Machine learning reveals connections between preclinical type 2 diabetes subtypes and brain health

Abstract Previous research has established type 2 diabetes mellitus as a significant risk factor for various disorders, adversely impacting human health. While evidence increasingly links type 2 diabetes to cognitive impairment and brain disorders, understanding the causal effects of its preclinical stage on brain health is yet to be fully known. This knowledge gap hinders advancements in screening and preventing neurological and psychiatric diseases. To address this gap, we employed a robust machine learning algorithm (Subtype and Stage Inference, SuStaIn) with cross-sectional clinical data from the UK Biobank (20 277 preclinical type 2 diabetes participants and 20 277 controls) to identify underlying subtypes and stages for preclinical type 2 diabetes. Our analysis revealed one subtype distinguished by elevated circulating leptin levels and decreased leptin receptor levels, coupled with increased body mass index, diminished lipid metabolism, and heightened susceptibility to psychiatric conditions such as anxiety disorder, depression disorder, and bipolar disorder. Conversely, individuals in the second subtype manifested typical abnormalities in glucose metabolism, including rising glucose and haemoglobin A1c levels, with observed correlations with neurodegenerative disorders. A >10-year follow-up of these individuals revealed differential declines in brain health and significant clinical outcome disparities between subtypes. The first subtype exhibited faster progression and higher risk for psychiatric conditions, while the second subtype was associated with more severe progression of Alzheimer’s disease and Parkinson’s disease and faster progression to type 2 diabetes. Our findings highlight that monitoring and addressing the brain health needs of individuals in the preclinical stage of type 2 diabetes is imperative.