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Individual predictions of long-term chronic disease progression from data of limited duration provide valuable insights into estimating patient outcomes and therapeutic needs. Statistical Restoration of Fragmented Time course (SReFT) was developed to address this challenge, yet it is computationally too intensive for large-scale datasets. Although diabetes is a representative chronic disease with significant medical needs, it has been challenging to analyze long-term changes using large-scale patient data due to this limitation. In this study, we aimed to develop a new method (SReFT-machine learning, SReFT-ML) by applying machine learning to the concept of SReFT, and to confirm its performance using synthetic data and the data from a clinical trial, the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial (N = 10,251). SReFT-ML has successfully analyzed both synthetic and clinical data, and reconstructed biomarker trajectories over a 30-year period in patients with diabetes. Decreases in diastolic blood pressure and renal function may be important indicators of disease progression. Furthermore, although age and mortality data were not included in the model, survival analysis demonstrated a clear trend of hazard increases in mortality and diabetes-related outcomes with disease progression. This study introduced machine learning to enhance long-term disease progression modeling. The resulting model characterized a 30-year trajectory of disease risk in diabetes. The results provide a clinically meaningful hypothesis that incorporating systemic factors such as renal function and blood pressure, in addition to classic glycemic control, may enhance comprehensive diabetes care. Trial Registration: ClinicalTrials.gov number: NCT00000620.