176-LB: Ethnic Differences in Cardiovascular Complications of Young-Onset Diabetes (YOD)
Bibliographic record
Abstract
Introduction: YOD (age at diagnosis [AAD]<40 years) is associated with greater cardiovascular disease (CVD) risk than usual-onset diabetes (UOD, AAD≥40 years). It is unknown whether this pattern varies by ethnicity. Methods: We included adults in Ontario, Canada with incident diabetes (2002-12, followed to 2018). We matched each case with ≤5 diabetes-free controls by age, sex, and ethnicity. The outcome was CVD (coronary artery disease, congestive heart failure, stroke, peripheral revascularization, lower extremity amputation). We constructed Cox proportional hazards models to estimate the association between AAD and CVD relative to controls. Results: We included 411,030 cases (3.5% South Asian; 4.3% Chinese) and 2,000,035 controls. The hazard ratios (HR) for CVD in UOD versus controls were higher in Chinese (2.38, 2.2-2.5) and South Asian (2.5, 2.4-2.7) people than White people (1.9, 1.9-1.9; pinteraction<0.0001). The HR for CVD in YOD versus controls were similar across ethnicities (Chinese: 5.2, 3.6-7.3; South Asian: 4.4, 3.7-5.3; White: 4.6, 4.4-4.8). In YOD and controls, Chinese and South Asian people had lower hazard of CVD than White people. In UOD, CVD hazard was lowest in Chinese people, and similar in South Asian and White people. Discussion: Although Chinese and South Asian people have a lower hazard of CVD compared to White people, YOD is associated with a 4- to 5-fold greater hazard of CVD across ethnicities. Disclosure C. Ke: None. T. Stukel: None. A. Luk: Research Support; Self; Bayer Healthcare Pharmaceuticals Inc., Roche Pharma. Other Relationship; Self; Merck Sharp & Dohme Corp. J.C. Chan: None. B.R. Shah: None. Funding Canadian Institutes of Health Research; South Asian Network Supporting Awareness and Research
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.004 | 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".