Bidirectional association between cognitive impairment and bone mineral density reduction in aging populations: A systematic review and meta-analysis of osteoporosis, osteopenia, and bone mineral content
Bibliographic record
Abstract
Osteoporosis is a skeletal disorder characterized by reduced bone mineral density (BMD), increasing fracture risk. Cognitive disorders (CD), including Alzheimer's disease, dementia, and mild cognitive impairment (MCI), cause cognitive decline and are prevalent among older adults. This systematic review and meta-analysis explores the bidirectional association between cognitive impairment and bone health, specifically low BMD, osteopenia, and osteoporosis. Comprehensive searches were performed in PubMed, Scopus, and the Cochrane Library through August 15, 2024. Eligible observational studies assessing the association between cognitive impairment and BMD were included. Two reviewers independently evaluated studies, with data analyzed using fixed and random-effects models in STATA 17. Risk of bias was assessed with the Newcastle-Ottawa Scale and AHRQ checklist. Fifteen studies involving 93 to 47,579 participants were analyzed. Results showed individuals with cognitive impairment had a significantly higher risk of osteoporosis (log RR: 0.59, 95 % CI: 0.27-0.92, p < 0.001) and a higher risk of osteopenia (log RR: -0.18, 95 % CI: -0.41-0.05). Conversely, those with osteoporosis were more likely to develop cognitive impairment (log RR: 0.34, 95 % CI: 0.19-0.48, p < 0.001). Comparing mean BMD between cognitively impaired individuals and controls revealed lower BMD in the cognitive impairment group: spine (Cohen's d: -0.26, 95 % CI: -0.57-0.04) and femur (Cohen's d: -0.39, 95 % CI: -0.63--0.16). These findings underscore the importance of bone health monitoring in patients with cognitive impairment and preventing cognitive decline in those with osteoporosis. Longitudinal studies with larger, diverse populations are warranted to confirm these results.
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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 | 0.013 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.030 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".