Heavy drinking and your brain; patients taking Ozempic for T1D; memory-inspired AI – Morning Medical Update

Author(s):

Austin Littrell

Key Takeaways

Heavy drinking is linked to increased risk of brain damage, including vascular lesions and Alzheimer's-related tau tangles, with effects persisting post-cessation.
GLP-1 medication use has risen 800% among type 1 diabetes patients, despite limited safety data, necessitating further randomized trials.
The "machine memory intelligence" (M2I) framework seeks to improve AI systems by emulating human memory processes, enhancing adaptability and efficiency.

The top news stories in medicine today.

Heavy drinking and the brain

People who have eight or more drinks per week may be at higher risk for brain damage, including vascular lesions and Alzheimer’s-related tau tangles, according to a new study published in Neurology. Heavy and formerly heavy drinkers had 133% and 89% greater odds of brain vessel injury than non-drinkers and were more likely to show cognitive decline and die earlier. The research shows that the brain impact lingers long after the drinking stops.

GLP-1 use jumps among patients with type 1 diabetes

Despite limited data regarding its safety, more people with type 1 diabetes are using medications like Ozempic and Mounjaro — originally designed for type 2. A Johns Hopkins study found an 800% rise in GLP-1 use among adults with obesity from 2008 to 2023. Although some patients report big benefits, researchers stress the need for randomized trials to understand risks like severe low blood sugar. CNN Health has more.

Human memory-inspired AI

A new study introduces “machine memory intelligence” (M2I), a brain-inspired framework that mimics how humans store and retrieve information. Published in Engineering, the research outlines how this approach could help AI systems like ChatGPT overcome key flaws — like high energy use, forgetfulness, and weak reasoning. By replicating memory mechanisms like spatiotemporal association and continual learning, M2I aims to make machines more adaptable, efficient, and human-like in how they learn and think.