Abstract
Obesity-related metabolic diseases include conditions linked to obesity, such as type 2 diabetes, hypertension, steatotic liver disease, and polycystic ovary syndrome. These disorders are primarily caused by insulin resistance, chronic inflammation, and excessive fat accumulation. They represent significant health challenges and often remain asymptomatic during their early stages. Traditional diagnostic tools, including blood glucose, lipid levels, blood pressure, and uric acid measurements, provide valuable insights but fall short of fully capturing the complexity of metabolic dysfunction. Consequently, there is a growing need for noninvasive, easily accessible biomarkers, especially those found in urine, to enable more accurate, sensitive, and patient-friendly diagnostic methods. Urine, with its diverse range of metabolites that reflect the body's metabolic changes, is an ideal sample for early detection. Recent advancements in urine metabolomics and proteomics have highlighted the potential of urinary biomarkers for diagnosing obesity-related metabolic diseases. Despite challenges such as the need for standardized detection techniques and clinical validation, the integration of artificial intelligence and multi-omics approaches holds significant promise for enhancing diagnostic accuracy and advancing disease management strategies.