摘要
目的 探讨磁共振成像(MRI)及血清骨钙素、基质金属蛋白酶(MMP-1)及白细胞介素-1(IL-1)对膝骨关节炎合并软骨损伤的预测价值.方法 回顾性分析2021年8月至2023年5月广西贵港市人民医院收治的84例膝骨关节炎患者的临床资料.入院后均行关节镜检查,依据检查结果是否合并软骨损伤分组,分为合并组(n=40)与未合并组(n=44).比较两组资料[性别、年龄、体重指数、合并疾病(高血压、高脂血症、糖尿病)、病变部位(左膝、右膝)、饮酒史、吸烟史]、MRI不同区域软骨T2值[(股骨外侧(LF)、股骨内侧(MF)、胫骨外侧(LT)、胫骨内侧(MT)、髌骨区(P)]以及血清骨钙素、MMP-1、IL-1水平.通过多因素采取非条件Logistic逐步回归分析明确膝骨关节炎合并软骨损伤的危险因素.通过受试者操作特征(ROC)曲线分析MRI不同区域软骨T2值、血清骨钙素、MMP-1、IL-1预测膝骨关节炎合并软骨损伤的价值.结果 两组性别构成比、年龄、体重指数、病变部位、合并高血压、高脂血症、糖尿病、饮酒史、吸烟史比较,差异均无统计学意义(P＞0.05);合并组MRI不同区域软骨T2值以及血清骨钙素、MMP-1、IL-1水平均显著高于未合并组,差异均有统计学意义(P＜0.05).多因素Logistic回归分析结果显示,MRI不同区域软骨T2值、骨钙素、MMP-1、IL-1是膝骨关节炎患者合并软骨损伤的危险因素(P＜0.05).经ROC分析证实,LF软骨T2值、MF软骨T2值、LT软骨T2值、MT软骨T2值、P软骨T2值、骨钙素、MMP-1、IL-1最佳截断值分别为39.705 ms、44.250 ms、36.855 ms、35.715 ms、38.005 ms、6.655 μg/L、7.335 μg/L、389.340 pg/mL,曲线下面积分别为 0.796、0.840、0.859、0.882、0.728、0.705、0.763、0.721,均有较高预测价值(P＜0.05).结论 MRI不同区域软骨T2值、血清骨钙素、MMP-1、IL-1是膝骨关节炎合并软骨损伤的危险因素;同时LF软骨T2值≥39.705 ms、MF软骨T2值≥44.250 ms、LT 软骨 T2 值≥36.855 ms、MT 软骨 T2 值≥35.715 ms、P 软骨 T2 值≥38.005 ms、骨钙素 ≥6.655 μg/L、MMP-1 ≥7.335 µg/L、IL-1≥389.340 pg/mL可用于预测此类患者合并风险.
Abstract
Objective To explore the predictive value of magnetic resonance imaging(MRI),serum bone glaprotein,matrix metallopro-teinase(MMP-1)and interleukin-1(IL-1)for knee osteoarthritis combined with cartilage injury.Methods A retrospective analysis was conducted on the clinical data of 84 patients with knee osteoarthritis admitted to Guigang People's Hospital of Guangxi Province from August 2021 to May 2023.After admission,all patients underwent arthroscopy and were divided into the combined group(n=40)and the unmerged group(n=44)based on whether cartilage damage was present.The baseline data[gender,age,body mass index,comorbid diseases(hypertension,hyperlipidemia,diabetes),lesion site(left knee,right knee),drinking history,smoking history],T2 values of cartilage in different regions of MRI[lateral region of femur(LF),medial region of femur(MF),lateral region of tibia(LT),medial region of tibia(MT),patella region(P)]and serum bone glaprotein,MMP-1,IL-1 levels were compared between the two groups.Finally,the risk factors for knee osteoarthritis com-bined with cartilage injury were identified through unconditional Logistic stepwise regression analysis using multiple factors.The value of MRI in predicting cartilage injury in knee osteoarthritis with different regions of cartilage T2 values,serum bone glaprotein,MMP-1,and IL-1 was ana-lyzed through receiver operating characteristics(ROC).Results There was no significant difference in sex,age,body mass index,lesion loca-tion,hypertension,hyperlipidemia,diabetes,drinking history,smoking history between the two groups(P＞0.05);the MRI T2 values of carti-lage in different regions and serum bone glaprotein,MMP-1,and IL-1 levels in the combined group were significantly higher than those in the non combined group,the differences were statistically significant(P＜0.05).The results of multivariate Logistic regression analysis showed that the T2 values,bone glaprotein,MMP-1,and IL-1 of cartilage in different regions of MRI were risk factors for cartilage injury in patients with knee osteoarthritis(P＜0.05).According to ROC analysis,the optimal cutoff values for LF cartilage T2 value,MF cartilage T2 value,LT carti-lage T2 value,MT cartilage T2 value,P cartilage T2 value,bone glaprotein,MMP-1,and IL-1 were 39.705 ms,44.250 ms,36.855 ms,35.715 ms,38.005 ms,6.655 μg/L,7.335 µg/L,and 389.340 pg/mL,respectively.The area under the curve was 0.796,0.840,0.859,0.882,0.728,0.705,0.763,and 0.721,all of which had high predictive value(P＜0.05).Conclusion T2 value of cartilage in different regions of MRI,serum bone glaprotein,MMP-1,and IL-1 are risk factors for knee osteoarthritis combined with cartilage injury.Simultaneous-ly,LF cartilage T2 value≥39.705 ms,MF cartilage T2 value≥44.250 ms,LT cartilage T2 value≥36.855 ms,MT cartilage T2 value≥35.715 ms,P cartilage T2 value≥38.005 ms,bone glaprotein≥6.655 µg/L,MMP-1 ≥7.335 μg/L,IL-1 ≥389.340 pg/mL can be used to predict the risk of comorbidities in such patients.
基金项目
广西壮族自治区科技计划(AD17739017)
广西贵港市自筹经费科研项目(贵科攻2200004)
维普
万方数据