1422-P: Different Diagnostic Criteria for Diabetes and Risk of Cardiovascular Disease in the Greenland Inuit
Bibliographic record
Abstract
Introduction: Diabetes prevalence in Greenland is high and increasing. Diabetes can be diagnosed by different criteria; currently HbA1c levels ≥ 48 mmol/mol, fasting plasma glucose (FPG) ≥ 7.0 mmol/l and/or two-hour plasma glucose (2hPG) ≥ 11.1 mmol/l after an oral glucose tolerance test (OGTT). We compared the risk of cardiovascular disease (CVD) by different diagnostic criteria, in the Greenland Inuit. Methods: Participants included in the population-based Inuit Health in Transition (IHIT) study in Greenland (2005-2010), had HbA1c, FPG and 2hPG levels measured at entry. We followed participants in the Danish and Greenlandic health registers for a composite endpoint of non-fatal and fatal CVD until 31/12-2017. Participants with known diabetes (n=77) and previous CVD (n=140), were excluded. We compared incidence rates for different diagnostic criteria, adjusted for various clinical and sociodemographic confounders in separate Poisson regressions, using multiple imputations for missing data and split follow-up into 1-year age-bands to account for a non-constant effect of age. Results: We included 2,898 adult participants, with 246 CVD events and 366 deaths during follow-up. In the crude analyses, incidence rate ratios (IRR) of CVD were highest among participants diagnosed by HbA1c (IRR:2.69; 95%CI (1.66-4.34)), followed by participants diagnosed by FPG (2.04 (1.27-3.27)), and both FPG and 2hPG (1.96 (1.31-2.94)), compared with normoglycemia. The IRR for all three diagnostic criteria were attenuated after confounder adjustment (HbA1c 1.14 (0.70-1.88); FPG: 0.81 (0.50-1.30); FPG and 2hPG: 0.78 (0.51-1.18)). Conclusions: IRR of CVD were highest among participants diagnosed by HbA1c in the crude analyses. None of the diagnostic criteria were associated with CVD after adjustment, especially for age. Future studies will investigate other diabetes related complications, competing risks from death, and interactions with genetic variants in the Greenland population. Disclosure S. Byberg: None. M. Tvermosegaard: None. L. Díaz: None. N.K. Senftleber: None. C.V.L. Larsen: None. P.F. Rønn: Research Support; Self; Amgen, Danish Diabetes Academy. Stock/Shareholder; Spouse/Partner; Novo Nordisk A/S. P. Bjerregaard: None. M.L. Pedersen: None. M.E. Jørgensen: Research Support; Self; Amgen, AstraZeneca, Boehringer Ingelheim Pharmaceuticals, Inc., Sanofi-Aventis. Stock/Shareholder; Self; Novo Nordisk A/S.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".