In selected sensitivity analyses, we also used the nutrient density method (Willett and Stampfer, 1998) and divided the energy intake from each fat by the total caloric intake while including total caloric intake in the model.
Our results remained unchanged when adding total energy intake, including energy due to alcohol intake, into the model.
The association for intake of acetaldehyde did not persist after inclusion of ethanol intake in the model (HR = 0.60 (95% CI: 0.24 1.52), trend analysis showed no dose response relationship; p value for the addition of the ethanol intake variable was <0.05.
Additionally, including coffee intake into the model did not change the relationship (RR=0.78, 95% CI=0.55 1.11, Ptrend=0.19).
Including background dietary intake in the model did not affect the associations.
Adding alcohol intake to the model did not change risk estimates (data not shown).
This association remained significant after multiple regression was used to incorporate important clinical covariates – age, BMI, level of activity, family history, and caffeine intake – into the model.
Therefore, for each 100 mg/day increase in dietary cholesterol intake in the model, a 1.9 mg/dL increase in LDL-C was expected.
In models evaluating the associations of the outcome variables with the low carbohydrate and the high protein scores, we repeated the analysis by also introducing the complementary score (that is, high protein in the models evaluating low carbohydrate and vice versa) without energy intake in the model.
Similar relationships were observed for non-heme iron and total iron intake from diet and supplements using an interaction term between iron intake and vitamin C intake in the models (Table IV).
