INFLAMM-AGING IS NOT A UNIVERSAL AGING MECHANISM ACROSS DIVERSE HUMAN POPULATIONS
Bibliographic record
Abstract
Abstract Inflamm-aging, defined by age-associated increases in inflammatory cytokines, is thought to be a universal mammalian aging mechanism associated with higher chronic disease risk, as observed in industrialized populations like the InCHIANTI cohort (Italy). However, consensus regarding its precise nature and a measurement framework is lacking. Its relevance and measurement applicability across diverse human populations, especially in non-industrialized societies with high infection burdens, remain unclear. We aimed to characterize inflamm-aging across four populations, examining its relation to age and chronic diseases. Through principal components and factor analysis, we studied immune variation within the InCHIANTI, Singapore Longitudinal Ageing Study (SLAS), Study of Health in Pomerania (SHIP), and Tsimane Health and Life History Project (THLHP) cohorts—Amerindian forager-horticulturalists from the Bolivian Amazon. We compared immune axes’ relationships with age and chronic diseases across cohorts. We observed major differences in the structure of inflammatory and immune variation across populations. Notably, the primary immune axis in the THLHP cohort diverged significantly from those in the InCHIANTI, SLAS and SHIP cohorts, with the latter two aligning more closely with the inflamm-aging phenomenon—albeit with variations in their manifestations. Furthermore, these axes did not consistently correlate with age across cohorts and exhibited differential impacts on chronic diseases, with the primary axes identified in InCHIANTI and SLAS showing stronger associations with chronic diseases. Our findings suggest inflamm-aging is not universal. There appear to be significant environmental and lifestyle impacts on inflammatory and immune variation leading to differences in how inflammation drives disease susceptibility across populations.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".