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To analyze the epidemiology, clinical features and costs of hospitalized patients with gout during the last decade in Spain. Retrospective observational study based on data from the Minimum Basic Data Set (MBDS) from the Spanish National Health Service database. Patients ≥ 18 years with any gout diagnosis at discharge who had been admitted to public or private hospitals between 2005 and 2015 were included. Patients were divided in two periods: p1 (2005–2010) and p2 (2011–2015) to compare the number of hospitalizations, mean costs and mortality rates. Data from 192,037 patients with gout was analyzed. There was an increase in the number of hospitalized patients with gout (p

The presence of rheumatoid factor (RF) or anti-cyclic citrullinated peptide (anti-CCP) autoantibodies contributes to the current rheumatoid arthritis (RA) classification criteria. These criteria involve stratification on antibody levels, which limits reproducibility, and underperform in the RA patients without RF and anti-CCP. Here, we have explored if two anti-acetylated peptide antibodies (AAPA), anti-acetylated lysine (AcLys) and anti-acetylated ornithine (AcOrn), could improve the performance of the current criteria. The analysis was done in 1062 prospectively-followed early arthritis (EA) patients. The anti-AcOrn were more informative than the anti-AcLys, the conventional RA antibodies and the anti-carbamylated protein antibodies. The anti-AcOrn produced a classification that did not require antibody levels and showed improved specificity (77.6% vs. 72.6%, p = 0.003) and accuracy (79.0% vs. 75.8%, p = 0.002) over the current criteria. These improvements were obtained with a scoring system that values concordance between anti-AcOrn, RF and anti-CCP. No significant gain was obtained in sensitivity (80.2% vs. 78.8%, p = 0.25) or in improving the classification of the RA patients lacking RF and anti-CCP, although the anti-AcOrn ranked first among the analysed new antibodies. Therefore, the anti-AcOrn antibodies could contribute to the improvement of RA classification criteria by exploiting antibody concordance., This work was supported by the Instituto de Salud Carlos III (Spain) through grants [RD16/0012/0014 and PI17/01606 to AG; RD16/0012/0012 to AB; PI14/00442 and RD16/0012/0011 to IG-A]. These grants are partially financed by the European Regional Development Fund of the EU (FEDER). LRM was supported by Xunta de Galicia (Spain) through a Gain pre-doctoral fellowship. CR was supported by Ministerio de Educacion Cultura y Deporte (Spain) through a FPU pre-doctoral fellowship [FPU15/03434].

The initial management of rheumatoid arthritis (RA) has a high impact on disease prognosis. Therefore, we need to select the most appropriate treatment as soon as possible. This goal requires biomarkers of disease severity and prognosis. One such biomarker may be the presence of anti-carbamylated protein antibodies (ACarPA) because it is associated with adverse long term outcomes as radiographic damage and mortality. Here, we have assessed the ACarPA as short-term prognostic biomarkers. The study was conducted in 978 prospective early arthritis (EA) patients that were followed for two years. Our results show the association of ACarPA with increased levels of all the disease activity measures in the first visit after arthritis onset. However, the associations were more significant with the high levels in local measures of inflammation and physician assessment than with the increases in systemic inflammation and patient-reported outcomes. More notably, disease activity was persistently increased in the ACarPA positive patients during the two years of follow-up. These differences were significant even after accounting for the presence of other RA autoantibodies. Therefore, the ACarPA could be considered short-term prognostic biomarkers of increased disease activity in the EA patients.

The COVID-19 pandemic was an inevitable outcome of a globalized world in which a highly infective disease is able to reach every country in a matter of weeks. While lockdowns and strong mobility restrictions have proven to be efficient to contain the exponential transmission of the virus, its pervasiveness has made it impossible for economies to maintain this kind of measures in time. Understanding precisely how the spread of the virus occurs from a territorial perspective is crucial not only to prevent further infections but also to help with policy design regarding human mobility. From the large spatial differences in the behavior of the virus spread we can unveil which areas have been more vulnerable to it and why, and with this information try to assess the risk that each community has to suffer a future outbreak of infection. In this work we have analyzed the geographical distribution of the cumulative incidence during the first wave of the pandemic in the region of Galicia (north western part of Spain), and developed a mathematical approach that assigns a risk factor for each of the different municipalities that compose the region. This risk factor is independent of the actual evolution of the pandemic and incorporates geographic and demographic information. The comparison with empirical information from the first pandemic wave demonstrates the validity of the method. Our results can potentially be used to design appropriate preventive policies that help to contain the virus.

The major environmental risk factor for rheumatoid arthritis (RA) is smoking, which according to a widely accepted model induces protein citrullination in the lungs, triggering the production of anti-citrullinated protein antibodies (ACPA) and RA development. Nevertheless, some research findings do not fit this model. Therefore, we obtained six independent cohorts with 2253 RA patients for a detailed analysis of the association between smoking and RA autoantibodies. Our results showed a predominant association of smoking with the concurrent presence of the three antibodies: rheumatoid factor (RF), ACPA and anti-carbamylated protein antibodies (ACarPA) (3 Ab vs. 0 Ab: OR = 1.99, p = 2.5 × 10–8). Meta-analysis with previous data (4491 patients) confirmed the predominant association with the concurrent presence of the three antibodies (3 Ab vs. 0 Ab: OR = 2.00, p = 4.4 ×10–16) and revealed that smoking was exclusively associated with the presence of RF in patients with one or two antibodies (RF+1+2vs. RF−0+1+2: OR = 1.32, p = 0.0002). In contrast, no specific association with ACPA or ACarPA was found. Therefore, these results showed the need to understand how smoking favors the concordance of RA specific antibodies and RF triggering, perhaps involving smoking-induced epitope spreading and other hypothesized mechanisms.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

The objective of the present study was to determine cytokine thresholds derived from predictive models for the diagnosis of chronic periodontitis, differentiating by smoking status. Seventy-five periodontally healthy controls and 75 subjects affected by chronic periodontitis were recruited. Sixteen mediators were measured in gingival crevicular fluid (GCF) using multiplexed bead immunoassays. The models were obtained using binary logistic regression, distinguishing between non-smokers and smokers. The area under the curve (AUC) and numerous classification measures were obtained. Model curves were constructed graphically and the cytokine thresholds calculated for the values of maximum accuracy (ACC). There were three cytokine-based models and three cytokine ratio-based models, which presented with a bias-corrected AUC > 0.91 and > 0.83, respectively. These models were (cytokine thresholds in pg/ml for the median ACC using bootstrapping for smokers and non-smokers): IL1alpha (46099 and 65644); IL1beta (4732 and 5827); IL17A (11.03 and 17.13); IL1alpha/IL2 (4210 and 7118); IL1beta/IL2 (260 and 628); and IL17A/IL2 (0.810 and 1.919). IL1alpha, IL1beta and IL17A, and their ratios with IL2, are excellent diagnostic biomarkers in GCF for distinguishing periodontitis patients from periodontally healthy individuals. Cytokine thresholds in GCF with diagnostic potential are defined, showing that smokers have lower threshold values than non-smokers. This work was supported by the Instituto de Salud Carlos III (General Division of Evaluation and Research Promotion, Madrid, Spain) and co-financed by FEDER (“A way of making Europe”) under Grant ISCIII/PI17/01722, and the Consellería de Cultura, Educación e Ordenación Universitaria da Xunta de Galicia (Spain) under Grant ED431B 2017/029 and A. Regueira-Iglesias support ED481A-2017 SI

Although a distinct cytokine profile has been described in the gingival crevicular fluid (GCF) of patients with chronic periodontitis, there is no evidence of GCF cytokine-based predictive models being used to diagnose the disease. Our objectives were: to obtain GCF cytokine-based predictive models; and develop nomograms derived from them. A sample of 150 participants was recruited: 75 periodontally healthy controls and 75 subjects affected by chronic periodontitis. Sixteen mediators were measured in GCF using the Luminex 100™ instrument: GMCSF, IFNgamma, IL1alpha, IL1beta, IL2, IL3, IL4, IL5, IL6, IL10, IL12p40, IL12p70, IL13, IL17A, IL17F and TNFalpha. Cytokine-based models were obtained using multivariate binary logistic regression. Models were selected for their ability to predict chronic periodontitis, considering the different role of the cytokines involved in the inflammatory process. The outstanding predictive accuracy of the resulting smoking-adjusted models showed that IL1alpha, IL1beta and IL17A in GCF are very good biomarkers for distinguishing patients with chronic periodontitis from periodontally healthy individuals. The predictive ability of these pro-inflammatory cytokines was increased by incorporating IFN gamma and IL10. The nomograms revealed the amount of periodontitis-associated imbalances between these cytokines with pro-inflammatory and anti-inflammatory effects in terms of a particular probability of having chronic periodontitis This work was supported by project EM2014/025 from Regional Ministry of Culture, Education and University (regional government of Galicia, Spain), which is integrated in Regional Plan of Research, Innovation and Development 2011–2015 SI

Classification of patients with rheumatoid arthritis (RA) as quickly as possible improves their prognosis. This reason motivates specially dedicated early arthritis (EA) clinics. Here, we have used 1062 EA patients with two years of follow-up to explore the value of anti-carbamylated protein (anti-CarP) antibodies, a new type of RA specific autoantibodies, for classification. Specifically, we aimed to determine whether the addition of anti-CarP antibodies to IgM rheumatoid factor (RF) and anti-cyclic citrullinated peptide (anti-CCP) antibodies, which are helpful in RA classification, improves it or not. Our analysis showed that incorporation of the anti-CarP antibodies to combinations of the other two antibodies (all joint by the OR Boolean operator) produces a modest increase in sensitivity (2.2% higher), at the cost of decreased specificity (8.1% lower). The cost-benefit ratio was more favorable in the patients lacking the other autoantibodies. However, it did not improve by considering different titer levels of the anti-CarP antibodies, or after exhaustively exploring other antibody combinations. Therefore, the place in RA classification of these antibodies is questionable in the context of current treatments and biomarkers. This conclusion does not exclude their potential value for stratifying patients in joint damage, disease activity, disability, or mortality categories.