Vascular repair and regeneration in cardiometabolic diseases
Bibliographic record
Abstract
Chronic cardiometabolic assaults during type 2 diabetes (T2D) and obesity induce a progenitor cell imbalance in the circulation characterized by overproduction and release of pro-inflammatory monocytes and granulocytes from the bone marrow alongside aberrant differentiation and mobilization of pro-vascular progenitor cells that generate downstream progeny for the coordination of blood vessel repair. This imbalance can be detected in the peripheral blood of individuals with established T2D and severe obesity using multiparametric flow cytometry analyses to discern pro-inflammatory vs. pro-angiogenic progenitor cell subsets identified by high aldehyde dehydrogenase activity, a conserved progenitor cell protective function, combined with lineage-restricted cell surface marker analyses. Recent evidence suggests that progenitor cell imbalance can be reversed by treatment with pharmacological agents or surgical interventions that reduce hyperglycaemia or excess adiposity. In this state-of-the-art review, we present current strategies to assess the progression of pro-vascular regenerative cell depletion in peripheral blood samples of individuals with T2D and obesity and we summarize novel clinical data that intervention using sodium-glucose co-transporter 2 inhibition or gastric bypass surgery can efficiently restore cell-mediated vascular repair mechanisms associated with profound cardiovascular benefits in recent outcome trials. Collectively, this thesis generates a compelling argument for early intervention using current pharmacological agents to prevent or restore imbalanced circulating progenitor content and maintain vascular regenerative cell trafficking to sites of ischaemic damage. This conceptual advancement may lead to the design of novel therapeutic approaches to prevent or reverse the devastating cardiovascular comorbidities currently associated with T2D and obesity.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".