ARF6: Biomarker, Therapeutic Target, and AI-Driven Precision Management for Type 2 Diabetes with Obesity

Type 2 diabetes combined with obesity is a prevalent metabolic comorbidity with high clinical heterogeneity, leading to inconsistent treatment efficacy and difficult long-term standardized management. Emerging evidence indicates that the small GTPase ADP-ribosylation factor 6 (ARF6) signaling axis plays a vital regulatory role in glycolipid metabolism disorders, insulin resistance and adipose tissue inflammation. Aberrant ARF6 expression is closely correlated with the occurrence and progression of type 2 diabetes and obesity, making it a promising pre-clinical candidate biomarker and potential druggable therapeutic target, which still requires large-scale clinical validation. Traditional clinical evaluation and intervention rely on conventional biochemical indicators and empirical treatment, which cannot meet the demands of individualized precision management. Artificial intelligence technology has gradually matured in endocrinology clinical practice, supporting accurate disease stratification, auxiliary diagnosis and optimized therapeutic decision-making. This review summarizes the pre-clinical significance of ARF6 in diabetic obesity and systematically discusses conceptual application prospects as well as major unresolved limitations of artificial intelligence in ARF6-oriented precision diagnosis and targeted intervention, aiming to provide practical references for the clinical translational research of metabolic precision management.