NOD1: An Interface Between Innate Immunity and Insulin Resistance
Bibliographic record
Abstract
Insulin resistance is driven, in part, by activation of the innate immune system. We have discussed the evidence linking nucleotide-binding oligomerization domain (NOD)1, an intracellular pattern recognition receptor, to the onset and progression of obesity-induced insulin resistance. On a molecular level, crosstalk between downstream NOD1 effectors and the insulin receptor pathway inhibits insulin signaling, potentially through reduced insulin receptor substrate action. In vivo studies have demonstrated that NOD1 activation induces peripheral, hepatic, and whole-body insulin resistance. Also, NOD1-deficient models are protected from high-fat diet (HFD)-induced insulin resistance. Moreover, hematopoietic NOD1 deficiency prevented HFD-induced changes in proinflammatory macrophage polarization status, thus protecting against the development of metabolic inflammation and insulin resistance. Serum from HFD-fed mice activated NOD1 signaling ex vivo; however, the molecular identity of the activating factors remains unclear. Many have proposed that an HFD changes the gut permeability, resulting in increased translocation of bacterial fragments and increased circulating NOD1 ligands. In contrast, others have suggested that NOD1 ligands are endogenous and potentially lipid-derived metabolites produced during states of nutrient overload. Nevertheless, that NOD1 contributes to the development of insulin resistance, and that NOD1-based therapy might provide benefit, is an exciting advancement in metabolic research.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".