Impact of pesticide exposure on adipose tissue development and function
Bibliographic record
Abstract
Obesity is a leading cause of morbidity, mortality and health care expenditure whose incidence is rapidly rising across the globe. Although the cause of the obesity epidemic is typically viewed as a product of an increased availability of high calorie foods and/or a reduction in physical activity, there is mounting evidence that exposure to synthetic chemicals in our environment may play an important role. Pesticides, are a class of chemicals whose widespread use has coincided with the global rise of obesity over the past two decades. Importantly, given their lipophilic nature many pesticides have been shown to accumulate with adipose tissue depots, suggesting they may be disrupting the function of white adipose tissue (WAT), brown adipose tissue (BAT) and beige adipose tissue to promote obesity and metabolic diseases such as type 2 diabetes. In this review, we discuss epidemiological evidence linking pesticide exposure with body mass index (BMI) and the incidence of diabetes. We then review preclinical studies in rodent models which have directly evaluated the effects of different classes of insecticides and herbicides on obesity and metabolic dysfunction. Lastly, we review studies conducted in adipose tissue cells lines and the purported mechanisms by which pesticides may induce alterations in adipose tissue function. The review of the literature reveals major gaps in our knowledge regarding human exposure to pesticides and our understanding of whether physiologically relevant concentrations promote obesity and elicit alterations in key signaling pathways vital for maintaining adipose tissue metabolism.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".