Higher HbA1c variability is associated with increased arterial stiffness in individuals with type 1 diabetes
Bibliographic record
Abstract
Abstract Background Both long-term glycaemic variability and arterial stiffness have been recognized as cardiovascular risk factors. This study aims to investigate whether an association between these phenomena exists in individuals with type 1 diabetes. Methods This cross-sectional study included 673 adults (305 men, 368 women) with type 1 diabetes and combined available retrospective laboratory data on HbA1c from the preceding 10 years with outcome data on arterial stiffness and clinical variables from a comprehensive study visit. HbA1c variability was calculated as adjusted standard deviation (adj-HbA1c-SD), coefficient of variation (HbA1c-CV) and average real variability (HbA1c-ARV). As measures of arterial stiffness, carotid-femoral pulse wave velocity (cfPWV; n = 335) and augmentation index (AIx; n = 653) were assessed using applanation tonometry. Results The study population had a mean age of 47.1 (± 12.0) years and a median duration of diabetes of 31.2 (21.2–41.3) years. The median number of HbA1c assessments per individual was 17 (12–26). All three indices of HbA1c variability were significantly correlated with both cfPWV and AIx after adjustment for sex and age (p < 0.001). In separate multivariable linear regression models, adj-HbA1c-SD and HbA1c-CV were significantly associated with cfPWV (p = 0.032 and p = 0.046, respectively) and AIx (p = 0.028 and p = 0.049, respectively), even after adjustment for HbA1c-mean. HbA1c-ARV was not associated with cfPWV or AIx in the fully adjusted models. Conclusions An association independent of HbA1c-mean was found between HbA1c variability and arterial stiffness, suggesting a need to consider multiple HbA1c metrics in studies assessing cardiovascular risk in type 1 diabetes. Longitudinal and interventional studies are needed to confirm any causal relationship and to find strategies for reducing long-term glycaemic variability.
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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 | 0.003 |
| 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 | 0.000 |
| Research integrity | 0.001 | 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".