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Our data support the concept that adiponectin is an important marker in the pathogenesis of atherosclerosis since adiponectin remains significantly associated with plaque morphology in a fully adjusted multivariate model containing age, sex, BMI, hypertension, diabetes mellitus, smoking, family history of CAD, LDL-C, HDL-C, triglycerides, hsCRP, medication and PAT volume.
A model containing age, sex and deprivation alone explained 28% of the uncertainty.
We first fit a model containing age and sex (model 1).
A model containing age, sex, age-by-sex interaction, deprivation and GP practice alone explained 22% of the observed uncertainty.
The strongest association with the BASFI (square root transformed) was found in a model containing age and CRP levels as independent variables (Table 3, Model 2).
We were able to explain 39% of the variance in the DI with a model containing age, sex, waist circumference, and three metabolic factors (Table 2).
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Our model contained age, lumen size, Fisher grade, and WFNS grade as predictors.
However, the best fitting model contained: age, gender, age*gender, East Midlands, Scotland, population density and non-car ownership.
Adding SRI to models containing age, race, and individual SRI components did not improve C-statistics.
Adding any of these risk factors to the models containing age group and the exposure variable of interest did not change the OR for the exposure variable by >2%, so to keep models parsimonious they were not included in the final models.
To test for associations with visual impairment and blindness, multiple logistic regression models were constructed containing age, sex, BMI, social class, education level and smoking status.
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CEO of Professional Science Editing for Scientists @ prosciediting.com