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Record W2751222302 · doi:10.14740/gr890w

Gastrin-Releasing Peptide and Glucose Metabolism Following Pancreatitis

2017· article· en· W2751222302 on OpenAlexvenueno aff
Sayali A. Pendharkar, Marie Drury, Monika Walia, Murray Korc, Maxim S. Petrov

Bibliographic record

VenueGastroenterology Research · 2017
Typearticle
Languageen
FieldNeuroscience
TopicNeuropeptides and Animal Physiology
Canadian institutionsnot available
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesHealth Research Council of New Zealand
KeywordsInternal medicineEndocrinologyPeptide YYMedicineVasoactive intestinal peptidePancreatitisGastrinGastrin-releasing peptideAmylinPancreatic polypeptideGlucagonInsulinNeuropeptide Y receptorSecretionBombesinNeuropeptideReceptor

Abstract

fetched live from OpenAlex

BACKGROUND: Gastrin-releasing peptide (GRP) is a pluripotent peptide that has been implicated in both gastrointestinal inflammatory states and classical chronic metabolic diseases such as diabetes. Abnormal glucose metabolism (AGM) after pancreatitis, an exemplar inflammatory disease involving the gastrointestinal tract, is associated with persistent low-grade inflammation and altered secretion of pancreatic and gut hormones as well as cytokines. While GRP is involved in secretion of many of them, it is not known whether GRP has a role in AGM. Therefore, we aimed to investigate the association between GRP and AGM following pancreatitis. METHODS: Fasting blood samples were collected to measure GRP, blood glucose, insulin, amylin, glucagon, pancreatic polypeptide (PP), somatostatin, cholecystokinin, gastric-inhibitory peptide (GIP), gastrin, ghrelin, glicentin, glucagon-like peptide-1 and 2, oxyntomodulin, peptide YY (PYY), secretin, vasoactive intestinal peptide, tumor necrosis factor-α (TNF-α), monocyte chemoattractant protein (MCP)-1, and interleukin-6. Modified Poisson regression analysis and linear regression analyses were conducted. Four statistical models were used to adjust for demographic, metabolic, and pancreatitis-related risk factors. RESULTS: A total of 83 individuals after an episode of pancreatitis were recruited. GRP was significantly associated with AGM, consistently in all four models (P -trend < 0.05), and fasting blood glucose contributed 17% to the variance of GRP. Further, GRP was significantly associated with glucagon (P < 0.003), MCP-1 (P < 0.025), and TNF-α (P < 0.025) - consistently in all four models. GRP was also significantly associated with PP and PYY in three models (P < 0.030 for both), and with GIP and glicentin in one model (P = 0.001 and 0.024, respectively). Associations between GRP and other pancreatic and gut hormones were not significant. CONCLUSION: GRP is significantly increased in patients with AGM after pancreatitis and is associated with increased levels of pro-inflammatory cytokines, as well as certain pancreatic and gut hormones. Detailed mechanistic studies are now warranted to investigate the exact role of GRP in derangements of glucose homeostasis following pancreatitis.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.098
GPT teacher head0.369
Teacher spread0.271 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations27
Published2017
Admission routes1
Has abstractyes

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