NOD-like Receptors—Emerging Links to Obesity and Associated Morbidities
Bibliographic record
Abstract
Obesity and its associated metabolic morbidities have been and still are on the rise, posing a major challenge to health care systems worldwide. It has become evident over the last decades that a low-grade inflammatory response, primarily proceeding from the adipose tissue (AT), essentially contributes to adiposity-associated comorbidities, most prominently insulin resistance (IR), atherosclerosis and liver diseases. In mouse models, the release of pro-inflammatory cytokines such as TNF-alpha (TNF-α) and interleukin (IL)-1β and the imprinting of immune cells to a pro-inflammatory phenotype in AT play an important role. However, the underlying genetic and molecular determinants are not yet understood in detail. Recent evidence demonstrates that nucleotide-binding and oligomerization domain (NOD)-like receptor (NLR) family proteins, a group of cytosolic pattern recognition receptors (PRR), contribute to the development and control of obesity and obesity-associated inflammatory responses. In this article, we review the current state of research on the role of NLR proteins in obesity and discuss the possible mechanisms leading to and the outcomes of NLR activation in the obesity-associated morbidities IR, type 2 diabetes mellitus (T2DM), atherosclerosis and non-alcoholic fatty liver disease (NAFLD) and discuss emerging ideas about possibilities for NLR-based therapeutic interventions of metabolic diseases.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".