Role of adiponectin and its receptors AdipoR1/2 in inflammatory bowel disease
Bibliographic record
Abstract
Intake of nutrients and water from diet to maintain life, a typical physiological function of gut, is highly dependent on the extensive immune network, whose imbalance is easy to induce inflammatory bowel disease (IBD) including Crohn's disease (CD) and ulcerative colitis (UC). Clinical strategies to completely cure IBD are poor, so it is urgent to develop novel drugs or targets. Adiponectin (APN), an adipokine from adipocytes, regulates energy metabolism and immune response. High levels APN are inversely associated with CD severity UC colonic fibrosis. However, the mechanism by which APN interferes with IBD remains unclear. This review aims to analyze correlation and molecular mechanism between APN and IBD. APN and AdipoR2 proteins are highly expressed in colon which is a primary organ of IBD, and the target intersection of APN and IBD is huge. APN may interfere with lipid metabolism in IBD individuals through AdipoR1/2, but regulates neural and peripheral immune by AdipoR1 but not AdipoR2 and mediates nutritional and energy homeostasis through AdipoR2 rather than AdipoR1. Besides, APN mediates CRP and IL-6 through AdipoR1/2, AMPK and TNF-α through AdipoR1 and PI3K-Akt, PPARA and PPARG through AdipoR2 to affect IBD progression, which depends on direct interaction between APPL1 and AdipoR1/2. Unexpectedly, AMPK and TNF-α may also interact directly with AdipoR1. APN regulates CD through AdipoR1/2-metabolism process and UC through AdipoR1-inflammation axis or AdipoR2-fibrosis process. APN analogues or AdipoRon which is a dual agonist of AdipoR1/2 potentially reduces colonic fibrosis in UC and fistulae in CD, promotes mucosal healing, repairs intestinal microbiota homeostasis and increases autophagy to alleviate IBD symptoms by weakening TNF-α, IL-6, NLRP3, TGFB1 activities and aggrandizing P-AKT, PPARA, PPARG, INS, IRS1/2, IGF-1, TIMP1, NOD2, SIRT1 levels.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".