SCIENCE MAGAZINE: The latest issue features ‘GLP-1 AND BEYOND’ – Targeting metabolism….

Going Beyond GLP-1

The growing complexity of targeting metabolism

Until recently, people with obesity were generally expected to manage their weight through diet and exercise, and pharmacological interventions were developed for comorbid conditions such as diabetes and cardiovascular disease. Some drugs were available to promote weight loss, but they did not become established therapies owing to intolerable side effects, limited effectiveness, or both. By far, the most reliable intervention to treat obesity was bariatric surgery, but it was reserved for the most severely affected patients because of the risks associated with the surgery itself as well as lifelong side effects.

GLP-1 receptor agonists in addiction therapy

Use of GLP-1–based therapies for addiction could provide a bridge toward whole-person health care

Glucagon-like peptide-1 receptor agonist (GLP-1RA) medications have transformed the landscape of obesity and diabetes treatment. Evidence is accruing that they also might reduce cravings for alcohol, nicotine, opioids, and stimulants. Most research into the mechanisms by which GLP-1RAs might help to treat substance use disorders has focused on their ability to modulate the dopamine reward system in the brain, which also contributes to suppressing the consumption of highly palatable food. However, emerging understanding about the biology of GLP-1RAs suggests that a neurocentric framework might be too narrow to interpret their effects, which arise from complex interactions between brain processes and other aspects of an individual’s physiology. As such, these medications provide an opportunity to reconsider the effects of drugs and addiction through a whole-person health lens. This framework considers how the brain’s reward pathway interacts with metabolic, inflammatory, hormonal, and cardiovascular processes throughout the body.

The scramble for your brain data

Companies are using wearable neurotechnologies to train AI models of the brain—without telling users