Subset scanning for multi-trait analysis using GWAS summary statistics.

MOTIVATION: Multi-trait analysis has been shown to have greater statistical power than single-trait analysis. Most of the existing multi-trait analysis methods only work with a limited number of traits and usually prioritize high statistical power over identifying relevant traits, which heavily rely on domain knowledge. RESULTS: To handle diseases and traits with obscure etiology, we developed TraitScan, a powerful and fast algorithm that identifies potential pleiotropic traits from a moderate or large number of traits (e.g. dozens to thousands) and tests the association between one genetic variant and the selected traits. TraitScan can handle either individual-level or summary-level GWAS data. We evaluated TraitScan using extensive simulations and found that it outperformed existing methods in terms of both testing power and trait selection when sparsity was low or modest. We then applied it to search for traits associated with Ewing Sarcoma, a rare bone tumor with peak onset in adolescence, among 754 traits in UK Biobank. Our analysis revealed a few promising traits worthy of further investigation, highlighting the use of TraitScan for more effective multi-trait analysis as biobanks emerge. We also extended TraitScan to search and test association with a polygenic risk score and genetically imputed gene expression. AVAILABILITY AND IMPLEMENTATION: Our algorithm is implemented in an R package "TraitScan" available at https://github.com/RuiCao34/TraitScan.

Addressing Disparities in Physician Access to Information in Support of Evidence-based Practice.

The purpose of this study is to determine if Minnesota physicians have access to information resources needed to support evidence-based practice (EBP), which supports a culture of safety and patient-centered care. A survey was used to determine Minnesota physicians' need for, and access to, evidence-based clinical information. A total of 877 responses (6.4% response rate) were included in the data analysis. Participants spent 24 min daily seeking answers to clinical questions and averaged 4.41 questions per day that could not be immediately answered. Physicians reported high levels of information needs met (85.8%), though they reported limited access to drug resources, citation databases, systematic reviews, and full-text books and articles. Results also showed use of unreliable sources to support decision-making. A key finding was the extent to which workplace affiliation broadens disparities in information access. National and regional approaches can work to support EBP by reducing the information gap caused by workplace affiliation and other barriers. Further research should be done to identify partnerships, funding, infrastructure, and support to address these gaps.

A New Analysis Tool for Continuous Glucose Monitor Data.

BACKGROUND: With the development of continuous glucose monitoring systems (CGMS), detailed glycemic data are now available for analysis. Yet analysis of this data-rich information can be formidable. The power of CGMS-derived data lies in its characterization of glycemic variability. In contrast, many standard glycemic measures like hemoglobin A1c (HbA1c) and self-monitored blood glucose inadequately describe glycemic variability and run the risk of bias toward overreporting hyperglycemia. Methods that adjust for this bias are often overlooked in clinical research due to difficulty of computation and lack of accessible analysis tools. METHODS: In response, we have developed a new R package rGV, which calculates a suite of 16 glycemic variability metrics when provided a single individual's CGM data. rGV is versatile and robust; it is capable of handling data of many formats from many sensor types. We also created a companion R Shiny web app that provides these glycemic variability analysis tools without prior knowledge of R coding. We analyzed the statistical reliability of all the glycemic variability metrics included in rGV and illustrate the clinical utility of rGV by analyzing CGM data from three studies. RESULTS: In subjects without diabetes, greater glycemic variability was associated with higher HbA1c values. In patients with type 2 diabetes mellitus (T2DM), we found that high glucose is the primary driver of glycemic variability. In patients with type 1 diabetes (T1DM), we found that naltrexone use may potentially reduce glycemic variability. CONCLUSIONS: We present a new R package and accompanying web app to facilitate quick and easy computation of a suite of glycemic variability metrics.

Structural Alterations in Deep Brain Structures in Type 1 Diabetes.

Even though well known in type 2 diabetes, the existence of brain changes in type 1 diabetes (T1D) and both their neuroanatomical and clinical features are less well characterized. To fill the void in the current understanding of this disease, we sought to determine the possible neural correlate in long-duration T1D at several levels, including macrostructural, microstructural cerebral damage, and blood flow alterations. In this cross-sectional study, we compared a cohort of 61 patients with T1D with an average disease duration of 21 years with 54 well-matched control subjects without diabetes in a multimodal MRI protocol providing macrostructural metrics (cortical thickness and structural volumes), microstructural measures (T1-weighted/T2-weighted [T1w/T2w] ratio as a marker of myelin content, inflammation, and edema), and cerebral blood flow. Patients with T1D had higher T1w/T2w ratios in the right parahippocampal gyrus, the executive part of both putamina, both thalami, and the cerebellum. These alterations were reflected in lower putaminal and thalamic volume bilaterally. No cerebral blood flow differences between groups were found in any of these structures, suggesting nonvascular etiologies of these changes. Our findings implicate a marked nonvascular disruption in T1D of several essential neural nodes engaged in both cognitive and motor processing.

Duration and onset of action of high dose U-500 regular insulin in severely insulin resistant subjects with type 2 diabetes.

AIM: Although regular human U-500 insulin (U-500) is frequently used for insulin resistant type 2 diabetics, pharmacokinetic and pharmacodynamic studies in these individuals are lacking. We set out to determine the rate of onset, duration of action and total glucose lowering effect of two doses of U-500 insulin in obese insulin resistant subjects with type 2 diabetes. MATERIALS AND METHODS: Randomized double-blind crossover study was designed to study subjects who were administered either 100 or 200 units SQ of U-500 insulin once and then were provided intravenous glucose as necessary to maintain euglycaemia. RESULTS: A total of 12 subjects were studied. The time during which intravenous glucose was required to maintain euglycaemia following a 200-unit dose of U-500 insulin was significantly greater than the time following a 100-unit dose. No differences were found between doses in measures related to the rate of onset or in the total amount of intravenous glucose required to maintain euglycaemia for the duration of the study. CONCLUSIONS: The duration of action of U-500 increases when dose is increased from 100 to 200 units. Neither dose of U-500 insulin has an onset of action before 2.5 hours after administration. This suggests that U-500 should not be used as a premeal bolus insulin to lower glucose two hours after a meal and that dosing intervals might need to be extended as dose is increased to avoid hypoglycaemia.