Association of type 2 diabetes with osteoporosis and fracture risk: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Osteoporosis, a systemic skeletal disease characterized by low bone mass and increased fracture risk, poses significant social and economic challenges globally, while type 2 diabetes mellitus (T2DM), a prevalent metabolic disorder, has been linked to complex effects on bone health, including contradictory findings on its relationship with osteoporosis and fracture risk. METHODS: We searched PubMed, Embase, Cochrane, and Web of Science Library to identify observational studies investigating whether people with T2DM have a higher risk of osteoporosis or fracture than people without diabetes. The time limit for literature retrieval was from the establishment of the database until March 2023. The quality of the studies was assessed using the Newcastle-Ottawa Scale and Agency for Healthcare Research and Quality checklist. The meta-analysis was conducted using Stata 15, and a random-effects model was used if I2 was > 50%. The Egger test was used to assess publication bias. RESULTS: The results demonstrated that people with T2DM have a higher risk of osteoporosis. (relative risk, 1.841; 95% confidence interval, 1.219-2.780; P = .004). Similar results were demonstrated for fractures (relative risk, 1.21; 95% confidence interval, 1.09-1.31; P < .001). However, the results of the subgroup analysis showed no significant correlation between T2DM and fractures in univariate analysis, cross-sectional studies, Asia, Europe, Oceania, and vertebral fractures. However, a significant correlation was found in other subgroup analyses. CONCLUSIONS: Osteoporosis and fractures are significantly associated with T2DM.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.034 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".