GRADE (Glycemia Reduction Approaches in Diabetes: A Comparative Effectiveness Study) is a 36-center unmasked, parallel treatment group, randomized controlled trial evaluating four diabetes medications added to metformin in people with type 2 diabetes (T2DM). We report baseline characteristics and compare GRADE participants to a National Health and Nutrition Examination Survey (NHANES) cohort. RESEARCH DESIGN AND METHODS Participants were age ‡30 years at the time of diagnosis, with duration of T2DM <10 years, HbA 1c 6.8-8.5% (51-69 mmol/mol), prescribed metformin monotherapy, and randomized to glimepiride, sitagliptin, liraglutide, or insulin glargine. RESULTS At baseline, GRADE's 5,047 randomized participants were 57.2 6 10.0 years of age, 63.6% male, with racial/ethnic breakdown of 65.7% white, 19.8% African American, 3.6% Asian, 2.7% Native American, 7.6% other or unknown, and 18.4% Hispanic/ Latino. Duration of diabetes was 4.2 6 2.8 years, with mean HbA 1c of 7.5 6 0.5% (58 6 5.3 mmol/mol), BMI of 34.3 6 6.8 kg/m 2 , and metformin dose of 1,944 6 204 mg/day. Among the cohort, 67% reported a history of hypertension, 72% a history of hyperlipidemia, and 6.5% a history of heart attack or stroke. Applying GRADE inclusion criteria to NHANES indicates enrollment of a representative cohort with T2DM on metformin monotherapy (NHANES cohort average age, 57.9 years; mean HbA 1c , 7.4% [57 mmol/mol]; BMI, 33.2 kg/m 2 ; duration, 4.2 6 2.5 years; and 7.2% with a history of cardiovascular disease). CONCLUSIONS The GRADE cohort represents patients with T2DM treated with metformin requiring a second diabetes medication. GRADE will inform decisions about the clinical effectiveness of the addition of four classes of diabetes medications to metformin. The optimal medication management of hyperglycemia in type 2 diabetes (T2DM) is not established. In addition to lifestyle intervention, metformin is the recommended initial medication in T2DM due to its glycemic effectiveness, lack of associated hypoglycemia or weight gain, low cost, and evidence of long-term benefit and safety

Alzheimer’s disease (AD) has devastating consequences for patients during its slow, progressive course. It is important to understand the pathology of AD onset. Recently, circular RNAs (circRNAs) have been found to participate in many human diseases including cancers and neurodegenerative conditions. In this study, we mined the published dataset on the AMP-AD Knowledge Portal from the Mount Sinai Brain Bank (MSBB) to describe the circRNA profiles at different AD stages in brain samples from four brain regions: anterior prefrontal cortex, superior temporal lobe, parahippocampal gyrus and inferior frontal gyrus. In total, we found 147 circRNAs to be differentially expressed (DE) for different AD severity levels in the four regions. We also characterized the mRNA-circRNA co-expression network and annotated the potential function of circRNAs based on the co-expressed modules. Based on our results, we found that the most circRNA-regulated region in AD patients with severe symptoms was the parahippocampal gyrus. The strongest negatively AD severity-correlated module in the parahippocampal gyrus was enriched in cognitive disability and pathological-associated pathways such as synapse organization and regulation of membrane potential. Finally, a regression model based on the expression pattern of DE circRNAs in the module could help to distinguish the disease severity of patients, further supporting a role for circRNAs in AD pathology. In conclusion, our findings indicate that circRNAs in parahippocampal gyrus are possible biomarkers and regulators of AD as well as potential therapeutic targets.

All source code, alignments and models are available at http://www.stats.ox.ac.uk/proteins/resources

Membrane proteins are estimated to be the targets of 50% of drugs that are currently in development, yet we have few membrane protein crystal structures. As a result, for a membrane protein of interest, the much-needed structural information usually comes from a homology model. Current homology modelling software is optimized for globular proteins, and ignores the constraints that the membrane is known to place on protein structure. Our Memoir server produces homology models using alignment and coordinate generation software that has been designed specifically for transmembrane proteins. Memoir is easy to use, with the only inputs being a structural template and the sequence that is to be modelled. We provide a video tutorial and a guide to assessing model quality. Supporting data aid manual refinement of the models. These data include a set of alternative conformations for each modelled loop, and a multiple sequence alignment that incorporates the query and template. Memoir works with both α-helical and β-barrel types of membrane proteins and is freely available at http://opig.stats.ox.ac.uk/webapps/memoir.