Outcome of creatine supplementation therapy in phosphoglucomutase-1 deficiency associated congenital disorders of glycosylation: Novel insights
Bibliographic record
Abstract
Background: result in phosphoglucomutase 1 (PGM1) deficiency that is one of the congenital disorders of glycosylation (CDG) (PGM1-CDG). Phenotypic spectrum includes congenital malformations, and muscular, cardiac, hepatic, endocrine and hematologic phenotypes. Current treatment consists of D-galactose therapy that results in clinical and biochemical improvements. To improve fatigue, and exercise intolerance, we started creatine supplementation therapy. Material and methods: We reviewed electronic patient chart. We applied Nijmegen Pediatric CDG Rating Scale (NPCRS) and The Functional Assessment of Chronic Illness Therapy Fatigue scale (FACIT-F). We measured creatine metabolism biomarkers. Results: This is a 29-year-old female with PGM1-CDG, confirmed diagnosis by clinical exome sequencing. She has been treated with D-galactose therapy which did not improve her fatigue and exercise intolerance. She was started on creatine supplementation therapy at the age of 27 years which led to decreased daytime sleeping, increased exercise capacity and improvements in her NPCRS, and FACIT-F. Her plasma guanidinoacetate was low. She had elevated urine galactitol on D-galactose therapy. Discussion: PGM1-CDG associated myopathy is likely due to combination of several factors including abnormal muscle carbohydrate metabolism, abnormal N-glycosylation of proteins involved in the muscle functions and creatine transport and altered muscle energy homeostasis. It was previously shown that creatine supplementation therapy improves myopathy in patients with mitochondrial cytopathies. We think that the use of creatine supplementation therapy coincided with improvements in fatigue and exercise intolerance subjectively and objectively in our patient.
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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 | 0.000 | 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) | 0.001 | 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".