The Vicious Circle of Left Ventricular Dysfunction and Diabetes: From Pathophysiology to Emerging Treatments
Bibliographic record
Abstract
CONTEXT: Diabetes and heart failure (HF) are 2 deadly and strictly related epidemic disorders. The aim of this review is to present an updated discussion of the epidemiology, pathophysiology, clinical presentation and treatment options for HF in diabetes. EVIDENCE ACQUISITION: Relevant references published up to February 2020 were identified through searches in PubMed. Quality was graded using the Newcastle-Ottawa score in observational studies and the Cochrane Collaboration tool in randomized studies. EVIDENCE SYNTHESIS: Metabolic and neurohumoral derangements, oxidative stress, inflammation, micro- and macroangiopathy all contribute through complex molecular and cellular mechanisms to cardiac dysfunction in diabetes, which in turn, results as one the most frequent underlying conditions affecting up to 42% of patients with HF and causing a 34% increased risk of cardiovascular death. On top of traditional guideline-based HF medical and device therapies, equally effective in patients with and without diabetes, a new class of glucose-lowering agents acting through the sodium-glucose cotransporter 2 (SGLT2) inhibition showed impressive results in reducing HF outcomes in individuals with diabetes and represents an active area of investigation. CONCLUSIONS: Diabetes and HF are strictly linked in a bidirectional and deadly vicious circle difficult to break. Therefore, preventive strategies and a timely diagnosis are crucial to improve outcomes in such patients. SGLT2 inhibitors represent a major breakthrough with remarkably consistent findings. However, it is still not clear whether their benefits may be definitely extended to patients with HF with preserved ejection fraction, to those without diabetes and in the acute setting.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".