Temporal variability analysis reveals biases in electronic health records due to hospital process reengineering interventions over seven years

[EN] Objective To evaluate the effects of Process-Reengineering interventions on the Electronic Health Records (EHR) of a hospital over 7 years. Materials and methods Temporal Variability Assessment (TVA) based on probabilistic data quality assessment was applied to the historic monthly-batched admission data of Hospital La Fe Valencia, Spain from 2010 to 2016. Routine healthcare data with a complete EHR was expanded by processed variables such as the Charlson Comorbidity Index. Results Four Process-Reengineering interventions were detected by quantifiable effects on the EHR: (1) the hospital relocation in 2011 involved progressive reduction of admissions during the next four months, (2) the hospital services re-configuration incremented the number of inter-services transfers, (3) the care-services re-distribution led to transfers between facilities (4) the assignment to the hospital of a new area with 80,000 patients in 2015 inspired the discharge to home for follow up and the update of the pre-surgery planned admissions protocol that produced a significant decrease of the patient length of stay. Discussion TVA provides an indicator of the effect of process re-engineering interventions on healthcare practice. Evaluating the effect of facilities¿ relocation and increment of citizens (findings 1, 3¿4), the impact of strategies (findings 2¿3), and gradual changes in protocols (finding 4) may help on the hospital management by optimizing interventions based on their effect on EHRs or on data reuse. Conclusions The effects on hospitals EHR due to process re-engineering interventions can be evaluated using the TVA methodology. Being aware of conditioned variations in EHR is of the utmost importance for the reliable reuse of routine hospitalization data.F.J.P.B, C.S., J.M.G.G. and J.A.C. were funded Universitat Politecnica de Valencia, project "ANALISIS DE LA CALIDAD Y VARIABILIDAD DE DATOS MEDICOS". www.upv.es. J.M.G.G.is also partially supported by: Ministerio de Economia y Competitividad of Spain through MTS4up project (National Plan for Scientific and Technical Research and Innovation 2013-2016, No. DPI2016-80054-R); and European Commission projects H2020-SC1-2016-CNECT Project (No. 727560) and H2020-SC1-BHC-2018-2020 (No. 825750). The funders did not play any role in the study design, data collection and analysis, decision to publish, nor preparation of the manuscript.Perez-Benito, FJ.; Sáez Silvestre, C.; Conejero, JA.; Tortajada, S.; Valdivieso, B.; Garcia-Gomez, JM. (2019). Temporal variability analysis reveals biases in electronic health records due to hospital process reengineering interventions over seven years. PLoS ONE. 14(8):1-19. https://doi.org/10.1371/journal.pone.0220369S119148Aguilar-Savén, R. S. (2004). Business process modelling: Review and framework. International Journal of Production Economics, 90(2), 129-149. doi:10.1016/s0925-5273(03)00102-6Poulymenopoulou, M. (2003). Journal of Medical Systems, 27(4), 325-335. doi:10.1023/a:1023701219563Dadam P, Reichert M, Kuhn K. Clinical Workflows -The Killer Application for Process-oriented Information Systems? Proceedings of the 4th International Conference on Business Information Systems. London: Springer London; 2000. pp. 36–59. doi: https://doi.org/10.1007/978-1-4471-0761-3Lenz, R., & Reichert, M. (2007). IT support for healthcare processes – premises, challenges, perspectives. Data & Knowledge Engineering, 61(1), 39-58. doi:10.1016/j.datak.2006.04.007Rebuge, Á., & Ferreira, D. R. (2012). Business process analysis in healthcare environments: A methodology based on process mining. Information Systems, 37(2), 99-116. doi:10.1016/j.is.2011.01.003Amour EAEH, Ghannouchi SA. Applying Data Mining Techniques to Discover KPIs Relationships in Business Process Context. 2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT). IEEE; 2017. pp. 230–237. doi: https://doi.org/10.1109/PDCAT.2017.00045Chou, Y.-C., Chen, B.-Y., Tang, Y.-Y., Qiu, Z.-J., Wu, M.-F., Wang, S.-C., … Chuang, W.-C. (2010). Prescription-Filling Process Reengineering of an Outpatient Pharmacy. Journal of Medical Systems, 36(2), 893-902. doi:10.1007/s10916-010-9553-5Leu, J.-D., & Huang, Y.-T. (2009). An Application of Business Process Method to the Clinical Efficiency of Hospital. Journal of Medical Systems, 35(3), 409-421. doi:10.1007/s10916-009-9376-4Gand K. Investigating on Requirements for Business Model Representations: The Case of Information Technology in Healthcare. 2017 IEEE 19th Conference on Business Informatics (CBI). IEEE; 2017. pp. 471–480. doi: https://doi.org/10.1109/CBI.2017.36Ferreira, G. S. A., Silva, U. R., Costa, A. L., & Pádua, S. I. D. de D. (2018). The promotion of BPM and lean in the health sector: main results. Business Process Management Journal, 24(2), 400-424. doi:10.1108/bpmj-06-2016-0115Abdulrahman Jabour RM. Cancer Reporting: Timeliness Analysis and Process. 2016; Available: https://search.proquest.com/openview/4ecf737c5ef6d2d503e948df8031fe54/1?pq-origsite=gscholar&cbl=18750&diss=yHewitt M, Simone J V. Enhancing Data Systems to Improve the Quality of Cancer Care [Internet]. National Academy Press; 2000. Available: http://www.nap.edu/catalog/9970.htmlWeiskopf, N. G., & Weng, C. (2013). Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research. Journal of the American Medical Informatics Association, 20(1), 144-151. doi:10.1136/amiajnl-2011-000681Saez C, Robles M, Garcia-Gomez JM. Comparative study of probability distribution distances to define a metric for the stability of multi-source biomedical research data. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. IEEE; 2013. pp. 3226–3229. doi: https://doi.org/10.1109/EMBC.2013.6610228Sáez, C., Rodrigues, P. P., Gama, J., Robles, M., & García-Gómez, J. M. (2014). Probabilistic change detection and visualization methods for the assessment of temporal stability in biomedical data quality. Data Mining and Knowledge Discovery, 29(4), 950-975. doi:10.1007/s10618-014-0378-6Sáez, C., Zurriaga, O., Pérez-Panadés, J., Melchor, I., Robles, M., & García-Gómez, J. M. (2016). Applying probabilistic temporal and multisite data quality control methods to a public health mortality registry in Spain: a systematic approach to quality control of repositories. Journal of the American Medical Informatics Association, 23(6), 1085-1095. doi:10.1093/jamia/ocw010International Ethical Guidelines for Epidemiological Studies [Internet]. Geneva: Council for International Organizations of Medical Sciences (CIOMS) in collaboration with the World Health Organization; 2009. Available: https://cioms.ch/wp-content/uploads/2017/01/International_Ethical_Guidelines_LR.pdfResearch Ethics Committee of the Universitari i Politècnic La Fe Hospital [Internet]. Available: https://www.iislafe.es/en/research/ethics-committees/Charlson, M. E., Pompei, P., Ales, K. L., & MacKenzie, C. R. (1987). A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation. Journal of Chronic Diseases, 40(5), 373-383. doi:10.1016/0021-9681(87)90171-8Schneeweiss, S., Wang, P. S., Avorn, J., & Glynn, R. J. (2003). Improved Comorbidity Adjustment for Predicting Mortality in Medicare Populations. Health Services Research, 38(4), 1103-1120. doi:10.1111/1475-6773.00165Quan, H., Sundararajan, V., Halfon, P., Fong, A., Burnand, B., Luthi, J.-C., … Ghali, W. A. (2005). Coding Algorithms for Defining Comorbidities in ICD-9-CM and ICD-10 Administrative Data. Medical Care, 43(11), 1130-1139. doi:10.1097/01.mlr.0000182534.19832.83Sáez Silvestre C. Probabilistic methods for multi-source and temporal biomedical data quality assessment [Internet]. Thesis. Universitat Politècnica de València. 2016. doi: https://doi.org/10.4995/Thesis/10251/62188Amari S, Nagaoka H. Methods of Information Geometry [Internet]. Amer. Math. Soc. and Oxford Univ. Press. American Mathematical Society; 2000. Available: https://books.google.es/books?hl=es&lr=&id=vc2FWSo7wLUC&oi=fnd&pg=PR7&dq=Methods+of+Information+geometry&ots=4HmyCCY4PX&sig=2-dpCuwMQvEC1iREjxdfIX0yEls#v=onepage&q=MethodsofInformationgeometry&f=falseCsiszár, I., & Shields, P. C. (2004). Information Theory and Statistics: A Tutorial. Foundations and Trends™ in Communications and Information Theory, 1(4), 417-528. doi:10.1561/0100000004Lin, J. (1991). Divergence measures based on the Shannon entropy. IEEE Transactions on Information Theory, 37(1), 145-151. doi:10.1109/18.61115M.Cover T. Elements Of Information Theory Notes [Internet]. 2006. Available: http://books.google.fr/books?id=VWq5GG6ycxMC&printsec=frontcover&dq=intitle:Elements+of+Information+Theory&hl=&cd=1&source=gbs_api%5Cnpapers2://publication/uuid/BAF426F8-5A4F-44A4-8333-FA8187160D9BBrandes, U., & Pich, C. (s. f.). Eigensolver Methods for Progressive Multidimensional Scaling of Large Data. Lecture Notes in Computer Science, 42-53. doi:10.1007/978-3-540-70904-6_6Liaw, S. T., Rahimi, A., Ray, P., Taggart, J., Dennis, S., de Lusignan, S., … Talaei-Khoei, A. (2013). Towards an ontology for data quality in integrated chronic disease management: A realist review of the literature. International Journal of Medical Informatics, 82(1), 10-24. doi:10.1016/j.ijmedinf.2012.10.001Arts, D. G. T. (2002). Defining and Improving Data Quality in Medical Registries: A Literature Review, Case Study, and Generic Framework. Journal of the American Medical Informatics Association, 9(6), 600-611. doi:10.1197/jamia.m1087Bray, F., & Parkin, D. M. (2009). Evaluation of data quality in the cancer registry: Principles and methods. Part I: Comparability, validity and timeliness. European Journal of Cancer, 45(5), 747-755. doi:10.1016/j.ejca.2008.11.032Parkin, D. M., & Bray, F. (2009). Evaluation of data quality in the cancer registry: Principles and methods Part II. Completeness. European Journal of Cancer, 45(5), 756-764. doi:10.1016/j.ejca.2008.11.033Fernandez-Llatas, C., Ibanez-Sanchez, G., Celda, A., Mandingorra, J., Aparici-Tortajada, L., Martinez-Millana, A., … Traver, V. (2019). Analyzing Medical Emergency Processes with Process Mining: The Stroke Case. Lecture Notes in Business Information Processing, 214-225. doi:10.1007/978-3-030-11641-5_17Fernandez-Llatas, C., Lizondo, A., Monton, E., Benedi, J.-M., & Traver, V. (2015). Process Mining Methodology for Health Process Tracking Using Real-Time Indoor Location Systems. Sensors, 15(12), 29821-29840. doi:10.3390/s151229769Van der Aalst, W., Weijters, T., & Maruster, L. (2004). Workflow mining: discovering process models from event logs. IEEE Transactions on Knowledge and Data Engineering, 16(9), 1128-1142. doi:10.1109/tkde.2004.47Weijters AJMM, Van Der Aalst WMP, Alves De Medeiros AK. Process Mining with the HeuristicsMiner Algorithm [Internet]. Available: https://pdfs.semanticscholar.org/1cc3/d62e27365b8d7ed6ce93b41c193d0559d086.pdfShim, S. J., & Kumar, A. (2010). Simulation for emergency care process reengineering in hospitals. Business Process Management Journal, 16(5), 795-805. doi:10.1108/14637151011076476Svolba, G., & Bauer, P. (1999). Statistical Quality Control in Clinical Trials. Controlled Clinical Trials, 20(6), 519-530. doi:10.1016/s0197-2456(99)00029-xKahn, M. G., Raebel, M. A., Glanz, J. M., Riedlinger, K., & Steiner, J. F. (2012). A Pragmatic Framework for Single-site and Multisite Data Quality Assessment in Electronic Health Record-based Clinical Research. Medical Care, 50, S21-S29. doi:10.1097/mlr.0b013e318257dd67Batini, C., Cappiello, C., Francalanci, C., & Maurino, A. (2009). Methodologies for data quality assessment and improvement. ACM Computing Surveys, 41(3), 1-52. doi:10.1145/1541880.1541883Heinrich, B., Klier, M., & Kaiser, M. (2009). A Procedure to Develop Metrics for Currency and its Application in CRM. Journal of Data and Information Quality, 1(1), 1-28. doi:10.1145/1515693.1515697Sirgo, G., Esteban, F., Gómez, J., Moreno, G., Rodríguez, A., Blanch, L., … Bodí, M. (2018). Validation of the ICU-DaMa tool for automatically extracting variables for minimum dataset and quality indicators: The importance of data quality assessment. International Journal of Medical Informatics, 112, 166-172. doi:10.1016/j.ijmedinf.2018.02.007Hinton, G. E., & Salakhutdinov, R. R. (2006). Reducing the Dimensionality of Data with Neural Networks. Science, 313(5786), 504-507. doi:10.1126/science.1127647Kohn LT, Corrigan JM. To err is human: building a safer health system. A report of the Committee on Quality of Health Care in America. 2000. p. 287. National Academies Press

Total and high molecular weight adiponectin have similar utility for the identification of insulin resistance

<p>Abstract</p> <p>Background</p> <p>Insulin resistance (IR) and related metabolic disturbances are characterized by low levels of adiponectin. High molecular weight adiponectin (HMWA) is considered the active form of adiponectin and a better marker of IR than total adiponectin. The objective of this study is to compare the utility of total adiponectin, HMWA and the HMWA/total adiponectin index (S_Aindex) for the identification of IR and related metabolic conditions.</p> <p>Methods</p> <p>A cross-sectional analysis was performed in a group of ambulatory subjects, aged 20 to 70 years, in Mexico City. Areas under the receiver operator characteristic (ROC) curve for total, HMWA and the S_Aindex were plotted for the identification of metabolic disturbances. Sensitivity and specificity, positive and negative predictive values, and accuracy for the identification of IR were calculated.</p> <p>Results</p> <p>The study included 101 men and 168 women. The areas under the ROC curve for total and HMWA for the identification of IR (0.664 <it>vs</it>. 0.669, <it>P </it>= 0.74), obesity (0.592 <it>vs</it>. 0.610, <it>P </it>= 0.32), hypertriglyceridemia (0.661 <it>vs</it>. 0.671, <it>P </it>= 0.50) and hypoalphalipoproteinemia (0.624 <it>vs</it>. 0.633, <it>P </it>= 0.58) were similar. A total adiponectin level of 8.03 μg/ml was associated with a sensitivity of 57.6%, a specificity of 65.9%, a positive predictive value of 50.0%, a negative predictive value of 72.4%, and an accuracy of 62.7% for the diagnosis of IR. The corresponding figures for a HMWA value of 4.25 μg/dl were 59.6%, 67.1%, 51.8%, 73.7% and 64.2%.</p> <p>The area under the ROC curve of the S_Aindex for the identification of IR was 0.622 [95% CI 0.554-0.691], obesity 0.613 [95% CI 0.536-0.689], hypertriglyceridemia 0.616 [95% CI 0.549-0.683], and hypoalphalipoproteinemia 0.606 [95% CI 0.535-0.677].</p> <p>Conclusions</p> <p>Total adiponectin, HMWA and the S_Aindex had similar utility for the identification of IR and metabolic disturbances.</p

Lack of replication of interactions between polymorphisms in rheumatoid arthritis susceptibility: case-control study

Introduction: Approximately 100 loci have been definitively associated with rheumatoid arthritis (RA) susceptibility. However, they explain only a fraction of RA heritability. Interactions between polymorphisms could explain part of the remaining heritability. Multiple interactions have been reported, but only the shared epitope (SE) × protein tyrosine phosphatase nonreceptor type 22 (PTPN22) interaction has been replicated convincingly. Two recent studies deserve attention because of their quality, including their replication in a second sample collection. In one of them, researchers identified interactions between PTPN22 and seven single-nucleotide polymorphisms (SNPs). The other showed interactions between the SE and the null genotype of glutathione S-transferase Mu 1 (GSTM1) in the anti-cyclic citrullinated peptide-positive (anti-CCP+) patients. In the present study, we aimed to replicate association with RA susceptibility of interactions described in these two high-quality studies. Methods: A total of 1,744 patients with RA and 1,650 healthy controls of Spanish ancestry were studied. Polymorphisms were genotyped by single-base extension. SE genotypes of 736 patients were available from previous studies. Interaction analysis was done using multiple methods, including those originally reported and the most powerful methods described. Results: Genotypes of one of the SNPs (rs4695888) failed quality control tests. The call rate for the other eight polymorphisms was 99.9%. The frequencies of the polymorphisms were similar in RA patients and controls, except for PTPN22 SNP. None of the interactions between PTPN22 SNPs and the six SNPs that met quality control tests was replicated as a significant interaction term the originally reported finding or with any of the other methods. Nor was the interaction between GSTM1 and the SE replicated as a departure from additivity in anti-CCP+ patients or with any of the other methods. Conclusions: None of the interactions tested were replicated in spite of sufficient power and assessment with different assays. These negative results indicate that whether interactions are significant contributors to RA susceptibility remains unknown and that strict standards need to be applied to claim that an interaction exists

Analysis of TNFAIP3, a feedback inhibitor of nuclear factor-κB and the neighbor intergenic 6q23 region in rheumatoid arthritis susceptibility

Introduction Genome-wide association studies of rheumatoid arthritis (RA) have identified an association of the disease with a 6q23 region devoid of genes. TNFAIP3, an RA candidate gene, flanks this region, and polymorphisms in both the TNFAIP3 gene and the intergenic region are associated with systemic lupus erythematosus. We hypothesized that there is a similar association with RA, including polymorphisms in TNFAIP3 and the intergenic region. Methods To test this hypothesis, we selected tag-single nucleotide polymorphisms (SNPs) in both loci. They were analyzed in 1,651 patients with RA and 1,619 control individuals of Spanish ancestry. Results Weak evidence of association was found both in the 6q23 intergenic region and in the TNFAIP3 locus. The rs582757 SNP and a common haplotype in the TNFAIP3 locus exhibited association with RA. In the intergenic region, two SNPs were associated, namely rs609438 and rs13207033. The latter was only associated in patients with anti-citrullinated peptide antibodies. Overall, statistical association was best explained by the interdependent contribution of SNPs from the two loci TNFAIP3 and the 6q23 intergenic region. Conclusions Our data are consistent with the hypothesis that several RA genetic factors exist in the 6q23 region, including polymorphisms in the TNFAIP3 gene, like that previously described for systemic lupus erythematosus

Taxonomic variations in the gut microbiome of gout patients with and without tophi might have a functional impact on urate metabolism

Objective: To evaluate the taxonomic composition of the gut microbiome in gout patients with and without tophi formation, and predict bacterial functions that might have an impact on urate metabolism. Methods: Hypervariable V3–V4 regions of the bacterial 16S rRNA gene from fecal samples of gout patients with and without tophi (n=33 and n=25, respectively) were sequenced and compared to fecal samples from 53 healthy controls. We explored predictive functional profles using bioinformatics in order to identify diferences in taxonomy and metabolic pathways. Results: We identifed a microbiome characterized by the lowest richness and a higher abundance of Phascolarctobacterium, Bacteroides, Akkermansia, and Ruminococcus_gnavus_group genera in patients with gout without tophi when compared to controls. The Proteobacteria phylum and the Escherichia-Shigella genus were more abundant in patients with tophaceous gout than in controls. Fold change analysis detected nine genera enriched in healthy controls compared to gout groups (Bifdobacterium, Butyricicoccus, Oscillobacter, Ruminococcaceae_UCG_010, Lachnospiraceae_ND2007_group, Haemophilus, Ruminococcus_1, Clostridium_sensu_stricto_1, and Ruminococcaceae_ UGC_013). We found that the core microbiota of both gout groups shared Bacteroides caccae, Bacteroides stercoris ATCC 43183, and Bacteroides coprocola DSM 17136. These bacteria might perform functions linked to one-carbon metabo‑ lism, nucleotide binding, amino acid biosynthesis, and purine biosynthesis. Finally, we observed diferences in key bacterial enzymes involved in urate synthesis, degradation, and elimination. Conclusion: Our fndings revealed that taxonomic variations in the gut microbiome of gout patients with and with‑ out tophi might have a functional impact on urate metabolism. Keywords: Gout, Gut microbiota, Uric acid metabolis

Factors associated with postprandial lipemia and apolipoprotein A-V levels in individuals with familial combined hyperlipidemia

Peer reviewe

Validation Study Of Genetic Biomarkers Of Response To Tnf Inhibitors In Rheumatoid Arthritis

Genetic biomarkers are sought to personalize treatment of patients with rheumatoid arthritis (RA), given their variable response to TNF inhibitors (TNFi). However, no genetic biomaker is yet sufficiently validated. Here, we report a validation study of 18 previously reported genetic biomarkers, including 11 from GWAS of response to TNFi. The validation was attempted in 581 patients with RA that had not been treated with biologic antirheumatic drugs previously. Their response to TNFi was evaluated at 3, 6 and 12 months in two ways: change in the DAS28 measure of disease activity, and according to the EULAR criteria for response to antirheumatic drugs. Association of these parameters with the genotypes, obtained by PCR amplification followed by single-base extension, was tested with regression analysis. These analyses were adjusted for baseline DAS28, sex, and the specific TNFi. However, none of the proposed biomarkers was validated, as none showed association with response to TNFi in our study, even at the time of assessment and with the outcome that showed the most significant result in previous studies. These negative results are notable because this was the first independent validation study for 12 of the biomarkers, and because they indicate that prudence is needed in the interpretation of the proposed biomarkers of response to TNFi even when they are supported by very low p values. The results also emphasize the requirement of independent replication for validation, and the need to search protocols that could increase reproducibility of the biomarkers of response to TNFi

Replication Study Of Polymorphisms Associated With Response To Methotrexate In Patients With Rheumatoid Arthritis

About 70 genetic studies have already addressed the need of biomarkers to predict the response of patients with rheumatoid arthritis (RA) to methotrexate (MTX) treatment. However, no genetic biomarker has yet been sufficiently validated. Here, we aimed to replicate a selection of 25 SNPs in the largest collection of patients up to date, which consisted of 915 patients treated with MTX. The change in disease activity (measured as.DAS28) from baseline was considered the primary outcome. In addition, response according to widely used criteria (EULAR) was taken as secondary outcome. We considered consistency between outcomes, P values accounting for the number of SNPs, and independence from potential confounders for interpretation of the results. Only the rs1801394 SNP in MTRR fulfilled the high association standards. Its minor allele was associated with less improvement than the major allele according to.DAS28 (p = 0.0016), and EULAR response (p = 0.004), with independence of sex, age, baseline DAS28, smoking, seropositivity, concomitant corticosteroid use or previous treatments. In addition, previous evidence suggests the association of this SNP with response to MTX in another autoimmune disease, juvenile idiopathic arthritis, and with high intracellular folate levels, which could contribute to poor response

Evaluation of 12 GWAS-drawn SNPs as biomarkers of rheumatoid arthritis response to TNF inhibitors. A potential SNP association with response to etanercept

Research in rheumatoid arthritis (RA) is increasingly focused on the discovery of biomarkers that could enable personalized treatments. The genetic biomarkers associated with the response to TNF inhibitors (TNFi) are among the most studied. They include 12 SNPs exhibiting promising results in the three largest genome-wide association studies (GWAS). However, they still require further validation. With this aim, we assessed their association with response to TNFi in a replication study, and a meta-analysis summarizing all nonredundant data. The replication involved 755 patients with RA that were treated for the first time with a biologic drug, which was either infliximab (n = 397), etanercept (n = 155) or adalimumab (n = 203). Their DNA samples were successfully genotyped with a single-base extension multiplex method. Lamentably, none of the 12 SNPs was associated with response to the TNFi in the replication study (p > 0.05). However, a drug-stratified exploratory analysis revealed a significant association of the NUBPL rs2378945 SNP with a poor response to etanercept (B = -0.50, 95% CI = -0.82, -0.17, p = 0.003). In addition, the metaanalysis reinforced the previous association of three SNPs: rs2378945, rs12142623, and rs4651370. In contrast, five of the remaining SNPs were less associated than before, and the other four SNPs were no longer associated with the response to treatment. In summary, our results highlight the complexity of the pharmacogenetics of TNFi in RA showing that it could involve a drug-specific component and clarifying the status of the 12 GWAS-drawn SNPsThis work was supported by the Instituto de Salud Carlos III (ISCIII, Spain) through grants PI14/01651, PI17/01606 and RD16/0012/0014 to AG and PI12/01909 to JJG-R. These grants are partially financed by the European Regional Development Fund of the EU (FEDER

Lack of validation of genetic variants associated with anti-tumor necrosis factor therapy response in rheumatoid arthritis: a genome-wide association study replication and meta-analysis

Introduction: In this study, our aim was to elucidate the role of four polymorphisms identified in a prior large genome-wide association study (GWAS) in which the investigators analyzed the responses of patients with rheumatoid arthritis (RA) to treatment with tumor necrosis factor inhibitors (TNFi). The authors of that study reported that the four genetic variants were significantly associated. However, none of the associations reached GWAS significance, and two subsequent studies failed to replicate these associations. Methods: The four polymorphisms (rs12081765, rs1532269, rs17301249 and rs7305646) were genotyped in a total of 634 TNFi-treated RA patients of Spanish Caucasian origin. Four outcomes were evaluated: changes in the Disease Activity Score in 28 joints (DAS28) after 6 and 12 months of treatment and classification according to the European League Against Rheumatism (EULAR) response criteria at the same time points. Association with DAS28 changes was assessed by linear regression using an additive genetic model. Contingency tables of genotype and allele frequencies between EULAR responder and nonresponder patients were compared. In addition, we combined our data with those of previously reported studies in a meta-analysis including 2,998 RA patients. Results: None of the four genetic variants showed an association with response to TNFi in any of the four outcomes analyzed in our Spanish patients. In addition, only rs1532269 yielded a suggestive association (P = 0.0033) with the response to TNFi when available data from previous studies were combined in the meta-analysis. Conclusion: Our data suggest that the rs12081765, rs1532269, rs17301249 and rs7305646 genetic variants do not have a role as genetic predictors of TNFi treatment outcomes