Differential roles of FOXO transcription factors on insulin action in brown and white adipose tissue
Bibliographic record
Abstract
Insulin and IGF-1 are essential for adipocyte differentiation and function. Mice lacking insulin and IGF-1 receptors in fat (FIGIR-KO, fat-specific IGF-1 receptor and insulin receptor-KO) exhibit complete loss of white and brown adipose tissue (WAT and BAT), glucose intolerance, insulin resistance, hepatosteatosis, and cold intolerance. To determine the role of FOXO transcription factors in the altered adipose phenotype, we generated FIGIR-KO mice with fat-specific KO of fat-expressed Foxos [Foxo1, Foxo3, Foxo4] (F-Quint-KO). Unlike FIGIR-KO mice, F-Quint-KO mice had normal BAT, glucose tolerance, insulin-regulated hepatic glucose production, and cold tolerance. However, loss of FOXOs only partially rescued subcutaneous WAT and hepatosteatosis, did not rescue perigonadal WAT or systemic insulin resistance, and led to even more marked hyperinsulinemia. Thus, FOXOs play different roles in insulin/IGF-1 action in different adipose depots, being most important in BAT, followed by subcutaneous WAT and then by visceral WAT. Disruption of FOXOs in fat also led to a reversal of insulin resistance in liver, but not in skeletal muscle, and an exacerbation of hyperinsulinemia. Thus, adipose FOXOs play a unique role in regulating crosstalk between adipose depots, liver, and β cells.
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 | 0.000 |
| 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".