Faculty Opinions recommendation of HSP72 protects against obesity-induced insulin resistance.
Bibliographic record
Abstract
Patients with type 2 diabetes have reduced gene expression of heat shock protein (HSP) 72, which correlates with reduced insulin sensitivity. Heat therapy, which activates HSP72, improves clinical parameters in these patients. Activation of several inflammatory signaling proteins such as c-jun amino terminal kinase (JNK), inhibitor of kappaB kinase, and tumor necrosis factor-alpha, can induce insulin resistance, but HSP 72 can block the induction of these molecules in vitro. Accordingly, we examined whether activation of HSP72 can protect against the development of insulin resistance. First, we show that obese, insulin resistant humans have reduced HSP72 protein expression and increased JNK phosphorylation in skeletal muscle. We next used heat shock therapy, transgenic overexpression, and pharmacologic means to overexpress HSP72 either specifically in skeletal muscle or globally in mice. Herein, we show that regardless of the means used to achieve an elevation in HSP72 protein, protection against diet- or obesity-induced hyperglycemia, hyperinsulinemia, glucose intolerance, and insulin resistance was observed. This protection was tightly associated with the prevention of JNK phosphorylation. These findings identify an essential role for HSP72 in blocking inflammation and preventing insulin resistance in the context of genetic obesity or high-fat feeding. PMID: 18223156 Funding information This work was supported by: NIDDK NIH HHS, United States Grant ID: DK60484 NIDDK NIH HHS, United States Grant ID: DK73227 NIDDK NIH HHS, United States Grant ID: R21 DK073227 NIDDK NIH HHS, United States Grant ID: K01 DK060484
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.626 | 0.471 |
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".