Effects of circulating inflammatory proteins on spinal degenerative diseases: Evidence from genetic correlations and Mendelian randomization study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: Numerous investigations have suggested links between circulating inflammatory proteins (CIPs) and spinal degenerative diseases (SDDs), but causality has not been proven. This study used Mendelian randomization (MR) to investigate the causal associations between 91 CIPs and cervical spondylosis (CS), prolapsed disc/slipped disc (PD/SD), spinal canal stenosis (SCS), and spondylolisthesis/spondylolysis. Methods: Genetic variants data for CIPs and SDDs were obtained from the genome-wide association studies (GWAS) database. We used inverse variance weighted (IVW) as the primary method, analyzing the validity and robustness of the results through pleiotropy and heterogeneity tests and performing reverse MR analysis to test for reverse causality. Results: The IVW results with Bonferroni correction indicated that beta-nerve growth factor (β-NGF), C-X-C motif chemokine 6 (CXCL6), and interleukin-6 (IL-6) can increase the risk of CS. Fibroblast growth factor 19 (FGF19), sulfotransferase 1A1 (SULT1A1), and tumor necrosis factor-beta (TNF-β) can increase PD/SD risk, whereas urokinase-type plasminogen activator (u-PA) can decrease the risk of PD/SD. FGF19 and TNF can increase SCS risk. STAM binding protein (STAMBP) and T-cell surface glycoprotein CD6 isoform (CD6 isoform) can increase the risk of spondylolisthesis/spondylolysis, whereas monocyte chemoattractant protein 2 (MCP2) and latency-associated peptide transforming growth factor beta 1 (LAP-TGF-β1) can decrease spondylolisthesis/spondylolysis risk. Conclusions: MR analysis indicated the causal associations between multiple genetically predicted CIPs and the risk of four SDDs (CS, PD/SD, SCS, and spondylolisthesis/spondylolysis). This study provides reliable genetic evidence for in-depth exploration of the involvement of CIPs in the pathogenic mechanism of SDDs and provides novel potential targets for SDDs.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it