The Pesticide Chlorpyrifos Promotes Obesity by Inhibiting Diet-Induced Thermogenesis
Bibliographic record
Abstract
Abstract Obesity is a major risk factor for type 2 diabetes (T2D), non-alcoholic fatty liver disease (NAFLD) and cardiovascular disease that arises from a caloric surplus of as little as 10–30 kcal per day. And while increased consumption of energy dense foods and reduced physical activity are commonly thought to be the major contributors to this caloric imbalance, diet-induced thermogenesis is a quantitatively important component of the energy balance equation. In adult humans, recent studies have indicated that diet-induced thermogenesis requires the activation of brown adipose tissue (BAT), however, the determinants regulating this process and why they may differ between individuals are not fully understood. We hypothesized that environmental toxicants commonly used as food additives or pesticides might reduce diet-induced thermogenesis through suppression of uncoupling protein 1, the defining protein of human BAT thermogenesis. Through a screening approach of pesticides/toxicants chosen from the Toxcast chem Library, we discovered that the organophosphate insecticide chlorpyrifos potently suppressed the expression of uncoupling protein 1 (UCP1) and mitochondrial respiration in brown adipocytes at concentrations as low as 1 pM. Chloropyrifos-induced suppression of brown adipocyte thermogenesis was also observed in mice fed a diet high in fat and housed at thermoneutrality where it promoted greater obesity, non-alcoholic fatty liver disease and insulin resistance. Reductions in thermogenesis by chlorpyrifos were associated with impaired activation of the β3-adrenergic receptor and protein kinases critical for regulating UCP1 and mitophagy. These data indicate that the commonly used pesticide chlorpyrifos, at doses found within the food supply, suppresses the activation of brown adipose tissue, suggesting that its use may contribute to the obesity epidemic.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".