Expression study of the atherosclerosis mouse aorta reveals significant disturbance of calcium signaling pathway

Atherosclerosis is a complex disease resulting from interactions of genetic and environmental risk factors leading to heart failure and stroke. Using an atherosclerotic mouse model (ldlr-/-, apobec1-/- designated as LDb), we performed microarray analysis to identify candidate genes and pathways, which are most perturbed in changes in the following risk factors: genetics (control C57BL/6 vs. LDb mice), shearstress (lesion-prone vs. lesion-resistant regions in LDb mice), diet (chow vs. high fat fed LDb mice) and age (2-month-old vs. 8-month old LDb mice). ^ Atherosclerotic lesion quantification and lipid profile studies were performed to assess the disease phenotype. A microarray study was performed on lesion-prone and lesion-resistant regions of each aorta. Briefly, 32 male C57BL/6 and LDb mice (n =16/each) were fed on either chow or high fat diet, sacrificed at 2- and 8-months old, and RNA isolated from the aortic lesion-prone and aortic lesion-resistant segments. Using 64 Affymetrix Murine 430 2.0 chips, we profiled differentially expressed genes with the cut off value of FDR ≤ 0.15 for t-test, and q <0.0001 for the ANOVA. The data were normalized using two normalization methods---invariant probe sets (Loess) and Quantile normalization, the statistical analysis was performed using t-tests and ANOVA, and pathway characterization was done using Pathway Express (Wayne State). The result identified the calcium signaling pathway as the most significant overrepresented pathway, followed by focal adhesion. In the calcium signaling pathway, 56 genes were found to be significantly differentially expressed out of 180 genes listed in the KEGG calcium signaling pathway. Nineteen of these genes were consistently identified by both statistical tests, 11 of which were unique to the test, and 26 were unique to the ANOVA test, using the cutoffs noted above. ^ In conclusion, this finding suggested that hypercholesterolemia drives the disease progression by altering the expression of calcium channels and regulators which subsequently results in cell differentiation, growth, adhesion, cytoskeletal change and death. Clinically, this pathway may serve as an important target for future therapeutic intervention, and thus the calcium signaling pathway may serve as an important target for future diagnostic and therapeutic intervention. ^

Assessment of the effect on statistical power of regression model misspecification by using techniques of mathematical statistics and simulation study

Objectives. This paper seeks to assess the effect on statistical power of regression model misspecification in a variety of situations. ^ Methods and results. The effect of misspecification in regression can be approximated by evaluating the correlation between the correct specification and the misspecification of the outcome variable (Harris 2010).In this paper, three misspecified models (linear, categorical and fractional polynomial) were considered. In the first section, the mathematical method of calculating the correlation between correct and misspecified models with simple mathematical forms was derived and demonstrated. In the second section, data from the National Health and Nutrition Examination Survey (NHANES 2007-2008) were used to examine such correlations. Our study shows that comparing to linear or categorical models, the fractional polynomial models, with the higher correlations, provided a better approximation of the true relationship, which was illustrated by LOESS regression. In the third section, we present the results of simulation studies that demonstrate overall misspecification in regression can produce marked decreases in power with small sample sizes. However, the categorical model had greatest power, ranging from 0.877 to 0.936 depending on sample size and outcome variable used. The power of fractional polynomial model was close to that of linear model, which ranged from 0.69 to 0.83, and appeared to be affected by the increased degrees of freedom of this model.^ Conclusion. Correlations between alternative model specifications can be used to provide a good approximation of the effect on statistical power of misspecification when the sample size is large. When model specifications have known simple mathematical forms, such correlations can be calculated mathematically. Actual public health data from NHANES 2007-2008 were used as examples to demonstrate the situations with unknown or complex correct model specification. Simulation of power for misspecified models confirmed the results based on correlation methods but also illustrated the effect of model degrees of freedom on power.^