A Management of Type 2 Diabetes: Mechanistic Insights, Dietary Models and Machine Learning Applications

Abstract

Type 2 diabetes is a rapidly increasing metabolic disease worldwide, driven by unhealthy dietary patterns, chronic inflammation, nutrient imbalances, sedentary behavior and disturbed circadian rhythm. This review summarizes mechanistic evidence linking high intakes of saturated fat, refined carbohydrates, and UPFs to insulin resistance through activation of inflammatory pathways, hepatic triglyceride accumulation, glucolipotoxicity, gut barrier impairment, and disturbances in gut microbiota. The roles of fiber, antioxidant nutrients, and micronutrients in preserving insulin sensitivity and β-cell function are highlighted. Major evidence-based dietary patterns, including the MD, DASH Diet, low-carbohydrate diets are also introduced. Other factors including intermittent fasting, lifestyle habits and psychological states are also mentioned in relation to blood sugar and lipid levels as well as metabolic risks. The review further discusses dietary strategies tailored to Chinese populations, established a dietary structure with cultural characteristics and emphasized traditional functional foods such as tea. Finally, we outline emerging applications of ML in diabetes care, including risk prediction, data identification and personalized dietary response. These insights support an integrated framework to improve long-term outcomes in T2D management.