Nutrition and Health Sciences Doctoral Program, Laney School of Graduate Studies, Emory University, Atlanta, Georgia, USA.
Department of Computer Science, Emory University, Atlanta, Georgia, USA.
Diabetes Technol Ther. 2021 Aug;23(8):555-564. doi: 10.1089/dia.2020.0672. Epub 2021 May 11.
To identify profiles of type 2 diabetes from continuous glucose monitoring (CGM) data using ambulatory glucose profile (AGP) indicators and examine the association with prevalent complications. Two weeks of CGM data, collected between 2015 and 2019, from 5901 adult type 2 diabetes patients were retrieved from a clinical database in Chennai, India. Non-negative matrix factorization was used to identify profiles as per AGP indicators. The association of profiles with existing complications was examined using multinomial and logistic regressions adjusted for glycated hemoglobin (HbA1c; %), sex, age at onset, and duration of diabetes. Three profiles of glycemic variability (GV) were identified based on CGM data-Profile 1 ["TIR Profile"] ( = 2271), Profile 2 ["Hypo"] ( = 1471), and Profile 3 ["Hyper"] ( = 2159). Compared with time in range (TIR) profile, those belonging to Hyper had higher mean fasting plasma glucose (202.9 vs. 167.1, mg/dL), 2-h postprandial plasma glucose (302.1 vs. 255.6, mg/dL), and HbA1c (9.7 vs. 8.6; %). Both "Hypo profile" and "Hyper profile" had higher odds of nonproliferative diabetic retinopathy ("Hypo": 1.44, 1.20-1.73; "Hyper": 1.33, 1.11-1.58), macroalbuminuria ("Hypo": 1.58, 1.25-1.98; "Hyper": 1.37, 1.10-1.71), and diabetic kidney disease (DKD; "Hypo": 1.65, 1.18-2.31; "Hyper": 1.88, 1.37-2.58), compared with "TIR profile." Those in "Hypo profile" (vs. "TIR profile") had higher odds of proliferative diabetic retinopathy (PDR; 2.84, 1.65-2.88). We have identified three profiles of GV from CGM data. While both "Hypo profile" and "Hyper profile" had higher odds of prevalent DKD compared with "TIR profile," "Hypo profile" had higher odds of PDR. Our study emphasizes the clinical importance of recognizing and treating hypoglycemia (which is often unrecognized without CGM) in patients with type 2 Diabetes Mellitus.
为了使用动态血糖谱(AGP)指标从连续血糖监测(CGM)数据中确定 2 型糖尿病患者的特征,并研究这些特征与现有并发症的关联。我们从印度钦奈的一个临床数据库中检索了 2015 年至 2019 年间 5901 例成年 2 型糖尿病患者的 2 周 CGM 数据。使用非负矩阵分解根据 AGP 指标识别特征。使用多变量和逻辑回归调整糖化血红蛋白(HbA1c;%)、性别、发病年龄和糖尿病病程来检查特征与现有并发症的关联。根据 CGM 数据确定了三种血糖变异性(GV)特征:特征 1 [TIR 特征]( = 2271)、特征 2 [低血糖]( = 1471)和特征 3 [高血糖]( = 2159)。与时间在范围内(TIR)特征相比,高血糖特征的平均空腹血糖(202.9 与 167.1,mg/dL)、餐后 2 小时血糖(302.1 与 255.6,mg/dL)和 HbA1c(9.7 与 8.6;%)更高。低血糖特征和高血糖特征的非增殖性糖尿病视网膜病变(低血糖:1.44,1.20-1.73;高血糖:1.33,1.11-1.58)、大量白蛋白尿(低血糖:1.58,1.25-1.98;高血糖:1.37,1.10-1.71)和糖尿病肾病(DKD;低血糖:1.65,1.18-2.31;高血糖:1.88,1.37-2.58)的可能性均高于 TIR 特征。与 TIR 特征相比,低血糖特征的增殖性糖尿病视网膜病变(PDR;2.84,1.65-2.88)可能性更高。我们从 CGM 数据中确定了三种 GV 特征。虽然低血糖特征和高血糖特征与 TIR 特征相比,更易发生现有 DKD,但低血糖特征更易发生 PDR。我们的研究强调了在 2 型糖尿病患者中识别和治疗低血糖(如果没有 CGM 通常无法识别)的临床重要性。