Bone Mineral Density as a Predictor of Cardiovascular Disease in Women: A Real-World Retrospective Study
Bibliographic record
Abstract
Background: Atherosclerotic cardiovascular disease (ASCVD) in women remains understudied, under-diagnosed, and under-treated. Traditional risk factors affect men’s and women’s hearts differently. However, the current risk stratification tools do not consider such sex-specific factors. We aimed to investigate the utility of bone mineral density (BMD) with dual-energy X-ray absorptiometry (DXA) scoring as a predictor of ASCVD in women. Methods: Data of 1,995 patients who underwent DXA scanning from 2012 to 2014 at multiple centers within our health system were collected through a chart review and using the SlicerDicer tool of Epic electronic medical records (EMR) to identify comorbidities and outcomes. Age, sex, race, history of hypertension (HTN), hyperlipidemia (HLD), diabetes mellitus (DM), body mass index (BMI), and smoking status were noted. The primary outcome was the composite of ASCVD events (stroke, myocardial infarction (MI) and cardiac death). Osteoporosis was defined as a T score of < -2.5, and osteopenia was defined as a combined T score between -1.5 to -2.5 in either hip, one of the femurs or combined. Results: Of the 1,995 female participants who underwent DXA scanning, 245 patients (10.8%) experienced ASCVD events during the mean follow-up of 9 years. After adjusting covariables, women with osteoporosis and combined low BMD have higher odds of the composite ASCVD events compared to normal BMD (odds ratio (OR) 4.60 (2.783 - 7.867), P < 0.0001). Low BMD in each site, the right femur, left femur, and hip is associated with an increased risk of ASCVD events (OR 6.50 (3.637 - 11.608), P < 0.0001; OR 5.07 (3.166 - 8.108), P < 0.000; OR 3.36 (2.127 - 5.312), P < 0.0001, respectively). Osteoporosis is independently linked to a 4.25-fold rise in MI incidence and a 3.64-fold rise in stroke. Osteopenia was not associated with ASCVD events (OR 1.29 (0.754 - 2.204), P = 0.35416). Conclusions: BMD measurement with DXA scan could stratify and predict the risk of ASCVD events in women, with no additional economic strain on healthcare. Further wide-scale studies are needed to utilize this potentially promising predictor and a commonly used test. J Endocrinol Metab. 2022;12(4-5):125-133 doi: https://doi.org/10.14740/jem840
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".