Hepatocyte-derived DPP4 regulates portal GLP-1 bioactivity, modulates glucose production, and when absent influences NAFLD progression
Bibliographic record
Abstract
Elevated circulating dipeptidyl peptidase-4 (DPP4) is a biomarker for liver disease, but its involvement in gluconeogenesis and metabolic associated fatty liver disease progression remains unclear. Here, we identified that DPP4 in hepatocytes but not TEK receptor tyrosine kinase-positive endothelial cells regulates the local bioactivity of incretin hormones and gluconeogenesis. However, the complete absence of DPP4 (Dpp4-/-) in aged mice with metabolic syndrome accelerates liver fibrosis without altering dyslipidemia and steatosis. Analysis of transcripts from the livers of Dpp4-/- mice displayed enrichment for inflammasome, p53, and senescence programs compared with littermate controls. High-fat, high-cholesterol feeding decreased Dpp4 expression in F4/80+ cells, with only minor changes in immune signaling. Moreover, in a lean mouse model of severe nonalcoholic fatty liver disease, phosphatidylethanolamine N-methyltransferase mice, we observed a 4-fold increase in circulating DPP4, in contrast with previous findings connecting DPP4 release and obesity. Last, we evaluated DPP4 levels in patients with hepatitis C infection with dysglycemia (Homeostatic Model Assessment of Insulin Resistance > 2) who underwent direct antiviral treatment (with/without ribavirin). DPP4 protein levels decreased with viral clearance; DPP4 activity levels were reduced at long-term follow-up in ribavirin-treated patients; but metabolic factors did not improve. These data suggest elevations in DPP4 during hepatitis C infection are not primarily regulated by metabolic disturbances.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".