Radiographic Progression of Lumbar Spine Disc Degeneration Is Influenced by Variation at Inflammatory Genes
Bibliographic record
Abstract
STUDY DESIGN: A candidate gene association study in a longitudinal cohort. OBJECTIVE: To investigate the association between polymorphisms at 25 candidate genes and progression of individual radiographic features of lumbar disc degeneration (LDD). SUMMARY OF BACKGROUND DATA: LDD is characterized radiographically by the presence of osteophytes and disc space narrowing and is known to have a genetic component. Because of the high prevalence of radiographic features, progression may be a more useful phenotype clinically to study than prevalence itself. METHODS: We tested the effect on radiographic progression of LDD of polymorphisms in 25 genes, 24 of which had been previously tested with regards to knee osteoarthritis. The progression traits used were the change in radiographic grade over 9 years in osteophytes, disc space narrowing, and summary Kellgren-Lawrence grade. Lumbar spine radiographs (L1-L5) at baseline and at follow-up were read for 720 women genotyped at the 25 genes participating in the Chingford study. RESULTS: Polymorphisms in MMP3, TIMP1, and COX2, which encode molecules involved in inflammatory pathways, were associated with radiographic progression of LDD. The strongest associations observed (statistically significant after correcting for multiple comparisons) were between COX2 and change in osteophyte grade (P < 0.001) and Kellgren-Lawrence grade (P < 2 x 10(-5)), and between the genes for vitamin D receptor (P < 0.002) and a thrombospondin (THSD2) (P < 0.002) and change in osteophyte grade. CONCLUSIONS: Our results suggest a role for genes regulating inflammatory pathways in the radiographic progression of spine degeneration. This could prove a fruitful area for future therapeutics for the spine and other joints.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".