Bibliographic record
Abstract
Chronic hyperglycemia due to impaired glucose tolerance (IGT) is generally associated with the metabolic syndrome and type 2 diabetes. Glucose homeostasis principally involves the liver, skeletal muscle and adipose tissue. To prevent the adverse effects that accrue from high and low glucose levels, the liver stores glycogen and produces glucose, so any chronic liver injury could potentially contribute to IGT via cellular damage. However, IGT particularly occurs in chronic hepatitis C, alcoholic liver disease and non-alcoholic fatty liver disease so additional mechanisms make significant contributions.1 Therapies designed to treat IGT aim to prevent diabetes onset. The most common therapy is metformin or acarbose. If this therapy fails, a thiazolidinedione such as pioglitazone or rosiglitazone may be used. In the obese, a trial of the lipase inhibitor, Orlistat (Roche, Basel, Switzerland), is also an option in IGT. Recently developed incretin therapies exploit the role of glucagon-like peptide-1 (GLP-1) and other incretin hormones that are released by intestinal L cells following food ingestion. GLP-1 stimulates pancreatic insulin release but has additional effects including slowing gastric emptying and activating hepatocyte glycogen synthesis. Several incretin hormones, notably GLP-1 and glucose insulinotropic peptide (GIP), are inactivated by the enzyme dipeptidyl peptidase IV (DPIV). DPIV is ubiquitous; it is expressed on lymphocytes and on epithelial cells including hepatocytes, but most relevant to GLP-1 inactivation is its expression on capillary endothelial cells and its activity in serum.2 In this issue of the Journal of Gastroenterology and Hepatology, Itou and colleagues report that serum levels of the active form of GLP-1 are lowered in chronic hepatitis C and that DPIV levels are increased in the ileum and serum of those patients.3 This observation points to a potential contribution of the DPIV–incretin axis to the mechanism of IGT in hepatitis C. The incretin therapies are of two types: either an oral DPIV inhibitor (sitagliptin; (2R)-4-oxo-4-[3-(trifluoromethyl)-5,6-dihydro[1,2,4]triazolo[4,3-a]pyrazin-7(8H)-yl]-1-(2,4,5-trifluorophenyl)butan-2-amine), released as Januvia by Merck (Whitehorse Station, NJ, USA) or injection of a DPIV-resistant GLP-1 analog. Such GLP-1 analogs include exenatide (Byetta; Eli Lilly, Indianapolis, IN, USA), derived from the gila monster lizard Heloderma suspectum, and liraglutide (Novo-Nordisk, Bagsvaerd, Denmark), which is a modified human GLP-1. Sitagliptin and exenatide are being prescribed in the USA. In the July 2007 issue of the Journal of the American Medical Association, the first meta-analysis of incretin therapy has been reported. This meta-analysis of 29 studies shows that either exenatide injection or DPIV inhibition lowers glycohemoglobin A1c (HbA1c) to the same extent as other hypoglycemic agents.4 Like exenatide, sitagliptin is effective as a combination therapy with either metformin or pioglitazone.5,6 Sitagliptin therapy for 24 weeks is associated with lowered fasting blood glucose and increased insulin sensitivity compared to placebo and less nausea and fewer hypoglycemic events than other therapies. Rodent studies indicate that improved glucose homeostasis from sitagliptin is associated with improved pancreatic beta cell function, and the human trials show increased insulin sensitivity. Metformin and thiazolidinediones cause weight gain, whereas DPIV inhibition does not (there is no weight change) and exenatide causes weight loss. DPIV has many substrates that could contribute to the overall outcomes of DPIV inhibition. For example, the DPIV substrate insulin-like growth factor 1 is crucial for hepatocyte proliferation and the hepatic response to growth hormone. In the clinic, the goal is achieving glucose homeostasis, so fasting blood glucose and HbA1c are key measurements for patient health. In addition to diet and exercise, insulin levels, insulin sensitivity and peripheral insulin resistance are important for glucose homeostasis, but their measurement and interpretation are complex; these indices are therefore more applicable in a research setting.7 Itou and colleagues observed high serum DPIV levels in HCV patients,3 a finding previously noted in other various inflammatory conditions.8 DPIV inhibitory compounds developed in the 1980s suppress immune responses,8 so when DPIV inhibition was first proposed as a type 2 diabetes therapy there was concern about therapeutic specificity. However, since the discovery of the DPIV-related peptidase, DP8,9 sitagliptin has been chosen by counterscreening against DP8 to be >2000-fold selective for DPIV inhibition. This selectivity appears to be the reason for the ability of sitagliptin to exhibit improved glucose tolerance without significant side-effects.10 The observations from Itou and colleagues provide a new possible link between hepatitis C pathogenesis, insulin resistance and disordered glucose homeostasis. We have previously shown increased DPIV expression on the basolateral surface of hepatocytes in cirrhosis due to hepatitis C or other causes11 and increased serum DPIV levels during experimental liver regeneration.12 The exciting possibility that such DPIV activity may contribute to impaired glucose tolerance in cirrhosis, thereby providing a novel therapeutic avenue in the form of DPIV inhibitors to reassert control of glucose homeostasis in cirrhosis-related diabetes, is an important direction for future studies.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".