目的：观察2型糖尿病(T2DM)继发干眼的危险因素，并构建适用于早期筛查的列线图风险预测模型。

方法：收集2020年3月至2024年4月就诊本院的T2DM患者347例，按照7：3比例分为训练集(242例)、验证集(105例)。观察训练集的无干眼患者(对照组，86例)与糖尿病继发干眼患者(干眼组，156例)的人口学资料、血糖指标、临床治疗情况。取其中存在统计学意义的指标纳入多因素Logistic回归分析中筛选继发干眼的危险因素，并将相关危险因素代入R软件构建列线图模型并结合验证集数据加以验证。

结果：T2DM患者242例中继发干眼患者占比为64.5%(156/242)。多因素Logistic回归分析发现，血糖变异度、糖化血清蛋白、视网膜病变、睑板腺功能状况、T2DM病程、睑板腺开口堵塞是T2DM继发干眼的危险因素(均OR>1，P<0.05)。基于上述6项指标构建列线图预测模型，训练集和验证集受试者工作特征(ROC)曲线下面积分别为0.994(95%CI：0.989-0.999)、0.990(95%CI：0.977-0.999)。训练集和验证集校准曲线斜率相近，经Hosmer-Lemeshow检验： χ²=1.461、1.566，P=0.993、0.992。列线图模型能够提供良好的临床决策效用。

结论：血糖变异度、糖化血清蛋白、视网膜病变、睑板腺功能状况、T2DM病程、睑板腺开口堵塞均会增加T2DM继发干眼风险，构建列线图模型能够为临床提供评估T2DM患者干眼风险的有力工具，有助于早期诊断和干预，提升患者生活质量。

METHODS: A total of 347 T2DM patients treated in our hospital between March 2020 and April 2024 were enrolled and randomly divided into training(242 cases)and validation(105 cases)sets at a 7:3 ratio. Demographic data, glycemic parameters, and clinical treatments were compared between non-dry eye syndrome(control group, 86 cases)and dry eye syndrome to type 2 diabetes mellitus(dry eye group, 156 cases)in the training set. Statistically significant indicators were incorporated into multivariate Logistic regression to identify risk factors for secondary dry eye. These factors were then used to construct a nomogram model using R software, which was subsequently validated using the validation set.

RESULTS:The percentage of patients with secondary dry eye syndrome in 242 cases of T2DM was 64.5%(156/242). Multifactorial Logistic regression revealed that blood glucose variability, glycosylated serum protein, retinopathy, meibomian gland functional status, duration of T2DM, and meibomian gland opening blockage were the risk factors for secondary dry eye(all OR>1, P<0.05). A nomogram prediction model was constructed based on the 6 indicators above, and the area under the receiver operating characteristics(ROC)curve of the training and validation sets was verified to be 0.994(95%CI: 0.989-0.999)and 0.990(95%CI: 0.977-0.999), respectively. The slopes of the calibration curves were similar, as tested by the Hosmer-Lemeshow test χ²=1.461, 1.566, P=0.993, 0.992. The nomogram model could provide good utility for clinical decision-making.

CONCLUSION:Glycemic variability, glycated serum protein, retinopathy, meibomian gland dysfunction, T2DM duration, and meibomian gland orifice obstruction significantly increase the risk of dry eye secondary to T2DM. The constructed nomogram model serves as a valuable tool for early risk assessment and intervention, and it is helpful for early diagnosis and intervention, thus improving patients' quality of life.