Triglyceride glucose-body mass index (TyG-BMI): mechanisms, multi-organ predictive value, and artificial intelligence-assisted risk stratification in type 2 diabetes mellitus

Abstract Type 2 diabetes mellitus (T2DM) has evolved into a global public health crisis characterized by high prevalence, chronic progression, and multisystem complications, including cardiovascular, renal, hepatic, and bone metabolic disorders. Insulin resistance (IR) serves as the core pathological driver of T2DM onset and subsequent multi-organ damage. Traditional IR evaluation indicators, such as the homeostatic model assessment for insulin resistance (HOMA-IR), rely on costly insulin detection and lack suitability for large-scale grassroots screening and long-term dynamic monitoring. The triglyceride glucose-body mass index (TyG-BMI), a novel non-invasive and cost-effective composite metabolic marker calculated from routine biochemical and anthropometric parameters, has recently gained extensive attention in metabolic and diabetes research. Accumulating clinical evidence demonstrates that TyG-BMI exhibits superior performance in evaluating IR and predicting diabetic complications compared with single metabolic or anthropometric indicators. However, existing reviews mainly focus on single-organ diabetic complications, lacking a systematic integration of TyG-BMI-associated cardiorenal, hepatic, and bone biomarker abnormalities. Furthermore, the combination of TyG-BMI and artificial intelligence (AI) predictive modeling, a cutting-edge hotspot in precision diabetes management, has not been comprehensively summarized. This review systematically retrieves and analyzes high-quality literature published from 2016 to 2026, elaborates on the generation mechanism and molecular pathway basis of TyG-BMI mediating multi-organ injury in T2DM, summarizes the quantitative correlation between TyG-BMI and multisystem biomarker abnormalities and adverse clinical outcomes, compares the predictive efficacy of TyG-BMI with traditional IR markers, and innovatively discusses the application value of AI machine learning models based on TyG-BMI in diabetic risk stratification. In addition, we clarify population heterogeneity, conflicting research conclusions, and clinical application limitations of TyG-BMI, and propose future research directions focusing on unified cut-off value formulation, prospective intervention verification, and multi-modal AI model optimization. Overall, TyG-BMI is a promising integrated metabolic risk stratification tool for T2DM. The combination of TyG-BMI and AI predictive technology provides a novel strategy for early warning, hierarchical management, and precise clinical intervention of multi-organ diabetic complications, which has broad grassroots clinical translation prospects.