Basu, Ananda

Abstract Context Circulating lactate concentration is an important determinant of exercise tolerance. Objective This work aimed to determine the role of hyperglycemia on lactate metabolism during exercise in individuals with type 1 diabetes (T1D). Methods The protocol at the University of Virginia compared 7 T1D participants and 7 participants without diabetes (ND) at euglycemia (5.5 mM) or hyperglycemia (9.2 mM) in random order in T1D and at euglycemia in ND. Intervention included [1-13C] lactate infusion, exercise at 65% maximal oxygen uptake (VO2max), euglycemia, and hyperglycemia visits. The main outcome measure was lactate turnover before, during, and after 60 minutes of exercise at 65% VO2max. Results A 2-compartment model with loss only from the peripheral compartment described lactate kinetics. Volume of distribution of the accessible compartment was similar between T1D and ND individuals (P = .76) and concordant with plasma volume (∼40 mL/kg). Circulating lactate concentrations were higher (P < .001) in T1D participants during exercise at hyperglycemia than euglycemia. Exercise-induced lactate appearance did not differ (P = .13) between hyperglycemia and euglycemia. However, lactate clearance (CL) was lower (P = .03) during hyperglycemia than euglycemia in T1D participants. There were no differences in any of the aforementioned parameters between T1D and ND participants during euglycemia. Conclusion Hyperglycemia modulates lactate metabolism during exercise by lowering CL, leading to higher circulating lactate concentrations in T1D individuals. This novel observation implies that exercise during hyperglycemia can lead to higher circulating lactate concentrations thus increasing the likelihood of reaching the lactate threshold sooner in T1D, and has high translational relevance both for providers and recreationally active people with T1D.

Background: A composite metric for the quality of glycemia from continuous glucose monitor (CGM) tracings could be useful for assisting with basic clinical interpretation of CGM data. Methods: We assembled a data set of 14-day CGM tracings from 225 insulin-treated adults with diabetes. Using a balanced incomplete block design, 330 clinicians who were highly experienced with CGM analysis and interpretation ranked the CGM tracings from best to worst quality of glycemia. We used principal component analysis and multiple regressions to develop a model to predict the clinician ranking based on seven standard metrics in an Ambulatory Glucose Profile: very low–glucose and low-glucose hypoglycemia; very high–glucose and high-glucose hyperglycemia; time in range; mean glucose; and coefficient of variation. Results: The analysis showed that clinician rankings depend on two components, one related to hypoglycemia that gives more weight to very low-glucose than to low-glucose and the other related to hyperglycemia that likewise gives greater weight to very high-glucose than to high-glucose. These two components should be calculated and displayed separately, but they can also be combined into a single Glycemia Risk Index (GRI) that corresponds closely to the clinician rankings of the overall quality of glycemia (r = 0.95). The GRI can be displayed graphically on a GRI Grid with the hypoglycemia component on the horizontal axis and the hyperglycemia component on the vertical axis. Diagonal lines divide the graph into five zones (quintiles) corresponding to the best (0th to 20th percentile) to worst (81st to 100th percentile) overall quality of glycemia. The GRI Grid enables users to track sequential changes within an individual over time and compare groups of individuals. Conclusion: The GRI is a single-number summary of the quality of glycemia. Its hypoglycemia and hyperglycemia components provide actionable scores and a graphical display (the GRI Grid) that can be used by clinicians and researchers to determine the glycemic effects of prescribed and investigational treatments.