Genetic predisposition to fracture non-union: a case control study of a preliminary single nucleotide polymorphisms analysis of the BMP pathway
Bibliographic record
Abstract
BACKGROUND: Despite the known multi-factorial nature of atrophic fracture non-unions, a possible genetic predisposition for the development of this complication after long bone fractures remains unknown. This pilot study aimed to address this issue by performing a preliminary SNP analysis of specific genes known to regulate fracture healing. METHODS: A total of fifteen SNPs within four genes of the Bone Morphogenetic Protein (BMP) pathway (BMP-2, BMP-7, NOGGIN and SMAD6) were examined, in 109 randomly selected patients with long bone fractures as a result of motor vehicle accident, fall or direct blow. There were sixty-two patients with atrophic non-union and forty-seven patients (54 fractures) with uneventful fracture union. Overall SNPs frequencies were computed with respect to patient's age, gender, smoking habits, fracture-associated parameters and the use of nonsteroidal anti-inflammatory drugs (NSAIDs), and tested for their association to the impaired bone healing process, using binary logistic regression (STATA 11.1; StataCorp, Texas USA). RESULTS: Statistical analysis revealed age to be an important covariate in the development of atrophic non-union (p = 0.01, OR 1.05 [per year]), and two specific genotypes (G/G genotype of the rs1372857 SNP, located on NOGGIN and T/T genotype of the rs2053423 SNP, located on SMAD6) to be associated with a greater risk of fracture non-union (p = 0.02, OR 4.56 and p = 0.04, OR 10.27, respectively, after adjustment for age). CONCLUSIONS: This is the first clinical study to investigate the potential existence of genetic susceptibility to fracture non-union. Even though no concrete conclusions can be obtained from this pilot study, our results indicate the existence of a potential genetically predetermined impairment within the BMP signalling cascade, initiated after a fracture and when combined with other risk factors could synergistically increase the susceptibility of a patient to develop non-union. Further research is desirable in order to clarify the genetic component and its role and interaction with other risk factors in the development of atrophic long bone non-union, as simple genetic testing may contribute to the early identification of patients at risk in the future and the on-time intervention at the biologic aspects of bone healing.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".