SCN9A Variants May be Implicated in Neuropathic Pain Associated With Diabetic Peripheral Neuropathy and Pain Severity
Bibliographic record
Abstract
OBJECTIVES: Previous studies have established the role of SCN9A in various pain conditions, including idiopathic small fiber neuropathy. In the present study, we interrogate the relationship between common and rare variants in SCN9A gene and chronic neuropathic pain associated with diabetic peripheral neuropathy. DESIGN: Using a cohort of 938 patients of European ancestry with chronic neuropathic pain associated with diabetic peripheral neuropathy enrolled in 6 clinical studies and 2 controls (POPRES, n=2624 and Coriell, n=1029), we examined the relationship between SCN9A variants and neuropathic pain in a case-control study using a 2-stage design. The exonic regions of SCN9A were sequenced in a subset of 244 patients with neuropathic pain, and the variants discovered were compared with POPRES control (stage 1). The top associated variants were followed up by genotyping in the entire case collection and Coriell controls restricting the analysis to the matching patients from the United States and Canada only (stage 2). RESULTS: Seven variants were found to be associated with neuropathic pain at the sequencing stage. Four variants (Asp1908Gly, Val991Leu/Met932Leu, and an intronic variant rs74449889) were confirmed by genotyping to occur at a higher frequency in cases than controls (odds ratios ∼2.1 to 2.6, P=0.05 to 0.009). Val991Leu/Met932Leu was also associated with the severity of pain as measured by pain score Numeric Rating Scale (NRS-11, P=0.047). Val991Leu/Met932Leu variants were in complete linkage disequilibrium and previously shown to cause hyperexcitability in dorsal root ganglia neurons. CONCLUSIONS: The association of SCN9A variants with neuropathic pain and pain severity suggests a role of SCN9A in the disease etiology of neuropathic pain.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".