Contributions of Increasing Obesity and Diabetes to Slowing Decline in Subclinical Coronary Artery Disease
Bibliographic record
Abstract
BACKGROUND: Our previous study of nonelderly adult decedents with nonnatural (accident, suicide, or homicide) cause of death (96% autopsy rate) between 1981 and 2004 revealed that the decline in subclinical coronary artery disease (CAD) ended in the mid-1990s. The present study investigated the contributions of trends in obesity and diabetes mellitus to patterns of subclinical CAD and explored whether the end of the decline in CAD persisted. METHODS AND RESULTS: We reviewed provider-linked medical records for all residents of Olmsted County, Minnesota, who died from nonnatural causes within the age range of 16 to 64 years between 1981 and 2009 and who had CAD graded at autopsy. We estimated trends in CAD risk factors including age, sex, systolic blood pressure, diabetes (qualifying fasting glucose or medication), body mass index, smoking, and diagnosed hyperlipidemia. Using multiple regression, we tested for significant associations between trends in CAD risk factors and CAD grade and assessed the contribution of trends in diabetes and obesity to CAD trends. The 545 autopsied decedents with recorded CAD grade exhibited significant declines between 1981 and 2009 in systolic blood pressure and smoking and significant increases in blood pressure medication, diabetes, and body mass index ≥30 kg/m(2). An overall decline in CAD grade between 1981 and 2009 was nonlinear and ended in 1994. Trends in obesity and diabetes contributed to the end of CAD decline. CONCLUSIONS: Despite continued reductions in smoking and blood pressure values, the previously observed end to the decline in subclinical CAD among nonelderly adult decedents was apparent through 2009, corresponding with increasing obesity and diabetes in that population.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".