Glycemic variability is associated with subclinical atherosclerosis in Chinese type 2 diabetic patients
Bibliographic record
Abstract
BACKGROUND: The contribution of glycemic variability to macrovascular complications remains unclear. We therefore investigated the association between glycemic variability and cervical and/or intracranial atherosclerosis in Chinese type 2 diabetic patients. METHODS: We conducted a cross-sectional study in 216 type 2 diabetic patients with a hemoglobin A1c of 8.3 ± 1.7% and a median diabetes duration of 9.0 years. The standard deviation of blood glucose values (SDBG) and the mean amplitude of glycemic excursion (MAGE) were calculated from continuous glucose monitoring system data for assessing glycemic variability while 24h mean blood glucose (MBG) was calculated for measuring overall blood glucose level. Magnetic resonance angiography (MRA) was used to detect cervical and/or intracranial plaque, and ultrasonography was used to quantify carotid intima-media thickness (IMT) as an index of subclinical atherosclerosis. RESULTS: One hundred and fifty-three patients (70.8%) presented with cervical and/or intracranial lesions on MRA among 216 patients in the study. Elder age, increased systolic blood pressure, increased MBG and elevated low density lipoprotein cholesterol were independent contributors to plaque formation. In patients without stenosis (n = 63), SDBG (r = 0.412, P = 0.001) and MAGE (r = 0.365, P = 0.005) were both correlated with carotid IMT and these relationships remained significant in multiple linear regression analysis (multiple R2 = 0.314 for the model including SDBG and multiple R2 = 0.268 for the model including MAGE). However, SDBG and MAGE were not significantly different among patients with different stenosis degrees. CONCLUSIONS: Glycemic variability is associated with subclinical atherosclerosis in Chinese type 2 diabetic 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".