Sex differences in GLP-1 signaling across species
Roseberry, T.; Grossrubatscher, I.; Krausz, T.; Wang, Y.; Schwartz, M. W.; Tingley, D.
Show abstract
Two billion humans are currently overweight or obese1. While glucagon-like peptide 1 receptor (GLP1R) agonists have emerged as the most promising treatment for this epidemic, side effects including nausea and vomiting constitute a significant obstacle to their use. Of the patients currently being treated, women represent nearly 70%. While early studies have noted sex differences in the response to these drugs, the nature of these differences remain poorly characterized. Using real world electronic medical record (EMR) data, we find that women experience more than double the rates of persistent nausea and vomiting when prescribed GLP1R agonists. To investigate this sex difference in greater detail, we developed novel, species-specific in vivo phenomic assays to quantify aversive behaviors. In both mice and rats, aversive responses to either semaglutide or tirzepatide were greater in females than males. To investigate the basis for this difference, we constructed a mouse single cell transcriptomic atlas of body and brain regions most relevant to the action of GLP-1. Using this atlas we find that multiple neuronal cell types involved in the processing of aversive stimuli and nausea had higher GLP1R expression in females than males. Heightened susceptibility of females to the aversive effects of GLP1R agonists could therefore involve increased activation of these brain circuits. Finally, we demonstrate that in mice, both the efficacy and tolerability of GLP1R agonists vary with the phase of the estrous cycle, being highest during proestrus (when estrogen levels peak) and lowest in diestrus (low estrogen levels). Similarly, we report that higher circulating estrogen levels in humans is associated with heightened risk of nausea and vomiting among women taking a GLP1R agonist. Based on these findings, we anticipate that women will continue to be disproportionately impacted by the adverse effects associated with all members of this drug class. Research to better understand and ultimately mitigate this heightened susceptibility is an important priority for new drug development in this area, and novel approaches to model the impact of endogenous hormone signaling will be critical to developing better treatments for more people.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Nucleus accumbens D1- and D2-expressing neurons control the balance between feeding and activity-mediated energy expenditure 95%
- Astrocyte glucocorticoid receptors mediate sex-specific changes in activity following stress 94%
- TrkB-expressing paraventricular hypothalamic neurons suppress appetite through multiple neurocircuits 93%
Similar papers in this journal
- POMC neurons control fertility through differential signaling of MC4R in Kisspeptin neurons 95%
- Metabolic-sensing in AgRP neurons integrates homeostatic state with dopamine signalling in the striatum 94%
- Smith-Magenis syndrome protein RAI1 regulates body weight homeostasis through hypothalamic BDNF-producing neurons and neurotrophin downstream signalling 94%
Similar papers in this journal
- Applying a computational transcriptomics-based drug repositioning pipeline to identify therapeutic candidates for endometriosis 94%
- Feeding Neurons Integrate Metabolic and Reproductive States in Mice 93%
- Humanized substitutions of Vmat1 in mice alter amygdala-dependent behaviors associated with the evolution of anxiety 93%
Similar papers in this journal
- A Peptide Triple Agonist of GLP-1, Neuropeptide Y1, and Neuropeptide Y2 Receptors Promotes Glycemic Control and Weight Loss 94%
- JNK1 And Downstream Signalling Hubs Regulate Anxiety-Like Behaviours In A Zebrafish Larvae Phenotypic Screen 94%
- Neuropeptide Neuromedin B does not alter body weight and glucose homeostasis nor does it act as an insulin-releasing peptide 94%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.