Adipose depot-specific upregulation of Ucp1 or mitochondrial oxidative complex proteins are early consequences of genetic insulin reduction in mice
Bibliographic record
Abstract
Abstract Hyperinsulinemia plays a causal role in adipose tissue expansion. Mice with reduced insulin have increased energy expenditure, but the mechanisms remained unclear. Here we investigated the effects of genetically reducing insulin production on uncoupling and oxidative mitochondrial proteins in liver, skeletal muscle, white adipose tissue (WAT), and brown adipose tissue (BAT). Male Ins1 +/+ or Ins1 +/- littermates were fed either a low-fat diet (LFD) or a high-fat diet (HFD) for 4 weeks, starting at 8 weeks of age. Replicating our previous observations, HFD increased fasting hyperinsulinemia, and Ins1 +/- mice had significantly lower circulating insulin compared with Ins1 +/+ littermates. Fasting glucose and body weight were not different between genotypes. We did not observe significant differences in liver in skeletal muscle. In mesenteric WAT, Ins1 +/- mice had reduced Ndufb8 and Sdhb. Ucp1 was increased in the context of the HFD, and HFD alone had a dramatic inhibitory effect on Pparg abundance. In inguinal WAT, Ins1 +/- mice exhibited significant increases in oxidative complex proteins, independent of diet, without affecting Ucp1, Pparg, or Prdm16:Pparg association. In BAT, lowered insulin increased Sdhb protein levels that had been reduced by HFD. Ucp1 protein, Prdm16:Pparg association, and Sirt3 abundance were all increased in the absence of diet-induced hyperinsulinemia. Our data show that reducing insulin upregulates oxidative proteins in inguinal WAT without affecting Ucp1, while in mesenteric WAT and BAT, reducing insulin upregulates Ucp1 in the context of HFD. Preventing hyperinsulinemia has early depot-specific effects on adipose tissue metabolism and help explain the increased energy expenditure previously reported in Ins1 +/- mice.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".