Nwadiugwu Martin C, Bastola Dhundy R, Haas Christian, Russell Doug
Department of Biomedical Informatics, University of Nebraska at Omaha, Omaha, NE 68182, USA.
Department of Information Systems and Quantitative Analysis, University of Nebraska at Omaha, Omaha, NE 68182, USA.
J Clin Med. 2021 Apr 2;10(7):1477. doi: 10.3390/jcm10071477.
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 ( < 0.05) with disorders of lipoprotein ( = 0.0014), fluid, electrolyte, and acid-base balance ( = 0.0032), while those with low CV were statistically significant for factors influencing health status such as screening for other disorders ( = 0.0137), long-term (current) drug therapy ( = 0.0019), and screening for malignant neoplasms ( = 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.
血糖变异性（GV）是有效控制血糖的障碍，也是糖尿病并发症的一个独立危险因素。因此，我们探讨了血糖波动幅度较好的糖尿病患者的样本数据，并将他们的疾病结局与控制不佳的组进行了比较。利用来自全人医疗（TWPH）的血红蛋白A1C（HbA1c）值在最近五个时间点的患者电子数据进行了一项回顾性研究。使用控制变异性网格分析（CVGA）图和变异系数（CV）来识别和聚类血糖波动。我们使用LASSO选择重要变量。使用卡方检验、Fisher精确检验、Bonferroni卡方调整残差分析和多变量Kruskal-Wallis检验来评估最终的疾病结局。CV非常高的患者与脂蛋白紊乱（=0.0014）、体液、电解质和酸碱平衡紊乱（=0.0032）密切相关（<0.05），而CV低的患者在影响健康状况的因素方面具有统计学意义，如筛查其他疾病（=0.0137）、长期（当前）药物治疗（=0.0019）和筛查恶性肿瘤（=0.0072）。降低血糖变异性可能平衡电解质变化并减少脂质谱差异，这可能有助于糖尿病患者的管理策略。
