目的 本研究应用扫描式葡萄糖监测技术（FGM）探讨血糖波动指标与2型糖尿病（T2DM）患者发生糖尿病足风险的相关性。 方法 回顾性分析2021年1月至2022年5月连云港市第一人民医院内分泌科住院治疗且佩戴FGM的伴或不伴有糖尿病足的T2DM患者233例。根据是否合并糖尿病足，将患者分为非糖尿病足组（n = 147）和糖尿病足组（n = 86）。比较两组患者一般临床资料、生化指标及血糖波动指标，同时进行亚组分析。采用二元logistic回归法分析T2DM患者发生糖尿病足的影响因素，通过受试者工作（ROC）曲线评估血糖波动指标对糖尿病足的预测价值。 结果 与非糖尿病足组相比，糖尿病足组患者病程长，体质量指数（BMI）、糖化血红蛋白（HbA_1c）、尿微量白蛋白/肌酐（UACR）、谷丙转氨酶（ALT）、尿酸（SUA）以及平均血糖波动幅度（MAGE）、血糖变异系数（CV）、日间血糖平均绝对差（MODD）和平均血糖（MBG）升高，而空腹C肽（FCP）、空腹胰岛素（FINS）、高密度脂蛋白胆固醇（HDL-C）以及葡萄糖目标范围内时间（TIR）降低，差异有统计学意义（P < 0.05）。亚组分析发现TIR水平与糖尿病足和糖尿病视网膜病变（DR）的发生率有关。二元logistic回归提示HbA_1c、MAGE、MODD和MBG是T2DM患者发生糖尿病足的危险因素，而TIR是其保护因素（P < 0.05）。ROC曲线分析提示HbA_1c、TIR、MAGE、MODD、MBG及联合预测T2DM患者发生糖尿病足的曲线下面积（AUC）分别为0.646、0.850、0.868、0.764、0.619及0.967，联合预测效果更佳。 结论 HbA_1c、TIR、MAGE、MODD和MBG是T2DM患者发生糖尿病足的影响因素，及早干预上述血糖波动指标可有助于减少糖尿病足的发生。

Objective To investigate the correlation between glycemic variability metrics and the risk of diabetic foot （DF） in patients with type 2 diabetes mellitus （T2DM） utilizing flash glucose monitoring （FGM） technology. Methods A retrospective analysis was conducted on 233 hospitalized patients with T2DM， with or without DF， who were treated in the Department of Endocrinology at Lianyungang First People's Hospital from January 2021 to May 2022 and monitored using FGM. Patients were categorized into a non?DF group （n = 147） and a DF group （n = 86） based on the presence of DF. The study compared general clinical characteristics， biochemical parameters， and glycemic variability metrics between the two groups and performed subgroup analyses. Binary logistic regression was employed to identify factors associated with the risk of DF， while receiver operating characteristic （ROC） curves were utilized to assess the predictive value of glycemic variability metrics for DF. Results Compared with the non?DF group， patients in the DF group exhibited significantly longer disease duration， higher body mass index （BMI）， glycated hemoglobin （HbA_1c）， urinary albumin?to?creatinine ratio （UACR）， alanine aminotransferase （ALT）， serum uric acid （SUA）， mean amplitude of glycemic excursions （MAGE）， coefficient of variation （CV）， mean of daily differences （MODD）， and mean blood glucose （MBG）， but lower fasting C?peptide （FCP）， fasting insulin （FINS）， high?density lipoprotein cholesterol （HDL?C）， and time in range （TIR）， with statistically significant differences （P < 0.05）. Subgroup analysis revealed that TIR was associated with the incidence of DF and diabetic retinopathy （DR）. Binary logistic regression analysis identified HbA_1c， MAGE， MODD， and MBG as risk factors for DF， while TIR was a protective factor （P < 0.05）. ROC curve analysis demonstrated that the area under the curve （AUC） for predicting DF using HbA_1c， TIR， MAGE， MODD， MBG， and their combination were 0.646， 0.850， 0.868， 0.764， 0.619， and 0.967， respectively， indicating superior performance of the combined prediction model. Conclusions HbA_1c， TIR， MAGE， MODD， and MBG are critical factors associated with the development of DF in patients with T2DM. Targeted early interventions aimed at optimizing these glycemic variability indicators may effectively reduce the incidence of DF.