Body mass index and the prevalence, severity, and risk of coronary artery disease: an international multicentre study of 13 874 patients
Bibliographic record
Abstract
AIMS: Obesity is associated with the presence of coronary artery disease (CAD) risk factors and cardiovascular events. We examined the relationship between body mass index (BMI) and the presence, extent, severity, and risk of CAD in patients referred for coronary computed tomographic angiography (CCTA). METHODS AND RESULTS: We evaluated 13,874 patients from a prospective, international, multicentre registry of individuals without known CAD undergoing CCTA. We compared risk factors, CAD findings, and risk of all-cause mortality and non-fatal myocardial infarction (MI) amongst individuals with underweight (18.5-20.0 kg/m(2)), normal (20.1-24.9 kg/m(2)), overweight (25-29.9 kg/m(2)), and obese (≥30 kg/m(2)) BMI. The mean follow-up was 2.4 ± 1.2 years with 143 deaths and 193 MIs. Among underweight, normal weight, overweight, and obese individuals, there was increasing prevalence of diabetes (7 vs.10% vs. 12 vs. 19%), hypertension (37 vs. 40% vs. 46 vs. 59%), and hyperlipidaemia (48 vs. 52% vs. 56 vs. 56%; P < 0.001 for trend). After multivariable adjustment, BMI was positively associated with the prevalence of any CAD [odds ratio (OR) 1.25 per +5 kg/m(2), 95% confidence interval (CI): 1.20-1.30, P < 0.001] and obstructive (≥50% stenosis) CAD (OR: 1.13 per +5 kg/m(2), 95% CI: 1.08-1.19, P < 0.001); a higher BMI was also associated with an increased number of segments with plaque (+0.26 segments per +5 kg/m(2), 95% CI: 0.22-0.30, P < 0.001). Larger BMI categories were associated with an increase in all-cause mortality (P = 0.004), but no difference in non-fatal MI. After multivariable adjustment, a higher BMI was independently associated with increased risk of MI (hazards ratio: 1.28 per +5 kg/m(2), 95% CI: 1.12-1.45, P < 0.001). CONCLUSIONS: Amongst patients with suspected CAD referred for CCTA, individuals with increased BMI have greater prevalence, extent, and severity of CAD that is not fully explained by the presence of traditional risk factors. A higher BMI is independently associated with increased risk of intermediate-term risk of myocardial infarction.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".