Does abdominal obesity have a similar impact on cardiovascular disease and diabetes? A study of 91 246 ambulant patients in 27 European Countries
Bibliographic record
Abstract
AIMS: Differences in cardiovascular risk factors across Europe provide an opportunity to examine the impact of adiposity on the frequency of diabetes and cardiovascular disease (CVD). METHODS AND RESULTS: The International Day for Evaluation of Abdominal obesity (IDEA) study evaluated the prevalence of abdominal obesity, elevated body mass index (BMI), and other cardiometabolic risk factors among primary care patients. Abdominal obesity predicted increased diabetes risk, despite socio-economic, demographic, and risk factor differences. Cardiovascular disease was at least two-fold more frequent in Eastern Europe vs. Northwest Europe (P < 0.0001) and 2.5-fold more vs. Southern Europe (P < 0.0001). Waist circumference (WC) predicted increased (P < 0.0001) age- and BMI-adjusted risks of CVD and diabetes. In women, odds ratios (95% confidence intervals) for CVD per 1 SD increase in WC were: Northwest Europe 1.28 (1.18-1.40); Southern Europe 1.26 (1.16-1.37); and Eastern Europe 1.10 (1.03-1.18). Values for diabetes were 1.72 (1.58-1.88), 1.45 (1.35-1.56), and 1.59 (1.46-1.73), with similar findings in men. CONCLUSION: Abdominal obesity impacted similarly on the frequency of diabetes across Europe, despite regional differences in cardiovascular risk factors and CVD rates. Increasing abdominal obesity may offset future declines in CVD, even where CVD rates are lower.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".