL cohort (p≤0.01). Increased levels of BLyS and decreased levels of IL-10 were associated with type 2 symptoms (p<0.05). SLE-CSQ scores, ACR scores, and accumulated AutoAb specificities correlated with levels of multiple inflammatory immune mediators (p<0.05), including BLyS, IL-2Rα, stem cell factor (SCF), soluble TNF receptors, and Th-1 type mediators and chemokines. Transition to SLE was associated with increased levels of SCF (p<0.05). ILE Rel also had increased levels of TNF-α and IFN-γ, offset by increased levels of regulatory IL-10 and TGF-β (p<0.05). Clinically unaffected Rel (vs. HC) had higher SLE-CSQ scores (p<0.001), increased serology (p<0.05), and increased inflammatory mediator levels, offset by increased IL-10 and TGF-β (p<0.01). These findings suggest that Rel at highest risk of transitioning to classified SLE have increased inflammation coupled with decreased regulatory mediators. In contrast, clinically unaffected Rel and Rel with ILE demonstrate increased inflammation offset with increased immune regulation, intimating a window of opportunity for early intervention and enrollment in prevention trials.

Objectives Moderate alcohol consumption has been associated with decreased systemic lupus erythematosus risk, but the biologic basis for this association is unknown. We aimed to determine whether moderate alcohol consumption was associated with lower concentrations of systemic lupus erythematosus-associated chemokines/cytokines in an ongoing cohort of female nurses without systemic lupus erythematosus, and whether the association was modified by the presence of systemic lupus erythematosus-related autoantibodies. Methods About 25% of participants from the Nurses’ Health Study ( n = 121,700 women) and Nurses’ Health Study 2 ( n = 116,429) donated a blood sample; of these, 1177 women were without systemic lupus erythematosus at time of donation. Cumulative average and current (within 4 years) intakes of beer, wine or liquor were assessed from pre-blood draw questionnaires. Chemokine/cytokine concentrations (stem cell factor, B-lymphocyte stimulator, interferon-inducible protein-10, interferon-alpha, interleukin-10) and antibodies against dsDNA and extractable nuclear antigens were obtained using enzyme-linked immunosorbent assays. Antinuclear antibodies were detected by indirect immunofluorescence on HEp-2 cells. Results At blood draw, the women’s mean age was 56 years and 22% were antinuclear antibody positive; 36% were African-American. About half (46%) reported consuming 0–5 g/day of alcohol. Stem cell factor levels were 0.5% lower ( p < 0.0001) for every gram per day increase in cumulative average alcohol consumption. Women who consumed >5 g/day had mean stem cell factor levels 7% lower ( p = 0.002) than non-drinkers. Other cytokines were not significantly associated with alcohol intake. Autoantibody status did not modify observed associations. Conclusion In this study of female nurses, moderate alcohol consumption was associated with lower stem cell factor levels, suggesting a plausible mechanism through which alcohol may lower systemic lupus erythematosus risk might be by decreasing circulating stem cell factor.

Systemic lupus erythematosus (SLE) and other autoimmune diseases are propelled by immune dysregulation and pathogenic, disease-specific autoantibodies. Autoimmunity against the lupus autoantigen Sm is associated with cross-reactivity to Epstein-Barr virus (EBV) nuclear antigen 1 (EBNA-1). Additionally, EBV latent membrane protein-1 (LMP1), initially noted for its oncogenic activity, is an aberrantly active functional mimic of the B cell co-stimulatory molecule CD40. Mice expressing a transgene (Tg) for the mCD40-LMP1 hybrid molecule (containing the cytoplasmic tail of LMP1) have mild autoantibody production and other features of immune dysregulation by 2–3 months of age, but no overt autoimmune disease. This study evaluates whether exposure to the EBV molecular mimic, EBNA-1, stimulates antigen-specific and concurrently-reactive humoral and cellular immunity, as well as lupus-like features. After immunization with EBNA-1, mCD40-LMP1 Tg mice exhibited enhanced, antigen-specific, cellular and humoral responses compared to immunized WT congenic mice. EBNA-1 specific proliferative and inflammatory cytokine responses, including IL-17 and IFN-γ, were significantly increased (p<0.0001) in mCD40-LMP1 Tg mice, as well as antibody responses to amino- and carboxy-domains of EBNA-1. Of particular interest was the ability of mCD40-LMP1 to drive EBNA-1 associated molecular mimicry with the lupus-associated autoantigen, Sm. EBNA-1 immunized mCD40-LMP1 Tg mice exhibited enhanced proliferative and cytokine cellular responses (p<0.0001) to the EBNA-1 homologous epitope PPPGRRP and the Sm B/B’ cross-reactive sequence PPPGMRPP. When immunized with the SLE autoantigen Sm, mCD40-LMP1 Tg mice again exhibited enhanced cellular and humoral immune responses to both Sm and EBNA-1. Cellular immune dysregulation with EBNA-1 immunization in mCD40-LMP1 Tg mice was accompanied by enhanced splenomegaly, increased serum blood urea nitrogen (BUN) and creatinine levels, and elevated anti-dsDNA and antinuclear antibody (ANA) levels (p<0.0001 compared to mCD40 WT mice). However, no evidence of immune-complex glomerulonephritis pathology was noted, suggesting that a combination of EBV and genetic factors may be required to drive lupus-associated renal disease. These data support that the expression of LMP1 in the context of EBNA-1 may interact to increase immune dysregulation that leads to pathogenic, autoantigen-specific lupus inflammation.

Systemic lupus erythematosus (SLE) is a complex and heterogeneous systemic autoimmune disease associated with innate and adaptive immune dysregulation. SLE occurs primarily in females of childbearing age, with increased prevalence and severity in minority populations. Despite improvements in treatment modalities, SLE patients frequently experience periods of heightened disease activity and flare that can lead to permanent organ damage, increased morbidity, and early mortality. Such outcomes impair quality of life and inflict a significant socioeconomic burden. Predicting changes in SLE disease activity could allow for closer monitoring and preemptive treatment, but existing clinical, demographic and serologic markers have been only modestly predictive. Novel, proactive approaches to clinical disease management are thus critically needed. Panels of blood biomarkers can detect a breadth of immune pathway dysregulation that captures SLE heterogeneity and disease activity. Alterations in the balance of pro-inflammatory and regulatory soluble mediators have been associated with changes in clinical disease activity and are detectable several weeks prior to clinical flare occurrence. A soluble mediator score has been highly predictive of impending flare in both European American and African American SLE patients, and this score does not require a priori knowledge of specific pathway activation in the patient. We review current concepts of disease activity and flare in SLE, focusing on the potential of novel blood biomarkers to characterize and predict changes in disease activity. Measuring the disordered immune response in SLE in this way promises to improve disease management and prevent organ damage in SLE.