Why Did Ancient People Have Atherosclerosis? From Autopsies to Computed Tomography to Potential Causes
Bibliographic record
Abstract
Computed tomographic findings of atherosclerosis in the ancient cultures of Egypt, Peru, the American Southwest and the Aleutian Islands challenge our understanding of the fundamental causes of atherosclerosis. Could these findings be true? Is so, what traditional risk factors might be present in these cultures that could explain this apparent paradox? The recent computed tomographic findings are consistent with multiple autopsy studies dating as far back as 1852 that demonstrate calcific atherosclerosis in ancient Egyptians and Peruvians. A nontraditional cause of atherosclerosis that could explain this burden of atherosclerosis is the microbial and parasitic inflammatory burden likely to be present in ancient cultures inherently lacking modern hygiene and antimicrobials. Patients with chronic systemic inflammatory diseases of today, including systemic lupus erythematosus, rheumatoid arthritis, and human immunodeficiency virus infection, experience premature atherosclerosis and coronary events. Might the chronic inflammatory load of ancient times secondary to infection have resulted in atherosclerosis? Smoke inhalation from the use of open fires for daily cooking and illumination represents another potential cause. Undiscovered risk factors could also have been present, potential causes that technologically cannot currently be measured in our serum or other tissue. A synthesis of these findings suggests that a gene-environmental interplay is causal for atherosclerosis. That is, humans have an inherent genetic susceptibility to atherosclerosis, whereas the speed and severity of its development are secondary to known and potentially unknown environmental factors.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".