Regression coefficients for dietary patterns and energy intake as predictors of body mass index. The parameter estimates are for each 1 SD increase of the nutrient dense factor score, the energy dense factor score, the difference between energy dense and nutrient dense factor score, and the log-tranformed energy intake (1 SD is roughly 36% change in energy intake). P-values for null hypothesis (from top to bottom) Younger Men: 0.080, 0.001, 0.001, 0.884; Older Men: 0.294, < 0.001, 0.002, 0.961; Premenopausal Women: 0.077, 0.126, 0.019, 0.683; Postmenopausal Women: 0.842, < 0.001, < 0.001, 0.096. Analyses were run for the two factor scores and for the difference between factor scores and energy intake separately due to multicollinearity between intake and factor scores. All models are adjusted for age, height, center, education, smoking, alcohol consumption, activity, sedentary time, milk consumption, supplements (vitamin D, calcium); and antiresorptives, corticosteroids, recent (< 5 years) menopause, oophorectomy, as relevant. A high nutrient dense score indicates a greater consumption of fruits, vegetables and whole grains relative to other foods, a high energy dense scores indicates a greater consumption of chips/fries, processed meat, soft drinks, and certain desserts relative to other foods. A high difference indicates more energy dense food relative to nutrient dense foods.