Targeted Therapies for Metabolic Myopathies Related to Glycogen Storage and Lipid Metabolism: a Systematic Review and Steps Towards a ‘Treatabolome’
Bibliographic record
Abstract
BACKGROUND: Metabolic myopathies are a heterogenous group of muscle diseases typically characterized by exercise intolerance, myalgia and progressive muscle weakness. Effective treatments for some of these diseases are available, but while our understanding of the pathogenesis of metabolic myopathies related to glycogen storage, lipid metabolism and β-oxidation is well established, evidence linking treatments with the precise causative genetic defect is lacking. OBJECTIVE: The objective of this study was to collate all published evidence on pharmacological therapies for the aforementioned metabolic myopathies and link this to the genetic mutation in a format amenable to databasing for further computational use in line with the principles of the "treatabolome" project. METHODS: A systematic literature review was conducted to retrieve all levels of evidence examining the therapeutic efficacy of pharmacological treatments on metabolic myopathies related to glycogen storage and lipid metabolism. A key inclusion criterion was the availability of the genetic variant of the treated patients in order to link treatment outcome with the genetic defect. RESULTS: Of the 1,085 articles initially identified, 268 full-text articles were assessed for eligibility, of which 87 were carried over into the final data extraction. The most studied metabolic myopathies were Pompe disease (45 articles), multiple acyl-CoA dehydrogenase deficiency related to mutations in the ETFDH gene (15 articles) and systemic primary carnitine deficiency (8 articles). The most studied therapeutic management strategies for these diseases were enzyme replacement therapy, riboflavin, and carnitine supplementation, respectively. CONCLUSIONS: This systematic review provides evidence for treatments of metabolic myopathies linked with the genetic defect in a computationally accessible format suitable for databasing in the treatabolome system, which will enable clinicians to acquire evidence on appropriate therapeutic options for their patient at the time of diagnosis.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".