Harnessing gut-derived bioactives and AI diagnostics for the next generation of type 2 diabetes solutions

The prevalence of type 2 diabetes (T2D) has significantly increased over the past 20 years, currently affecting over 500 million people worldwide. Projections suggest that this number could rise to over 700 million in the next two decades. Despite advancements in medication and global health strategies that promote healthy lifestyles, T2D remains a complex disease that impacts the quality of life. Traditional treatment methods are becoming less effective, highlighting the need for innovative approaches to prevention, diagnosis, and treatment.

Two promising areas of research that could transform the management of T2D are the use of biologically active substances derived from the intestines and the integration of artificial intelligence (AI) in clinical diagnostics. The human intestinal microbiota plays a crucial role in metabolic processes, including glucose regulation and insulin sensitivity. Microbial metabolites, including bile acids and short-chain fatty acids, have potential as therapeutic agents for metabolic disorders. As digital medicine advances, AI is increasingly utilized for real-time monitoring and personalized risk assessments. The medical field is evolving from merely using biosensors for glucose tracking to employing machine learning to analyze various biological indicators and electronic medical records.

Recent research at the intersection of microbiome studies and AI may improve diagnostic accuracy and support tailored treatment strategies. This study aims to analyze global experiences with the implementation of bioactive substances from the intestines and the diagnostic potential of AI in developing a new approach to enhancing the quality of life and treating T2D.

We examine the diverse functions of microbial metabolites and the current landscape of their therapeutic applications. Additionally, the review examines the current state of AI in diagnostics, with a particular focus on microbiome parameters. As a result, we propose a novel model that combines these two fields into an adaptive and personalized approach to treating patients with T2D and improving their quality of life.