15 research outputs found
Defective chaperone-mediated autophagy is a hallmark of joint disease in patients with knee osteoarthritis
[Abstract] Objective: Defects in autophagy contribute to joint aging and Osteoarthritis (OA). Identifying specific autophagy types could be useful for developing novel treatments for OA.
Design: An autophagy-related gene array was performed in blood from non-OA and knee OA subjects from the Prospective Cohort of A Coruña (PROCOAC). The differential expression of candidate genes was confirmed in blood and knee cartilage and a regression analysis was performed adjusting for age and BMI. HSP90A, a chaperone mediated autophagy (CMA) marker was validated in human knee joint tissues, as well as, in mice with aging-related and surgically-induced OA. The consequences of HSP90AA1 deficiency were evaluated on OA pathogenesis. Finally, the contribution of CMA to homeostasis was studied by assessing the capacity to restore proteostasis upon ATG5-mediated macroautophagy deficiency and genetic HSP90AA1 overexpression.
Results: 16 autophagy-related genes were significantly down-regulated in blood from knee OA subjects. Validation studies showed that HSP90AA1 was down-regulated in blood and human OA cartilage and correlated with risk incidence of OA. Moreover, HSP90A was reduced in human OA joints tissues and with aging and OA in mice. HSP90AA1 knockdown was linked to defective macroautophagy, inflammation, oxidative stress, senescence and apoptosis. However, macroautophagy deficiency increased CMA, highlighting the CMA-macroautophagy crosstalk. Remarkably, CMA activation was sufficient to protect chondrocytes from damage.
Conclusions: We show that HSP90A is a key chaperone for chondrocyte homeostasis, while defective CMA contributes to joint damage. We propose that CMA deficiency is a relevant disease mechanism and could represent a therapeutic target for OA.Instituto de Salud Carlos III; PI17/02059Instituto de Salud Carlos III; PI20/0064
Lipoprotein(A) Concentrations In Rheumatoid Arthritis On Biologic Therapy: Results From The Cardiovascular In Rheumatology [Carma] Study Project
Background
Plasma concentrations of lipoprotein (a) (Lp(a)), a lipoprotein with atherogenic and thrombogenic properties, have a strong genetic basis, although high concentrations of Lp(a) have also been reported in the context of inflammation, as in rheumatoid arthritis (RA). Few studies evaluate the impact of biologic therapies (BT) on Lp(a) in RA, taking into account that with these new therapies a better control of inflammation is achieved.
Objective
The aim of the study was to evaluate the plasma concentrations of Lp(a) in Spanish RA patients on BT attending rheumatology outpatient clinics.
Methods
Baseline analysis of the CARdiovascular in rheuMAtology project, a 10-year prospective study, evaluating the risk of cardiovascular events in RA and other forms of inflammatory arthritis. RA patients were classified according to treatment: no biologic, anti-tumor necrosis factor, anti-interleukin-6 receptor tocilizumab (TCZ), and other biologic (rituximab or abatacept). A model of linear multivariate regression was built in which the dependent variable was Lp(a) concentration and the explanatory variable was BT. The model was adjusted for confounding factors.
Results
Seven hundred and seventy-five RA patients were analyzed. Plasma concentrations of total cholesterol and triglyceride were significantly higher in TCZ-treated patients. Nevertheless, no significant difference in the atherogenic index between TCZ-treated patients and patients without BT was found. After adjusting for confounding factors, patients with BT had lower concentrations of Lp(a) than those without BT; however, only TCZ-treated patients achieved statistically significant differences (?: ?0.303, 95% confidence interval: ?0.558 to ?0.047; P = .02).
Conclusions
RA patients treated with TCZ show lower plasma concentrations of Lp(a) compared with patients without BT.This project has been supported by an unrestricted grant from Abbvie, Spain. The design, analysis, interpretation of results, and preparation of the article have been done independently of Abbvie. Dr González-Gay's studies have been supported by grants from “Fondo de Investigaciones Sanitarias” PI06/0024, PS09/00748, and PI12/00060 and RD12/0009/0013 (RIER) from “Instituto de Salud Carlos III” (ISCIII) (Spain)
Urine metabolome profiling of immune-mediated inflammatory diseases
Background: Immune-mediated inflammatory diseases (IMIDs) are a group of complex and prevalent diseases where disease diagnostic and activity monitoring is highly challenging. The determination of the metabolite profiles of biological samples is becoming a powerful approach to identify new biomarkers of clinical utility. In order to identify new metabolite biomarkers of diagnosis and disease activity, we have performed the first large-scale profiling of the urine metabolome of the six most prevalent IMIDs: rheumatoid arthritis, psoriatic arthritis, psoriasis, systemic lupus erythematosus, Crohn?s disease, and ulcerative colitis. Methods: Using nuclear magnetic resonance, we analyzed the urine metabolome in a discovery cohort of 1210 patients and 100 controls. Within each IMID, two patient subgroups were recruited representing extreme disease activity (very high vs. very low). Metabolite association analysis with disease diagnosis and disease activity was performed using multivariate linear regression in order to control for the effects of clinical, epidemiological, or technical variability. After multiple test correction, the most significant metabolite biomarkers were validated in an independent cohort of 1200 patients and 200 controls. Results: In the discovery cohort, we identified 28 significant associations between urine metabolite levels and disease diagnosis and three significant metabolite associations with disease activity (PFDR < 0.05). Using the validation cohort, we validated 26 of the diagnostic associations and all three metabolite associations with disease activity (PFDR < 0.05). Combining all diagnostic biomarkers using multivariate classifiers we obtained a good disease prediction accuracy in all IMIDs and particularly high in inflammatory bowel diseases. Several of the associated metabolites were found to be commonly altered in multiple IMIDs, some of which can be considered as hub biomarkers. The analysis of the metabolic reactions connecting the IMID-associated metabolites showed an overrepresentation of citric acid cycle, phenylalanine, and glycine-serine metabolism pathways. Conclusions: This study shows that urine is a source of biomarkers of clinical utility in IMIDs. We have found that IMIDs show similar metabolic changes, particularly between clinically similar diseases and we have found, for the first time, the presence of hub metabolites. These findings represent an important step in the development of more efficient and less invasive diagnostic and disease monitoring methods in IMIDs
Severe Disease in Patients With Recent-Onset Psoriatic Arthritis. Prediction Model Based on Machine Learning
To identify patient- and disease-related characteristics that make it possible to predict higher disease severity in recent-onset PsA. We performed a multicenter observational prospective study (2-year follow-up, regular annual visits). The study population comprised patients aged ≥ 18 years who fulfilled the CASPAR criteria and less than 2 years since the onset of symptoms. Severe disease was defined at each visit as fulfillment of at least 1 of the following criteria: need for systemic treatment, Health Assessment Questionnaire (HAQ) > 0.5, polyarthritis. The dataset contained data for the independent variables from the baseline visit and follow-up visit number 1. These were matched with the outcome measures from follow-up visits 1 and 2, respectively. We trained a logistic regression model and random forest-type and XGBoost machine learning algorithms to analyze the association between the outcome measure and the variables selected in the bivariate analysis. The sample comprised 158 patients. At the first follow-up visit, 78.2% of the patients who attended the clinic had severe disease. This percentage decreased to 76.4% at the second visit. The variables predicting severe disease were patient global pain, treatment with synthetic DMARDs, clinical form at diagnosis, high CRP, arterial hypertension, and psoriasis affecting the gluteal cleft and/or perianal area. The mean values of the measures of validity of the machine learning algorithms were all ≥ 80%. Our prediction model of severe disease advocates rigorous control of pain and inflammation, also addressing cardiometabolic comorbidities, in addition to actively searching for hidden psoriasis
Lipoprotein(a) Concentrations in Rheumatoid Arthritis on Biologic Therapy: Results from the Cardiovascular in Rheumatology [CARMA] Study Project
Sin financiación6.918 JCR (2016) Q1, 3/30 RheumatologyUE
Characteristics associated with the perception of high-impact disease (PsAID ≥4) in patients with recent-onset psoriatic arthritis. Machine learning-based model
To evaluate which patient and disease characteristics are associated with the perception of high-impact disease (PsAID ≥4) in recent-onset psoriatic arthritis. We performed a multicenter observational prospective study (2-year follow-up, regular annual visits). The study population comprised patients aged ≥18 years who fulfilled the CASPAR criteria and less than 2 years since the onset of symptoms. The dataset was generated using data for each patient at the 3 visits (baseline, first year, and second year of follow-up) matched with the PsAID values at each of the 3 visits. PsAID was categorized into two groups (<4 and ≥4). We trained a logistic regression model and random forest-type and XGBoost machine learning algorithms to analyze the association between the outcome measure and the variables selected in the bivariate analysis. A k-fold cross-validation with k = 5 was performed. The sample comprised 158 patients. Of the patients who attended the clinic, 45.8% scored PsAID ≥4 at baseline; 27.1%, at the first follow-up visit, and in 23.0%, at the second follow-up visit. The variables associated with PsAID ≥4 were, in decreasing order of importance: HAQ, pain, educational level, and physical activity. Higher HAQ (logistic regression coefficient 10.394; IC95% 7.777,13.011), higher pain (5.668; 4.016, 7.320), lower educational level (-2.064; -3.515, -0.613) and high level of physical activity (1.221; 0.158, 2.283) were associated with a higher frequency of PsAID ≥4. The mean values of the measures of validity of the algorithms were all ≥85%. Despite the higher weight given to pain when scoring PsAID, we observed a greater influence of physical function on disease impact
Secukinumab como tratamiento biológico en la artritis psoriásica en práctica clínica real
Association of serum anti-centromere protein F antibodies with clinical response to infliximab in patients with rheumatoid arthritis: a prospective study
[Abstract] Background: One-third of rheumatoid arthritis (RA) patients demonstrate no clinical improvement after receiving tumor necrosis factor inhibitors (TNFi). The presence of serum autoantibodies is a hallmark in RA and may provide information on future response to treatment. The aim of this prospective study was to search for novel serum autoantibodies useful to predict clinical response to TNFi.
Methods: The autoantibody repertoire was profiled on RA patients treated with TNFi as a first line of biologic therapy (N = 185), who were recruited in three independent cohorts. The presence and levels of autoantibodies in serum at baseline were analysed in association with the clinical response after 24 weeks follow-up. A multiplex bead array built using antigens selected from an initial untargeted screening was employed to identify the autoantibodies on a discovery cohort (N = 50) and to verify and validate the results on verification (N = 61) and validation (N = 74) cohorts. Non-parametric tests, meta-analysis and Receiver Operating Curves (ROC) were performed in order to assess the clinical relevance of the observed findings.
Results: Novel autoantibodies were associated with the clinical response to TNFi, showing different reactivity profiles among the different TNFi. The baseline levels of IgG antibodies against Centromere protein F (CENPF), a protein related to cell proliferation, were significantly (p<0.05) increased in responders (N = 111) to infliximab (IFX) compared to non-responders (N = 44). The addition of anti-CENPF antibodies to demographic and clinical variables (age, sex, DAS28-ESR) resulted in the best model to discriminate responders, showing an area under the curve (AUC) of 0.756 (95% CI [0.639-0.874], p = 0.001). A further meta-analysis demonstrated the significant association of anti-CENPF levels with the patient's subsequent response to IFX, showing a standardized mean difference (SMD) of -0.65 (95% CI [-1.02;-0. 27], p = 0.018).
Conclusions: Our study reveals for the first time the potential of circulating anti-CENPF antibodies to predict the clinical response to IFX before starting the treatment. This finding could be potentially useful to guide therapeutic decisions and may lead to further studies focusing on the role of CENPF on RA pathology.Instituto de Salud Carlos III; PI14/01707Instituto de Salud Carlos III; PI16/02124Instituto de Salud Carlos III; PI17/00404Instituto de Salud Carlos III; PI19/01206Instituto de Salud Carlos III; CIBER-CB06/01/0040Instituto de Salud Carlos III; RETIC-RIER-RD16/0012/0002Instituto de Salud Carlos III; PRB3-ISCIII-PT17/0019/0014)
Urine metabolome profiling of immune-mediated inflammatory diseases
Background: immune-mediated inflammatory diseases (IMIDs) are a group of complex and prevalent diseases where disease diagnostic and activity monitoring is highly challenging. The determination of the metabolite profiles of biological samples is becoming a powerful approach to identify new biomarkers of clinical utility. In order to identify new metabolite biomarkers of diagnosis and disease activity, we have performed the first large-scale profiling of the urine metabolome of the six most prevalent IMIDs: rheumatoid arthritis, psoriatic arthritis, psoriasis, systemic lupus erythematosus, Crohn's disease, and ulcerative colitis. Methods: using nuclear magnetic resonance, we analyzed the urine metabolome in a discovery cohort of 1210 patients and 100 controls. Within each IMID, two patient subgroups were recruited representing extreme disease activity (very high vs. very low). Metabolite association analysis with disease diagnosis and disease activity was performed using multivariate linear regression in order to control for the effects of clinical, epidemiological, or technical variability. After multiple test correction, the most significant metabolite biomarkers were validated in an independent cohort of 1200 patients and 200 controls. Results: in the discovery cohort, we identified 28 significant associations between urine metabolite levels and disease diagnosis and three significant metabolite associations with disease activity (PFDR < 0.05). Using the validation cohort, we validated 26 of the diagnostic associations and all three metabolite associations with disease activity (PFDR < 0.05). Combining all diagnostic biomarkers using multivariate classifiers we obtained a good disease prediction accuracy in all IMIDs and particularly high in inflammatory bowel diseases. Several of the associated metabolites were found to be commonly altered in multiple IMIDs, some of which can be considered as hub biomarkers. The analysis of the metabolic reactions connecting the IMID-associated metabolites showed an over-representation of citric acid cycle, phenylalanine, and glycine-serine metabolism pathways. Conclusions: this study shows that urine is a source of biomarkers of clinical utility in IMIDs. We have found that IMIDs show similar metabolic changes, particularly between clinically similar diseases and we have found, for the first time, the presence of hub metabolites. These findings represent an important step in the development of more efficient and less invasive diagnostic and disease monitoring methods in IMIDs