Diabetes management in maternally inherited diabetes and deafness ( <scp>MIDD</scp> ): A review and a proposed treatment algorithm
Bibliographic record
Abstract
Maternally inherited diabetes and deafness (MIDD) is a mitochondrial disorder usually caused by the variant m.3243A>G in the MT-TL1 gene. We have proposed that diabetes in MIDD arises from a combination of insulin resistance and impaired β-cell function that is more likely to occur in the presence of high skeletal muscle heteroplasmy and moderate β-cell heteroplasmy for m.3243A>G and to be driven by oxidative stress as a major pathophysiologic mechanism. There are no randomised trials specifically on the management of MIDD. An approach to MIDD informed by its pathophysiology could optimise management and be a framework for future trials. In this narrative review, we discuss the effects of existing anti-hyperglycaemic medications on oxidative stress, mitochondrial function, and cardiorenal protection. We also review the published case reports and series on the management of diabetes associated with MIDD, and the safety of insulin and non-insulin medications in MIDD. We found that glucagon-like peptide-1 receptor agonists and sodium-glucose cotransporter-2 inhibitors have favourable properties in addressing oxidative stress and mitochondrial function. They also harbour cardiorenal protection properties independent from their effect on glucose control that are fully relevant in MIDD, making them ideal candidates as first-line agents for the management of MIDD. Accordingly, we share our perspective on a disease-specific algorithm for the management of MIDD diabetes that could delay or prevent the development of cardiovascular and renal complications associated with this mitochondrial disease.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".