Hyperglycemia Inhibits Cardiac Stem Cell–Mediated Cardiac Repair and Angiogenic Capacity
Bibliographic record
Abstract
BACKGROUND: The impact of diabetes mellitus on the cardiac regenerative potential of cardiac stem cells (CSCs) is unknown yet critical, given that individuals with diabetes mellitus may well require CSC therapy in the future. Using human and murine CSCs from diabetic cardiac tissue, we tested the hypothesis that hyperglycemic conditions impair CSC function. METHODS AND RESULTS: CSCs cultured from the cardiac biopsies of patients with diabetes mellitus (hemoglobin A1c, 10±2%) demonstrated reduced overall cell numbers compared with nondiabetic sourced biopsies (P=0.04). When injected into the infarct border zone of immunodeficient mice 1 week after myocardial infarction, CSCs from patients with diabetes mellitus demonstrated reduced cardiac repair compared with nondiabetic patients. Conditioned medium from CSCs of patients with diabetes mellitus displayed a reduced ability to promote in vitro blood vessel formation (P=0.02). Similarly, conditioned medium from CSCs cultured from the cardiac biopsies of streptozotocin-induced diabetic mice displayed impaired angiogenic capacity (P=0.0008). Somatic gene transfer of the methylglyoxal detoxification enzyme, glyoxalase-1, restored the angiogenic capacity of diabetic CSCs (diabetic transgenic versus nondiabetic transgenic; P=0.8). Culture of nondiabetic murine cardiac biopsies under high (25 mmol/L) glucose conditions reduced CSC yield (P=0.003), impaired angiogenic (P=0.02) and chemotactic (P=0.003) response, and reduced CSC-mediated cardiac repair (P<0.05). CONCLUSIONS: Diabetes mellitus reduces the ability of CSCs to repair injured myocardium. Both diabetes mellitus and preconditioning CSCs in high glucose attenuated the proangiogenic capacity of CSCs. Increased expression of glyoxalase-1 restored the proangiogenic capacity of diabetic CSCs, suggesting a means of reversing diabetic CSC dysfunction by interfering with the accumulation of reactive dicarbonyls.
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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) | 0.000 | 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".