Genetic tools for coronary risk assessment in type 2 diabetes: A cohort study from the ACCORD clinical trial

OBJECTIVE We evaluated whether the increasing number of genetic loci for coronary artery disease (CAD) identified in the general population could be used to predict the risk of major CAD events (MCE) among participants with type 2 diabetes at high cardiovascular risk. RESEARCH DESIGN AND METHODS A weighted genetic risk score (GRS) derived from 204 variants representative of all the 160 CAD loci identified in the general population as of December 2017 was calculated in 5,360 and 1,931 white participants in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) and Outcome Reduction With Initial Glargine Intervention (ORIGIN) studies, respectively. The association between GRS and MCE (combining fatal CAD events, nonfatal myocardial infarction, and unstable angina) was assessed by Cox proportional hazards regression. RESULTS The GRS was associated with MCE risk in both ACCORD and ORIGIN (hazard ratio [HR] per SD 1.27, 95% CI 1.18–1.37, P = 4 3 10210, and HR per SD 1.35, 95% CI 1.16–1.58, P = 2 3 1024, respectively). This association was independent from interventions tested in the trials and persisted, though attenuated, after adjustment for classic cardiovascular risk predictors. Adding the GRS to clinical predictors improved incident MCE risk classification (relative integrated discrimination improvement +8%, P = 7 3 1024). The performance of this GRS was superior to that of GRS based on the smaller number of CAD loci available in previous years. CONCLUSIONS When combined into a GRS, CAD loci identified in the general population are associated with CAD also in type 2 diabetes. This GRS provides a significant improvement in the ability to correctly predict future MCE, which may increase further with the discovery of new CAD loci

Exctractable soil lipids and microbial activity as affected by Bt and non Bt maize grown in a silty loam soil

International audiencePyrolysis-gas (Py-GC) chromatography was used to characterize extractable lipids from Bt and non-Bt maize shoots and soils collected at time of harvesting. Py-GC-MS (mass spectrometry) showed that the concentrations of total alkenes identified in non-Bt shoots and soils were 47.9 and 21.3% higher than in Bt maize shoots and soils, respectively. N-alkanes identified were of similar orders of magnitude in Bt and non-Bt maize shoots, but were 28.6% higher in Bt than in non-Bt soils. Bt maize shoots contained 29.7% more n-fatty acids than non-Bt maize shoots, whereas the concentrations of n-fatty acids in Bt soils were twice as high as those in non-Bt soils. Concentrations of unsaturated fatty acids in Bt maize shoots were 22.1% higher than those in non-Bt maize shoots, while concentrations of unsaturated fatty acids were 22.5% higher in non-Bt than in Bt soils. The cumulative CO2-C evolved from soils under Bt and non-Bt crops was 30.5% lower under Bt as compared to non-Bt crops, whereas when maize shoots were added to Bt and non-Bt soils, the decrease in CO2-C evolved were 16.5 and 23.6%, respectively. Our data showed that the cultivation of Bt maize significantly increased the saturated to unsaturated lipid ratios in soils which appeared to negatively affect microbial activity

Picture, Archiving and Communication System in the Italian NHS: A Primer on Diffusion and Evaluation Analysis

This contribution focuses on picture archiving and communication systems (PACS) in the Italian National Healthcare System (NHS). It finally aims to test the Chiefs Radiology Department’s perceptions about PACS along the main evaluation dimensions emerging from the literature. First, a brief review of the main literature concerning PACS evaluation leads the authors to classify the different approaches undertaken and highlight the main variables of investigation. Second, the evidence emerging from a survey is presented and discussed in the light of the literature review. The survey aims to: (a) map out the degree of PACSs diffusion and their main features in the Italian NHS; (b) verify whether and how PACS impact the dimensions analyzed in many evaluation studies carried out to date; (c) test the relationship between some measured impacts and specific PACS features