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. 2010 Jan 28;11:20.
doi: 10.1186/1471-2474-11-20.

Dietary Patterns in Canadian Men and Women Ages 25 and Older: Relationship to Demographics, Body Mass Index, and Bone Mineral Density

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Dietary Patterns in Canadian Men and Women Ages 25 and Older: Relationship to Demographics, Body Mass Index, and Bone Mineral Density

Lisa Langsetmo et al. BMC Musculoskelet Disord. .
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Abstract

Background: Previous research has shown that underlying dietary patterns are related to the risk of many different adverse health outcomes, but the relationship of these underlying patterns to skeletal fragility is not well understood. The objective of the study was to determine whether dietary patterns in men (ages 25-49, 50+) and women (pre-menopause, post-menopause) are related to femoral neck bone mineral density (BMD) independently of other lifestyle variables, and whether this relationship is mediated by body mass index.

Methods: We performed an analysis of 1928 men and 4611 women participants in the Canadian Multicentre Osteoporosis Study, a randomly selected population-based longitudinal cohort. We determined dietary patterns based on the self-administered food frequency questionnaires in year 2 of the study (1997-99). Our primary outcome was BMD as measured by dual x-ray absorptiometry in year 5 of the study (2000-02).

Results: We identified two underlying dietary patterns using factor analysis and then derived factor scores. The first factor (nutrient dense) was most strongly associated with intake of fruits, vegetables, and whole grains. The second factor (energy dense) was most strongly associated with intake of soft drinks, potato chips and French fries, certain meats (hamburger, hot dog, lunch meat, bacon, and sausage), and certain desserts (doughnuts, chocolate, ice cream). The energy dense factor was associated with higher body mass index independent of other demographic and lifestyle factors, and body mass index was a strong independent predictor of BMD. Surprisingly, we did not find a similar positive association between diet and BMD. In fact, when adjusted for body mass index, each standard deviation increase in the energy dense score was associated with a BMD decrease of 0.009 (95% CI: 0.002, 0.016) g/cm(2) for men 50+ years old and 0.004 (95% CI: 0.000, 0.008) g/cm(2) for postmenopausal women. In contrast, for men 25-49 years old, each standard deviation increase in the nutrient dense score, adjusted for body mass index, was associated with a BMD increase of 0.012 (95% CI: 0.002, 0.022) g/cm(2).

Conclusions: In summary, we found no consistent relationship between diet and BMD despite finding a positive association between a diet high in energy dense foods and higher body mass index and a strong correlation between body mass index and BMD. Our data suggest that some factor related to the energy dense dietary pattern may partially offset the advantages of higher body mass index with regard to bone health.

Figures

Figure 1
Figure 1
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.
Figure 2
Figure 2
Regression coefficients for dietary patterns and energy intake as predictors of femoral neck BMD without adjustment for 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: 0.057, 0.120, 0.770, 0.008; Older Men: 0.381, 0.284. 0.202, 0.357; Premenopausal Women: 0.907, 0.232, 0.449, 0.874; Postmenopausal Women: 0.607. 0.451, 0.905, 0.324.
Figure 3
Figure 3
Regression coefficients for dietary patterns and energy intake as predictors of femoral neck BMD with adjustment for 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: 0.028, 0.552, 0.258, 0.030; Older Men: 0.118, 0.007, 0.005, 0.399; Premenopausal Women: 0.300, 0.374, 0.904, 0.425; Postmenopausal Women: 0.353, 0.032, 0.028, 0.110.

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