缺血性脑卒中患者血浆1-磷酸鞘氨醇表达水平及风险列线图构建

Construction of Plasma Sphingosine 1-Phosphate Expression Level and Risk Nomogram in Patients with Ischemic Stroke

摘要: 目的：探索缺血性脑卒中(Ischemic stroke, IS)患者血浆1-磷酸鞘氨醇(Sphingosine 1-phosphate, S1P)的水平变化并构建风险列线图模型。方法：选择2022年9月至2023年9月牡丹江红旗医院神经内科收治的113例IS患者为病例组。选取同期本院健康体检者146例为健康对照组。采用液相色谱联用质谱检测两组血浆S1P水平并绘ROC曲线，进行Logistic回归分析，并构建列线图风险模型。H-L拟合优度检验及校准曲线评价该列线图模型的预测效能。结果：IS组血浆S1P水平明显低于健康对照组为120.02 (97.90, 153.40) ng/mL比193.2 (158.77, 281.31) ng/mL，P < 0.001。血浆S1P水平诊断IS的截断值为150.82 ng/mL，曲线下面积为0.8441，灵敏度为78.8%，特异度为74.3%。二元Logistic回归分析表明，性别、BMI、血糖和高血压是IS发生的危险因素(P < 0.05)。模型ROC曲线下面积为0.974，95%CI为0.959~0.989；H-L拟合优度检验χ² = 4.529，P = 0.873；模型预测IS患者发生的观测值和模型预测值基本一致，该模型具有较好的预测效能。结论：IS患者血浆S1P水平显著下降，S1P可能作为IS诊断的潜在生物标志物，为脑卒中诊治提供新的策略且构建的关于IS发生风险的列线图结果表明模型可以较好地预测IS发生的风险，用于指导临床实践。

Abstract: Objective: To explore the changes in plasma sphingosine 1-phosphate (S1P) levels of Ischemic stroke patients and to construct a risk nomogram model for ischemic stroke patients. Methods: 113 IS patients admitted to the Department of Neurology of Mudanjiang Red Flag Hospital from September 2022 to September 2023 were selected as the case group. A total of 146 healthy subjects were selected as the control group. The plasma S1P level of the two groups was detected by liquid chromatography combined with mass spectrometry and an ROC curve was drawn, Logistic regression analysis was performed, and the risk model of the nomogram was constructed. H-L goodness of fit test and calibration curve were used to evaluate the prediction efficiency of the nomogram. Results: Plasma S1P level in the IS group was significantly lower than that in the healthy control group (120.02 (97.90, 153.40) ng/mL vs. 193.2 (158.77, 281.31) ng/mL, P < 0.001). ROC curve showed that the cutoff value of plasma S1P level for the diagnosis of IS was 150.82 ng/mL, the area under the curve was 0.8441, the sensitivity was 78.8%, and the specificity was 74.3%. Multivariate Logistic regression analysis showed that gender, BMI, blood glucose, and hypertension were risk factors for the development of IS (P < 0.05). The area under the ROC curve predicted by the model was 0.974, and the 95%CI was 0.959~0.989. H-L goodness of fit test χ² = 4.529, P = 0.873; the overall trend of the correction curve predicted by the model was consistent with the ideal curve, indicating that the model had good goodness of fit and good prediction efficiency. Conclusion: Plasma S1P levels are significantly decreased in patients with IS. S1P may be used as a potential biomarker for the diagnosis of IS, providing a new strategy for the diagnosis and treatment of stroke. The results of the constructed nomogram on the risk of IS development indicate that the model can better predict the risk of IS development, and can be used to guide clinical practice.