The susceptibility to develop coeliac disease (CD) has a strong genetic component, which is not entirely explained by HLA associations. Two previous genome wide linkage studies have been performed to identify additional loci outside this region. These studies both used a sib-pair design and produced conflicting results.Our aim is to identify non-MHC genetic loci contributing to coeliac disease using a family based linkage study. We performed a genome wide search in 16 highly informative multiply affected pedigrees using 400 microsatellite markers with an average spacing of 10 cM. Linkage analysis was performed using lod score and model free methods.We identified two new potential susceptibility loci with lod scores of 1n9, at 10q23n1, and 16q23n3. Significant, but lower lod scores were found for 6q14 (1n2), 11p11 (1n5), and 19q13n4 (0n9), areas implicated in a previous genome wide study. Lod scores of 0n9 were obtained for both D7S507, which lies 1 cM from the γT-cell receptor gene, and for D2S364, which lies 12 cM from the CTLA4 gene. Coeliac disease (CD) is a gluten sensitive enteropathy in which dietary exposure to wheat, barley, rye, and possibly oats results in small bowel mucosal atrophy and consequent malabsorption. There is a strong genetic component to disease development as demonstrated by a disease concordance among monozygotic twins of 70-100 % (Polanco et al. 1981 ;Salazar de Souza et al. 1987), and a 30-50 % concordance in Correspondence : Prof. PJ Ciclitira,

We studied prospectively the relationship between serum Upids and Dupuytren's disease of the band in 85 patients, 65 men and 20 women. The Dupuytren patients bad significantly higher fasting serum cholesterol and triglyceride levels than did the controls (p < 0.001). The raised levels of serum Upids appeared to be associated with the pathogenesis of Dupuytren's disease, and this may help to explain the high incidence of Dupuytren's disease in alcobo6c, diabetic and epileptic patients, since these conditions are also assoc iated with raised serum Upid levels.

Background Novel consumer and lifestyle data, such as those collected by supermarket loyalty cards or mobile phone exercise tracking apps, offer numerous benefits for researchers seeking to understand diet- and exercise-related risk factors for diseases. However, limited research has addressed public attitudes toward linking these data with individual health records for research purposes. Data linkage, combining data from multiple sources, provides the opportunity to enhance preexisting data sets to gain new insights. Objective The aim of this study is to identify key barriers to data linkage and recommend safeguards and procedures that would encourage individuals to share such data for potential future research. Methods The LifeInfo Survey consulted the public on their attitudes toward sharing consumer and lifestyle data for research purposes. Where barriers to data sharing existed, participants provided unstructured survey responses detailing what would make them more likely to share data for linkage with their health records in the future. The topic modeling technique latent Dirichlet allocation was used to analyze these textual responses to uncover common thematic topics within the texts. Results Participants provided responses related to sharing their store loyalty card data (n=2338) and health and fitness app data (n=1531). Key barriers to data sharing identified through topic modeling included data safety and security, personal privacy, requirements of further information, fear of data being accessed by others, problems with data accuracy, not understanding the reason for data linkage, and not using services that produce these data. We provide recommendations for addressing these issues to establish the best practice for future researchers interested in using these data. Conclusions This study formulates a large-scale consultation of public attitudes toward this kind of data linkage, which is an important first step in understanding and addressing barriers to participation in research using novel consumer and lifestyle data.

The UK government plans to limit price‐based and location‐based promotions for products high in saturated fat, salt and sugars. The 2004/2005 UK Nutrient Profiling Model (NPM) is the proposed legislative basis, but may be superseded by the draft 2018 NPM. This study develops an algorithm to apply both NPMs to a large food composition database (FCDB), and assesses implementation challenges. UK NPMs were applied algorithmically to the myfood24 FCDB, representing ~45 000 retail products. Pass rates – indicating free or restricted promotions – and micronutrient compositions were compared. Challenges were assessed, and recommendations addressed the legislation’s public consultation questions. For products in scope (75% of total), 6% fewer passed the 2018 NPM (36%, P < 0.001) compared with the 2004/2005 NPM (42%). Beverages showed the greatest reduction in pass rate (75%). Under both models, micronutrient contents (per 100 g of product) were generally lower for products that passed; except folate, vitamin C and vitamin D were no different for passed and failed products. Compared with products passing the 2004/2005 NPM, products passing the 2018 NPM on average had marginally higher amounts of iron (0.05 mg, 95% CI: 0.02, 0.08, P < 0.001) and magnesium (1.00 mg, 95% CI: 0.00, 1.17, P = 0.029), but marginally lower levels of calcium (−0.42 mg, 95% CI: −2.00, −0.40, P = 0.025). Missing ingredient information and heterogeneous product categories were challenges for both NPMs. Free sugars calculation further complicated 2018 NPM application. To balance feasibility and public health benefit, the proposed legislative basis may not be appropriate.

Poor diet is a leading cause of death in the United Kingdom (UK) and around the world. Methods to collect quality dietary information at scale for population research are time consuming, expensive and biased. Novel data sources offer potential to overcome these challenges and better understand population dietary patterns. In this research we will use 12 months of supermarket sales transaction data, from 2016, for primary shoppers residing in the Yorkshire and Humber region of the UK (n = 299,260), to identify dietary patterns and profile these according to their nutrient composition and the sociodemographic characteristics of the consumer purchasing with these patterns. Results identified seven dietary purchase patterns that we named: Fruity; Meat alternatives; Carnivores; Hydrators; Afternoon tea; Beer and wine lovers; and Sweet tooth. On average the daily energy intake of loyalty card holders -who may buy as an individual or for a household- is less than the adult reference intake, but this varies according to dietary purchase pattern. In general loyalty card holders meet the recommended salt intake, do not purchase enough carbohydrates, and purchase too much fat and protein, but not enough fibre. The dietary purchase pattern containing the highest amount of fibre (as an indicator of healthiness) is bought by the least deprived customers and the pattern with lowest fibre by the most deprived. In conclusion, supermarket sales data offer significant potential for understanding population dietary patterns.