Targeting Fat Cell Receptors Promotes Weight Loss in Mice, Offering New Hope for Obesity Treatments.

Targeting Fat Cell Receptors Promotes Weight Loss in Mice, Offering New Hope for Obesity Treatments.

A recent study published in Cell Metabolism has uncovered a new potential strategy for weight loss by targeting receptors in fat cells. Researchers at the University of Texas Southwestern Medical Center (UTSW) found that stimulating the glucose-dependent insulinotropic polypeptide receptor (GIPR) in adipose tissue can boost energy expenditure and lead to lasting weight loss in mice. These findings introduce an alternative therapeutic approach to combat obesity and metabolic diseases beyond current GLP-1R-based drugs such as Ozempic and Wegovy.

GLP-1 receptor agonists like Ozempic and Wegovy are widely used for weight loss and blood sugar control. These drugs work by stimulating insulin secretion and increasing feelings of fullness. However, researchers have been exploring combinations of GLP-1R agonists with other compounds, such as those targeting GIPR, to enhance therapeutic benefits. While GLP-1R mechanisms are well understood, the role of GIPR—particularly in fat tissue—has remained unclear until now.

Christine Kusminski, lead investigator of the study, noted that GIPRs are commonly studied in the brain and pancreas, but her team aimed to understand their role within adipose tissue, which plays a critical part in metabolic regulation. Initial investigations revealed widespread expression of GIPRs across multiple human tissues. Focusing on white adipose tissue, the researchers developed a tetracycline-inducible system in mice that allowed controlled overexpression of GIPR in fat cells. When drug administration stopped, GIPR levels returned to normal.

Mice engineered to overexpress GIPR gained significantly less weight on a high-fat diet compared to their unmodified counterparts. More strikingly, obese mice with induced GIPR overexpression shed nearly one-third of their body weight during the treatment period—a result that surprised the researchers.

To uncover the underlying mechanisms, the team performed RNA sequencing on the fat cells. They discovered that GIPR activation upregulated genes linked to the sarco/endoplasmic reticulum calcium-ATPase (SERCA) pathways, which are involved in energy-consuming calcium transport. One branch of this pathway engages in a "futile calcium cycle," burning energy without transporting calcium, effectively wasting energy and increasing heat production in fat tissue. When researchers disrupted one of the genes in this pathway, they observed reduced tissue temperature, confirming the pathway’s role in energy dissipation.

The study also addressed a known issue with GLP-1R agonist treatments: rapid weight regain after discontinuation. In trials, both normal and GIPR-overexpressing mice lost weight on GLP-1R agonists, but only the GIPR-enhanced group maintained the weight loss after treatment ended. Even after turning off GIPR overexpression, these mice did not regain the lost weight, suggesting a form of "metabolic memory" influenced by GIPR signaling in fat cells.

Philipp Scherer, coauthor and physiologist at UTSW, emphasized that these findings expand the understanding of how dual agonist therapies function. He highlighted the importance of adipose tissue in regulating weight and metabolism, proposing that future treatments may target fat tissue directly rather than focusing solely on the brain and pancreas.

Jacqueline Beaudry, an adipocyte biologist not involved in the study, praised the research for highlighting fat cells as key players in GIPR-mediated weight loss. However, she cautioned that further studies are needed to determine if these results will translate to human patients.

As researchers continue to investigate the metabolic pathways in fat cells, this study represents a promising step toward more effective and lasting treatments for obesity.

Source:https://www.the-scientist.com/targeting-a-fat-cell-receptor-to-drive-weight-loss-72929

This is non-financial/medical advice and made using AI so could be wrong.

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