Persistent organic pollutant concentrations in human pancreas and peripancreatic adipose tissues correlate with markers of beta cell dysfunction
Bibliographic record
Abstract
Abstract Epidemiological studies consistently report associations between circulating concentrations of persistent organic pollutants (POPs) and increased type 2 diabetes risk. Measures of POP concentrations in pancreas are limited; given the role of the endocrine pancreas in diabetes pathogenesis, this is an important gap in the literature. Additionally, no studies have correlated POP concentrations with direct measures of beta cell function in humans. We hypothesized that lipophilic POPs accumulate in human pancreas and correlate with markers of diabetes risk. To test this hypothesis, we measured POP concentration from 3 chemical classes – dioxins/furans, polychlorinated biphenyls (PCBs), and organochlorine pesticides (OCPs) – in pancreas and peripancreatic adipose tissue biopsies obtained from 31 human organ donors via the Alberta Diabetes Institute IsletCore. Indeed, POPs were consistently detected in human pancreas, and for some pollutants, at higher concentrations than in adipose. We next assessed correlations between POP concentrations and systemic indicators of diabetes risk (BMI, age, and %HbA1c) and direct measures of beta cell function. To this end, we measured insulin secretion in response to numerous secretagogues (i.e. glucose, fatty acids, amino acids, exendin-4, or KCl) in isolated islets from the same 31 donors. Pancreas PCBs and OCPs positively correlated with BMI, age and basal insulin secretion but negatively correlated with stimulation index (ratio of insulin secretion under high glucose / low glucose conditions). In contrast, pancreas dioxins/furans positively correlated with fatty acid- and amino acid-stimulated insulin secretion. These data confirm that lipophilic pollutants accumulate in human pancreas and positively correlate with markers of diabetes risk.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".