13 research outputs found
Circadian gene variants and susceptibility to type 2 diabetes : a pilot study
Background
Disruption of endogenous circadian rhythms has been shown to increase the risk of developing type 2 diabetes, suggesting that circadian genes might play a role in determining disease susceptibility. We present the results of a pilot study investigating the association between type 2 diabetes and selected single nucleotide polymorphisms (SNPs) in/near nine circadian genes. The variants were chosen based on their previously reported association with prostate cancer, a disease that has been suggested to have a genetic link with type 2 diabetes through a number of shared inherited risk determinants.
Methodology/Principal Findings
The pilot study was performed using two genetically homogeneous Punjabi cohorts, one resident in the United Kingdom and one indigenous to Pakistan. Subjects with (N = 1732) and without (N = 1780) type 2 diabetes were genotyped for thirteen circadian variants using a competitive allele-specific polymerase chain reaction method. Associations between the SNPs and type 2 diabetes were investigated using logistic regression. The results were also combined with in silico data from other South Asian datasets (SAT2D consortium) and white European cohorts (DIAGRAM+) using meta-analysis. The rs7602358G allele near PER2 was negatively associated with type 2 diabetes in our Punjabi cohorts (combined odds ratio [OR] = 0.75 [0.66–0.86], p = 3.18×10−5), while the BMAL1 rs11022775T allele was associated with an increased risk of the disease (combined OR = 1.22 [1.07–1.39], p = 0.003). Neither of these associations was replicated in the SAT2D or DIAGRAM+ datasets, however. Meta-analysis of all the cohorts identified disease associations with two variants, rs2292912 in CRY2 and rs12315175 near CRY1, although statistical significance was nominal (combined OR = 1.05 [1.01–1.08], p = 0.008 and OR = 0.95 [0.91–0.99], p = 0.015 respectively).
Conclusions/significance
None of the selected circadian gene variants was associated with type 2 diabetes with study-wide significance after meta-analysis. The nominal association observed with the CRY2 SNP, however, complements previous findings and confirms a role for this locus in disease susceptibility
Some Algorithmic Problems in Polytope Theory
Convex polytopes, i.e.. the intersections of finitely many closed affine halfspaces in R^d, are important objects in various areas of mathematics and other disciplines. In particular, the compact..
Institutional Monitoring and REIT CEO Compensation
Institional ownership, CEO compensation, REIT, Mornitoring,
Are There Rational Speculative Bubbles in REITs?
This study tests for the presence of rational speculative bubbles in the Equity REIT industry. We analyze REIT prices using a vector of macroeconomic fundamentals. Using the unit root test and cointegration procedures, we find no evidence of rational bubbles in the REIT market. Tests for duration dependence in the returns series show no evidence of negative duration dependence, suggesting that REIT markets are not affected by rational bubbles. Applying the same tests, we find no evidence of rational speculative bubbles in the Russell 2000 index, a proxy for small-cap stocks. Copyright Springer Science + Business Media, Inc. 2006REITs, Russell 2000, Rational expectations bubbles, Duration dependence,
Strategies of Dietary Carbohydrate Manipulation and Their Effects on Performance in Cycling Time Trials
Integrating Archaeological Theory and Predictive Modeling: a Live Report from the Scene
Archaeological predictive modeling has been used successfully for over20 years as a decision-making tool in cultural resources management. Itsappreciation in academic circles however has been mixed because of its perceivedtheoretical poverty. In this paper, we discuss the issue of integrating currentarchaeological theoretical approaches and predictive modeling. We suggest amethodology for doing so based on cognitive archaeology, middle range theory,and paleoeconomic modeling. We also discuss the problems associated with testingpredictive models