The Hijacking of Cellular Signaling and the Diabetes Epidemic: Mechanisms of Environmental Disruption of Insulin Action and Glucose Homeostasis
Bibliographic record
Abstract
The burgeoning epidemic of metabolic disease causes significant societal and individual morbidity and threatens the stability of health care systems around the globe. Efforts to understand the factors that contribute to metabolic derangements are critical for reversing these troubling trends. While excess caloric consumption and physical inactivity superimposed on a susceptible genetic background are central drivers of this crisis, these factors alone fail to fully account for the magnitude and rapidity with which metabolic diseases have increased in prevalence worldwide. Recent epidemiological evidence implicates endocrine disrupting chemicals in the pathogenesis of metabolic diseases. These compounds represent a diverse array of chemicals to which humans are exposed via multiple routes in adulthood and during development. Furthermore, a growing ensemble of animal- and cell-based studies provides preclinical evidence supporting the hypothesis that environmental contaminants contribute to the development of metabolic diseases, including diabetes. Herein are reviewed studies linking specific endocrine disruptors to impairments in glucose homeostasis as well as tying these compounds to disturbances in insulin secretion and impairments in insulin signal transduction. While the data remains somewhat incomplete, the current body of evidence supports the hypothesis that our chemically polluted environment may play a contributing role in the current metabolic crisis.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".