Differential effect of obesity on bone mineral density in White, Hispanic and African American women: a cross sectional study
Bibliographic record
Abstract
Osteoporosis is a major public health problem with low bone mass affecting nearly half the women aged 50 years or older. Evidence from various studies has shown that higher body mass index (BMI) is a protective factor for bone mineral density (BMD). Most of the evidence, however, is from studies with Caucasian women and it is unclear to what extent ethnicity plays a role in modifying the effect of BMI on BMD.A cross sectional study was performed in which records of postmenopausal women who presented for screening for osteoporosis at 2 urban medical centres were reviewed. Using logistic regression, we examined the interaction of race and BMI after adjusting for age, family history of osteoporosis, maternal fracture, smoking, and sedentary lifestyle on BMD. Low BMD was defined as T-score at the lumbar spine < -1.Among 3,206 patients identified, the mean age of the study population was 58.3 +/- 0.24 (Years +/- SEM) and the BMI was 30.6 kg/m2. 2,417 (75.4%) were African Americans (AA), 441(13.6%) were Whites and 348 (10.9%) were Hispanics. The AA women had lower odds of having low BMD compared to Whites [Odds ratio (OR) = 0.079 (0.03-0.24) (95% CI), p < 0.01]. The odds ratio of low BMD was not statistically significant between White and Hispanic women. We examined the interaction between race and BMD. For White women; as the BMI increases by unity, the odds of low BMD decreases [OR = 0.9 (0.87-0.94), p < 0.01; for every unit increase in BMI]. AA women had slightly but significantly higher odds of low BMD compared to Whites [OR 1.015 (1.007-1.14), p <0.01 for every unit increase in BMI]. This effect was not observed when Hispanic women were compared to Whites.There is thus a race-dependent effect of BMI on BMD. With each unit increase in BMI, BMD increases for White women, while a slight but significant decrease in BMD occurs in African American women.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".