Female mice exposed to low-doses of dioxin during pregnancy and lactation have increased susceptibility to diet-induced obesity and diabetes
Bibliographic record
Abstract
Abstract Objective Exposure to persistent organic pollutants is consistently associated with increased diabetes risk in humans. We investigated the short- and long-term impact of chronic low-dose dioxin (aka 2,3,7,8-tetrachlorodibenzo- p -dioxin, TCDD) exposure during pregnancy and lactation on glucose homeostasis and beta cell function in female mice, including their response to a metabolic stressor later in life. Methods Female mice were injected with either corn oil (CO; vehicle control) or 20 ng/kg/d TCDD 2x/week throughout mating, pregnancy, and lactation, and then tracked for 6-10 weeks after chemical exposure stopped. A subset of CO- and TCDD-exposed dams were then transferred to a 45% high fat diet (HFD) or remained on standard chow diet for an additional 11 weeks to assess long-term effects of TCDD on adaptability to a metabolic stressor. Results Dioxin-exposed dams were hypoglycemic at birth but otherwise had normal glucose homeostasis during and post-dioxin exposure. However, dioxin-exposed dams on chow diet were modestly heavier than controls starting 5 weeks after the last dioxin injection, and their weight gain accelerated after transitioning to a HFD. Dioxin-exposed dams also had accelerated onset of hyperglycemia, dysregulated insulin secretion, reduced islet size, increased MAFA - beta cells, and increased proinsulin accumulation following HFD feeding compared to control dams. Conclusions Our mouse model suggests that chronic low-dose dioxin exposure may be a contributing factor to obesity and diabetes pathogenesis in females.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".