Abstract
Glycemic variability (GV) is an obstacle to effective blood glucose control and an autonomous risk factor for diabetes complications. We, therefore, explored sample data of patients with diabetes mellitus who maintained better amplitude of glycemic fluctuations and compared their disease outcomes with groups having poor control. A retrospective study was conducted using electronic data of patients having hemoglobin A1C (HbA1c) values with five recent time points from Think Whole Person Healthcare (TWPH). The control variability grid analysis (CVGA) plot and coefficient of variability (CV) were used to identify and cluster glycemic fluctuation. We selected important variables using LASSO. Chi-Square, Fisher’s exact test, Bonferroni chi-Square adjusted residual analysis, and multivariate Kruskal–Wallis tests were used to evaluate eventual disease outcomes. Patients with very high CV were strongly associated (p < 0.05) with disorders of lipoprotein (p = 0.0014), fluid, electrolyte, and acid–base balance (p = 0.0032), while those with low CV were statistically significant for factors influencing health status such as screening for other disorders (p = 0.0137), long-term (current) drug therapy (p = 0.0019), and screening for malignant neoplasms (p = 0.0072). Reducing glycemic variability may balance alterations in electrolytes and reduce differences in lipid profiles, which may assist in strategies for managing patients with diabetes mellitus.
Highlights
The signs and severity of diabetes mellitus differ among people of different ethnic group and countries, and there are currently no cures for the disease [1]
The modified control variability grid analysis (CVGA) plot is a minimum/maximum plot of the hemoglobin A1C (HbA1c) values for a patient observed over an arbitrary period
The plot is split into zones; the minimum HbA1c value is plotted on the x-axis, while the maximum HbA1c value is plotted on the y-axis and the difference represents the amplitude observed for a patient
Summary
The signs and severity of diabetes mellitus differ among people of different ethnic group and countries, and there are currently no cures for the disease [1]. It is currently estimated that an average lifetime economic cost of about $85,000 (or more with increasing age) is needed for clinically managing the disease and its associated comorbidities [5]. The clinical treatment goal in managing patients with diabetes mellitus is to prevent the onset of associated comorbidities by relieving symptoms and controlling high and low blood glucose episodes upon diagnosis [1]. Apart from high and low blood glucose levels, glucose variability is another associated risk for complications in diabetes mellitus, necessitating therapeutic approaches aimed at avoiding low glycemic episodes and maintaining a balance in glucose levels [9]. Glucose variability (GV) is more studied in type 1 diabetes(T1D) where it has been suggested to be higher than in type 2 diabetes (T2D) [9]
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