Adipocyte FMO3-derived TMAO induces WAT dysfunction and metabolic disorders by promoting inflammasome activation in ageing
Bibliographic record
Abstract
Trimethylamine N-oxide (TMAO) contributes to cardio-metabolic diseases, with hepatic flavin-containing monooxygenase 3 (FMO3) recognized as its primary source. Here we demonstrate that elevated adipocyte FMO3 and its derived TMAO trigger white adipose tissue (WAT) dysfunction and its related metabolic disorders in ageing. In adipocytes, ageing or p53 activation upregulates FMO3 and TMAO levels. Adipocyte-specific ablation of FMO3 attenuates TMAO accumulation in WAT and circulation, leading to enhanced glucose metabolism and energy and lipid homeostasis in ageing and obese mice. These improvements are associated with reduced senescence, fibrosis and inflammation in WAT. Proteomics analysis identified TMAO-interacting proteins involved in inflammasome activation in adipocytes and macrophages. Mechanistically, TMAO binds to the central inflammasome adaptor protein ASC, promoting caspase-1 activation and interleukin-1β production. Our findings uncover a pivotal role for adipocyte FMO3 in modulating TMAO production and WAT dysfunction by promoting inflammasome activation in ageing via an autocrine and paracrine manner. White adipose tissue (WAT) dysfunction leads to ageing-related metabolic disorders. Here, the authors show that the FMO3-TMAO axis in adipocytes induces senescence and inflammation in WAT microenvironment via NLRP3 inflammasome, leading to systemic glucose, lipid, and energy dysregulation in ageing
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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 | 0.001 |
| 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".