ASSESSING THE IMPACT OF CARDIOPROTECTIVE THERAPIES ON VASCULAR REGENERATIVE CELL CONTENT IN PEOPLE AT RISK FOR ADVERSE CARDIOVASCULAR EVENTS
Bibliographic record
Abstract
Atherosclerotic cardiovascular disease (ASCVD) is a leading cause of death globally and is exacerbated by cardiometabolic risk factors such as type 2 diabetes (T2D) and dyslipidemia. These conditions promote systemic inflammation and oxidative stress that drives cardiac event risk. Using high-throughput flow cytometry previously showed that T2D and obesity deplete circulating vascular regenerative (VR) progenitor cell pools, which are essential for coordinating blood vessel repair and maintaining homeostasis. Moreover, VR cell stores could be restored with empagliflozin in T2D and bariatric surgery in obesity. Herein, we hypothesized that the restoration of VR cell content is a conserved mechanism in therapies that lower cardiovascular event risk. We sought to determine (1) the impact of empagliflozin on VR cell content in individuals without T2D with cardiac risk factors, (2) the effects of icosapent ethyl (IPE) in individuals with established ASCVD and/or T2D, and (3) the relationship between renal health and VR cell content. Our findings indicate that non-diabetic individuals randomized to receive empagliflozin exhibited similar boosts in VR cell content as demonstrated in people with T2D, and that the use of IPE led to increased VR cell content and increases in early granulocyte precursor cell content. Additionally, we have demonstrated that clinical metrics (i.e. eGFR) may predict VR cell deficiencies. This thesis offers novel insights into the cardioprotective mechanisms of SGLT2 inhibitors and omega-3 therapies and demonstrates their ability to modulate circulating VR cell populations that may explain, in part, their ability to reduce cardiovascular event risk reduction and support a role for VR cell characterization as a surrogate biomarker for cardiovascular risk and treatment efficacy.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".