| 1. |
- Berglund, Lisa, et al.
(författare)
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Glucose-Dependent Insulinotropic Polypeptide (GIP) Stimulates Osteopontin Expression in the Vasculature via Endothelin-1 and CREB.
- 2016
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Ingår i: Diabetes. - : American Diabetes Association. - 1939-327X .- 0012-1797. ; 65:1, s. 239-254
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Tidskriftsartikel (refereegranskat)abstract
- Glucose-dependent insulinotropic polypeptide (GIP) is an incretin hormone with extrapancreatic effects beyond glycemic control. Here we demonstrate unexpected effects of GIP signaling in the vasculature. GIP induces the expression of the pro-atherogenic cytokine osteopontin (OPN) in mouse arteries, via local release of endothelin-1 (ET-1) and activation of cAMP response element binding protein (CREB). Infusion of GIP increases plasma OPN levels in healthy individuals. Plasma ET-1 and OPN levels are positively correlated in patients with critical limb ischemia. Fasting GIP levels are higher in individuals with a history of cardiovascular disease (myocardial infarction, stroke) when compared to controls. GIP receptor (GIPR) and OPN mRNA levels are higher in carotid endarterectomies from patients with symptoms (stroke, transient ischemic attacks, amaurosis fugax) than in asymptomatic patients; and expression associates to parameters characteristic of unstable and inflammatory plaques (increased lipid accumulation, macrophage infiltration and reduced smooth muscle cell content). While GIPR expression is predominantly endothelial in healthy arteries from human, mouse, rat and pig; remarkable up-regulation is observed in endothelial and smooth muscle cells upon culture conditions yielding a "vascular disease-like" phenotype. Moreover, a common variant rs10423928 in the GIPR gene associated with increased risk of stroke in type 2 diabetes patients.
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| 2. |
- Burgess, S., et al.
(författare)
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Bayesian methods for meta-analysis of causal relationships estimated using genetic instrumental variables
- 2010
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Ingår i: Statistics in medicine. - : Wiley. - 1097-0258 .- 0277-6715. ; 29:12, s. 1298-311
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Tidskriftsartikel (refereegranskat)abstract
- Genetic markers can be used as instrumental variables, in an analogous way to randomization in a clinical trial, to estimate the causal relationship between a phenotype and an outcome variable. Our purpose is to extend the existing methods for such Mendelian randomization studies to the context of multiple genetic markers measured in multiple studies, based on the analysis of individual participant data. First, for a single genetic marker in one study, we show that the usual ratio of coefficients approach can be reformulated as a regression with heterogeneous error in the explanatory variable. This can be implemented using a Bayesian approach, which is next extended to include multiple genetic markers. We then propose a hierarchical model for undertaking a meta-analysis of multiple studies, in which it is not necessary that the same genetic markers are measured in each study. This provides an overall estimate of the causal relationship between the phenotype and the outcome, and an assessment of its heterogeneity across studies. As an example, we estimate the causal relationship of blood concentrations of C-reactive protein on fibrinogen levels using data from 11 studies. These methods provide a flexible framework for efficient estimation of causal relationships derived from multiple studies. Issues discussed include weak instrument bias, analysis of binary outcome data such as disease risk, missing genetic data, and the use of haplotypes.
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