Atglistatin ameliorates functional decline in heart failure via adipocyte-specific inhibition of adipose triglyceride lipase
Bibliographic record
Abstract
Despite advancements in therapies for cardiovascular disease and heart failure (HF), the incidence and prevalence of HF are increasing. Previous work has suggested that inhibiting adipose triglyceride lipase (ATGL) in adipose tissue during HF development may assist in the treatment of HF. The ability to specifically target the adipocyte as a potential treatment for HF is a novel approach that could significantly influence the management of HF in the future. Our objectives were to assess the cardiac structural and functional effects of pharmacological inhibition of ATGL in mice with HF, to assess whether ATGL inhibition works in an adipocyte-autonomous manner, and to determine the role that adiposity and glucose homeostasis play in this HF treatment approach. Using a known ATGL inhibitor, atglistatin, as well as mice with germline deletion of adipocyte-specific ATGL, we tested the effectiveness of ATGL inhibition in mice with pressure overload-induced HF. Here, we show that atglistatin can prevent the functional decline in HF and provide evidence that specifically targeting ATGL in the adipocyte is sufficient to prevent worsening of HF. We further demonstrate that the benefit resulting from atglistatin in HF is not dependent on previously suggested improvements in glucose homeostasis, nor are the benefits derived from increased adiposity. Overall, the results of this study suggest that adipocyte-specific pharmacological inhibition of ATGL may represent a novel therapeutic option for HF. NEW & NOTEWORTHY This work shows for the first time that the adipose triglyceride lipase (ATGL)-specific inhibitor atglistatin can prevent worsening heart failure. Furthermore, using mice with adipocyte-specific ATGL ablation, this study demonstrates that ATGL inhibition works in an adipocyte-autonomous manner to ameliorate a functional decline in heart failure. Overall, this work demonstrates that specifically targeting the adipocyte to inhibit ATGL is a potential treatment for 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".