Author Correction:Discovery of drug-omics associations in type 2 diabetes with generative deep-learning models

In the version of this article initially published, Cristina Leal Rodríguez (Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark) was omitted from the author list. The error has been corrected in the HTML and PDF versions of the article</p

Sharing data for future research-engaging participants' views about data governance beyond the original project:a DIRECT Study

Purpose: Biomedical data governance strategies should ensure that data are collected, stored, and used ethically and lawfully. However, research participants’ preferences for how data should be governed is least studied. The Diabetes Research on Patient Stratification (DIRECT) project collected substantial amounts of health and genetic information from patients at risk of, and with type II diabetes. We conducted a survey to understand participants’ future data governance preferences. Results will inform the postproject data governance strategy. Methods: A survey was distributed in Denmark, Sweden, The Netherlands, and the United Kingdom. Results: In total 855 surveys were returned. Ninety-seven percent were supportive of sharing data postproject, and 90% were happy to share data with universities, and 56% with commercial companies. The top three priorities for data sharing were highly secure database, DIRECT researchers to monitor data used by other researchers, and researchers cannot identify participants. Respondents frequently suggested that a postproject Data Access Committee should involve a DIRECT researcher, diabetes clinician, patient representative, and a DIRECT participant. Conclusion: Preferences of how data should be governed, and what data could be shared and with whom varied between countries. Researchers are considered as key custodians of participant data. Engaging participants aids in designing governance to support their choices

The gut microbiome in cardio-metabolic health

With the prevalence of cardio-metabolic disorders reaching pandemic proportions, the search for modifiable causative factors has intensified. One such potential factor is the vast microbial community inhabiting the human gastrointestinal tract, the gut microbiota. For the past decade evidence has accumulated showing the association of distinct changes in gut microbiota composition and function with obesity, type 2 diabetes and cardiovascular disease. Although causality in humans and the pathophysiological mechanisms involved have yet to be decisively established, several studies have demonstrated that the gut microbiota, as an environmental factor influencing the metabolic state of the host, is readily modifiable through a variety of interventions. In this review we provide an overview of the development of the gut microbiome and its compositional and functional changes in relation to cardio-metabolic disorders, and give an update on recent progress in how this could be exploited in microbiota-based therapeutics. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13073-015-0157-z) contains supplementary material, which is available to authorized users

Impact of <i>PTBP1</i> rs11085226 on glucose-stimulated insulin release in adult Danes

BACKGROUND: The variant rs11085226 (G) within the gene encoding polypyrimidine tract binding protein 1 (PTBP1) was reported to associate with reduced insulin release determined by an oral glucose tolerance test (OGTT) as well as an intravenous glucose tolerance test (IVGTT). The aim of the present study was to validate the association of the rs11085226 G-allele of PTBP1 with previously investigated OGTT- and IVGTT-derived diabetes-related metabolic quantitative phenotypes, to conduct exploratory analyses of additional measures of beta-cell function, and to further investigate a potential association with type 2 diabetes. METHODS: PTBP1 rs11085226 was genotyped in 20,911 individuals of Danish Caucasian ethnicity ascertained from 9 study samples. Case control analysis was performed on 5,634 type 2 diabetic patients and 11,319 individuals having a normal fasting glucose level as well as 4,641 glucose tolerant controls, respectively. Quantitative trait analyses were performed in up to 13,605 individuals subjected to an OGTT or blood samples obtained after an overnight fast, as well as in 596 individuals subjected to an IVGTT. RESULTS: Analyses of fasting and OGTT-derived quantitative traits did not show any significant associations with the PTBP1 rs11085226 variant. Meta-analysis of IVGTT-derived quantitative traits showed a nominally significant association between the variant and reduced beta-cell responsiveness to glucose (β = −0.1 mmol · kg(−1) · min(−1); 95% CI: −0.200.20 – −0.024; P = 0.01) assuming a dominant model of inheritance, but failed to replicate a previously reported association with area under the curve (AUC) for insulin. Case control analysis did not show an association of the PTBP1 rs11085226 variant with type 2 diabetes. CONCLUSIONS: Despite failure to replicate the previously reported associations of PTBP1 rs11085226 with OGTT- and IVGTT-derived measures of beta-cell function, we did find a nominally significant association with reduced beta-cell responsiveness to glucose during an IVGTT, a trait not previously investigated, leaving the potential influence of this variant in PTBP1 on glucose stimulated insulin release open for further investigation. However, the present study does not support the hypothesis that the variant confers risk of type 2 diabetes. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12881-015-0160-7) contains supplementary material, which is available to authorized users

Plasma proteome profiling reveals metabolic and immunologic differences between Anorexia Nervosa subtypes

AIMS/HYPOTHESIS: Anorexia Nervosa (AN) is a severe psychiatric disorder of an unknown etiology with a crude mortality rate of about 5 % per decade, making it one of the deadliest of all psychiatric illnesses. AN is broadly classified into two main subtypes, restricting and binge/purging disorder. Despite extensive research efforts during several decades, the underlying pathophysiology of AN remains poorly understood. In this study, we aimed to identify novel protein biomarkers for AN by performing a proteomics analysis of fasting plasma samples from 78 females with AN (57 restrictive and 21 binge/purge type) and 70 healthy controls.METHODS: Using state-of-the-art mass spectrometry-based proteomics technology in conjunction with an advanced bioinformatics pipeline, we quantify &gt;500 plasma proteins.RESULTS: Differential expression analysis and correlation of proteomics data with clinical variables led to identification of a panel of novel protein biomarkers with potential pathophysiological significance for AN. Our findings demonstrate evidence of a humoral immune system response, altered lipid metabolism and potential alteration of plasma cells in AN patients. Additionally, we stratified AN patients based on the quantified proteins and suggest a potential autoimmune nature in the restrictive subtype of AN.CONCLUSIONS/INTERPRETATION: In summary, on top of biomarkers of AN subtypes, this study provides a comprehensive map of plasma proteins that constitute a resource for further studies of the pathophysiology of AN.</p

Motivations for data sharing—views of research participants from four European countries:A DIRECT study

The purpose of this study was to explore and compare different countries in what motivated research participants’ decisions whether to share their de-identified data. We investigated European DIRECT (Diabetes Research on Patient Stratification) research project participants’ desire for control over sharing different types of their de-identified data, and with who data could be shared in the future after the project ends. A cross-sectional survey was disseminated among DIRECT project participants. The results found that there was a significant association between country and attitudes towards advancing research, protecting privacy, and beliefs about risks and benefits to sharing data. When given the choice to have control, some participants (<50% overall) indicated that having control over what data is shared and with whom was important; and control over what data types are shared was less important than respondents deciding who data are shared with. Danish respondents indicated higher odds of desire to control data types shared, and Dutch respondents showed higher odds of desire to control who data will be shared with. Overall, what research participants expect in terms of control over data sharing needs to be considered and aligned with sharing for future research and re-use of data. Our findings show that even with de-identified data, respondents prioritise privacy above all else. This study argues to move research participants from passive participation in biomedical research to considering their opinions about data sharing and control of de-identified biomedical data