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Record W2136915688 · doi:10.2337/db14-0685

Intranasal Insulin Suppresses Endogenous Glucose Production in Humans Compared With Placebo in the Presence of Similar Venous Insulin Concentrations

2014· article· en· W2136915688 on OpenAlexaff
Satya Dash, Changting Xiao, Cecilia Morgantini, Khajag Koulajian, Gary F. Lewis

Bibliographic record

VenueDiabetes · 2014
Typearticle
Languageen
FieldNeuroscience
TopicRegulation of Appetite and Obesity
Canadian institutionsUniversity of Toronto
FundersEli Lilly and Company
KeywordsInsulinInternal medicineEndocrinologyMedicineEndogenyCrossover studyNasal administrationVenous bloodDiabetes mellitusPlaceboCerebrospinal fluidPancreatic hormoneStimulationInsulin resistancePharmacology

Abstract

fetched live from OpenAlex

Intranasal insulin (INI) has been shown to modulate food intake and food-related activity in the central nervous system in humans. Because INI increases insulin concentration in the cerebrospinal fluid, these effects have been postulated to be mediated via insulin action in the brain, although peripheral effects of insulin cannot be excluded. INI has been shown to lower plasma glucose in some studies, but whether it regulates endogenous glucose production (EGP) is not known. To assess the role of INI in the regulation of EGP, eight healthy men were studied in a single-blind, crossover study with two randomized visits (one with 40 IU INI and the other with intranasal placebo [INP] administration) 4 weeks apart. EGP was assessed under conditions of an arterial pancreatic clamp, with a primed, constant infusion of deuterated glucose and infusion of 20% dextrose as required to maintain euglycemia. Between 180 and 360 min after administration, INI significantly suppressed EGP by 35.6% compared with INP, despite similar venous insulin concentrations. In conclusion, INI lowers EGP in humans compared with INP, despite similar venous insulin concentrations. INI may therefore be of value in treating excess liver glucose production in diabetes.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score0.377

Codex and Gemma teacher scores by category

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

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 head0.027
GPT teacher head0.235
Teacher spread0.208 · 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 teacher head, 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

Citations107
Published2014
Admission routes1
Has abstractyes

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