Relative Impact of GLP-1 Agonist Use on Microvascular Versus Macrovascular Complications
Bibliographic record
Abstract
BACKGROUND: Diabetes and obesity contribute to vascular complications. The effects of glucagon-like peptide-1 receptor agonists (GLP-1 RAs) on microvascular and macrovascular outcomes in the general population remain less well understood. OBJECTIVE: To compare the adjusted odds of microvascular and macrovascular complications among adults in the United States (US) using GLP-1 RAs. METHODS: We conducted a survey-weighted logistic regression analysis using National Health and Nutrition Examination Survey (NHANES) data from 2011-2018. Microvascular complications were defined as albuminuria or diabetic retinopathy, while macrovascular complications included myocardial infarction, stroke, or coronary artery disease. Models adjusted for demographic, socioeconomic, and clinical factors. RESULTS: In the adjusted models, the use of GLP-1 RA was linked to increased odds of microvascular complications (OR 2.29, 95% CI 1.05-4.97, p=0.037). No significant association was observed with macrovascular complications (OR 1.27, 95% CI 0.65-2.51, p=0.478). Established risk factors, including older age, higher BMI, lower income, and smoking, were independently associated with higher odds of vascular complications. CONCLUSION: The use of GLP-1 RAs was linked to increased odds of microvascular complications following adjustment for the confounders, even as no significant association was reported with macrovascular complications. This possibly reflects confounding through indication, given that such medications/agents are mainly prescribed to persons with either longer duration or increasingly severe diabetes. These findings indicate the need for longitudinal studies to explain the temporal correlations between the use of GLP-1 RA and complication risk.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".