Progression of Atherosclerosis and Hypothesis on the Relationship Between Microvascular Dysfunction, Cardiovascular Risk, and Cancer
Bibliographic record
Abstract
Background: The extent of atherosclerosis in healthy men and women was measured using ultrasound in the carotid artery and whether the progression could be predicted using the classic risk factors was investigated. A hypothesis was also formulated as to how microvascular dysfunction and atherosclerosis, on the one hand, and cardiovascular disease and cancer, on the other hand, might be related. To describe the relationship between atherosclerosis and cancer, the classic risk factors and plaque burden were compared. Methods: From 2009 to 2024, 10,597 subjects (44% women) aged 20 - 65 years without signs of cardiovascular disease were examined using ultrasound of the carotid artery. The sum of all plaque areas (total plaque area (TPA)) and the maximum plaque thickness were measured. Follow-up examinations were carried out on 4,520 subjects (42% women) aged 40 - 65 years. In 2,397 men without cancer or cardiovascular events during follow-up, the classic risk factors and plaque burden were compared between 55 men who had a solid tumor and 188 men who had a cardiovascular event, and whether patients with cancer and a cardiovascular event exhibited different risk profiles was examined. Results: In the age group of 35 - 65 years, 12% of men and 4.2% of women had advanced atherosclerosis (types III and IVb). In the age group of 40 - 65 years, 2,592 men and 1,928 women were followed up. Low to moderate atherosclerosis was present in 2,052 (79.2%) men and 1,761 (91.3%) women. Advanced atherosclerosis developed in 139 (6.8%) men and 39 (2.2%) women, which could not be predicted by the classical risk factors (P > 0.05). The mean follow-up time was 73 months (6.1 years) for men and 75 months (6.3 years) for women. Patients without cancer or cardiovascular disease have lower risk factors and lower plaque burden compared to men with cancer or cardiovascular events. Patients with cancer have very similar high-risk factors and high plaque burden compared to patients with cardiovascular disease. Conclusion: By measuring the plaque burden on the carotid artery, a good risk stratification can be achieved in every age group. A follow-up examination every 3 - 5 years is advisable, as a rapid progression of the disease cannot be predicted taking into account the classic risk factors. Early treatment of advanced atherosclerosis improves the prognosis for cardiovascular diseases and possibly also for certain types of cancer. Patients with cancer and cardiovascular events show a similar risk profile and plaque burden.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".