Dietary patterns in Canadian men and women ages 25 and older: relationship to demographics, body mass index, and bone mineral density
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".