Predicting complications of diabetes with longitudinal metabolic trajectories

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ABSTRACT The prevalence of diabetes is increasing worldwide, with an estimated 463 million cases in 2019 (31 million cases in the United States). Patients with diabetes suffer from a number of common and morbid complications, including peripheral neuropathy (PN) and chronic kidney disease (CKD). In addition to diabetes, the metabolic syndrome (MetS) has been firmly established as a risk factor for these complications. Unfortunately, interventions that improve metabolic risk factors for patients with long-term metabolic impairment do not completely prevent or reverse diabetic complications, which may be the result of the current timing of interventions after metabolic risk factors have been present for many years. Importantly, very few studies have assessed the longitudinal association between the MetS and diabetic complications; therefore, the ideal timing of these interventions is unknown. Metabolic profiles worsen as age increases and there is a substantial amount of heterogeneity between patient metabolic trajectories. What remains unknown is how changes in specific aspects of longitudinal metabolic profiles (such as rate of change, cumulative effects, and changes during specific risk-periods) affect the progression of these complications. It is also unknown whether the onset of diabetic complications can be accurately predicted using longitudinal metabolic trajectories. Machine learning algorithms are flexible and powerful analytic tools for making predictions with complex data; therefore, they offer an ideal approach to predict diabetic complications using complex characteristics from metabolic trajectories. Our initial aims are to (1) determine the association between metabolic trajectories, PN, and CKD, and (2) develop a machine learning algorithm to predict these complications with detailed characteristics of metabolic trajectories. We will accomplish these initial aims using three complementary and powerful databases with differential metabolic complexity and sample size. First, we will determine if metabolic trajectories are associated with and are predictive of diabetic complications using two cohorts of Pima American Indians that have undergone detailed longitudinal metabolic and diabetic complication phenotyping. Then, to determine if our results can be implemented in a large-scale, integrated United States health system, our final aim is to (3) develop a comprehensive machine learning algorithm to predict complications for patients with diabetes using the Veterans Affairs Corporate Data Warehouse database, which contains detailed longitudinal medical information on over 3.1 million Veterans with diabetes from 1999-2021. Results from this study will provide a novel understanding of the associations between metabolic trajectories and diabetic complications. Ultimately, this data will inform future disease modifying interventions that reverse the metabolic trajectories that are most predictive of diabetic complications.