The Extending Spectrum of NPC1-Related Human Disorders: From Niemann–Pick C1 Disease to Obesity
Bibliographic record
Abstract
The Niemann-Pick type C1 (NPC1) protein regulates the transport of cholesterol and fatty acids from late endosomes/lysosomes and has a central role in maintaining lipid homeostasis. NPC1 loss-of-function mutations in humans cause NPC1 disease, a rare autosomal-recessive lipid-storage disorder characterized by progressive and lethal neurodegeneration, as well as liver and lung failure, due to cholesterol infiltration. In humans, genome-wide association studies and post-genome-wide association studies highlight the implication of common variants in NPC1 in adult-onset obesity, body fat mass, and type 2 diabetes. Heterozygous human carriers of rare loss-of-function coding variants in NPC1 display an increased risk of morbid adult obesity. These associations have been confirmed in mice models, showing an important interaction with high-fat diet. In this review, we describe the current state of knowledge for NPC1 variants in relationship to pleiotropic effects on metabolism. We provide evidence that NPC1 gene variations may predispose to common metabolic diseases by modulating steroid hormone synthesis and/or lipid homeostasis. We also propose several important directions of research to further define the complex roles of NPC1 in metabolism. This review emphasizes the contribution of NPC1 to obesity and its metabolic complications.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".