A Comprehensive Evaluation of Thrombolytic Protein Therapy in Non-Insulin Dependent Diabetes: Integrating Bioinformatics with Clinical Parameters

Abstract

Non-insulin dependent diabetes mellitus (NIDDM) increases the risk of cardiovascular thrombotic events. Thrombolytic protein therapy is used to treat such events, but response varies between individuals. Integrating clinical and molecular data using bioinformatics could improve prediction of therapy outcomes.

Objective: Develop predictive models for thrombolytic protein therapy response in NIDDM patients by analyzing clinical parameters, molecular signatures, and genetic factors using bioinformatics approaches.

Methods: Protein interaction networks and pathway analyses identified molecular targets and pathways related to thrombosis in NIDDM. Clinical data from NIDDM patients receiving thrombolytic therapy was collected. Predictive models integrating genetic, molecular and clinical data were developed using machine learning. Correlations between clinical parameters and treatment response were analyzed.

Results: Key proteins and pathways involved in platelet activation, coagulation, and endothelial dysfunction were identified. Predictive models demonstrated variable treatment responses based on patient characteristics. Duration of diabetes and glycemic control correlated with response.

Conclusion: A systems approach identified molecular mechanisms of thrombosis in NIDDM. Predictive models suggested personalized therapy based on patients' profiles could optimize outcomes. Understanding factors influencing response informs tailored treatment strategies