Introduction: Type 2 diabetes mellitus (T2DM) is a complex metabolic disorder characterized by heterogeneity in treatment response. Identifying reliable biomarkers to predict therapeutic outcomes can improve personalized treatment strategies and optimize disease management. This study investigates the role of biological markers in predicting treatment efficacy in T2DM patients.
Methods: A systematic review and meta-analysis of clinical and experimental studies were conducted to identify potential biomarkers associated with glycemic control and treatment response. Biomarkers such as HbA1c, C-peptide levels, inflammatory markers (e.g., IL-6, TNF-α), and genetic polymorphisms were analyzed. Advanced statistical modeling and machine learning algorithms were employed to assess the predictive value of these biomarkers and their integration into clinical decision-making frameworks.
Results: Key biomarkers, including baseline HbA1c, fasting insulin, and C-peptide levels, showed significant predictive power for glycemic response to therapies such as metformin, GLP-1 receptor agonists, and SGLT2 inhibitors. Inflammatory markers correlated with treatment resistance, while genetic polymorphisms in genes such as SLC22A1 and TCF7L2 influenced drug efficacy. Machine learning models combining multiple biomarkers demonstrated high accuracy in predicting individual patient responses, facilitating the development of tailored treatment plans.
Conclusion: Biomarkers play a critical role in predicting therapeutic outcomes in T2DM, enabling a shift towards more personalized and precise treatment strategies. Integrating biomarker analysis into routine clinical practice can enhance glycemic control, reduce complications, and improve patient quality of life. Future efforts should focus on validating these biomarkers in diverse populations and incorporating them into accessible diagnostic platforms for widespread clinical use.