Notice bibliographique
Résumé
The investigation of endocrine dysregulation continues to be an important part of understanding the etiology of obesity-induced metabolic disease. The gastrointestinal hormone, glucagon-like peptide-1 (GLP-1), is a key regulator of insulin release and appetite. This enhancement of glucose-stimulated insulin secretion by GLP-1 released by the intestinal L-cell after a meal is consumed is known as the incretin effect (reviewed in Ref. 1). GLP-1-based therapies are currently used in the treatment of type 2 diabetes and, more recently, obesity (reviewed in Ref. 2). However, how this hormone is regulated, or dysregulated in disease, remains a major focus of metabolic health research. In this issue, the article by Dusaulcy et al (3) examines the idea that some animals are able to adapt and prevent the development of high-fat diet (HFD)-induced hyperglycemia. The authors shed light on an important reality of studying obesity and metabolic dysfunction in animal models, not all animals will become hyperglycemic. Compensation may occur, and incretin hormones may be responsible for this compensation. What is responsible for this adaptation of the L-cell and how these findings are relevant to incretin dysfunction in obese and diabetic humans remain 2 key questions. In their study, male mice were given a HFD or low-fat diet for 16 weeks. At the end of this period, animals receiving HFD all had similar weight gain, hyperinsulinemia, and insulin resistance. However, when hemoglobin A1c (HbA1c) levels were examined, a high degree of variability was found. This led the authors to subdivide their HFD mice into 2 groups, those with a normal HbA1c (<4.2) as glucose-intolerant HFD (I-HFD) and those with HbA1c more than 4.5 as hyperglycemic HFD (H-HFD). When they examined the cause of this hyperglycemia, they determined H-HFD mice had impaired suppression of glucagon release during an oral glucose tolerance test (OGTT) in vivo and in α-cell primary culture. Indeed, elevated glucagon levels are a known contributor to the pathophysiology of diabetes (reviewed in Ref. 4). Of particular interest, GLP-1 and GLP-1-based therapies are also known to suppress the release of glucagon (5). When they examined GLP-1 levels after the oral glucose challenge, Dusaulcy et al (3) found that, although both HFD groups had elevated GLP-1 relative to low-fat diet animals, the I-HFD mice had a significantly greater GLP-1 response. Of key importance, this study was done in transgenic mice that express a Venus fluorochrome in proglucagon-producing cells (6). Using this model, they were able to closely examine the alterations and adaptations of the L-cells in the 2 groups. Surprisingly, the hyperglycemic (H-HFD) animals had a greater degree of hyperplasia in their L-cells compared with the I-HFD group. However, regardless of cell number, the ability of primary L-cell cultures to secrete GLP-1 was greatest in I-HFD mice. When they examined the molecular details of this, genes required for GLP-1 biosynthesis were highest in the I-HFD animals. It would appear that these L-cells had adapted to the increased demands for GLP-1 rather than becoming dysfunctional (7). To confirm that it was indeed the increased GLP-1 that was responsible for maintenance of glucagon secretion and normoglycemia, the authors pretreated the I-HFD mice with the GLP-1 receptor antagonist, Ex4(9–39) before an OGTT. Indeed, Ex4(9–39) pretreated mice had markedly impaired glucose tolerance with a loss in GLP-1-induced glucagon suppression. One critical question that emerges from this is why do the L-cells of some animals adapt while others do not? The authors acknowledge that other endocrine regulators such as leptin and insulin may play a role; however, these were similar in both I-HFD and H-HFD mice. Instead, they suggest that a potential endocrine modulator that may have been altered between these 2 groups of HFD mice is the gut microbiome. Indeed, several groups have demonstrated a role for bacterial metabolites including short chain fatty acids (8, 9) and indole (10), in the regulation of GLP-1. If microbial metabolites are indeed driving incretin secretion and intestinal endocrine cell adaptation (11), this presents an exciting new frontier in examining the roles of pro-, pre-, and postbiotics in the treatment of metabolic disease. One additional note is that, if differences in endocrine regulation can be attributed to microbial metabolites, then researchers should take special care when examining metabolic profiles from one group of cohoused animals to another. It is interesting to speculate that the enhanced release of GLP-1 may be the initial response of the L-cell to the HFD and insulin resistance, and that long term obesity will lead to eventual decline in the GLP-1 secretion. The latter appears to be the case from studies in long-term obese and diabetic humans, where typically a drop in GLP-1 secretion is observed (12–14), although not always, as reviewed in detail in Ref. 15. Data on incretin levels in humans with early-stage obesity are scarce; however, a recent study examining GLP-1 responses to an OGTT in obese and normal weight (nondiabetic) prepubertal boys found obese boys had significantly elevated GLP-1 secretion (16). Perhaps the elevated GLP-1 demand from the L-cell may eventually lead to the dysfunction and hyperplasia of L-cells, similar to what was observed in the H-HFD mice. Regardless, it is clear that longitudinal information on GLP-1 secretion is lacking for both prediabetic/overweight humans and long-term obesity in rodent models. Future work will hopefully close these gaps. Funded by Banting Research Foundation. Disclosure Summary: The author has nothing to disclose. glucagon-like peptide-1 high-fat diet hyperglycemic HFD glucose-intolerant HFD hemoglobin A1c oral glucose tolerance test.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,011 | 0,013 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,004 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».