Dysregulation of Cytokine Response in Canadian First Nations Communities: Is There an Association with Persistent Organic Pollutant Levels?
Bibliographic record
Abstract
In vitro and animal studies report that some persistent organic pollutants (POPs) trigger the secretion of proinflammatory cytokines. Whether POP exposure is associated with a dysregulation of cytokine response remains to be investigated in humans. We studied the strength of association between plasma POP levels and circulating cytokines as immune activation markers. Plasma levels of fourteen POPs and thirteen cytokines were measured in 39 Caucasians from a comparator sample in Québec City (Canada) and 72 First Nations individuals from two northern communities of Ontario (Canada). Caucasians showed significantly higher levels of organochlorine insecticides (β-HCH, p,p'-DDE and HCB) compared to First Nations. Conversely, First Nations showed higher levels of Mirex, Aroclor 1260, PCB 153, PCB 170, PCB 180 and PCB 187 compared to Caucasians. While there was no difference in cytokine levels of IL-4, IL-6, IL-10 and IL-22 between groups, First Nations had significantly greater average levels of IFNγ, IL-1β, IL-2, IL-5, IL-8, IL-12p70, IL-17A, TNFα and TNFβ levels compared to Caucasians. Among candidate predictor variables (age, body mass index, insulin resistance and POP levels), high levels of PCBs were the only predictor accounting for a small but significant effect of observed variance (∼7%) in cytokine levels. Overall, a weak but significant association is detected between persistent organochlorine pollutant exposure and elevated cytokine levels. This finding augments the already existing information that environmental pollution is related to inflammation, a common feature of several metabolic disorders that are known to be especially prevalent in Canada's remote First Nations communities.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".