Role of the type 3 cytokines IL-17 and IL-22 in modulating metabolic dysfunction-associated steatotic liver disease
Bibliographic record
Abstract
Metabolic dysfunction-associated steatotic liver disease (MASLD) comprises a spectrum of liver diseases that span simple steatosis, metabolic dysfunction-associated steatohepatitis (MASH) and fibrosis and may progress to cirrhosis and cancer. The pathogenesis of MASLD is multifactorial and is driven by environmental, genetic, metabolic and immune factors. This review will focus on the role of the type 3 cytokines IL-17 and IL-22 in MASLD pathogenesis and progression. IL-17 and IL-22 are produced by similar adaptive and innate immune cells such as Th17 and innate lymphoid cells, respectively. IL-17-related signaling is upregulated during MASLD resulting in increased chemokines and proinflammatory cytokines in the liver microenvironment, enhanced recruitment of myeloid cells and T cells leading to exacerbation of inflammation and liver disease progression. IL-17 may also act directly by activating hepatic stellate cells resulting in increased fibrosis. In contrast, IL-22 is a pleiotropic cytokine with a dominantly protective signature in MASLD and is currently being tested as a therapeutic strategy. IL-22 also exhibits beneficial metabolic effects and abrogates MASH-related inflammation and fibrosis development via inducing the production of anti-oxidants and anti-apoptotic factors. A sex-dependent effect has been attributed to both cytokines, most importantly to IL-22 in MASLD or related conditions. Altogether, IL-17 and IL-22 are key effectors in MASLD pathogenesis and progression. We will review the role of these two cytokines and cells that produce them in the development of MASLD, their interaction with host factors driving MASLD including sexual dimorphism, and their potential therapeutic benefits.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".