Serum Neurofilament Light Chain and Structural and Functional Nerve Fiber Loss in Painful and Painless Diabetic Polyneuropathy
Bibliographic record
Abstract
AIMS: To explore associations between the axonal protein neurofilament light chain (NfL) and severity of diabetic polyneuropathy (DPN) and pain. METHODS: We performed cross-sectional analysis of a subset of the PiNS/DOLORisk cohort of people with DPN with and without neuropathic pain. Biobank samples were analyzed for serum NfL (s-NfL) using single molecule array. DPN was defined by Toronto criteria for probable or confirmed DPN. Painful DPN (PDPN) was evaluated according to IASP criteria. Measures of DPN severity included clinical DPN scales, quantitative sensory testing (QST) and intraepidermal nerve fiber density (IENFD). RESULTS: Participants with confirmed (N = 172) or probable DPN (N = 29) were included. There was no s-NfL difference between participants with DPN (N = 79, 22.8 ng/L [IQR 17.4; 31.3]) and PDPN (N = 122, 22.2 ng/L [16.0; 34.4]). S-NfL was not associated with pain severity or DPN severity evaluated by clinical DPN scales. Higher s-NfL was associated with lower IENFD (13.6 % [95 % CI 3.1; 22.9], unit = 1 fiber/mm, N = 24) and more pronounced loss of nerve fiber function measured by QST (p-trend = 0.02). CONCLUSIONS: Higher s-NfL was associated with nerve fiber dysfunction and loss quantified by QST and IENFD, but not with pain or clinical DPN scales. S-NfL may reflect the severity of nerve fiber damage underlying DPN.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".