CGV in Newly Diagnosed Type 2 Diabetes Patients: Effect of Dietary Interventions in the Human Microbiome
CGV in Newly Diagnosed Type 2 Diabetes Patients: Effect of Dietary Interventions in the Human Microbiome
The proposed study is an integrative scientific undertaking, reflecting the input of experts in the behavioral sciences, mHealth technology, computational biology, endocrinology, biochemistry, and nutrition. The project features a unique translational approach, connecting behavior and biology in an economical manner that provides mechanistic insights into the progression of type 2 diabetes (T2D) and the biologic consequences of behavior change. The project may have profound implications for the clinical management of early T2D.
T2D poses a significant public health problem. Hyperglycemia is associated with a variety of vascular complications, but clinical trials of medication regimes designed to achieve near-normal HbA1c have been mostly negative. The lack of benefit observed in these trials may be due to the fact that they targeted HbA1c rather than glucose variability, and were conducted in patients with established T2D that had already experienced complications of the disease. In early T2D, management of postprandial glycemia may be particularly important for minimization of metabolic memory effects and preservation of beta cell function. Postprandial glycemia in early T2D is largely driven by dietary intake, but research findings that guide clinicians regarding the best dietary approach to prescribe are mixed. Studies done, to-date, have used one-size-fits-all dietary regimes that do not take into consideration the person's unique glycemic response to food. The investigators know from decades of research that behavioral methods are required to engage patients in lifestyle behavior change. The investigators argue that tailoring the approach to the participant's glycemic response and gut microbiome will enhance self-efficacy in ways that cannot be achieved through standard behavioral methods alone and, in turn, reduce glycemic variability and metabolic memory effects.
Co-PI Segal et al. demonstrated that subjects have high between-subject variability in postprandial response to the same food, suggesting that universal dietary recommendations are of limited utility for controlling elevated postprandial glucose. The machine learning algorithm devised by the group integrates multidimensional data to accurately predict personalized postprandial glycemic responses. In a subsequent validation study in 100 participants, they showed that a personally-tailored intervention based on the predicted response resulted in significantly improved postprandial glycemic response, and were accompanied by beneficial changes in gut microbiota.
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