Diabetes and carotid artery disease: a narrative review
Bibliographic record
Abstract
Diabetes mellitus (DM) has been linked to an increased prevalence and severity of carotid artery disease, as well as polyvascular disease. Carotid disease is also associated with obesity and abnormal peri-organ and intra-organ fat (APIFat) deposition (i.e., excess fat accumulation in several organs such as the liver, heart and vessels). In turn, DM is associated with APIFat. The coexistence of these comorbidities confers a greater risk of vascular events. Clinicians should also consider that carotid bruits may predict cardiovascular risk. DM has been related to a greater risk of adverse outcomes after carotid endarterectomy or stenting. Whether modifying risk factors (e.g., glycaemia and dyslipidaemia) in DM patients can improve the outcomes of these procedures needs to be established. Furthermore, DM is a risk factor for contrast-induced acute kidney injury (CI-AKI). The latter should be recorded in DM patients undergoing carotid stenting since it can influence both short- and long-term outcomes. From a pathophysiological perspective, functional changes in the carotid artery may precede morphological ones. Furthermore, carotid plaque characteristics are increasingly being studied in terms of vascular risk stratification and monitoring short-term changes attributed to treatment. The present narrative review discusses the recent (2019) literature on the associations between DM and carotid artery disease. Physicians and vascular surgeons looking after patients with carotid disease and DM should consider these links that may influence outcomes. Further research in this field is also needed to optimise the treatment of such patients.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".