Limb-girdle muscular dystrophy type 2B causes HDL-C abnormalities in patients and statin-resistant muscle wasting in dysferlin-deficient mice
Bibliographic record
Abstract
Limb-girdle muscular dystrophy (MD) type 2B (LGMD2B) and Duchenne MD (DMD) are caused by mutations to the Dysferlin and Dystrophin genes, respectively. We have recently demonstrated in typically mild dysferlin- and dystrophin-deficient mouse models that increased plasma cholesterol levels severely exacerbate muscle wasting, and that DMD patients display primary dyslipidemia characterized by elevated plasma cholesterol and triglycerides. Herein, we investigate lipoprotein abnormalities in LGMD2B and if statin therapy protects dysferlin-deficient mice (Dysf) from muscle damage. Herein, lipoproteins and liver enzymes from LGMD2B patients and dysferlin-null (Dysf) mice were analyzed. Simvastatin, which exhibits anti-muscle wasting effects in mouse models of DMD and corrects aberrant expression of key markers of lipid metabolism and endogenous cholesterol synthesis, was tested in Dysf mice. Muscle damage and fibrosis were assessed by immunohistochemistry and cholesterol signalling pathways via Western blot. LGMD2B patients show reduced serum high-density lipoprotein cholesterol (HDL-C) levels compared to healthy controls and exhibit a greater prevalence of abnormal total cholesterol (CHOL)/HDL-C ratios despite an absence of liver dysfunction. While Dysf mice presented with reduced CHOL and associated HDL-C and LDL-C-associated fractions, simvastatin treatment did not prevent muscle wasting in quadriceps and triceps muscle groups or correct aberrant low-density lipoprotein receptor (LDLR) and 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGCR) protein expression. LGMD2B patients present with reduced serum concentrations of HDL-C, a major metabolic comorbidity, and as a result, statin therapy is unlikely to prevent muscle wasting in this population. We propose that like DMD, LGMD2B should be considered as a new type of genetic dyslipidemia.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".