Abstract 11965: Cellular Senescence Produces Diastolic Dysfunction and Cardiac Hypertrophy With Aging in Mice
Bibliographic record
Abstract
Background: Age is a major risk factor for heart failure with preserved ejection fraction (HFpEF); however, the underlying mechanisms are poorly understood. Cellular senescence is an aging and stress related response; senescent cells (SCs) secrete bioactive substances that produce a variety of pathological changes. The role of senescence in HFpEF is unknown. Objectives: To study the effect of targeted deletion of SCs on cardiac function, structure and left ventricular (LV) pressure in an aging mouse model of HFpEF. Methods: We studied INK ATTAC mice, in which SCs undergo targeted clearance following treatment with a dimerizing agent, AP20187 (AP). Mice were treated with blinded AP or vehicle from 12 months to 18 months of age. Cardiac function and structure were studied with echocardiography. Contractility parameters were measured with a Millar catheter. The percentage of SCs in different cardiac-cell populations was analyzed with the use of Fluorescence-Activated Cell Sorting (FACS). Results: From 12 to 18 months, LV mass/LV diameter at end diastole (LVDd) and the LV anterior wall thickness at end diastole (LVAWd) increased in the vehicle group while they decreased in the AP group (LV mass/LVDd: %Δvehicle=+7.9± 3%, %ΔAP=-2±1.2%, P=0.005; LVAWd: %Δvehicle=+5.3±2.5%, %ΔAP=-3.5±1.6%, P=0.007) (Fig1A, B). E/e’ (echo) and isovolumic relaxation time indicated diastolic dysfunction in vehicle mice, and improved with AP (Fig1C, D). GFP bicistronic with p16 indicated SCs; FACS-purified fibroblasts (FBs) showed reduced GFP fluorescence (P=0.056) in the AP group, whereas immune and endothelial cells were not affected, suggesting that the anti-HFpEF effects of SC-clearance might be mediated through the clearance of senescent FBs (Fig1E). Conclusions: Our study points to a significant role of SCs, likely senescent FBs, in the pathophysiology of HFpEF. Modulating senescence might provide the basis for novel therapeutic approaches to HFpEF prevention and/or therapy.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.010 | 0.004 |
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".