In BL/6J mice, statistical analysis revealed a reduced saccharin intake (factor group: F1,22 = 12.30; p<0.01).
After four weeks CMS exposure, these findings could be replicated for an independent batch of BL6/J mice (data not shown) regarding their saccharin intake (factor group: F1,18 = 24.98; p<0.01; interaction group×time: F5,90 = 4.33; p<0.01; ε = 0.584) but not in respect of their saccharin preference.
Secondly, one item which asks for the recording of blood glucose levels showed indeed a loading of 0.44 on the 'blood glucose measurement/ medication intake' factor, but it was primarily related to 'contact with health-care professionals' (0.61).
The differences in mean urinary InAs%, MMA%, and DMA% between those in the highest and lowest tertiles of each dietary intake factor and blood micronutrient are presented in Table 3, ranked in order of the magnitude of the absolute differences, excluding those factors having a p-value > 0.2.
For all cases and controls, we ascertained demographic (e.g., age, sex, body mass index) and lifestyle (e.g., smoking, alcohol intake) factors, as well as general comorbidities at the index date; the distribution of these factors among cases and controls has been previously presented [4].
For brevity, we refer to the dietary intake factors, along with the blood micronutrients, as "nutritional factors".
Findings from the current study provide convergent evidence that intake factors should be used as clinical predictors for pain and disability outcomes.
Although A1C values do not differ between locations, prepregnancy care will have addressed the impact of teratogenic drugs, rubella screening, folic acid uptake, and smoking and alcohol intake factors known to influence pregnancy outcome.
With the lowest quartile of intake as the referent category and controlling for daily caloric intake, factors predicting intake of these compounds included maternal race/ethnicity, education, body mass index, household income, area of residence, folate intake, and percent of daily calories from dietary fat.
Although randomization presumes achieving no major differences between two groups in the other than statin intake factors, we will control the analysis for age, as the age would have a significant variation within each group, and also for gender, smoking status and other variables, which would retain in the final model if they change the β coefficients by more than 5%.
For colon cancer, there was a significant positive interaction between 10 or more years of exposure above 5 mg/L nitrate-N and both low vitamin C and high meat intake, factors likely to increase endogenous NOC formation (De Roos et al. 2003).
