【摘要】 目的 探讨2型糖尿病（type 2 diabetes mellitus,T2DM）患者发生下肢动脉硬化闭塞症（arteriosclerosis obliterans,ASO）的危险因素并构建其列线图预测模型。方法 采用病例对照研究。根据纳入和排除标准回顾性收集2017年1月至2022年12月期间于新疆医科大学第一附属医院收治的T2DM合并下肢ASO的患者及该院同期单纯T2DM患者，比较二者的基本情况、血液、甲状腺激素等相关指标，并采用多因素logistic回归分析T2DM患者发生下肢ASO的危险因素，同时构建其列线图预测模型。结果 本研究纳入单纯T2DM患者119例，T2DM合并下肢ASO患者114例，比较发现，二者在吸烟史、白细胞计数、中性粒细胞计数、淋巴细胞计数、血小板计数、全身免疫炎症指数、系统炎症反应指数、全身免疫炎症反应指数、高密度脂蛋白胆固醇、载脂蛋白A1、载脂蛋白α、胱抑素C、游离三碘甲状腺原氨酸、三碘甲状腺原氨酸总量、游离三碘甲状腺原氨酸/三碘甲状腺原氨酸总量比值、纤维蛋白原、纤维蛋白原降解产物及血浆D-二聚体方面的差异呈现出有统计学意义（P<0.05），进一步对此进行多因素logistic回归分析发现，有吸烟史增加T2DM患者发生下肢ASO的概率[OR(95%CI)=2.921(1.023,4.227),P=0.003]，随纤维蛋白原水平和系统炎症反应指数增高T2DM患者发生下肢ASO的概率增大[OR(95%CI)=2.641(1.810,4.327),P<0.001;OR(95%CI)=1.020(1.004,1.044),P=0.018]，随载脂蛋白A1和游离三碘甲状腺原氨酸增高T2DM患者发生下肢ASO的概率降低[OR(95%CI)=0.231(0.054,0.782),P=0.021;OR(95%CI)=0.503(0.352,0.809),P=0.002]。根据筛选出来的这几个影响因素构建的列线图预测模型对T2DM患者发生下肢ASO的区分度良好[受试者操作特征曲线下面积（95%可信区间）为0.788(0.730,0.846）]，其预测曲线与实际发生曲线均接近理想曲线（Hosmer-Lemeshow拟合优度检验，χ~2=5.952,P=0.653），临床决策分析曲线评估列线图预测模型的阈值概率在0.18～0.80范围内对T2DM患者进行干预的临床净收益优于对所有患者不进行和进行干预。结论 从本组资料分析结果提示，对于有吸烟史、纤维蛋白原水平增高、载脂蛋白A1和游离三碘甲状腺原氨酸降低的T2DM患者需要密切关注其下肢发生ASO的风险，根据这几个特征构建的列线图预测模型对T2DM患者发生下肢ASO具有一定的区分度，其预测价值还需要进一步探讨。更多还原

【Abstract】 Objective To explore the risk factors affecting occurrence of arteriosclerosis obliterans (ASO) for patients with type 2 diabetes mellitus (T2DM) and to develop a nomogram predictive model using these risk factors.Methods A case-control study was conducted.The patients with T2DM accompanied with ASO and those with T2DM alone,admitted to the First Affiliated Hospital of Xinjiang Medical University from January 2017 to December 2022,were retrospectively collected according to the inclusion and exclusion criteria.The basic characteristics,blood,thyroid hormones,and other relevant indicators of the paitents in two groups were compared.The multivariate logistic regression analysis was used to identify the risk factors for the occurrence of ASO in the patients with T2DM,and then a nomogram predictive model was developed.Results There were 119 patients with T2DM alone and 114 patients with T2DM accompanied with lower extremity ASO in this study.The significant differences were observed between the two groups in terms of smoking history,white blood cell count,neutrophil count,lymphocyte count,platelet count,systemic immuneinflammation index,systemic inflammatory response index (SIRI),high-density lipoprotein cholesterol,apolipoprotein A1(ApoA1),apolipoprotein α (Apoα),serum cystatin C,free-triiodothyronine (FT3),total triiodothyronine,FT3/total triiodothyronine ratio,fibrinogen (Fib),fibrinogen degradation products,and plasma D-dimer (P<0.05).Further the results of the multivariate logistic regression analysis revealed that the history of smoking,increased Fib level and SIRI value increased the probabilities of ASO occurrence in the patients with T2DM[OR (95%CI)=2.921 (1.023,4.227),P=0.003;OR (95%CI)=2.641 (1.810,4.327),P<0.001;OR (95%CI)=1.020 (1.004,1.044),P=0.018],whereas higher levels of ApoA1 and FT3 were associated with reduced probabilities of ASO occurrence in the patients with T2DM[OR(95%CI)=0.231 (0.054,0.782),P=0.021;OR (95%CI)=0.503 (0.352,0.809),P=0.002].The nomogram predictive model based on these factors demonstrated a good discrimination for predicting the ASO occurrence in the T2DM patients[area under the receiver operating characteristic curve (95%CI)=0.788 (0.730,0.846)].The predicted curve closely matched the ideal curve (Hosmer-Lemeshow goodness-of-fit test,χ~2=5.952,P=0.653).The clinical decision analysis curve showed that the clinical net benefit of intervention based on the nomogram model was higher within a threshold probability range of0.18 to 0.80 compared to no intervention or universal intervention.Conclusions The analysis results indicate that T2DM patients with a smoking history,elevated Fib level and SIRI value,as well as decreased ApoA1 and FT3 levels should be closely monitored for ASO risk.The nomogram predictive model based on these features has a good discriminatory power for ASO occurrence in T2DM patients,though its value warrants further investigation.更多还原