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Glucose complexity inversely linked to insulin resistance in patients with, without type 1 diabetes
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Glucose complexity assessed by continuous glucose monitoring-derived sample entropy is negatively associated with insulin resistance in individuals without diabetes and those with type 1 diabetes.
Laurent Crenier, MD, of the department of endocrinology at ULB-Hôpital Erasme in Belgium, and colleagues evaluated 37 individuals aged 12 to 65 years without diabetes and 49 adults aged 26 to 72 years with longstanding type 1 diabetes who were on a multiple daily insulin injection regimen.
Researchers recorded blinded CGM readings (iPro 2 with Enlite sensor, Medtronic Minimed) in the healthy participants, and the participants were taught to perform self-monitoring of blood glucose, in addition to four tests per day. Blinded CGM readings were also evaluated for the participants with diabetes. Researchers determined sample entropy and detrended fluctuation analysis scaling exponents on 72 hours of the CGM recordings.
The researchers found that the healthy group demonstrated a higher sample entropy vs. the diabetes group (P < .0001). A negative association was demonstrated between sample entropy and each of the glucose variability measures in both groups, but multivariate analysis demostrated that only coefficient of variation of glucose was persistently significant when both groups were combined (P < .0001).
An inverse correlation was seen between sample entropy and homeostasis model of assessment for insulin resistance (HOMA-IR; P = .0008), HOMA-beta cell function (P = .013) and BMI z score (P = .019) in the healthy group; a positive relation was found between sample entropy and the Quantitative Insulin Sensitivity Check Index (P = .0011). No significant relationships were found between any of the glucose variability measures and HOMA-IR, the Quantitative Insulin sensitivity Check Index or BMI z score. Sample entropy was the only measure independently linked with BMI z score across both groups, and the negative association was strongest when both groups were combined (P < .0001).
“We showed that glucose complexity measured by CGM-derived [sample entropy] is inversely correlated with insulin resistance both in high entropy glucose profiles of healthy subjects and in low entropy glucose profiles of patients with [type 1 diabetes],” the researchers wrote. “In nondiabetic subjects, [sample entropy] is more sensitive and could probably be an earlier sign of glucose regulation failure than glucose variability. In healthy volunteers and patients with [type 1 diabetes], the relationship between [sample entropy] and insulin sensitivity is dependent on the richness of [sample entropy]-sensitive fast glucose oscillations in the CGM profiles.” – by Jennifer Byrne
Disclosure: Crenier reports receiving speaker fees from Medtronic.
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