Sudden Cardiac Death in Diabetes and Obesity: Mechanisms and Therapeutic Strategies
Bibliographic record
Abstract
Ventricular arrhythmias and sudden cardiac death (SCD) occur most frequently in the setting of coronary artery disease, cardiomyopathy and heart failure but are also increasingly observed in persons suffering from diabetes mellitus and obesity. The incidence of these metabolic disorders is rising in Western countries, but adequate prevention and treatment of arrhythmias and SCD in affected patients is limited because of our incomplete knowledge of the underlying disease mechanisms. Here, an overview is presented of the prevalence of electrophysiological disturbances, ventricular arrhythmias, and SCD in the clinical setting of diabetes and obesity. Experimental studies are reviewed, which have identified disease pathways and associated modulatory factors, in addition to pro-arrhythmic mechanisms. Key processes are discussed, including mitochondrial dysfunction, oxidative stress, cardiac structural derangements, abnormal cardiac conduction, ion channel dysfunction, prolonged repolarization, and dysregulation of intracellular sodium and calcium homeostasis. In addition, the recently identified pro-arrhythmic effects of dysregulated branched chain amino acid metabolism, a common feature in patients with metabolic disorders, are addressed. Finally, current management options are discussed in addition to the potential development of novel preventive and therapeutic strategies based on recent insight gained from translational studies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.002 |
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".