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  • 1
    Online Resource
    Online Resource
    BMJ ; 1999
    In:  Archives of Disease in Childhood Vol. 81, No. 5 ( 1999-11-01), p. 444-445
    In: Archives of Disease in Childhood, BMJ, Vol. 81, No. 5 ( 1999-11-01), p. 444-445
    Type of Medium: Online Resource
    ISSN: 0003-9888 , 1468-2044
    Language: English
    Publisher: BMJ
    Publication Date: 1999
    detail.hit.zdb_id: 1481191-1
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  • 2
    Online Resource
    Online Resource
    BMJ ; 2013
    In:  Archives of Disease in Childhood Vol. 98, No. 10 ( 2013-10-01), p. 798-798
    In: Archives of Disease in Childhood, BMJ, Vol. 98, No. 10 ( 2013-10-01), p. 798-798
    Type of Medium: Online Resource
    ISSN: 0003-9888 , 1468-2044
    Language: English
    Publisher: BMJ
    Publication Date: 2013
    detail.hit.zdb_id: 1481191-1
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  • 3
    In: Annals of the Rheumatic Diseases, BMJ, Vol. 80, No. Suppl 1 ( 2021-06), p. 469-470
    Abstract: TNF inhibitors (TNFi) represent an extraordinary advance in the management of Rheumatoid Arthritis (RA). Despite their benefits, there is a percentage of patients (20–40%) that do not achieve clinical improvement. Therefore, it is necessary to search for new and easily accessible biomarkers predictive of therapeutic response that might guide precision medicine. Objectives: 1. To explore changes in the molecular profile of RA patients following TNFi therapy in serum samples. 2. To search for new and reliable biomarkers predictive of TNFi response, based on clinical and molecular profiles of RA patients, by using machine learning algorithms. Methods: In a prospective multicenter study, 79 RA patients undergoing TNFi and 29 healthy donors (HD) were enrolled. Twenty-two RA patients were further included as a validation cohort. Serum samples were obtained before and after 6 months of treatment, and therapeutic efficacy was evaluated. Patients’ response was determined following EULAR response criteria. Serum inflammatory profile was analyzed by a multiplex immunoassay, along with oxidative and NETotic profiles, evaluated by commercial kits. A circulating miRNA array was also performed by next-generation sequencing. Clustering analysis was carried out to identify groups of patients with distinctive molecular signatures. Then, clinical and molecular changes induced by TNFi were delineated after 6 months of therapy. Finally, integrative clinical and molecular signatures as predictors of response were assessed at baseline by supervised machine learning methods, using regularized logistic regressions. Results: Inflammatory, oxidative stress and NETosis-derived biomolecules were found altered in RA patients versus HD, closely interconnected and associated with several deregulated miRNAs. This altered molecular profile at baseline allowed the unsupervised division of three clusters of RA patients with distinctive clinical phenotypes, further linked to TNFi effectiveness. Cluster 1 included RA patients with low levels of pro-inflammatory cytokines, associated with a medium-low disease activity score and good clinical response. Clusters 2-3 comprised patients with high levels of pro-inflammatory cytokines, associated with a high disease activity and a non-response rate of 30%. After 6 months of therapy the molecular profile found altered in RA patients was reversed in responder patients, who achieved a molecular phenotype similar to HDs. However, non-responder patients’ molecular profile remained significantly deregulated, including alterations in inflammatory mediators (IL-6, L-8, TNFα, VEGF, IL-1RA, IL-5, IL-15, GMCSF, GCSF, FGFb), oxidative stress markers (LPO) and NETosis-derived products (Elastase), along with specific miRNAs (miR-199a-5p). These molecular changes further correlated with changes in disease activity score. Machine-learning algorithms identified clinical (Creatinine, IgM, Vitamin D, Swollen Joints, C4, Disease Duration and Tryglicerides) and molecular (Nucleosomes, IL-10, miR-106a-5p, IL-13, IL-12p70, IL-15 and LPO) signatures as potential predictors of response to TNFi treatment with high accuracy. Furthermore, the integration of both features in a combined model increased the predictive value of these signatures (AUC: 0.91). These results were further confirmed in an independent validation cohort. Conclusion: 1. RA patients display distinctive altered molecular profiles directly linked to their clinical status and associated with TNFi effectiveness. 2. Clinical response was associated with a specific modulation of the inflammatory profile, the reestablishment of the altered oxidative status, the reduction of NETosis and the reversion of related altered miRNAs. 3. The integrative analysis of the clinical and molecular profiles using machine learning allows the identification of novel signatures as potential predictors of therapeutic response to TNFi therapy. Disclosure of Interests: None declared
    Type of Medium: Online Resource
    ISSN: 0003-4967 , 1468-2060
    RVK:
    Language: English
    Publisher: BMJ
    Publication Date: 2021
    detail.hit.zdb_id: 1481557-6
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  • 4
    In: Annals of the Rheumatic Diseases, BMJ, Vol. 79, No. Suppl 1 ( 2020-06), p. 951.2-952
    Abstract: To evaluate the changes promoted in levels of circulating inflammatory mediators in RA patients in response to TNF-α inhibitors (TNFi) and anti-CD20 therapies, in order to identify biomarkers of clinical efficacy and potential predictors of therapeutic response to these drugs. Methods: In a prospective RA cohort multicenter study, we collected serum from RA patients with moderate or high disease activity prior and after 6 months of treatment with TNFi or rituximab (RTX), and analyzed levels of 27 proteins that constitute a multi-biomarker test of the inflammatory profile of these samples, using a multiplex immunoassay. Patients’ response was determined according to the EULAR response criteria (good/moderate/no). We compared basal levels of inflammatory molecules between the differential response patient groups and analyzed their discriminative ability. Logistic prediction models were created to assess the added value of potential inflammatory predictors. Results: Among 111 total RA patients, 50 of 85 (59%) patients in the TNFi group and 18 of 26 patients in the RTX group (69%) responded to the biologic treatment. High DAS28 or SDAI scores, or titers of auto-antibodies (RF or ACPA) at baseline were not predictive of response to any treatment. Instead, smoking habit and hyperlipidemia at baseline were predictors of a worse response to any of these bDMARDs. Of the molecules analyzed by the multiplex assay, 14 inflammatory mediators showed a significant downregulation on patients’ responders to TNFi therapy. Moreover, the decline on 7 biomolecules was related to reduced DAS28. After RTX treatment, 15 inflammatory mediators were reduced in patients with good clinical response; downregulation in 4 of those biomolecules correlated with reduced DAS28. In the search for predictors of response to each bDMARD, by using the MetaboAnalyst software, we could classify patients with distinctive therapeutic response based on the baseline levels of the inflammatory molecules analyzed. Receiver operating characteristic (ROC) analyses for those multiple biomarkers allowed us to further identify specific signatures of inflammatory biomolecules that may serve as predictors of response to each bDMARD therapy with high sensitivity and specificity. Thus, a signature of five molecules was identified as potential predictor of TNFi response [Vascular endothelial growth factor (VEGF), Eotaxin, RANTES, IL7 and IL-17]. Indeed, a signature including three highly expressed cytokines/chemokines in RA serum were identified as predictors of RTX response [interferon-inducible protein 10 (IP10), Eotaxin and monocyte chemotactic protein 1 (MCP-1)] . Conclusion: The extensive analysis of serum inflammatory profile allowed to identify specific and distinctive signatures of biomolecules that, in coordination with known clinical and serological profiles, might predict the response of RA patients to TNFi or RTX treatments. Acknowledgments : Funded by Junta de Andalucía (PI-0285-2017), ISCIII, (PI18/00837 and RIER RD16/0012/0015) co-funded with FEDER Disclosure of Interests: María Luque-Tévar: None declared, Carlos Perez-Sanchez: None declared, Font Ugalde Pilar: None declared, Montserrat Romero-Gómez: None declared, Alejandra M. Patiño-Trives: None declared, Desiree Ruiz: None declared, Iván Arias de la Rosa: None declared, Maria del Carmen Abalos-Aguilera: None declared, Rafaela Ortega Castro: None declared, Alejandro Escudero Contreras: None declared, Carlos Rodríguez-Escalera Speakers bureau: Lilly, GSK, Novartis and Sanofi, José Javier Pérez Venegas: None declared, María Dolores Ruiz Montesinos: None declared, Carmen Dominguez: None declared, Carmen Romero Barco: None declared, Antonio Fernandez-Nebro: None declared, Natalia Mena-Vázquez: None declared, Jose Luis Marenco Speakers bureau: ABbvie, Pfzer, lilly, Julia Uceda: None declared, Mª Dolores Toledo-Coello: None declared, Nuria Barbarroja Puerto Grant/research support from: ROCHE and Pfizer., Speakers bureau: ROCHE and Celgene., Maria A Aguirre: None declared, Chary Lopez-Pedrera Grant/research support from: ROCHE and Pfizer., Eduardo Collantes-Estévez Grant/research support from: ROCHE and Pfizer., Speakers bureau: ROCHE, Lilly, Bristol and Celgene.
    Type of Medium: Online Resource
    ISSN: 0003-4967 , 1468-2060
    RVK:
    Language: English
    Publisher: BMJ
    Publication Date: 2020
    detail.hit.zdb_id: 1481557-6
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  • 5
    In: Annals of the Rheumatic Diseases, BMJ, Vol. 74, No. Suppl 2 ( 2015-06), p. 1057.1-1057
    Type of Medium: Online Resource
    ISSN: 0003-4967 , 1468-2060
    RVK:
    Language: English
    Publisher: BMJ
    Publication Date: 2015
    detail.hit.zdb_id: 1481557-6
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  • 6
    In: Annals of the Rheumatic Diseases, BMJ, Vol. 75, No. Suppl 2 ( 2016-06), p. 237.1-237
    Type of Medium: Online Resource
    ISSN: 0003-4967 , 1468-2060
    RVK:
    Language: English
    Publisher: BMJ
    Publication Date: 2016
    detail.hit.zdb_id: 1481557-6
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  • 7
    In: Annals of the Rheumatic Diseases, BMJ, Vol. 81, No. Suppl 1 ( 2022-06), p. 475.1-475
    Abstract: The clinical outcome of the most common therapeutic options of rheumatoid arthritis (RA) patients, such as conventional disease-modifying antirheumatic drugs (DMARDs) and TNF inhibitors (TNFi) is still unpredictable, since a high percentage of patients do not response to the therapy. Innovative analyses combining high-throughput technologies and thorough clinical assessments are needed to gain insight about the management of this prevalent autoimmune disorder. Objectives To evaluate the systemic inflammatory proteome of RA patients, to identify useful biomarkers associated with distinctive clinical outcomes. Methods Serum samples from 140 subjects, including 40 healthy donors (HC) and 100 RA patients with high activity disease (mean DAS28=4.7), were profiled with the innovative proteomic methodology “proximity extension assay” (Olink) which analyses one panel of 92 pro-inflammatory proteins. Samples from RA active patients included 40 from newly-diagnosed RA patients before taking conventional DMARDs and 60 from biologics-naïve patients (mean disease duration=10 years) before receiving TNFi drugs. Clinical outcomes were evaluated following EULAR criteria after 6 months of treatment and patients were classified as responders or non-responders to the different therapeutic interventions. Unsupervised hierarchical clustering methodologies were applied to identify subgroup pf patients based on the proteomic profiles. Gene ontology enrichment were used to interrogate the biological meaning of the distinctive molecular signatures identified. Results The inflammatory proteome analysis identified 33 proteins differentially expressed and upregulated in RA patients compared with HC including several chemokines (CCL-11, -19, -20, -23, -28; CXCL-10, -11, -9; MCP-1, -3), interleukins (IL-6, -8, -18, -10, -17c), and other relevant proinflammatory mediators (VEGFA, CD40, MMP-1, CSF-1, OPG, FGF23) among others (FDR 〈 0.05). Most of these molecules were associated with disease activity (DAS28) and the autoimmunity profile (Rheumatoid factor and ACPAs antibodies) of RA patients. The unsupervised clustering analysis using the proteomic profile of RA patients before TNFi identified two subgroups of patients. Cluster 1 (C1) was characterised by patients with higher levels of several pro-inflammatory mediators compared with Cluster 2 (C2), where a signature of 16 chemokines was significantly enriched (CCL-3, -4, -10, -11, -20, -23; CX3CL1; CXCL-1, -10, -11, -5, -6, -9; MCP-1, -3, -4). Clinically, 25% of the non-responders’ patients was included in C2, while 75% was located in C1, suggesting that a prominent circulating chemotaxis profile prior therapy is associated with a poor clinical outcome. These data were similarly observed in patients before receiving DMARDs, where a signature of upregulated chemokines and pro-inflammatory mediators characterised a cluster with a high % of non-responder patients. Conclusion A pro-inflammatory signature, where chemokines are predominantly up-regulated in the serum of RA patients before therapy, is associated with a poor clinical outcome. This newly identified signature, which deserves a more in-depth analysis, might be clinically useful guiding precision medicine and novel therapeutic approaches. Acknowledgements Supported by ISCIII (PI21/005991 y RICOR-RD21/0002/0033) co-financed by FEDER, Fundacion Andaluza de Reumatología (FAR) and Consejería de Conocimiento, Investigación y Universidad de la Junta de Andalucía (P20_01367). Disclosure of Interests None declared.
    Type of Medium: Online Resource
    ISSN: 0003-4967 , 1468-2060
    RVK:
    Language: English
    Publisher: BMJ
    Publication Date: 2022
    detail.hit.zdb_id: 1481557-6
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  • 8
    In: Annals of the Rheumatic Diseases, BMJ, Vol. 81, No. Suppl 1 ( 2022-06), p. 645.2-646
    Abstract: Systemic Lupus Erythematosus (SLE) is a chronic autoimmune disease, characterized by a wide variety of clinical manifestations, as well as continuous relapses and exacerbation of symptoms. This complex panorama complicates an early and proper diagnosis, treatment, and follow-up of the people with SLE and therefore has a significant impact on their health-related quality of life (HRQoL). Although the importance of assessing HRQoL in SLE has become evident in recent years, in Mexico there is no epidemiological surveillance system nor national registry that conveys this information. Objectives To evaluate health related quality of life (HRQoL) in Mexican individuals with SLE using the data from the Mexican Register of Lupus (Lupus RGMX). Methods The Mexican Lupus Registry (Lupus RGMX) is an ongoing online register. This cohort includes sociodemographic and clinical data of Mexican individuals with SLE. In this study we assessed and compared HRQoL in patients with SLE and a matched control group of Mexican individuals without SLE diagnosis. We estimated QoL using the World Health Organization Quality of Life (WHOQOL-bref) and Short Form-36 (SF36) questionnaires. For both WHOQOL-bref and SF36, higher scores mean better HRQoL. Statistical analysis was performed using R 4.1.2 (R Core Team, 2021). Results A total of 631 SLE and 272 control registers were analyzed (Table 1). Significant lower scores on HRQoL were observed on participants with SLE for both SF36 and WHOQOL-bref questionnaires, compared with the matched control group. All score components were lower in SLE individuals. Physical role functioning, bodily pain and general health exhibited the lowest scores among the SF36 factors, whereas physical factor was the lowest for WHOQOL-bref (Table 1). Table 1. SF36 and WHOQOL-bref median scores (25-75 IQR) of SLE participants and controls. Controls SLE p-value (n=272) (n=631) Female sex 211 (77.6%) 596 (94.4%) 〈 0.001 n (%)# Age* 28 (24-35) 35 (28-43) 〈 0.001 SF36 (max 100 points per function ) * Physical functioning 100 (95-100) 65 (45-85) 〈 0.001 Physical role functioning 100 (100-100) 25 (0-100) 〈 0.001 Bodily pain 84 (61-100) 31 (22-52) 〈 0.001 General health 77 (62-100) 35 (20-47) 〈 0.001 Vitality 65 (50-100) 40 (25-55) 〈 0.001 Social functioning 100 (62.5-100) 50 (37.5-75) 〈 0.001 Emotional role functioning 100 (66.7-100) 66.7 (0-100) 〈 0.001 Mental health 76 (60-100) 56 (42-74) 〈 0.001 WHOQOL-bref (max 20 points per function ) * Physical 16.6 (14.8-17.7) 11.4 (9.1-13.1) 〈 0.001 Psychological 15.3 (13.3-19.3) 12.7 (10.7-14.7) 〈 0.001 Social relations 14.7 (12-16) 12 (9.3-14.7) 〈 0.001 Environmental 15.5 (14-17) 13 (11-15) 〈 0.001 # Chi-square test, *U Mann-Whitney test Conclusion In Mexican people with SLE, a significant decrease in HRQoL was detected compared with controls. The implementation of a national register in Mexico (Lupus RGMX) will provide additional psychosocial and clinical information to deepen our knowledge of this disease. References [1]Louthrenoo, W., et al. Arthritis Res Ther 2020; 22, 8. [2]Shi Y, et al. Autoimmun Rev. 2021 Jan;20(1):102691. Acknowledgements ALHL is a doctoral student from Programa de Doctorado en Ciencias Biomédicas, Universidad Nacional Autónoma de México (UNAM) and recieved fellowship 790972 from CONACYT (CVU 711015). AMR was supported by CONACYT-FORDECYT-PRONACES grant no [11311], and Programa de Apoyo a Proyectos de Investigación e Innovación Tecnológica-Universidad Nacional Autónoma de México (PAPIIT-UNAM) grant nos. IA203021 Disclosure of Interests Ana Laura Hernández-Ledesma: None declared, Karen Julia Nuñez-Reza: None declared, Andrea Yojany Tapia-Atilano: None declared, Víctor Flores-Ocampo: None declared, Juan Ernesto Villarreal del Moral: None declared, Talía V. Román-López: None declared, Sandra Valentina Vera del Valle: None declared, Donaji Domínguez-Zúñiga: None declared, Estefania Torres-Valdez: None declared, Gabriel Frontana-Vázquez: None declared, Sarael Alcauter: None declared, Miguel Enrique Rentería: None declared, Alejandra Evelyn Ruíz-Contreras: None declared, Deshire Alpizar-Rodriguez Consultant of: Scientific advisor GKS, Alejandra Medina-Rivera: None declared
    Type of Medium: Online Resource
    ISSN: 0003-4967 , 1468-2060
    RVK:
    Language: English
    Publisher: BMJ
    Publication Date: 2022
    detail.hit.zdb_id: 1481557-6
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  • 9
    In: Annals of the Rheumatic Diseases, BMJ, Vol. 79, No. Suppl 1 ( 2020-06), p. 1128.2-1128
    Abstract: The etiopathogenesis of axial spondyloarthritis (AxSpA) is multifactorial. The possible role of alteration in gut microbiome (dysbiosis) has been recently suggested. However, the association of dysbiosis with structural damage is still unknown and further studies are needed to assess its association with disease activity. Objectives: To determine the alterations in the gut microbiota in AxSpA patients. To evaluate whether changes in the gut microbiota in AxSpA patients are associated with structural damage or disease activity. Methods: Fifteen AxSpA patients and 15 healthy donors (HDs) were included in a cross-sectional study. Disease activity variables such as C-reactive protein and ESR were measured. Structural damage was determined by lateral X-rays of the cervical and lumbar spine to establish the mSASSS index. Axial mobility was evaluated using the BASMI index and the enthesis affectation was evaluated using ultrasound to obtain the MASEI index. Gut microbiota was measured using the Ion Torrent S5 platform and sequences were processed using the QIIME2. Chi-square and Mann-Whitney U were used, and correlations were determined using the Spearman Rho test. Significant differences were considered p 〈 0.05. Results: Alpha diversity indicators, such as the number of observed OTUs group and the faith index, showed a greater richness in AxSpA compared to HDs (p=0.03 and p=0.01). A significant decrease in family Bacteroidaceae (p=0.006) and an increase in families Synergistaceae and Bifidobacteriaceae were found in the microbiota of AxSpA (p=0.036, p=0.049). According to genera, Bacteroides decreased in AxSpA (p=0.006), while Dialister and Bifidobacterium increased (p=0.010 and p=0.046). Positive correlation among lumbar mSASSS (r=0.508, p=0.019) and Synergistaceae was found. This family was also increased along with the increase in enthesis damage (MASEI index (r=0.656, p=0.028)) and axial mobility by the BASMI index (r=0.529, p=0.011). Correlation studies between the decrease in Bacteroidaceae and Bacteroides with disease activity measured by ASDAS (r=-0.697, p=0.025; r=-0.770, p=0.009) was also significant. Positive correlation was observed between Dialister with mSASSS (r=0.549, p=0.010) and BASMI (r=0.512, p=0.015). Conclusion: 1) AxSpA patients had a significant alteration of the gut microbiota. 2) These alterations are associated with radiographic damage, disease activity, affectation of enthesis and axial mobility. Acknowledgments: PRL, supported by “Sara Borrell” (CD19/00216), IMI supported by “Miguel Servet tipo I” (CP16/00163), CGR supported by JdC Incorporación (IJCI-2017-33065). This work is supported by JA PI-0151-2018. Pablo Rodríguez Bada metagenomic platform CIBER-IBIMA. Disclosure of Interests: Gómez García Ignacio: None declared, Isabel Moreno-Indias: None declared, María del Carmen Castro Villegas: None declared, Maria del Carmen Abalos-Aguilera: None declared, MLourdes Ladehesa Pineda: None declared, Inmaculada Concepcion Aranda-Valera: None declared, Carolina Gutierrez Repiso: None declared, Alejandro Escudero Contreras Grant/research support from: ROCHE and Pfizer, Speakers bureau: ROCHE, Lilly, Bristol and Celgene., Jiménez Gómez Yolanda: None declared, Nuria Barbarroja: None declared, Francisco Jose Tinahones: None declared, Eduardo Collantes Estevez Grant/research support from: ROCHE and Pfizer, Speakers bureau: ROCHE, Lilly, Bristol and Celgene, Patricia Ruiz-Limon: None declared
    Type of Medium: Online Resource
    ISSN: 0003-4967 , 1468-2060
    RVK:
    Language: English
    Publisher: BMJ
    Publication Date: 2020
    detail.hit.zdb_id: 1481557-6
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  • 10
    In: Annals of the Rheumatic Diseases, BMJ, Vol. 74, No. Suppl 2 ( 2015-06), p. 1045.2-1045
    Type of Medium: Online Resource
    ISSN: 0003-4967 , 1468-2060
    RVK:
    Language: English
    Publisher: BMJ
    Publication Date: 2015
    detail.hit.zdb_id: 1481557-6
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