scholarly journals BAFF Predicts Immunogenicity in Older Patients With Rheumatoid Arthritis Treated With TNF Inhibitors

2020 ◽  
Author(s):  
Borja Hernández-Breijo ◽  
Victoria Navarro-Compán ◽  
Chamaida Plasencia-Rodríguez ◽  
Ioannis Parodis ◽  
Johanna E. Gehin ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Older Patients ◽  
Operating Characteristic ◽  
Certolizumab Pegol ◽  
Positive Likelihood Ratio ◽  
Characteristic Analysis ◽  
Serum Samples ◽  
Chi Square ◽  
Baseline Serum ◽  
Logistic Regression Models

Abstract Background: Immunogenicity related to treatment with TNF inhibitors (TNFi) is one of the causes for the decreased attainment of clinical response in patients with rheumatoid arthritis (RA). The B-cell activating factor (BAFF) may be playing a role in the development of immunogenicity. The objective of this study was to analyse the association of baseline concentration of serum BAFF with immunogenicity after 6 months of TNFi treatment.Methods: A total of 139 patients with RA starting a TNFi (infliximab, adalimumab, certolizumab pegol or golimumab) were followed-up for 6 months. Serum samples were obtained at baseline and at 6 months and anti-drug antibody (ADA) and BAFF concentrations were measured. Logistic regression models were employed in order to analyse the association between BAFF concentrations and immunogenicity. Receiver operating characteristic analysis was performed to determine the BAFF concentrations with a greater likelihood of showing immunogenicity association.Results: At 6 months, 39 patients (28%) developed ADA. A significant interaction between the age and baseline BAFF concentration was found for the development of ADA (Wald chi-square value=5.30; p=0.02); therefore, subsequent results were stratified according to mean age (≤/>55 years). Baseline serum BAFF concentration was independently associated with ADA development only in patients over 55 years (OR=1.55; 95% CI: 1.03-2.12). Baseline serum BAFF≥1034pg/mL predicted the presence of ADA at 6 months (positive likelihood ratio=3.7).Conclusions: Our results suggest that the association of BAFF concentration and immunogenicity depends on the patient’s age. Baseline serum BAFF concentration predicts the presence of ADA within 6 months of TNFi therapy in older patients with RA.

scholarly journals BAFF predicts immunogenicity in older patients with rheumatoid arthritis treated with TNF inhibitors

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Borja Hernández-Breijo ◽  
Victoria Navarro-Compán ◽  
Chamaida Plasencia-Rodríguez ◽  
Ioannis Parodis ◽  
Johanna E. Gehin ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
B Cell ◽  
Older Patients ◽  
Certolizumab Pegol ◽  
Positive Likelihood Ratio ◽  
Tnf Inhibitors ◽  
Characteristic Analysis ◽  
Serum Samples ◽  
Baseline Serum ◽  
B Cell Activating Factor

AbstractImmunogenicity related to treatment with TNF inhibitors (TNFi) is one of the causes for the decreased attainment of clinical response in patients with rheumatoid arthritis (RA). The B-cell activating factor (BAFF) may be playing a role in the development of immunogenicity. The objective of this study was to analyse the association of baseline concentration of serum B-cell activating factor (BAFF) with immunogenicity after 6 months of TNFi treatment. A total of 127 patients with RA starting a TNFi (infliximab, adalimumab, certolizumab pegol or golimumab) were followed-up for 6 months. Serum samples were obtained at baseline and at 6 months and anti-drug antibody (ADA) and BAFF concentrations were measured. Logistic regression models were employed in order to analyse the association between BAFF concentrations and immunogenicity. Receiver operating characteristic analysis was performed to determine the BAFF concentrations with a greater likelihood of showing immunogenicity association. At 6 months, 31 patients (24%) developed ADA. A significant interaction between the age and baseline BAFF concentration was found for the development of ADA (Wald chi-square value = 5.30; p = 0.02); therefore, subsequent results were stratified according to mean age (≤ / > 55 years). Baseline serum BAFF concentration was independently associated with ADA development only in patients over 55 years (OR = 1.51; 95% CI 1.03–2.21). Baseline serum BAFF ≥ 1034 pg/mL predicted the presence of ADA at 6 months (AUC = 0.81; 95% confidence interval (CI) 0.69–0.93; p = 0.001; positive likelihood ratio = 3.7). In conclusion, our results suggest that the association of BAFF concentration and immunogenicity depends on the patient’s age. Baseline serum BAFF concentration predicts the presence of ADA within 6 months of TNFi therapy in older patients with RA.

scholarly journals FRI0596 NOVEL AUTOANTIBODY BIOMARKERS FOR THE PREDICTION OF THERAPY RESPONSE IN RHEUMATOID ARTHRITIS

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 904.1-904
Author(s):  
P. Vandormael ◽  
A. Pues ◽  
E. Sleurs ◽  
P. Verschueren ◽  
V. Somers
Keyword(s):  
Rheumatoid Arthritis ◽  
Therapy Response ◽  
Autoimmune Disorder ◽  
First Line ◽  
Research Support ◽  
Serum Samples ◽  
Glucocorticoid Treatment ◽  
Baseline Serum ◽  
Disease Remission ◽  
Antibody Reactivity

Background:Rheumatoid arthritis (RA) is an autoimmune disorder that is characterized by chronic inflammation of the joint synovium and presence of autoantibodies in most patients. For RA, many treatments are currently available but each treatment will only induce disease remission in a subset of patients. Moreover, finding out which patients respond well to first-line therapy with classical synthetic disease modifying anti-rheumatic drugs (csDMARDs), still largely depends on trial and error.Objectives:In this study, we aim to find novel RA autoantibody biomarkers that predict therapy response to csDMARDs before the initiation of treatment.Methods:In the CareRA trial, a Flemish multicenter study of different treatment regimes, serum samples were collected from RA patients that did or did not show disease remission (DAS28(CRP)<2.6) in response to csDMARDs, combined with a step down glucocorticoid treatment. In our study, baseline samples, collected before the start of treatment, were used to determine predictive antibody reactivity. A cDNA phage display library, representing the antigens from RA synovial tissue, was constructed and screened for antibody reactivity in baseline serum samples of RA patients that failed to reach remission at week 16. Using enzyme-linked immunosorbent assays (ELISA), antibody reactivity against the identified antigens was initially determined in pooled baseline serum samples of RA patients that did (n=50) or did not (n=40) reach disease remission at week 16. Antigenic targets that showed increased antibody reactivity in pools from patients that did not reach disease remission, were further validated in individual serum samples of 69 RA patients that did not reach DAS28(CRP) remission at week 16, and 122 RA patients that did.Results:Screening and validation of antibody reactivity resulted in 41 novel antigens. The retrieved antigenic sequences correspond to (parts of) known proteins and to randomly formed peptides. A panel of 3 of these peptide antigens could be composed, whose baseline antibody reactivity correlated with lack of therapy response at week 16. Presence of antibodies against at least one of these 3 antigens was significantly higher in individual samples of RA patients that did not reach DAS28(CRP) remission (43 vs. 29%, p=0.041), or that failed to reach ACR 70 (42 vs. 26%, p=0.029) response criteria at week 16, compared to RA patients that did reach these respective criteria. In addition, RA patients which were positive for this antibody panel at baseline, also showed less DAS(CRP) remission at week 4 and week 8.Conclusion:We have identified a set of 3 antibody biomarkers that can predict failure of early disease remission after first-line RA therapy, which might contribute to personalized medicine decisions.Disclosure of Interests:Patrick Vandormael: None declared, Astrid Pues: None declared, Ellen Sleurs: None declared, Patrick Verschueren Grant/research support from: Pfizer unrestricted chair of early RA research, Speakers bureau: various companies, Veerle Somers Grant/research support from: Research grant from Pfizer and BMS

scholarly journals Identification of an optimal prolactin threshold to determine prolactinoma size using receiver operating characteristic analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Bianca M. Leca ◽  
Maria Mytilinaiou ◽  
Marina Tsoli ◽  
Andreea Epure ◽  
Simon J. B. Aylwin ◽  
...  
Keyword(s):  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Tumour Size ◽  
Area Under The Curve ◽  
Serum Prolactin ◽  
Pituitary Neoplasms ◽  
Characteristic Analysis ◽  
Roc Curve Analysis ◽  
Baseline Serum ◽  
Receiver Operating

AbstractProlactinomas represent the most common type of secretory pituitary neoplasms, with a therapeutic management that varies considerably based on tumour size and degree of hyperprolactinemia. The aim of the current study was to evaluate the relationship between serum prolactin (PRL) concentrations and prolactinoma size, and to determine a cut-off PRL value that could differentiate micro- from macro-prolactinomas. A retrospective cohort study of 114 patients diagnosed with prolactinomas between 2007 and 2017 was conducted. All patients underwent gadolinium enhanced pituitary MRI and receiver operating characteristic (ROC) analyses were performed. 51.8% of patients in this study were men, with a mean age at the time of diagnosis of 42.32 ± 15.04 years. 48.2% of the total cohort were found to have microadenomas. Baseline serum PRL concentrations were strongly correlated to tumour dimension (r = 0.750, p = 0.001). When performing the ROC curve analysis, the area under the curve was 0.976, indicating an excellent accuracy of the diagnostic method. For a value of 204 μg/L (4338 mU/L), sensitivity and specificity were calculated at 0.932 and 0.891, respectively. When a cut off value of 204 μg/L (4338 mU/L) was used, specificity was 93.2%, and sensitivity 89.1%, acceptable to reliably differentiate between micro- and macro- adenomas.

Antimodified protein antibody response pattern influences the risk for disease relapse in patients with rheumatoid arthritis tapering disease modifying antirheumatic drugs

2016 ◽  
Vol 76 (2) ◽  
pp. 399-407 ◽  
Author(s):  
Camille P Figueiredo ◽  
Holger Bang ◽  
Jayme Fogagnolo Cobra ◽  
Matthias Englbrecht ◽  
Axel J Hueber ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Antibody Response ◽  
Multivariate Logistic Regression Analysis ◽  
Sustained Remission ◽  
Disease Relapse ◽  
Serum Samples ◽  
Baseline Serum ◽  
Dmard Therapy ◽  
Relapse Risk ◽  
Disease Modifying

ObjectiveTo perform a detailed analysis of the autoantibody response against post-translationally modified proteins in patients with rheumatoid arthritis (RA) in sustained remission and to explore whether its composition influences the risk for disease relapse when tapering disease modifying antirheumatic drug (DMARD) therapy.MethodsImmune responses against 10 citrullinated, homocitrullinated/carbamylated and acetylated peptides, as well as unmodified vimentin (control) and cyclic citrullinated peptide 2 (CCP2) were tested in baseline serum samples from 94 patients of the RETRO study. Patients were classified according to the number of autoantibody reactivities (0–1/10, 2–5/10 and >5/10) or specificity groups (citrullination, carbamylation and acetylation; 0–3) and tested for their risk to develop relapses after DMARD tapering. Demographic and disease-specific parameters were included in multivariate logistic regression analysis for defining the role of autoantibodies in predicting relapse.ResultsPatients varied in their antimodified protein antibody response with the extremes from recognition of no (0/10) to all antigens (10/10). Antibodies against citrullinated vimentin (51%), acetylated ornithine (46%) and acetylated lysine (37%) were the most frequently observed subspecificities. Relapse risk significantly (p=0.011) increased from 18% (0–1/10 reactivities) to 34% (2–5/10) and 55% (>5/10). With respect to specificity groups (0–3), relapse risk significantly (p=0.021) increased from 18% (no reactivity) to 28%, 36% and finally to 52% with one, two or three antibody specificity groups, respectively.ConclusionsThe data suggest that the pattern of antimodified protein antibody response determines the risk of disease relapse in patients with RA tapering DMARD therapy.Trial registration number2009-015740-42; Results.

Receiver operating characteristic analysis of milk lactosefor identification of mastitis in buffaloes

2016 ◽  
Author(s):  
T. K. Patbandha ◽  
K. Ravikala ◽  
B. R. Maharana ◽  
Rupal Pathak ◽  
S. Marandi ◽  
...  
Keyword(s):  
Receiver Operating Characteristic ◽  
Roc Analysis ◽  
Operating Characteristic ◽  
Positive Likelihood Ratio ◽  
Threshold Values ◽  
Characteristic Analysis ◽  
Statistical Tool ◽  
Milk Samples ◽  
Diagnostic Indicator ◽  
Receiver Operating

Receiver operating characteristic (ROC) analysis is a simple statistical tool used to classify a diagnostic indicator in terms of area under a ROC curve (AUC) and to develop potential threshold values of a diagnostic indicator. Milk lactose was analyzed by ROC analysis to see its accuracy to discriminate infected and healthy udder quarters, and to develope an optimum threshold value along with corresponding sensitivity (Se), specificity (Sp) and positive likelihood ratio (LR+) value. Data for the present study comprised of 1516 milk samples collected from Jaffrabadi buffaloes. Milk lactose was estimated by milk analyzer ‘LACTOSCAN’ and further samples were checked for sub-clinical mastitis by California mastitis test (CMT). The threshold values of milk lactose for identification of moderate and severe infection were found to be 5.31g% (Se, 58.82%; Sp, 58.28%) and 5.23g% (Se, 70.97%; Sp, 64.41%), respectively by ROC analysis. Milk samples with lactose content below 5.31g% were 1.41 times more likely come from moderately infected quarters (LR+ = 1.41); whereas, below 5.23g% were 1.99 times more likely come from severely infected quarters (LR+ = 1.99). The overall accuracy of milk lactose for discrimination of normal quarters from moderately infected quarters was 64% (AUC=0.64) and from severely infected quarters was 72% (AUC=0.72) (P<0.001). Thus, the present study indicated that milk lactose classified mastitic and healthy udder quarters in Jaffrabadi buffaloes with moderate accuracy.

Untargeted lipidomics reveals specific lipid abnormalities in Sjögren’s syndrome

Rheumatology ◽  
2020 ◽  
Author(s):  
Jiawei Lu ◽  
Yunke Guo ◽  
Yan Lu ◽  
Wei Ji ◽  
Lili Lin ◽  
...  
Keyword(s):  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Receiver Operating Characteristic Analysis ◽  
Lipid Biomarkers ◽  
Acid Oxidation ◽  
Healthy Controls ◽  
Characteristic Analysis ◽  
Serum Samples ◽  
Latent Structures ◽  
Receiver Operating

Abstract Objective The relationship between serum lipid variations in SS and healthy controls was investigated to identify potential predictive lipid biomarkers. Methods Serum samples from 230 SS patients and 240 healthy controls were collected. The samples were analysed by ultrahigh-performance liquid chromatography coupled with Q Exactive™ spectrometry. Potential lipid biomarkers were screened through orthogonal projection to latent structures discriminant analysis and further evaluated by receiver operating characteristic analysis. Results A panel of three metabolites [phosphatidylcholine (18:0/22:5), triglyceride (16:0/18:0/18:1) and acylcarnitine (12:0)] was identified as a specific biomarker of SS. The receiver operating characteristic analysis showed that the panel had a sensitivity of 84.3% with a specificity of 74.8% in discriminating patients with SS from healthy controls. Conclusion Our approach successfully identified serum biomarkers associated with SS patients. The potential lipid biomarkers indicated that SS metabolic disturbance might be associated with oxidized lipids, fatty acid oxidation and energy metabolism.

scholarly journals Serum MALAT1 Assumes Signifying Capacity in Gastric Cancer Diagnosis.

2020 ◽  
Author(s):  
Keqian Zhang ◽  
Tianqi Mao ◽  
Zhicheng He ◽  
Xiaojiao Wu ◽  
Yu Peng ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Roc Analysis ◽  
Operating Characteristic ◽  
Area Under The Curve ◽  
Diagnostic Value ◽  
Chain Reaction ◽  
Serum Samples ◽  
Chi Square ◽  
Chi Square Test ◽  
Polymerase Chain

Abstract Background: Gastric cancer (GC) represents one of the most serious cancers worldwide with the increasing mortality. Metastasis associated lung adenocarcinoma transcript 1 (MALAT1), a kind of lncRNAs, has been reported to be involved in the progression of cancers. This study aimed to assess serum expression pattern of MALAT1 and its clinical significance in diagnosis of GC.Methods: Serum specimens were collected from 120 GC patients and 58 healthy individuals. The expression profile of MALAT1 was examined using quantitative real-time polymerase chain reaction (qRT-PCR), and its association with clinical parameters was estimated by chi-square test. The diagnostic value of MALAT1 in GC was evaluated by the receiver operating characteristic (ROC) analysis.Results: Upregulated expression of MALTA1 was found in GC patients compared with the healthy controls (P<0.05). The overexpression of MALAT1 was positively correlated with lymph node metastasis (P=0.041) and TNM stage (P=0.005). An area under the curve (AUC) was 0.897 in ROC analysis, suggesting the high diagnostic value of MALAT1. Conclusion: The expression of MALAT1 was upregulated in GC serum samples, and its expression might serve as a potential diagnostic biomarker in patients with GC.

scholarly journals Proteomic Approaches to Defining Remission and the Risk of Relapse in Rheumatoid Arthritis

2021 ◽  
Vol 12 ◽  
Author(s):  
Liam J. O’Neil ◽  
Pingzhao Hu ◽  
Qian Liu ◽  
Md. Mohaiminul Islam ◽  
Victor Spicer ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Machine Learning ◽  
Serum Proteins ◽  
Learning Algorithm ◽  
Characteristic Curve ◽  
Serum Samples ◽  
Das28 Score ◽  
Baseline Serum ◽  
Disease Flare ◽  
Serum Proteomics

ObjectivesPatients with Rheumatoid Arthritis (RA) are increasingly achieving stable disease remission, yet the mechanisms that govern ongoing clinical disease and subsequent risk of future flare are not well understood. We sought to identify serum proteomic alterations that dictate clinically important features of stable RA, and couple broad-based proteomics with machine learning to predict future flare.MethodsWe studied baseline serum samples from a cohort of stable RA patients (RETRO, n = 130) in clinical remission (DAS28&lt;2.6) and quantified 1307 serum proteins using the SOMAscan platform. Unsupervised hierarchical clustering and supervised classification were applied to identify proteomic-driven clusters and model biomarkers that were associated with future disease flare after 12 months of follow-up and RA medication withdrawal. Network analysis was used to define pathways that were enriched in proteomic datasets.ResultsWe defined 4 proteomic clusters, with one cluster (Cluster 4) displaying a lower mean DAS28 score (p = 0.03), with DAS28 associating with humoral immune responses and complement activation. Clustering did not clearly predict future risk of flare, however an XGboost machine learning algorithm classified patients who relapsed with an AUC (area under the receiver operating characteristic curve) of 0.80 using only baseline serum proteomics.ConclusionsThe serum proteome provides a rich dataset to understand stable RA and its clinical heterogeneity. Combining proteomics and machine learning may enable prediction of future RA disease flare in patients with RA who aim to withdrawal therapy.

scholarly journals Delineating a Retesting Zone Using Receiver Operating Characteristic Analysis on Serial QuantiFERON Tuberculosis Test Results in US Healthcare Workers

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
Wendy Thanassi ◽  
Art Noda ◽  
Beatriz Hernandez ◽  
Jeffery Newell ◽  
Paul Terpeluk ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Receiver Operating Characteristic ◽  
Healthcare Workers ◽  
Operating Characteristic ◽  
Receiver Operating Characteristic Analysis ◽  
Low Risk ◽  
Separation Point ◽  
Characteristic Analysis ◽  
Chi Square ◽  
Receiver Operating

Objective. To find a statistically significant separation point for the QuantiFERON Gold In-Tube (QFT) interferon gamma release assay that could define an optimal “retesting zone” for use in serially tested low-risk populations who have test “reversions” from initially positive to subsequently negative results.Method. Using receiver operating characteristic analysis (ROC) to analyze retrospective data collected from 3 major hospitals, we searched for predictors of reversion until statistically significant separation points were revealed. A confirmatory regression analysis was performed on an additional sample.Results. In 575 initially positive US healthcare workers (HCWs), 300 (52.2%) had reversions, while 275 (47.8%) had two sequential positive tests. The most statistically significant (Kappa = 0.48, chi-square = 131.0,P<0.001) separation point identified by the ROC for predicting reversion was the tuberculosis antigen minus-nil (TBag-nil) value at 1.11 International Units per milliliter (IU/mL). The second separation point was found at TBag-nil at 0.72 IU/mL (Kappa = 0.16, chi-square = 8.2,P<0.01). The model was validated by the regression analysis of 287 HCWs.Conclusion. Reversion likelihood increases as the TBag-nil approaches the manufacturer's cut-point of 0.35 IU/mL. The most statistically significant separation point between those who test repeatedly positive and those who revert is 1.11 IU/mL. Clinicians should retest low-risk individuals with initial QFT results < 1.11 IU/mL.

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