Cadmium acute exposure induces metabolic and transcriptomic perturbations in human mature adipocytes
Bibliographic record
Abstract
Obesity is considered as a major public health concern with strong economic and social burdens. Exposure to pollutants such as heavy metals can contribute to the development of obesity and its associated metabolic disorders, including type 2 diabetes and cardiovascular diseases. Adipose tissue is an endocrine and paracrine organ that plays a key role in the development of these diseases and is one of the main target of heavy metal accumulation. In this study, we determined by inductively coupled plasma mass spectrometry cadmium concentrations in human subcutaneous and visceral adipose tissues, ranging between 2.5 nM and 2.5 µM. We found a positive correlation between cadmium levels and age, sex and smoking status and a negative correlation between cadmium and body mass index. Based on cadmium adipose tissue concentrations found in humans, we investigated the effects of cadmium exposure, at concentrations between 1 nM and 10 µM, on adipose-derived human mesenchymal stem cells differentiated into mature adipocytes in vitro. Transcriptomic analysis highlighted that such exposure altered the expression of genes involved in trace element homeostasis and heavy metal detoxification, such as Solute Carrier Family transporters and metallothioneins. This effect correlated with zinc level alteration in cells and cellular media. Interestingly, dysregulation of zinc homeostasis and transporters has been particularly associated with the development of obesity and type 2 diabetes. Moreover, we found that cadmium exposure induces the pro-inflammatory state of the adipocytes by enhancing the expression of genes such as IL-6, IL-1B and CCL2, cytokines also induced in obesity. Finally, cadmium modulates various adipocyte functions such as the insulin response signaling pathway and lipid homeostasis. Collectively, our data identified some of the cellular mechanisms by which cadmium alters adipocyte functions, thus highlighting new facets of its potential contribution to the progression of metabolic disorders.
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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) | 0.001 | 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".