Vascular function and skeletal fragility: a study of tonometry, brachial hemodynamics, and bone microarchitecture
Bibliographic record
Abstract
Osteoporosis and cardiovascular disease frequently occur together in older adults; however, a causal relationship between these 2 common conditions has not been established. By the time clinical cardiovascular disease develops, it is often too late to test whether vascular dysfunction developed before or after the onset of osteoporosis. Therefore, we assessed the association of vascular function, measured by tonometry and brachial hemodynamic testing, with bone density, microarchitecture, and strength, measured by HR-pQCT, in 1391 individuals in the Framingham Heart Study. We hypothesized that decreased vascular function (pulse wave velocity, primary pressure wave, brachial pulse pressure, baseline flow amplitude, and brachial flow velocity) contributes to deficits in bone density, microarchitecture and strength, particularly in cortical bone, which is less protected from excessive blood flow pulsatility than the trabecular compartment. We found that individuals with increased carotid-femoral pulse wave velocity had lower cortical volumetric bone mineral density (tibia: -0.21 [-0.26, -0.15] standardized beta [95% CI], radius: -0.20 [-0.26, -0.15]), lower cortical thickness (tibia: -0.09 [-0.15, -0.04], radius: -0.07 [-0.12, -0.01]) and increased cortical porosity (tibia: 0.20 [0.15, 0.25], radius: 0.21 [0.15, 0.27]). However, these associations did not persist after adjustment for age, sex, height, and weight. These results suggest that vascular dysfunction with aging may not be an etiologic mechanism that contributes to the co-occurrence of osteoporosis and cardiovascular disease in older adults. Further study employing longitudinal measures of HR-pQCT parameters is needed to fully elucidate the link between vascular function and 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".