Plasma protein profiling of multiple sclerosis using proximity extension assays
Bibliographic record
Abstract
Abstract Background Multiple sclerosis (MS) is an inflammatory disease characterized by demyelination and neuro-axonal degeneration in the central nervous system. Except for neurofilament light protein, identification of biomarkers has been difficult to assess in the blood, presumably due partly to sensitivity. To detect traces of disease activities in the periphery and identify low-abundance protein biomarkers, this study conducts an exploratory examination of the plasma proteome of MS using proximity extension technology, a high-sensitivity multiplex PCR-based immunoassay. Methods A case-control cohort consisting of 52 MS cases (relapsing-remitting=30, progressive=22) and 17 healthy controls were enrolled at the Karolinska University Hospital. EDTA plasma was analyzed for 1157 unique protein targets across thirteen proximity extension assays. Protein associations to disease outcomes and related clinical measures were assessed using a multivariable linear regression model corrected for sex and age at sampling. Results AHCY and CHR levels were higher among MS cases than controls, while FABP2 was lower among those with relapsing-remitting disease than controls (P discovery <0.05, P replication <0.05), although not significant after multiple test corrections. Furthermore, PTN and CYR61 levels were higher in progressive MS than in relapsing-remitting disease (P<0.0002, P FDR <0.05), and CRNN and CXCL13 were associated with more severe disability at sampling (P<0.0001, P FDR <0.05), independent of disease course. CTSF was positively correlated with disease duration (P=4.1×10 −5 , P FDR =0.044), while RRM2B level correlated with intrathecal immunoglobulin production (IgG Index) in relapsing-remitting MS (P=1.7×10 −5 , P FDR =0.018). Conclusion We provide several candidates for characterizing MS, particularly progressive disease, which may help monitor disease progression and treatment response in a clinical setting.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".