Adipose Tissue Inflammation Is Directly Linked to Obesity-Induced Insulin Resistance, while Gut Dysbiosis and Mitochondrial Dysfunction Are Not Required
Bibliographic record
Abstract
Obesity is associated with adipose tissue hypertrophy, systemic inflammation, mitochondrial dysfunction, and intestinal dysbiosis. Rodent models of high-fat diet (HFD)-feeding or genetic deletion of multifunctional proteins involved in immunity and metabolism are often used to probe the etiology of obesity; however, these models make it difficult to divorce the effects of obesity, diet composition, or immunity on endocrine regulation of blood glucose. We, therefore, investigated the importance of adipose inflammation, mitochondrial dysfunction, and gut dysbiosis for obesity-induced insulin resistance using a spontaneously obese mouse model. We examined metabolic changes in skeletal muscle, adipose tissue, liver, the intestinal microbiome, and whole-body glucose control in spontaneously hyperphagic C57Bl/6J mice compared to lean littermates. A separate subset of lean and obese mice was subject to 8 weeks of obesogenic HFD feeding, or to pair feeding of a standard rodent diet. Hyperphagia, obesity, adipose inflammation, and insulin resistance were present in obese mice despite consuming a standard rodent diet, and these effects were blunted with caloric restriction. However, hyperphagic obese mice had normal mitochondrial respiratory function in all tissues tested and no discernable intestinal dysbiosis relative to lean littermates. In contrast, feeding mice an obesogenic HFD altered the composition of the gut microbiome, impaired skeletal muscle mitochondrial bioenergetics, and promoted poor glucose control. These data show that adipose inflammation and redox stress occurred in all models of obesity, but gut dysbiosis and mitochondrial respiratory dysfunction are not always required for obesity-induced insulin resistance. Rather, changes in the intestinal microbiome and mitochondrial bioenergetics may reflect physiological consequences of HFD feeding.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".