The role of genetic and epigenetic factors in determining the risk of spinal fragility fractures: new insights in the management of spinal osteoporosis
Bibliographic record
Abstract
Osteoporosis predisposes patients to spinal fragility fractures. Imaging plays a key role in the diagnosis and prognostication of these osteoporotic vertebral fractures (OVF). However, the current imaging knowledge base for OVF is lacking sufficient standardisation to enable effective risk prognostication. OVF have been shown to be more prevalent in Caucasian patient cohorts in comparison to the Eastern Asian population. These population-based differences in risk for developing OVF suggest that there could be genetic and epigenetic factors that drive the pathogenesis of osteoporosis, low bone mineral density (BMD) and OVF. Several genetic loci have been associated with a higher vertebral fracture risk, although at varying degrees of significance. The present challenge is clarifying whether these associations are specific to vertebral fractures or osteoporosis more generally. Furthermore, these factors could be exploited for diagnostic interpretation as biomarkers [including novel long non-coding (lnc)RNAs, micro (mi)RNAs and circular (circ)RNAs]. The extent of methylation of genes, alongside post-translational histone modifications, have shown to affect several interlinked pathways that converge on the regulation of bone deposition and resorption, partially through their influence on osteoblast and osteoclast differentiation. Lastly, in addition to biomarkers, several exciting new imaging modalities could add to the established dual-energy X-ray absorptiometry (DXA) method used for BMD assessment. New technologies, and novel sequences within existing imaging modalities, may be able to quantify the quality of bone in addition to the BMD and bone structure; these are making progress through various stages of development from the pre-clinical sphere through to deployment in the clinical setting. In this mini review, we explore the literature to clarify the genetic and epigenetic factors associated with spinal fragility fractures and delineate the causal genes, pathways and interactions which could drive different risk profiles. We also outline the cutting-edge imaging modalities which could transform diagnostic protocols for OVF.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".