Ethnic differences in the association between age at diagnosis of diabetes and the risk of cardiovascular complications: a population-based cohort study
Bibliographic record
Abstract
BACKGROUND: We examined ethnic differences in the association between age at diagnosis of diabetes and the risk of cardiovascular complications. METHODS: We conducted a population-based cohort study in Ontario, Canada among individuals with diabetes and matched individuals without diabetes (2002-18). We fit Cox proportional hazards models to determine the associations of age at diagnosis and ethnicity (Chinese, South Asian, general population) with cardiovascular complications. We tested for an interaction between age at diagnosis and ethnicity. RESULTS: There were 453,433 individuals with diabetes (49.7% women) and 453,433 matches. There was a significant interaction between age at diagnosis and ethnicity (P < 0.0001). Young-onset diabetes (age at diagnosis < 40) was associated with higher cardiovascular risk [hazard ratios: Chinese 4.25 (3.05-5.91), South Asian: 3.82 (3.19-4.57), General: 3.46 (3.26-3.66)] than usual-onset diabetes [age at diagnosis ≥ 40 years; Chinese: 2.22 (2.04-2.66), South Asian: 2.43 (2.22-2.66), General: 1.83 (1.81-1.86)] versus ethnicity-matched individuals. Among those with young-onset diabetes, Chinese ethnicity was associated with lower overall cardiovascular [0.44 (0.32-0.61)] but similar stroke risks versus the general population; while South Asian ethnicity was associated with lower overall cardiovascular [0.75 (0.64-0.89)] but similar coronary artery disease risks versus the general population. In usual-onset diabetes, Chinese ethnicity was associated with lower cardiovascular risk [0.44 (0.42-0.46)], while South Asian ethnicity was associated with lower cardiovascular [0.90 (0.86-0.95)] and higher coronary artery disease [1.08 (1.01-1.15)] risks versus the general population. CONCLUSIONS: There are important ethnic differences in the association between age at diagnosis and risk of cardiovascular complications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".