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Metabolic state determines the brain and direct islet effects of liraglutide on enhanced insulin secretion

Chiara Saponaro, Monica Imbernon, Isaline Louvet, Eleonora Deligia, Shiqian Chen, Iona Davies, Ana Acosta-Montalvo, Maria Moreno-Lopez, Eve Wemelle, Lakshmi Kothegala, Begoña Porteiro, Florent Auger, Lorea Zubiaga, Nathalie Dellalau, Julien Thevenet, Markus Mühlemann, Gianni Pasquetti, Valery Gmyr, Frank W. Pfrieger, Ruben NogueirasMarkus Schwaninger, Patrik Rorsman, Bart Staels, Julie Kerr-Conte, Claude Knauf, Ben Jones, François Pattou, Vincent Prevot*, Caroline Bonner*

*Corresponding author for this work

Abstract

Aims/hypothesis: Liraglutide, a glucagon-like peptide-1 receptor (GLP-1R) agonist for type 2 diabetes and obesity management, shows variable patient responses. We investigated the metabolic state-dependent mechanisms underlying this heterogeneity and how liraglutide’s mode of action shifts across stages of metabolic dysfunction. Methods: We employed human pancreatic islets from donors across metabolic states (normoglycaemic [HbA1c <42 mmol/l (<6.0%)], glucose intolerance [HbA1c 42–47 mmol/l (6.0–6.4%)] and type 2 diabetes [HbA1c ≥48 mmol/l (≥6.5%)]) using dynamic perifusion and static incubation techniques to assess glucose-stimulated insulin secretion. GLP-1R mRNA levels were measured in 112 donor islets stratified by HbA1c. Mechanistic investigations used tanycyte-specific GLP-1R knockdown (GLP-1RTanycyteKD) mice and botulinum toxin B-expressing (iBot) mice to distinguish between central and peripheral pathways. Oral glucose tolerance tests, pyruvate tolerance tests and positron emission tomography were performed to assess in vivo metabolic effects. Results: Liraglutide (25 nmol/l) enhanced glucose-stimulated insulin secretion specifically in donors with glucose intolerance (n=7, p=0.021), with no effect in normoglycaemic islets (n=7), despite preserved GLP-1 (7–36) responsiveness. In type 2 diabetes islets, GLP-1R mRNA levels progressively decreased with rising HbA1c (p=0.015, normoglycaemic [n=48] vs type 2 diabetes [n=10]). In chow-fed mice, liraglutide’s insulin-stimulating effects required tanycyte-mediated hypothalamic access, as demonstrated by abolished responses in GLP-1RTanycyteKD mice. However, during metabolic dysfunction (a 12-week high-fat diet), direct islet responsiveness was restored independent of tanycyte function. Advanced metabolic disease (a 27-week high-fat diet) maintained islet responsiveness ex vivo while losing in vivo insulin enhancement, revealing insulin-independent glucose-lowering mechanisms involving hepatic gluconeogenesis suppression and enhanced peripheral glucose uptake. Conclusions/interpretation: Liraglutide operates through complementary, metabolic state-dependent pathways: tanycyte-mediated brain actions predominate in healthy conditions, direct islet effects emerge during glucose intolerance and insulin-independent mechanisms maintain efficacy across metabolic states. This mechanistic framework enables potential patient stratification in type 2 diabetes therapy, suggesting that matching liraglutide’s predominant mechanism to individual metabolic profiles could optimise treatment outcomes.

Original languageEnglish
JournalDiabetologia
Volume69
Issue number9
Pages (from-to)2534-2553
Number of pages20
ISSN0012-186X
DOIs
Publication statusPublished - 01.2026

Funding

FundersFunder number
Juvenile Diabetes Research Foundation United Kingdom
Novo Nordisk AIS
Société Francophone du Diabète 2015
Imperial College London
National Institute for Health and Care Research
European Foundation for the Study of Diabetes
Imperial Clinical Research Facility
NIHR Biomedical Research Centre Funding Scheme
European Consortium for Islet Transplantation
Conseil Regional Nord-Pas de Calais
Lilly-2016
Agence Nationale de la RechercheANR-15-CE14-0025, ANR-18-CE14-0007-01 ENDIABAC
Diabetes UK20/0006307
I-SITE ULNEANR-16-IDEX-0004
Medical Research CouncilMR/R010676/1
H2020 Marie Skłodowska-Curie Actions748134
Universite Paris-Sud XIInternational Research Project Grant NeuroMicrobiota
European Genomic Institute for DiabetesANR-10-LABX-0046
European Research Council810331

    UN SDGs

    This output contributes to the following UN Sustainable Development Goals (SDGs)

    1. SDG 3 - Good Health and Well-being
      SDG 3 Good Health and Well-being

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