Plasma gangliosides correlate with disease stages and symptom severity in Huntington’s disease carriers
Bibliographic record
Abstract
ABSTRACT Background Gangliosides - glycosphingolipids that modulate cell signaling and neuronal functions - are decreased in Huntington’s disease (HD) models and patients’ brains. Restoring ganglioside GM1 has therapeutic benefits in HD mice, slowing neurodegeneration and improving symptoms. This suggests gangliosides might contribute to HD pathogenesis. However, their link to disease severity and progression in patients remains unclear. Objectives This study examined plasma ganglioside differences between HD gene carriers and controls, and their prognostic potential. Methods Plasma gangliosides were quantified in 67 HD carriers and 46 healthy participants, using liquid chromatography-tandem mass spectrometry. Statistical modelling assessed associations with clinical measures and prognostic potential. Results Levels of most gangliosides were similar between groups, but GM3 was higher and GT1b lower in HD carriers. Within the HD group, higher GM2 levels correlated with better cognition, and higher GM1 and GD1a with greater functional capacity and independence. Higher GM1 predicted HD status, but its decline and an increase in GD3 were strongly associated with disease progression. Individual gangliosides had limited disease classification ability. Conclusions The correlation between higher GM2, GD1a and GM1 and milder symptoms suggests a protective role of these gangliosides in HD. The association between higher GM1 levels and HD status, along with its decline predicting disease progression, suggests GM1 increase may be a compensatory neuroprotective mechanism that deteriorates over time. While plasma gangliosides are not strong disease classifiers, our findings provide novel insights into their role in HD progression and prognostic potential.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".