Inflammaging is minimal among forager-horticulturalists in the Bolivian Amazon
Bibliographic record
Abstract
An increase in chronic systemic inflammation in later life, termed inflammaging, is implicated in health risk. However, it is unclear whether inflammaging develops in all human populations, or if it is the product of environmental mismatch. We assessed inflammaging in Tsimane forager-horticulturalists of the Bolivian Amazon, using serum cytokines in a primarily cross-sectional sample (1134 samples from n = 714 individuals, age 39–94, 51.3% female). IL-6 was positively associated with age ( β = 0.013, p < 0.01). However, other pro-inflammatory markers, including IL-1β and TNF-α, did not increase with age ( β = −0.005 and β = −0.001, respectively). We then compared the Moseten, a neighbouring population that has experienced greater market integration (423 samples from n = 380 individuals, age 39–85, 48.2% female). The Moseten also showed a positive age association for IL-6 that attenuated at later ages (age β = 0.025, p < 0.01; age 2 β = −0.001, p < 0.05). Further, IL-1β and TNF-α were both positively associated with age ( β = 0.021, p < 0.05 and β = 0.011, p < 0.01, respectively). Our results demonstrate minimal inflammaging in the Tsimane, highlighting variation across populations in this age-related process. They also suggest that inflammaging is exacerbated by lifestyle shifts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".