Pathogenic hypothalamic extracellular matrix promotes metabolic disease
Bibliographic record
Abstract
Metabolic diseases such as obesity and type 2 diabetes are marked by insulin resistance1,2. Cells within the arcuate nucleus of the hypothalamus (ARC), which are crucial for regulating metabolism, become insulin resistant during the progression of metabolic disease3–8, but these mechanisms are not fully understood. Here we investigated the role of a specialized chondroitin sulfate proteoglycan extracellular matrix, termed a perineuronal net, which surrounds ARC neurons. In metabolic disease, the perineuronal net of the ARC becomes augmented and remodelled, driving insulin resistance and metabolic dysfunction. Disruption of the perineuronal net in obese mice, either enzymatically or with small molecules, improves insulin access to the brain, reversing neuronal insulin resistance and enhancing metabolic health. Our findings identify ARC extracellular matrix remodelling as a fundamental mechanism driving metabolic diseases. Experiments in mice show that the perineural net has a key role in metabolic disease by controlling insulin access to neurons in the arcuate nucleus of the hypothalamus.
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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) | 0.002 | 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".