Type 2 diabetes mellitus (T2DM) is associated with alterations in the blood brain barrier, neuronal damage, and arterial stiffness, thus affecting cerebral metabolism and brain perfusion. We develop a machine learning method to investigate T2DM-related covariance pattern and its association with cognitive performance/disease severity. Our pipeline is superior to the traditional method and the pattern-related individual scores are associated to diabetes severity variables, mobility and cognitive performance at baseline. Besides, the longitudinal score change is associated with change of HbA1c, and baseline cholesterol, indicating that this score is a promising biomarker for tracing the disease progression of individual T2DM patients.
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