Giant axonal neuropathy (GAN): cross-sectional data on phenotypes, genotypes, and proteomic signature from a German cohort
Bibliographic record
Abstract
Giant axonal neuropathy (GAN) is a progressive neurodegenerative disease affecting the peripheral and central nervous system and is caused by bi-allelic variants in the GAN gene, leading to loss of functional gigaxonin protein. A treatment does not exist, but a first clinical trial using a gene therapy approach has recently been completed. Here, we conducted the first systematic study of GAN patients treated by German-speaking child neurologists. We collected clinical, genetic, and epidemiological data from a total of 15 patients representing one of the largest cohorts described thus far. Average age of patients was 11.7 years at inclusion. The most frequently reported symptoms (HPO coded) were gait disturbance and muscle weakness, abnormality of muscle size, and abnormal reflexes. In line with the frequency of homozygous variants, in five families, parents reported being at least distantly related. In 14 patients, diagnosis was confirmed by molecular genetic testing, revealing eight different GAN variants, four being reported as pathogenic in the literature. Proteomics of white blood cells derived from four patients was conducted to obtain unbiased insights into the underlying pathophysiology and revealed dysregulation of 111 proteins implicated in diverse biological processes. Of note, diverse of these proteins is known to be crucial for proper synaptic function and transmission and affection of intermediate filament organisation and proteolysis, which is in line with the known functions of gigaxonin.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".