Identifying Causes of Fracture Beyond Bone Mineral Density: Evidence From Human Genetics
Bibliographic record
Abstract
New therapies may help to prevent osteoporotic fractures other than through increasing bone mineral density (BMD). Because fracture risk has an important genetic component, we aim to identify loci increasing fracture risk that do not decrease BMD, using a recently-proposed structural equation model adapted to remove genetic influences of BMD on fracture risk. We used summary statistics of the largest genome-wide association studies (GWASs) for BMD and for fracture in these analyses. We next estimated the genetic correlation between the non-BMD or BMD-related genetic effects and other clinical risk factors for fracture. Last, based on white British participants in the UK Biobank, we conducted genetic risk score analyses to assess whether the aggregated genetic effects conferred increased major osteoporotic fracture risk. We found that only three loci affecting fracture risk exhibited genetic effects not mediated by BMD: SOST, CPED1-WNT16, and RSPO3, while these three loci simultaneously conferred BMD-related effects. No strong genetic associations between non-BMD or BMD-related effects and 16 clinical risk factors were observed. However, non-BMD effects might be genetic correlated with hip bone size. In the UK Biobank, a 1 standard deviation (1-SD) increase in the non-BMD genetic risk score conferred an odds ratio of 1.17 for incident major osteoporotic fracture, compared to 1.29 by a BMD-related genetic risk score. Our study suggests that the majority of common genetic predisposition toward fracture risk acts upon BMD. Although non-BMD genetic effects may exist, they are not strongly correlated with most traditional clinical risk factors. Risk loci harboring non-BMD genetic effects may influence other perspectives of bone quality, or confer effects that existing GWASs fail to capture, but they demonstrate weaker impact on fracture risk than BMD-related genetic effects. These findings suggest that most successful drug development programs for osteoporosis should focus on pathways identified through BMD-associated loci. © 2022 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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".