Sex-Specific Associations Between Cardiac Workload, Peripheral Vascular Calcification, and Bone Mineral Density: The Gambian Bone and Muscle Aging Study
Bibliographic record
Abstract
Abstract Noncommunicable diseases (NCD) are rapidly rising in Africa, with multimorbidity increasing the burden on health and social care. Osteoporosis and cardiovascular disease (CVD) share common risk factors; both often remain undiagnosed until a major life-threatening event occurs. We investigated the associations between cardiac workload, peripheral vascular calcification (PVC), and bone parameters in Gambian adults. The Gambian Bone and Muscle Aging Study (GamBAS) recruited 249 women and 239 men aged 40 to 75+ years. Body composition and areal bone mineral density (aBMD) were measured using dual-energy X-ray absorptiometry; peripheral quantitative computed tomography (pQCT) scans were performed at the radius and tibia. Supine blood pressure and heart rate were measured and used to calculate rate pressure product and pulse pressure. Presence of PVC was determined from tibia pQCT scans. Sex interactions were tested (denoted as p-int); adjustments were made for residuals of appendicular lean mass (ALM) and fat mass (FM). There were negative associations between rate pressure product and aBMD in women only, all p-int < .05; after adjustment for ALM residuals, for every 10% increase in rate pressure product, aBMD was lower at the whole body (−0.6% [−1.2, −0.1]), femoral neck (−0.9% [−1.8, −0.05]), L1 to L4 (−0.6% [−1.7, 0.5]), and radius (−1.9% [−2.8, −0.9]); there were similar associations when adjusted for FM residuals. Similar negative associations were found between pulse pressure and aBMD in women only. PVC were found in 26.6% men and 22.5% women; women but not men with calcification had poorer cardiac health and negative associations with aBMD (all sites p-int < .001). There were consistent associations with cardiac parameters and pQCT outcomes at the radius and tibia in women only. Multiple markers of cardiac health are associated with poorer bone health in Gambian women. In the context of epidemiological transition and changing NCD burden, there is a need to identify preventative strategies to slow/prevent the rising burden in CVD and osteoporosis in Sub-Saharan Africa. © 2020 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".