Abstract 441: Relationship Between Diabetes, Glucose Control, And Vascular Health: Findings From The American Heart Association Cardiometabolic Health Strategically Focused Research Network
Bibliographic record
Abstract
Background: Diabetes mellitus (DM) increases the risk of vascular endothelial damage and subsequent cardiovascular (CV) events. This study was designed to investigate mechanisms of endothelial cell (EC) damage in DM. Methods: CHORD (CHOlesterol lowering and Residual Risk in Diabetes) is an ongoing study evaluating mechanisms of CV disease (CVD) risk in patients with 1) LDL-C >100 mg/dL, 2) no known CVD and 3) with or without DM. In a subset of participants, EC harvesting was performed at baseline by inserting a J-wire through an angiocatheter into the brachial vein. ECs were isolated with magnetic beads directed against CD146 and transcript expression assessed using next generation RNA sequencing. Results: We performed EC harvesting on participants with DM (n=11, mean age 47 ± 15 years, 45% female, mean HbA1c 6.4%) and controls (n=11, mean age 50 ± 15 years, 56% female, mean HbA1c 5.2%). Comparison of brachial vein EC RNA sequencing in DM vs controls after adjustment for age, sex, and race/ethnicity demonstrated 1043 upregulated genes and 26 downregulated genes with excellent supervised hierarchical clustering between groups (p<0.05). Using a linear regression model adjusting for age, sex, and race/ethnicity, 899 EC genes were significantly associated with HbA1c (p<0.05). Of the original 1069 EC genes differentially expressed in diabetes (over control), only 257 genes displayed a concordant relationship to HbA1c (HbA1c dependent) while the rest (n=812) were HbA1c independent. (Figure 1). Pathways related to EC genes that were HbA1c dependent included those related to endosomal processing whereas HbA1c independent pathways included those related to energy utilization (e.g. PGC-1 alpha phosphorylation), and cell signaling (RAC1, TGFBR1). Conclusion: Our data demonstrate the impact of DM on EC abnormalities and suggest differences in EC gene expression between glucose dependent and independent processes.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".