Nutritional and exercise-based therapies in the treatment of mitochondrial disease
Bibliographic record
Abstract
PURPOSE OF REVIEW: This review will critically summarize the nutritional and exercise-based interventions that have been used to treat mitochondrial disease, with a focus on the biochemical or molecular rationale for their use as well as recent advances in the field. RECENT FINDINGS: Many nutritional-based treatment strategies have been used in an attempt to target energy impairment and its sequelae. Recently, coenzyme Q10, idebenone and triacylglycerol have been shown to bypass defective respiratory enzymes or scavenge free radicals, whereas creatine monohydrate has provided an alternative energy source. Thiamine has been used to decrease lactate levels and increase flux through aerobic metabolism, and riboflavin has been used as a precursor to complexes I and II. Several therapies employing various antioxidants in combination with other supplements have been effective at targeting several of the final common pathways of mitochondrial disease. Miscellaneous supplements, such as L-arginine and uridine, have also had recent success. However, although positive responses have been reported with these agents, many reports have shown no benefit, and there is widespread disparity in the literature. An alternative approach to treatment is exercise training. Both resistance and endurance exercise training have had positive outcomes in patients with mitochondrial disease, although several questions remain to be answered. SUMMARY: There is no currently recognized treatment for mitochondrial disease. Future clinical trials are needed, as well as research into the potential for in-vitro screening of various compounds within affected cells from patients. Until this time, an accurate diagnosis will facilitate treatment on a case-by-case basis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".