The Association Between Phosphorylated Neurofilament Heavy Chain (pNF-H) and Small Fiber Neuropathy (SFN) in Patients with Impaired Glucose Tolerance
Bibliographic record
Abstract
INTRODUCTION: Small fiber neuropathy (SFN)-the early stage of diabetic peripheral neuropathy (DPN)-progresses gradually and is difficult to diagnose using neurophysiological tests. To facilitate the early diagnosis of SFN, biomarkers for SFN must be identified. The purpose of this study was to investigate the characteristics of SFN in prediabetic patients and the relationship between pNF-H and SFN. METHODS: 44 IGT patients (inpatients and outpatients) were selected at random. 33 healthy subjects served as controls. Data on clinical characteristics and laboratory parameters were collected. Quantitative sensory testing (QST), electromyography (EMG), and Sudoscan were performed, and pNF-H was measured by ELISA. RESULTS: 24 of the 44 patients with impaired glucose tolerance (IGT) were diagnosed with SFN according to the modified Toronto Diabetic Neuropathy Expert Group consensus criteria. The thermal sensory thresholds of the IGT-SFN group were significantly different from those of the CTRL group (p < 0.05), except for the heat pain threshold. The sensory nerve action potential (SNAP) of the sural nerve was 12.39 in the IGT-SFN group, which was significantly lower than those in the other groups. No significant difference in nerve conduction velocity (NCV) was observed among the three groups. The electrochemical skin conductance (ESC) in the IGT-SFN group was 69.78 ± 14.03uS, which was significantly lower than that in the CTRL group. The pNF-H in the IGT-SFN group was 170.6 (140.0, 223.6) pg/ml, which was significantly higher than those in the CTRL and IGT-non-SFN groups (76.55 and 64.7 pg/ml, respectively). Multivariate regression analysis demonstrated that pNF-H and 2h plasma glucose were independently correlated with SFN; the ORs (95% CI) were 1.429 (1.315, 1.924) and 2.375 (1.157, 4.837), respectively. CONCLUSIONS: Serum pNF-H may be associated with SFN in IGT patients, and serum pNF-H could therefore serve as a sensitive biomarker for the detection of SFN.
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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 | 0.000 | 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".