Muscarinic Receptor Antagonist Improves Nerve Fiber Function in Subjects with Type 2 Diabetes and Peripheral Neuropathy
Bibliographic record
Abstract
Degeneration of nerve fibers due to diabetic peripheral neuropathy (DPN) has been linked to mitochondrial dysfunction. Manipulation of mitochondrial dysfunction through antagonism of muscarinic receptors (MR) promotes neurite outgrowth in adult sensory neurons in vitro and provides neuroprotection in rodent models of DPN. The aim of the study was to assess the efficacy of MR antagonist topical 3% oxybutynin in structural and functional measures of nerve fiber function in subjects with type 2 diabetes (T2DM) and DPN. Pilot, randomized, placebo-controlled, double-blinded study in 40 subjects assessed at baseline and after 20 weeks of treatment with oxybutynin or placebo with the following: intraepidermal nerve fiber density (IENFD) on proximal and distal leg; neuropathy scores and quality of life (Norfolk QoL DN) questionnaire. Baseline demographic characteristics were similar between the treatment groups. IENFD improved significantly after 20 weeks for the treatment group. Neuropathy scores and Norfolk QoL DN also improved significantly in the treatment group (Table 1). No improvements were seen in the placebo group. In this study, oxybutynin proves to be efficacious in improving structural and functional measures of small fiber function, and quality of life in T2DM subjects. These results offer a promising novel therapeutic approach for DPN that needs to be explored further. Disclosure A.I. Vinik: None. N.A. Calcutt: Stock/Shareholder; Self; WinSanTor, Inc.. J.F. Edwards: None. J.R. Weaver: None. M.D. Bailey: None. P. Fernyhough: Stock/Shareholder; Self; WinSanTor, Inc.. L.B. Cundra: None. K.E. Frizzi: None. H. Parson: None. C.M. Casellini: None.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".