Cardiac Involvement in Myotonic Dystrophy Type 1: Mechanisms, Clinical Perspectives, and Emerging Therapeutic Strategies
Bibliographic record
Abstract
gene, leading to RNA toxicity and widespread splicing defects. These splicing abnormalities affect multiple systems, including the respiratory, skeletal, cardiac, nervous, and endocrine systems, resulting in aggressive symptoms that significantly impact quality of life and survival. Cardiac complications are the second leading cause of deaths in DM1, after respiratory insufficiency. Current research is largely focused on understanding cardiac pathology in DM1. This review highlights recent advancements in the clinical and pathological characterization of DM1 cardiac involvement, preclinical models used to study cardiac dysfunction, and emerging therapeutic strategies that target the molecular basis of DM1. Promising approaches include RNA-targeting strategies such as antisense oligonucleotides (ASOs), gene-editing tools like CRISPR-Cas9, and small molecules that modulate RNA splicing. ASOs aim to reduce toxic RNA accumulation, CRISPR-based approaches aim to excise or correct the expanded CTG repeats, and repurposed small-molecule drugs, such as vorinostat, tideglusib, and metformin, could serve as potential therapeutic agents for DM1 patients with cardiac complications. Despite this progress, several challenges remain: the heterogeneity of cardiac manifestations, unpredictable and often silent progression of arrhythmias, limited therapeutic options beyond implantable cardioverter-defibrillator (ICD)/pacemaker implantations, and complex interplay with the multisystemic nature of DM1. More research and well-designed clinical trials are urgently needed to translate these promising strategies into effective treatments for DM1-associated cardiac disease. Here, we discuss the current knowledge in DM1 cardiac pathology and preclinical models as well as the benefits and pitfalls of the available therapeutic approaches.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".