Three common assumptions about inflammation, aging, and health that are probably wrong
Bibliographic record
Abstract
Chronic inflammation contributes to the onset and progression of cardiovascular disease and other degenerative diseases of aging. But does it have to? This article considers the associations among inflammation, aging, and health through the lens of human population biology and suggests that chronic inflammation is not a normal nor inevitable component of aging. It is commonly assumed that conclusions drawn from research in affluent, industrialized countries can be applied globally; that aging processes leading to morbidity and mortality begin in middle age; and that inflammation is pathological. These foundational assumptions have shifted focus away from inflammation as a beneficial response to infection or injury and toward an understanding of inflammation as chronic, dysregulated, and dangerous. Findings from community-based studies around the world-many conducted in areas with relatively high burdens of infectious disease-challenge these assumptions by documenting substantial variation in levels of inflammation and patterns of association with disease. They also indicate that nutritional, microbial, and psychosocial environments in infancy and childhood play important roles in shaping inflammatory phenotypes and their contributions to diseases of aging. A comparative, developmental, and ecological approach has the potential to generate novel insights into the regulation of inflammation and how it relates to human health over the life course.
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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 | 0.025 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.091 |
| Scholarly communication | 0.009 | 0.021 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.008 | 0.026 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".