Mendelian randomization study of B-type natriuretic peptide and type 2 diabetes: evidence of causal association from population studies

<p>Background: Genetic and epidemiological evidence suggests an inverse association between B-type natriuretic peptide (BNP) levels in blood and risk of type 2 diabetes (T2D), but the prospective association of BNP with T2D is uncertain, and it is unclear whether the association is confounded.</p> <p>Methods and Findings: We analysed the association between levels of the N-terminal fragment of pro-BNP (NT-pro-BNP) in blood and risk of incident T2D in a prospective case-cohort study and genotyped the variant rs198389 within the BNP locus in three T2D case-control studies. We combined our results with existing data in a meta-analysis of 11 case-control studies. Using a Mendelian randomization approach, we compared the observed association between rs198389 and T2D to that expected from the NT-pro-BNP level to T2D association and the NT-pro-BNP difference per C allele of rs198389. In participants of our case-cohort study who were free of T2D and cardiovascular disease at baseline, we observed a 21% (95% CI 3%-36%) decreased risk of incident T2D per one standard deviation (SD) higher log-transformed NT-pro-BNP levels in analysis adjusted for age, sex, body mass index, systolic blood pressure, smoking, family history of T2D, history of hypertension, and levels of triglycerides, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol. The association between rs198389 and T2D observed in case-control studies (odds ratio = 0.94 per C allele, 95% CI 0.91-0.97) was similar to that expected (0.96, 0.93-0.98) based on the pooled estimate for the log-NT-pro-BNP level to T2D association derived from a meta-analysis of our study and published data (hazard ratio = 0.82 per SD, 0.74-0.90) and the difference in NT-pro-BNP levels (0.22 SD, 0.15-0.29) per C allele of rs198389. No significant associations were observed between the rs198389 genotype and potential confounders.</p> <p>Conclusions: Our results provide evidence for a potential causal role of the BNP system in the aetiology of T2D. Further studies are needed to investigate the mechanisms underlying this association and possibilities for preventive interventions.</p&gt

Ecosystem heterogeneity and diversity mitigate Amazon forest resilience to frequent extreme droughts

© 2018 The Authors. New Phytologist © 2018 New Phytologist Trust The impact of increases in drought frequency on the Amazon forest's composition, structure and functioning remain uncertain. We used a process- and individual-based ecosystem model (ED2) to quantify the forest's vulnerability to increased drought recurrence. We generated meteorologically realistic, drier-than-observed rainfall scenarios for two Amazon forest sites, Paracou (wetter) and Tapajós (drier), to evaluate the impacts of more frequent droughts on forest biomass, structure and composition. The wet site was insensitive to the tested scenarios, whereas at the dry site biomass declined when average rainfall reduction exceeded 15%, due to high mortality of large-sized evergreen trees. Biomass losses persisted when year-long drought recurrence was shorter than 2–7 yr, depending upon soil texture and leaf phenology. From the site-level scenario results, we developed regionally applicable metrics to quantify the Amazon forest's climatological proximity to rainfall regimes likely to cause biomass loss > 20% in 50 yr according to ED2 predictions. Nearly 25% (1.8 million km2) of the Amazon forests could experience frequent droughts and biomass loss if mean annual rainfall or interannual variability changed by 2σ. At least 10% of the high-emission climate projections (CMIP5/RCP8.5 models) predict critically dry regimes over 25% of the Amazon forest area by 2100

Practical computational toolkits for dendrimers and dendrons structure design

Dendrimers and dendrons offer an excellent platform for developing novel drug delivery systems and medicines. The rational design and further development of these repetitively branched systems are restricted by difficulties in scalable synthesis and structural determination, which can be overcome by judicious use of molecular modelling and molecular simulations. A major difficulty to utilise in silico studies to design dendrimers lies in the laborious generation of their structures. Current modelling tools utilise automated assembly of simpler dendrimers or the inefficient manual assembly of monomer precursors to generate more complicated dendrimer structures. Herein we describe two novel graphical user interface (GUI) toolkits written in Python that provide an improved degree of automation for rapid assembly of dendrimers and generation of their 2D and 3D structures. Our first toolkit uses the RDkit library, SMILES nomenclature of monomers and SMARTS reaction nomenclature to generate SMILES and mol files of dendrimers without 3D coordinates. These files are used for simple graphical representations and storing their structures in databases. The second toolkit assembles complex topology dendrimers from monomers to construct 3D dendrimer structures to be used as starting points for simulation using existing and widely available software and force fields. Both tools were validated for ease-of-use to prototype dendrimer structure and the second toolkit was especially relevant for dendrimers of high complexity and size.Peer reviewe

Social research on neglected diseases of poverty: Continuing and emerging themes

Copyright: © 2009 Manderson et al.Neglected tropical diseases (NTDs) exist and persist for social and economic reasons that enable the vectors and pathogens to take advantage of changes in the behavioral and physical environment. Persistent poverty at household, community, and national levels, and inequalities within and between sectors, contribute to the perpetuation and re-emergence of NTDs. Changes in production and habitat affect the physical environment, so that agricultural development, mining and forestry, rapid industrialization, and urbanization all result in changes in human uses of the environment, exposure to vectors, and vulnerability to infection. Concurrently, political instability and lack of resources limit the capacity of governments to manage environments, control disease transmission, and ensure an effective health system. Social, cultural, economic, and political factors interact and influence government capacity and individual willingness to reduce the risks of infection and transmission, and to recognize and treat disease. Understanding the dynamic interaction of diverse factors in varying contexts is a complex task, yet critical for successful health promotion, disease prevention, and disease control. Many of the research techniques and tools needed for this purpose are available in the applied social sciences. In this article we use this term broadly, and so include behavioral, population and economic social sciences, social and cultural epidemiology, and the multiple disciplines of public health, health services, and health policy and planning. These latter fields, informed by foundational social science theory and methods, include health promotion, health communication, and heath education