Integrating local knowledge into a national programme: Evidence from a community-based diabetes prevention education programme

Type 2 diabetes prevention is a major priority for healthcare services and public health. This study aimed to evaluate how a local authority in England piloted a diabetes prevention programme. The South Gloucestershire Diabetes Prevention (Pilot) Programme (SGDPP) comprised a group health education course over six weeks with subsequent support provision up to six months post-enrolment. Of the 300 patients invited onto the programme, 32% enrolled and 29% completed the full six-month programme. There was an attendance rate of 84% throughout group sessions and at a six-month follow-up. There were significant improvements across most measures at six months, including a 4 kg mean weight loss and a 3.45 mmol/mol mean HbA1c reduction. Clear goals, high quality organization and personal qualities of educators were identified as central for the programme’s success. The unit costs were similar to pilots of other healthy lifestyle programmes. The evaluation found evidence of reduced type 2 diabetes risk markers, positive impacts for dietary and physical activity, and potential cost-effectiveness for this format of group-based diabetes prevention intervention. Feedback from multiple stakeholders provided insight on how to successfully embed and scale-up delivery of diabetes prevention work. This evidence enables the integration of learning in local service delivery and provides a basis to support development of the national diabetes prevention programme

Will fish be part of future healthy and sustainable diets?

First paragraph: The adoption of healthy and sustainable diets and food systems is recognised as a means to address the global challenge of malnutrition and poor-quality diets, and unprecedented environmental damage from food production and consumption.1 Sustainable diets have also been recognised as a key strategy to achieve the Sustainable Development Goals. Reducing consumption of animal-source foods is frequently presented as key to improving the sustainability of food systems.2 Fish and seafood can have a lower environmental impact and in many cases are considered more efficient than terrestrial animal production (albeit with wide variation) depending on the type of production or capture method,3 yet remain largely absent, or insufficiently articulated in the sustainable diets literature, rendering their future role in healthy diets unclear.4 This absence of specific consideration of fish and seafood extends to food security literature, in which the role of fish remains under-recognised and undervalued.5 Legitimate concerns exist regarding the environmental sustainability of fisheries and aquaculture systems; however, we argue that an overemphasis on the so-called doomsday portrayal of fish—which often dominates literature and the broader media—masks the myriad of positive contributions of the fisheries sector to nutrition and sustainability and limits its scope in contributing to healthy and sustainable food systems

Flavonoid Biosynthesis Genes Putatively Identified in the Aromatic Plant Polygonum minus via Expressed Sequences Tag (EST) Analysis

P. minus is an aromatic plant, the leaf of which is widely used as a food additive and in the perfume industry. The leaf also accumulates secondary metabolites that act as active ingredients such as flavonoid. Due to limited genomic and transcriptomic data, the biosynthetic pathway of flavonoids is currently unclear. Identification of candidate genes involved in the flavonoid biosynthetic pathway will significantly contribute to understanding the biosynthesis of active compounds. We have constructed a standard cDNA library from P. minus leaves, and two normalized full-length enriched cDNA libraries were constructed from stem and root organs in order to create a gene resource for the biosynthesis of secondary metabolites, especially flavonoid biosynthesis. Thus, large-scale sequencing of P. minus cDNA libraries identified 4196 expressed sequences tags (ESTs) which were deposited in dbEST in the National Center of Biotechnology Information (NCBI). From the three constructed cDNA libraries, 11 ESTs encoding seven genes were mapped to the flavonoid biosynthetic pathway. Finally, three flavonoid biosynthetic pathway-related ESTs chalcone synthase, CHS (JG745304), flavonol synthase, FLS (JG705819) and leucoanthocyanidin dioxygenase, LDOX (JG745247) were selected for further examination by quantitative RT-PCR (qRT-PCR) in different P. minus organs. Expression was detected in leaf, stem and root. Gene expression studies have been initiated in order to better understand the underlying physiological processes

Appropriateness of treatment recommendations for PPI in hospital discharge letters

International audienc

Common variants at theCHEK2gene locus and risk of epithelial ovarian cancer

Genome-wide association studies have identified 20 genomic regions associated with risk of epithelial ovarian cancer (EOC), but many additional risk variants may exist. Here, we evaluated associations between common genetic variants [single nucleotide polymorphisms (SNPs) and indels] in DNA repair genes and EOC risk. We genotyped 2896 common variants at 143 gene loci in DNA samples from 15 397 patients with invasive EOC and controls. We found evidence of associations with EOC risk for variants at FANCA, EXO1, E2F4, E2F2, CREB5 and CHEK2 genes (P ≤ 0.001). The strongest risk association was for CHEK2 SNP rs17507066 with serous EOC (P = 4.74 x 10(-7)). Additional genotyping and imputation of genotypes from the 1000 genomes project identified a slightly more significant association for CHEK2 SNP rs6005807 (r (2) with rs17507066 = 0.84, odds ratio (OR) 1.17, 95% CI 1.11-1.24, P = 1.1×10(-7)). We identified 293 variants in the region with likelihood ratios of less than 1:100 for representing the causal variant. Functional annotation identified 25 candidate SNPs that alter transcription factor binding sites within regulatory elements active in EOC precursor tissues. In The Cancer Genome Atlas dataset, CHEK2 gene expression was significantly higher in primary EOCs compared to normal fallopian tube tissues (P = 3.72×10(-8)). We also identified an association between genotypes of the candidate causal SNP rs12166475 (r (2) = 0.99 with rs6005807) and CHEK2 expression (P = 2.70×10(-8)). These data suggest that common variants at 22q12.1 are associated with risk of serous EOC and CHEK2 as a plausible target susceptibility gene.Other Research Uni

Variants in genes encoding small GTPases and association with epithelial ovarian cancer susceptibility

Epithelial ovarian cancer (EOC) is the fifth leading cause of cancer mortality in American women. Normal ovarian physiology is intricately connected to small GTP binding proteins of the Ras superfamily (Ras, Rho, Rab, Arf, and Ran) which govern processes such as signal transduction, cell proliferation, cell motility, and vesicle transport. We hypothesized that common germline variation in genes encoding small GTPases is associated with EOC risk. We investigated 322 variants in 88 small GTPase genes in germline DNA of 18,736 EOC patients and 26,138 controls of European ancestry using a custom genotype array and logistic regression fitting log-additive models. Functional annotation was used to identify biofeatures and expression quantitative trait loci that intersect with risk variants. One variant, ARHGEF10L (Rho guanine nucleotide exchange factor 10 like) rs2256787, was associated with increased endometrioid EOC risk (OR=1.33, p=4.46 x 10-6). Other variants of interest included another in ARHGEF10L, rs10788679, which was associated with invasive serous EOC risk (OR=1.07, p=0.00026) and two variants in AKAP6 (A-kinase anchoring protein 6) which were associated with risk of invasive EOC (rs1955513, OR=0.90, p=0.00033; rs927062, OR =0.94, p=0.00059). Functional annotation revealed that the two ARHGEF10L variants were located in super-enhancer regions and that AKAP6 rs927062 was associated with expression of GTPase gene ARHGAP5 (Rho GTPase activating protein 5). Inherited variants in ARHGEF10L and AKAP6, with potential transcriptional regulatory function and association with EOC risk, warrant investigation in independent EOC study populations

Identification of six new susceptibility loci for invasive epithelial ovarian cancer.

Genome-wide association studies (GWAS) have identified 12 epithelial ovarian cancer (EOC) susceptibility alleles. The pattern of association at these loci is consistent in BRCA1 and BRCA2 mutation carriers who are at high risk of EOC. After imputation to 1000 Genomes Project data, we assessed associations of 11 million genetic variants with EOC risk from 15,437 cases unselected for family history and 30,845 controls and from 15,252 BRCA1 mutation carriers and 8,211 BRCA2 mutation carriers (3,096 with ovarian cancer), and we combined the results in a meta-analysis. This new study design yielded increased statistical power, leading to the discovery of six new EOC susceptibility loci. Variants at 1p36 (nearest gene, WNT4), 4q26 (SYNPO2), 9q34.2 (ABO) and 17q11.2 (ATAD5) were associated with EOC risk, and at 1p34.3 (RSPO1) and 6p22.1 (GPX6) variants were specifically associated with the serous EOC subtype, all with P < 5 × 10(-8). Incorporating these variants into risk assessment tools will improve clinical risk predictions for BRCA1 and BRCA2 mutation carriers.COGS project is funded through a European Commission's Seventh Framework Programme grant (agreement number 223175 ] HEALTH ]F2 ]2009 ]223175). The CIMBA data management and data analysis were supported by Cancer Research.UK grants 12292/A11174 and C1287/A10118. The Ovarian Cancer Association Consortium is supported by a grant from the Ovarian Cancer Research Fund thanks to donations by the family and friends of Kathryn Sladek Smith (PPD/RPCI.07). The scientific development and funding for this project were in part supported by the US National Cancer Institute GAME ]ON Post ]GWAS Initiative (U19 ]CA148112). This study made use of data generated by the Wellcome Trust Case Control consortium. Funding for the project was provided by the Wellcome Trust under award 076113. The results published here are in part based upon data generated by The Cancer Genome Atlas Pilot Project established by the National Cancer Institute and National Human Genome Research Institute (dbGap accession number phs000178.v8.p7). The cBio portal is developed and maintained by the Computational Biology Center at Memorial Sloan ] Kettering Cancer Center. SH is supported by an NHMRC Program Grant to GCT. Details of the funding of individual investigators and studies are provided in the Supplementary Note. This study made use of data generated by the Wellcome Trust Case Control consortium, funding for which was provided by the Wellcome Trust under award 076113. The results published here are, in part, based upon data generated by The Cancer Genome Atlas Pilot Project established by the National Cancerhttp://dx.doi.org/10.1038/ng.3185This is the Author Accepted Manuscript of 'Identification of six new susceptibility loci for invasive epithelial ovarian cancer' which was published in Nature Genetics 47, 164–171 (2015) © Nature Publishing Group - content may only be used for academic research

Polygenic risk modeling for prediction of epithelial ovarian cancer risk

Polygenic risk scores (PRS) for epithelial ovarian cancer (EOC) have the potential to improve risk stratification. Joint estimation of Single Nucleotide Polymorphism (SNP) effects in models could improve predictive performance over standard approaches of PRS construction. Here, we implemented computationally efficient, penalized, logistic regression models (lasso, elastic net, stepwise) to individual level genotype data and a Bayesian framework with continuous shrinkage, "select and shrink for summary statistics" (S4), to summary level data for epithelial non-mucinous ovarian cancer risk prediction. We developed the models in a dataset consisting of 23,564 non-mucinous EOC cases and 40,138 controls participating in the Ovarian Cancer Association Consortium (OCAC) and validated the best models in three populations of different ancestries: prospective data from 198,101 women of European ancestries; 7,669 women of East Asian ancestries; 1,072 women of African ancestries, and in 18,915 BRCA1 and 12,337 BRCA2 pathogenic variant carriers of European ancestries. In the external validation data, the model with the strongest association for non-mucinous EOC risk derived from the OCAC model development data was the S4 model (27,240 SNPs) with odds ratios (OR) of 1.38 (95% CI: 1.28-1.48, AUC: 0.588) per unit standard deviation, in women of European ancestries; 1.14 (95% CI: 1.08-1.19, AUC: 0.538) in women of East Asian ancestries; 1.38 (95% CI: 1.21-1.58, AUC: 0.593) in women of African ancestries; hazard ratios of 1.36 (95% CI: 1.29-1.43, AUC: 0.592) in BRCA1 pathogenic variant carriers and 1.49 (95% CI: 1.35-1.64, AUC: 0.624) in BRCA2 pathogenic variant carriers. Incorporation of the S4 PRS in risk prediction models for ovarian cancer may have clinical utility in ovarian cancer prevention programs

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency–Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research