BackgroundNot all obese subjects have an adverse metabolic profile predisposing them to developing type 2 diabetes or cardiovascular disease. The BioSHaRE-EU Healthy Obese Project aims to gain insights into the consequences of (healthy) obesity using data on risk factors and phenotypes across several large-scale cohort studies. Aim of this study was to describe the prevalence of obesity, metabolic syndrome (MetS) and metabolically healthy obesity (MHO) in ten participating studies.MethodsTen different cohorts in seven countries were combined, using data transformed into a harmonized format. All participants were of European origin, with age 18–80 years. They had participated in a clinical examination for anthropometric and blood pressure measurements. Blood samples had been drawn for analysis of lipids and glucose. Presence of MetS was assessed in those with obesity (BMI ≥ 30 kg/m2) based on the 2001 NCEP ATP III criteria, as well as an adapted set of less strict criteria. MHO was defined as obesity, having none of the MetS components, and no previous diagnosis of cardiovascular disease.ResultsData for 163,517 individuals were available; 17% were obese (11,465 men and 16,612 women). The prevalence of obesity varied from 11.6% in the Italian CHRIS cohort to 26.3% in the German KORA cohort. The age-standardized percentage of obese subjects with MetS ranged in women from 24% in CHRIS to 65% in the Finnish Health2000 cohort, and in men from 43% in CHRIS to 78% in the Finnish DILGOM cohort, with elevated blood pressure the most frequently occurring factor contributing to the prevalence of the metabolic syndrome. The age-standardized prevalence of MHO varied in women from 7% in Health2000 to 28% in NCDS, and in men from 2% in DILGOM to 19% in CHRIS. MHO was more prevalent in women than in men, and decreased with age in both sexes.ConclusionsThrough a rigorous harmonization process, the BioSHaRE-EU consortium was able to compare key characteristics defining the metabolically healthy obese phenotype across ten cohort studies. There is considerable variability in the prevalence of healthy obesity across the different European populations studied, even when unified criteria were used to classify this phenotype.

Background: Research in modern biomedicine and social science requires sample sizes so large that they can often only be achieved through a pooled co-analysis of data from several studies. But the pooling of information from individuals in a central database that may be queried by researchers raises important ethico-legal questions and can be controversial. In the UK this has been highlighted by recent debate and controversy relating to the UK’s proposed ‘care.data’ initiative, and these issues reflect important societal and professional concerns about privacy, confidentiality and intellectual property. DataSHIELD provides a novel technological solution that can circumvent some of the most basic challenges in facilitating the access of researchers and other healthcare professionals to individual-level data.Methods: Commands are sent from a central analysis computer (AC) to several data computers (DCs) storing the data to be co-analysed. The data sets are analysed simultaneously but in parallel. The separate parallelized analyses are linked by non-disclosive summary statistics and commands transmitted back and forth between the DCs and the AC. This paper describes the technical implementation of DataSHIELD using a modified R statistical environment linked to an Opal database deployed behind the computer firewall of each DC. Analysis is controlled through a standard R environment at the AC.Results: Based on this Opal/R implementation, DataSHIELD is currently used by the Healthy Obese Project and the Environmental Core Project (BioSHaRE-EU) for the federated analysis of 10 data sets across eight European countries, and this illustrates the opportunities and challenges presented by the DataSHIELD approach.Conclusions: DataSHIELD facilitates important research in settings where: (i) a co-analysis of individual-level data from several studies is scientifically necessary but governance restrictions prohibit the release or sharing of some of the required data, and/or render data access unacceptably slow; (ii) a research group (e.g. in a developing nation) is particularly vulnerable to loss of intellectual property—the researchers want to fully share the information held in their data with national and international collaborators, but do not wish to hand over the physical data themselves; and (iii) a data set is to be included in an individual-level co-analysis but the physical size of the data precludes direct transfer to a new site for analysis.

Rationale:The relationship between airway inflammation and obesity in severe asthma is poorly understood. Objectives: We sought to determine the relationship between sputum mediator profiles and the distribution of eosinophilic inflammation and obesity in people with severe asthma. Methods: Clinical parameters and eight mediators in sputum were assessed in 131 subjects with severe asthma from a single center categorized into lean, overweight, and obese groups defined by their body mass index. In an independent group of people with severe asthma (n ¼ 45) and healthy control subjects (n ¼ 19) eosinophilic inflammation was enumerated in bronchial submucosa, blood, and sputum and related to their body mass index. Measurements and Main Results: Sputum IL-5 geometric mean (95% confidence interval) (pg/ml) was elevated in the obese (1.8 [1.2-2.6]) compared with overweight (1.1 [0.8-1.3]; P ¼ 0.025) and lean (0.9 [0.6-1.2]; P ¼ 0.018) subjects with asthma and was correlated with body mass index (r ¼ 0.29; P , 0.001). There was no relationship among body mass index, the sputum cell count, or other sputum mediators. In the bronchoscopy group the submucosal eosinophil number in the subjects with asthma was correlated with body mass index (Spearman rank correlation, r s ¼ 0.38; P ¼ 0.013) and the median (interquartile range) number of submucosal eosinophils was increased in obese (19.4 [11.8-31.2]) (cells per square millimeter) versus lean subjects (8.2 [5.4-14.6]) (P ¼ 0.006). There was no significant association between sputum or peripheral blood eosinophil counts and body mass index. Conclusions: Sputum IL-5 and submucosal eosinophils, but not sputum eosinophils, are elevated in obese people with severe asthma. Whether specific antieosinophilic therapy is beneficial, or improved diet and lifestyle in obese asthma has antiinflammatory effects beyond weight reduction, requires further study.Keywords: asthma; obesity; cytokines; phenotypes; eosinophil Asthma is a common, complex inflammatory disorder affecting about 5% of adults in the general population, of which approximately 5-10% suffer from severe disease (1, 2). Severe disease is often associated with comorbidities, such as obesity, and is particularly important because these patients suffer from substantial morbidity and consume a disproportionately high amount of the overall healthcare resources spent on asthma management (3-6). Asthma, and in particular severe asthma, is a heterogeneous disease as highlighted by the different phenotypes identified using cluster analysis of clinical data (7-10) and cytokine profiles of airway samples (11-13). The key benefit of dividing a multidimensional disease, such as asthma, into distinct phenotypes is expected to be more effective treatment targeting. This has been effectively shown with the success of eosinophilic airway inflammationdirected corticosteroid (14-16) and anti-IL-5 treatment (17-19) to prevent asthma exacerbations in eosinophilic subjects with asthma. The association of obesity and asthma has been evident Support...

SummaryBackground Immunological biomarkers are the key to the diagnosis of allergic bronchopulmonary aspergillosis (ABPA) and fungal sensitisation, but how these relate to clinically relevant outcomes is unclear. Objectives To assess how fungal immunological biomarkers are related to fixed airflow obstruction and radiological abnormalities in moderate to severe asthma. Methods Cross-sectional study of 431 asthmatics. Inflammatory biomarkers, lung function and an IgE fungal panel to colonising filamentous fungi, yeasts and fungal aeroallergens were measured. CT scans were scored for the presence of radiological abnormalities. Factor analysis informed the variables used in a k-means cluster analysis. Fixed airflow obstruction and radiological abnormalities were then mapped to these immunological variables in the cluster analysis. Results 329 (76.3%) subjects were sensitised to ≥ 1 fungi. Sensitisation to Aspergillus fumigatus and/or Penicillium chrysogenum was associated with a lower post-bronchodilator FEV 1 compared with those not sensitised to fungi ((73.0 (95% CI 70.2-76) vs. 82.8 (95% CI 78.5-87.2)% predicted, P < 0.001), independent of atopic status (P = 0.005)), and an increased frequency of bronchiectasis (54.5%, P < 0.001), tree-in-bud (18.7%, P < 0.001) and collapse/consolidation (37.5%, P = 0.002). Cluster analysis identified three clusters: (i) hypereosinophilic (n = 71, 16.5%), (ii) high immunological biomarker load and high frequency of radiological abnormalities (n = 34, 7.9%) and (iii) low levels of fungal immunological biomarkers (n = 326, 75.6%). Conclusions and Clinical Relevance IgE sensitisation to thermotolerant filamentous fungi, in particular A. fumigatus but not total IgE, is associated with fixed airflow obstruction and a number of radiological abnormalities in moderate to severe asthma. All patients with IgE sensitisation to A. fumigatus are at risk of lung damage irrespective of whether they meet the criteria for ABPA.

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Mental health lived experience narratives are first-person accounts of people with experience of mental health problems. They have been published in journals, books and online, and used in healthcare interventions and anti-stigma campaigns. There are concerns about their potential misuse. A four-language systematic review was conducted of published literature characterizing uses and misuses of mental health lived experience narratives within healthcare and community settings. 6531 documents in four languages (English, Danish, Swedish, Norwegian) were screened and 78 documents from 11 countries were included. Twenty-seven uses were identified in five categories: political, societal, community, service level and individual. Eleven misuses were found, categorized as relating to the narrative (narratives may be co-opted, narratives may be used against the author, narratives may be used for different purpose than authorial intent, narratives may be reinterpreted by others, narratives may become patient porn, narratives may lack diversity), relating to the narrator (narrator may be subject to unethical editing practises, narrator may be subject to coercion, narrator may be harmed) and relating to the audience (audience may be triggered, audience may misunderstand). Four open questions were identified: does including a researcher’s personal mental health narrative reduce the credibility of their research?: should the confidentiality of narrators be protected?; who should profit from narratives?; how reliable are narratives as evidence?)

Background and objective People with psychosis report experiences of highly traumatic events. Positive change or post-traumatic growth (PTG) can occur as a result of traumatic experiences. Yet there is limited attention on PTG in psychosis, possibly due to the negative impact of psychotic symptoms on functioning and quality of life. The aim of this review was to identify significant correlates and mediators of PTG in psychosis, and to develop a conceptual framework synthesising facilitators of PTG in psychosis. Method Ten electronic databases were searched in seven languages, and five journals and grey literature were searched in English. Quantitative studies were eligible if examining correlates, mediators, or the temporal relationship between PTG and one or more variables. Qualitative studies were eligible if describing PTG arising from experiences of psychosis. Findings from quantitative papers were grouped by analysis method, with significant correlates, mediators, and temporal relationships descriptively reported upon. Narrative synthesis was conducted on findings in qualitative papers. Results Thirty-seven papers were included. Significant correlates and mediators of PTG were identified. Mediators of PTG in psychosis included meaning in life, coping self-efficacy, core beliefs, and self-reported recovery. No studies describing the temporal relationship between PTG and psychosis were identified. The narrative synthesis identified seven facilitators of PTG in psychosis: Personal identity and strength, Receiving support, Opportunities and possibilities, Strategies for coping, Perspective shift, Emotional experience, and Relationships, giving the acronym PROSPER. Conclusions Individuals with psychosis can be supported to grow from traumatic experiences. Clinicians can support PTG through the provision of trauma-informed care that supports positively valued identity changes. For researchers, the findings provide an evidence-based theoretical framework for conceptualising PTG, which can be validated through longitudinal cohort studies and underpin the development of new clinical interventions.

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