Concurrent diabetes and heart failure: interplay and novel therapeutic approaches
Bibliographic record
Abstract
Diabetes mellitus increases the risk of developing heart failure, and the co-existence of both diseases worsens cardiovascular outcomes, hospitalization, and the progression of heart failure. Despite current advancements on therapeutic strategies to manage hyperglycaemia, the likelihood of developing diabetes-induced heart failure is still significant, especially with the accelerating global prevalence of diabetes and an ageing population. This raises the likelihood of other contributing mechanisms beyond hyperglycaemia in predisposing diabetic patients to cardiovascular disease risk. There has been considerable interest in understanding the alterations in cardiac structure and function in diabetic patients, collectively termed as 'diabetic cardiomyopathy'. However, the factors that contribute to the development of diabetic cardiomyopathies are not fully understood. This review summarizes the main characteristics of diabetic cardiomyopathies, and the basic mechanisms that contribute to its occurrence. This includes perturbations in insulin resistance, fuel preference, reactive oxygen species generation, inflammation, cell death pathways, neurohormonal mechanisms, advanced glycated end-products accumulation, lipotoxicity, glucotoxicity, and post-translational modifications in the heart of the diabetic. This review also discusses the impact of antihyperglycaemic therapies on the development of heart failure, as well as how current heart failure therapies influence glycaemic control in diabetic patients. We also highlight the current knowledge gaps in understanding how diabetes induces heart failure.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.005 | 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".