The Relationship Between Dysglycemia and Atherosclerosis in South Asian, Chinese, and European Individuals in Canada
Bibliographic record
Abstract
OBJECTIVE: Raised glucose levels extending from normal into the diabetic range (dysglycemia) are an emerging risk factor for clinical cardiovascular events. The relationship between dysglycemia and atherosclerosis (AS) in the general population and in different ethnic groups remains controversial. RESEARCH DESIGN AND METHODS: Glucose tolerance, HbA1c, other metabolic risk factors for AS, and carotid mean maximal intimal media thickness were assessed in a random sample of 979 Canadians of South Asian, Chinese, and European descent. RESULTS: The age and sex-adjusted intimal medial thickness increased 0.026 mm for every 0.9% increase in HbA1c in all participants (P < 0.0001) and in those of South Asian (P = 0.018), Chinese (P = 0.002), and European (P < 0.0001) descent. This progressive curvilinear relationship was most apparent at HbA1c levels >5.7%. The HbA1c-AS relationship persisted after adjustment for ethnicity, age, sex, diabetes status, abdominal obesity, insulin resistance, insulin secretion, fasting free fatty acids, blood pressure, and/or dyslipidemia (P < 0.004). Both diabetes (P = 0.002) and HbA1c (P < 0.0001) were determinants of the intimal medial thickness when included in separate statistical models. When included together in a single model, HbA1c (P < 0.0001) but not diabetes (P = 0.6) was a significant determinant. CONCLUSIONS: The degree of AS is related to the level of HbA1c irrespective of diabetes status and independent of abdominal obesity and other markers of the metabolic syndrome. This progressive relationship between HbA1c and AS was observed within different ethnic groups.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".