Reduced Peripheral Nerve Conduction Velocity is Associated with Alzheimer’s Disease: A Cross-Sectional Study from China
Bibliographic record
Abstract
PURPOSE: Elderly individuals with degenerative diseases of the central nervous system are more likely to develop peripheral neuropathy; however, research is limited as to whether the decline in peripheral nerve conduction can be used as a biomarker of Alzheimer's disease (AD). PATIENTS AND METHODS: This study enrolled 74 patients with mild cognitive impairment (MCI), 21 with AD, and 82 healthy elderly individuals. All participants underwent a peripheral nerve conduction and neuropsychological evaluation. Nicolet EDX was used to assess peripheral nerve conduction in the limbs and comparisons were made between the three cognitive groups. Furthermore, the relationship between peripheral nerve conduction and cognitive function was investigated. RESULTS: A ladder-shaped difference was found in the median (p < 0.001) and common peroneal (p < 0.001) motor nerve velocity, with the control group > MCI group > AD group, even after controlling for variables. The median motor nerve amplitude in the AD group was lower than that in the control group (P = 0.017). After controlling for age, sex, education, and height, the median motor nerve velocity was positively correlated with the Montreal Cognitive Assessment (r = 0.196, p = 0.015), and the common peroneal motor nerve velocity was positively correlated with verbal fluency task-idioms (r = 0.184, p = 0.026). The median (AUC: 0.777, p < 0.001) and common peroneal motor nerve velocities (AUC: 0.862; p < 0.001) were significantly associated with the diagnosis of AD. The accuracy rate of these two motor nerve velocities to predict AD was 51.5%. CONCLUSION: Our study found that peripheral motor nerve velocity may correlate with early cognitive impairment in AD. However, the accuracy of different cognitive classifications and the value of early diagnosis are not ideal when peripheral motor nerve velocity is used alone. Whether peripheral nerve function can be used as a marker for early diagnosis of AD needs further clarification but provides a new possibility for the future of biomarker research.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".