Key result
Patients with type 2 diabetes exhibited a significantly different and more homogeneous distribution of glycemic oscillatory patterns across 2 to 6 days compared to controls.
Why the study?
Although patients with T1D exhibit glycemic variability with specific circadian oscillatory patterns, it had not yet been determined whether an oscillation pattern represents glycemic variability in T2D.
Does continuous glucose monitoring reveal specific, predictable circadian oscillatory patterns of glycemia in patients with type 2 diabetes mellitus compared to controls?
Observational (n=52)
No
Does continuous glucose monitoring reveal specific, predictable circadian oscillatory patterns of glycemia in patients with type 2 diabetes mellitus compared to controls?
Answers are grounded in this paper and cite their sources.
Patients with type 2 diabetes exhibit specific, predictable circadian oscillatory patterns of glycemic control, which may help personalize treatment strategies.
BACKGROUND: Diabetes mellitus (DM) has glucose variability that is of such relevance that the appearance of vascular complications in patients with DM has been attributed to hyperglycemic and dysglycemic events. It is known that T1D patients mainly have glycemic variability with a specific oscillatory pattern with specific circadian characteristics for each patient. However, it has not yet been determined whether an oscillation pattern represents the variability of glycemic in T2D. This is why our objective is to determine the characteristics of glycemic oscillations in T2D and generate a robust predictive model. RESULTS: Showed that glycosylated hemoglobin, glycemia, and body mass index were all higher in patients with T2D than in controls (all p < 0.05). In addition, time in hyperglycemia and euglycemia was markedly higher and lower in the T2D group (p < 0.05), without significant differences for time in hypoglycemia. Standard deviation, coefficient of variation, and total power of glycemia were significantly higher in the T2D group than Control group (all p < 0.05). The oscillatory patterns were significantly different between groups (p = 0.032): the control group was mainly distributed at 2-3 and 6 days, whereas the T2D group showed a more homogeneous distribution across 2-3-to-6 days. CONCLUSIONS: The predictive model of glycemia showed that it is possible to accurately predict hyper- and hypoglycemia events. Thus, T2D patients exhibit specific oscillatory patterns of glycemic control, which are possible to predict. These findings may help to improve the treatment of DM by considering the individual oscillatory patterns of patients.
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Vásquez-Muñoz et al. (2022) conducted an observational in Type 2 Diabetes Mellitus (n=52). Type 2 Diabetes Mellitus vs. Controls was evaluated on Distribution of glycemic oscillation days (p=0.032). Patients with type 2 diabetes exhibited a significantly different and more homogeneous distribution of glycemic oscillatory patterns across 2 to 6 days compared to controls.
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