To investigate the influencing factors of glucose variability in patients with T1DM by using flash glucose monitoring system (FGMS) , and to provide basis for future clinical use of targeted hypoglycemic treatment.

Using convenience sampling method, 85 patients with T1DM admitted to the Affiliated Huaian No.1 People's Hospital of Nanjing Medical University from May 2019 to April 2020 were selected as the research objects. The gender, age, diabetesduration, marital status, education level, smoking history, drinking history and other general data to determine body mass index (BMI) , waist hip ratio (WHR) , blood pressure (BP) , glycosylated hemoglobin (HbA_1c) , total cholesterol (TC) , total triglycerides (TG) , high-density lipoprotein cholesterol (HDL-C) , low-density lipoprotein cholesterol (HDL-C) , estimated glomerular filtration rate (eGFR) , and urinary albumin creatinine ratio (UACR) of patients were collected. According to whether the mean amplitude of glycemic excursions (MAGE) of patient is higher than the overall average value of 0.82 mmol/L, patients were divided into high blood glucose fluctuation group and low blood glucose fluctuation group. The scores of the Summary of Diabetes Self Care Activities (SDSCA) and the Diabetes Empowerment Scale-Short Form (DES-SF) were calculated. Multiple linear regression was used to analyze the influencing factors of blood glucose fluctuation.

There were statistically significant difference between two groups in age, diabetes duration, HbA_1c, TG, UACR, MEAN, SD, TIR, DES-SF scores and SDSCA scores (P<0.05) . Multiple linear regression analysis showed that age was the influencing factor of MEAN (β=-0.272, P=0.019) , SD (β=-0.300, P=0.009) , and MAGE (β=-0.254, P=0.007) , diabetes durationwas the influencing factor of MEAN (β=0.466, P=0.029) , HbA_1cwas the influencing factor of MEAN (β=0.416, P<0.001) , SD (β=0.330, P=0.004) , TIR (β=-0.287, P=0.014) , MAGE (β=0.182, P<0.001) , UACR was the influencing factor of SD (β=0.264, P=0.040) , TIR (β=-0.350, P=0.006) , MAGE (β=0.236, P=0.009) , the total score of SDSCA was the influencing factor of MEAN (β=0.416, P<0.001) , SD (β=0.330, P=0.004) and TIR (β=-0.287, P=0.014) , the total score of DES-SF was the influencing factor of MEAN (β=-0.271, P=0.045) and TIR (β=0.865, P=0.016) .

Age, diabete duration, HbA_1c, UACR, self-management behavior and self management potential were the influencing factors of glucose variability in patients with T1DM, individual hypoglycemic strategies should be formulated for patients according to these factors, so as to reduce patients' blood glucose fluctuations and delay the occurrence and development of the complications.

应用瞬感扫描式血糖监测系统（FGMS）探讨T1DM患者血糖波动的影响因素，为未来临床采取针对性降糖治疗措施提供依据。

应用便利抽样法选取2019年5月至2020年4月南京医科大学附属淮安第一医院收治的85例T1DM患者作为研究对象。收集患者性别、年龄、糖尿病病程、婚姻状况、文化程度、吸烟、饮酒等基线资料，测定体质指数（BMI）、腰臀比（WHR）、血压（BP）、糖化血红蛋白（HbA_1c）、总胆固醇（TC）、三酰甘油（TG）、高密度脂蛋白胆固醇（HDL-C）、低密度脂蛋白胆固醇（HDL-C）、估算肾小球滤过率（eGFR）和尿微量白蛋白/尿肌酐比值（UACR）等临床资料，依据患者平均血糖波动幅度（MAGE）是否高于总体均值0.82 mmol/L将患者分为血糖波动高组与血糖波动低组，评估糖尿病自我管理行为量表（SDSCA）总分及糖尿病授权简化量表（DES-SF）总分，采用多元线性回归分析探讨T1DM患者血糖波动的影响因素。

两组患者年龄、糖尿病病程、HbA_1c、TG、UACR、平均血糖浓度（MEAN）、血糖标准差（SD）、血糖在正常范围内时间（TIR）、DES-SF得分和SDSCA得分比较，差异均有统计学意义（P<0.05）。多元线性回归分析结果显示，年龄是MEAN（β=-0.272，P=0.019）、SD（β=-0.300，P=0.009）、MAGE（β=-0.254，P=0.007）的影响因素；糖尿病病程是MEAN（β=0.466，P=0.029）的影响因素；HbA_1c是MEAN（β=0.416，P<0.001）、SD（β=0.330，P=0.004）、TIR（β=-0.287，P=0.014）、MAGE（β=0.182，P<0.001）的影响因素；UACR是SD（β=0.264，P=0.040）、TIR（β=-0.350，P=0.006）、MAGE（β=0.236，P=0.009）的影响因素；SDSCA总分是MEAN（β=0.416，P<0.001）、SD（β=0.330，P=0.004）、TIR（β=-0.287，P=0.014）的影响因素；DES-SF总分是MEAN（β=-0.271，P=0.045）、TIR（β=0.865，P=0.016）的影响因素。

T1DM患者血糖波动的影响因素包括年龄、糖尿病病程、HbA_1c、UACR、自我管理行为以及自我管理潜能，临床应根据这些因素为患者制订个体化降糖策略，从而减少患者的血糖波动，延缓并发症的发生、发展。