Plasma Proteomic Biomarkers of Degenerative Cervical Myelopathy in the UK Biobank
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The diagnosis of spinal cord dysfunction in degenerative cervical myelopathy (DCM) is currently based on correlating clinical signs and symptoms with neuroimaging findings. Plasma proteomics represents an opportunity to establish objective, blood-based biomarkers, which predict DCM severity or response to surgical intervention. These methods may also support novel insights into its pathophysiological relationship with other neurological diseases, such as multiple sclerosis (MS), which also can present with myelopathy, or peripheral neuropathy (PN), which has overlapping symptoms with DCM. In this study, we examined the relationships between circulating blood protein levels with DCM, MS, and PN using data from the UK Biobank Pharma Proteomics Project. METHODS: The plasma proteomic profiles of participants with a diagnosis of DCM within 5 years of blood draw, who had not undergone surgical intervention (n = 42) were compared with those without known neural injury or neurodegenerative disease (n = 39 519) using case-control matching. To further characterize the difference between central and peripheral nerve injury, case-control analyses were also performed using participants with a diagnosis of MS (n = 125) and PN (n = 101) within 5 years of blood draw. RESULTS: The top proteins significantly associated with DCM included neuron markers RNA-binding Fox-1 homolog 3 and neurofilament light chain. These proteins were also significantly elevated in the PN group relative to controls, and neurofilament light was elevated in MS. CONCLUSION: These findings suggest that neural injury in DCM can be detected using blood protein levels and may hold value in diagnostic, monitoring, and potentially prognostic applications.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".