The National Niemann-Pick Type C1 Disease Database: correlation of lipid profiles, mutations, and biochemical phenotypes
Bibliographic record
Abstract
Niemann-Pick type C1 disease (NPC1) is an autosomal recessive lysosomal storage disorder characterized by neonatal jaundice, hepatosplenomegaly, and progressive neurodegeneration. The present study provides the lipid profiles, mutations, and corresponding associations with the biochemical phenotype obtained from NPC1 patients who participated in the National NPC1 Disease Database. Lipid profiles were obtained from 34 patients (39%) in the survey and demonstrated significantly reduced plasma LDL cholesterol (LDL-C) and increased plasma triglycerides in the majority of patients. Reduced plasma HDL cholesterol (HDL-C) was the most consistent lipoprotein abnormality found in male and female NPC1 patients across age groups and occurred independent of changes in plasma triglycerides. A subset of 19 patients for whom the biochemical severity of known NPC1 mutations could be correlated with their lipid profile showed a strong inverse correlation between plasma HDL-C and severity of the biochemical phenotype. Gene mutations were available for 52 patients (59%) in the survey, including 52 different mutations and five novel mutations (Y628C, P887L, I923V, A1151T, and 3741_3744delACTC). Together, these findings provide novel information regarding the plasma lipoprotein changes and mutations in NPC1 disease, and suggest plasma HDL-C represents a potential biomarker of NPC1 disease severity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".