血清IL-6和TNF-α对重症急性胰腺炎的早期诊断价值分析

何健; 俞隼; 张静

doi:10.3969/j.issn.1001-5256.2023.07.020

血清IL-6和TNF-α对重症急性胰腺炎的早期诊断价值分析

DOI: 10.3969/j.issn.1001-5256.2023.07.020

苏州大学附属常熟医院(常熟市第一人民医院)重症医学科，江苏常熟 215501

基金项目:

苏州市科技计划项目 (cswsq201710);

苏州市科技计划项目 (cs202221)

伦理学声明：本研究方案于2017年6月1日经常熟市第一人民医院伦理委员会审批，批号：2017伦审(申报)批第4号，所纳入患者均签署知情同意书。

利益冲突声明：本文不存在任何利益冲突。

作者贡献声明：何健、俞隼负责设计研究思路，论文撰写；张静负责收集临床资料，统计分析。

详细信息

通信作者:
张静，csyyhj02@163.com (ORCID：0000-0003-1050-3608)

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出版历程
- 收稿日期: 2022-10-12
- 录用日期: 2022-12-27
- 出版日期: 2023-07-20

Value of serum interleukin-6 and tumor necrosis factor-α in early diagnosis of severe acute pancreatitis

Department of Critical Care Medicine, Changshu Hospital Affiliated to Suzhou University & Changshu First People's Hospital, Changshu, Jiangsu 215501, China

Research funding:

Science and Technology Planning Project of Suzhou (cswsq201710);

Science and Technology Planning Project of Suzhou (cs202221)

More Information

Corresponding author: ZHANG Jing, csyyhj02@163.com (ORCID: 0000-0003-1050-3608)

摘要

摘要: 目的评估血清细胞因子在重症急性胰腺炎(SAP)早期诊断中的价值, 并利用LASSO算法构建复合指标的数理模型以提高对SAP诊断的准确性。方法纳入2019年1月-2022年6月在常熟市第一人民医院就诊的130例急性胰腺炎患者, 其中SAP患者73例, 非SAP患者57例。收集所有患者的外周血清样本并通过Luminex xMAP液相芯片技术完成13种血清细胞因子的精准检测。同时, 所有患者均进行APACHE Ⅱ、BISAP和CTSI评分。使用Kolmogorov-Smirnov法进行正态性检验, 对符合正态分布的计量资料两组间比较采用成组t检验; 对非正态分布的计量资料两组间比较采用Mann-Whitney U检验。计数资料两组间比较采用χ²检验。此外, 通过二元Logistic回归分析评估细胞因子对SAP的影响, 应用线性回归分析评估细胞因子与SAP疾病的严重程度之间的关联。偏相关分析在校正协变量(年龄、性别、BMI、高血压、糖尿病病史)后分析细胞因子与SAP疾病的严重程度评分的关联性。利用LASSO算法构建复合指标的数理模型, 并采用受试者工作特征曲线(ROC曲线)分析血清细胞因子对SAP临床诊断的效能, 计算曲线下面积(AUC)。结果非SAP组APACHE Ⅱ、BISAP和CTSI评分、改良Marshall评分均低于SAP组, 差异均有统计学意义(P值均 < 0.001)。SAP组患者IFN-γ、IL-1β、IL-6、IL-8、TNF-α水平均高于非SAP组, IL-12水平明显低于非SAP组, 差异均有统计学意义(P值均 < 0.05)。Logistic回归分析结果显示, IFN-γ(OR=1.190, 95%CI: 1.036~1.367, P=0.014)、IL-6(OR=1.148, 95%CI: 1.070~1.231, P < 0.001)和TNF-α(OR=1.100, 95%CI: 1.048~1.155, P < 0.001)为SAP的独立影响因素。偏相关分析提示, 在校正了性别、年龄、BMI、慢性疾病史(糖尿病、高血压)后, SAP患者IL-6和TNF-α的水平与APACHE Ⅱ评分均呈显著正相关(IL-6:r=0.503, P < 0.001;TNF-α: r=0.557, P < 0.001)。线性回归分析显示, SAP患者中IL-6和TNF-α水平均与APACHE Ⅱ评分有关(IL-6:β=0.049, P=0.044;TNF-α: β=0.054, P=0.046), 且IL-6和TNF-α存在交互作用, 影响APACHE Ⅱ评分。ROC曲线分析显示, LASSO算法联合IL-6和TNF-α构建的风险评分区分SAP和非SAP的AUC值最大(AUC=0.925), 而IL-6和TNF-α的AUC分别为0.885、0.878;偏相关分析发现, 在校正性别、年龄、BMI、慢性疾病史(糖尿病、高血压)后, SAP患者风险评分与APACHE Ⅱ评分呈显著正相关(r=0.565, P < 0.001)。结论血清IL-6和TNF-α水平可反映AP疾病严重程度。联合血清IL-6和TNF-α构建的风险评分可显著提高SAP早期诊断的准确性, 对SAP的临床诊疗具有重要的临床价值。
- 胰腺炎 /
- 白细胞介素6 /
- 肿瘤坏死因子α /
- LASSO算法
Abstract: Objective To investigate the value of serum cytokines in the early diagnosis of severe acute pancreatitis (SAP), and to improve the accuracy of the diagnosis of SAP by establishing a mathematical model with composite indices based on LASSO algorithm. Methods A total of 130 patients with acute pancreatitis (AP) who attended Changshu First People's Hospital from January 2019 to June 2022 were enrolled, among whom there were 73 SAP patients and 57 non-SAP patients.Peripheral serum samples were collected from all patients, and Luminex xMAP liquid chip technique was used to measure 13 serum cytokines.Meanwhile, Acute Physiology and Chronic Health Evaluation Ⅱ(APACHE Ⅱ), Bedside Index for Severity in Acute Pancreatitis (BISAP), and Computed Tomography Severity Index (CTSI) scores were determined for all patients.The Kolmogorov-Smirnov method was used for normality test; the independent-samples t test was used for comparison of normally distributed continuous data between two groups, and the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between two groups; the chi-square test was used for comparison of categorical data between two groups.Furthermore, the binary logistic regression analysis was used to evaluate the effect of cytokines on SAP, and the linear regression analysis was used to investigate the correlation between cytokines and SAP severity.The partial correlation analysis was used to evaluate the correlation between cytokines and SAP severity score after adjustment for covariates[age, sex, body mass index (BMI), and history of hypertension and diabetes].The LASSO algorithm was used to establish a mathematical model with composite indices; the receiver operating characteristic (ROC) curve was used to assess the performance of serum cytokines in the clinical diagnosis of SAP, and the area under the ROC curve (AUC) was calculated. Results Compared with the SAP group, the non-SAP group had significantly lower APACHE Ⅱ, BISAP, CTSI, and modified Marshall scores (all P < 0.001).Compared with the non-SAP group, the SAP group had significantly higher levels of interferon-γ(IFN-γ), interleukin-6(IL-6), interleukin-8, and tumor necrosis factor-α(TNF-α) and a significantly lower level of interleukin-12(all P < 0.05).The logistic regression analysis showed that IFN-γ(odds ratio[OR]=1.190, 95% confidence interval[CI]: 1.036-1.367, P=0.014), IL-6 (OR=1.148, 95%CI: 1.070-1.231, P < 0.001), and TNF-α (OR=1.100, 95%CI: 1.048-1.155, P < 0.001) were independent influencing factors for SAP.The partial correlation analysis showed that after adjustment for sex, age, BMI, and history of chronic diseases (diabetes and hypertension), the levels of IL-6 and TNF-α were positively correlated with APACHE Ⅱ score in SAP patients (IL-6:r=0.503, P < 0.001;TNF-α: r=0.557, P < 0.001).The linear regression analysis showed that the levels of IL-6 and TNF-α were associated with APACHE Ⅱ score in SAP patients (IL-6:β=0.049, P=0.044;TNF-α: β=0.054, P=0.046), and there was an interaction between IL-6 and TNF-α, which affected APACHE Ⅱ score.The ROC curve analysis showed that the risk score based on IL-6 and TNF-α using LASSO algorithm had the largest AUC of 0.925 in distinguishing SAP from non-SAP, while IL-6 or TNF-α alone had an AUC of 0.885 and 0.878, respectively.The partial correlation analysis showed that after adjustment for sex, age, BMI, and history of chronic diseases (diabetes and hypertension), the risk score was positively correlated with APACHE Ⅱ score in SAP patients (r=0.565, P < 0.001). Conclusion The serum levels of IL-6 and TNF-α can reflect the severity of AP.The risk score combining serum IL-6 and TNF-α can significantly improve the accuracy of the early diagnosis of SAP, which has an important clinical value in the clinical diagnosis and treatment of SAP.
- Pancreatitis /
- Interleukin-6 /
- Tumor Necrosis Factor-alpha /
- LASSO Algorithm

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图 1 SAP组患者细胞因子水平与APACHE Ⅱ评分的关联分析

注：a，IL-6水平与APACHE Ⅱ评分的相关性；b，TNF-α水平与APACHE Ⅱ评分的相关性；c，IL-6和TNF-α水平交互作用对APACHE Ⅱ评分的影响(基于SAP患者APACHE Ⅱ评分的中位数分为低APACHE Ⅱ评分组和高APACHE Ⅱ评分组)。

Figure 1. Association of cytokine levels with APACHE Ⅱscore in SAP patients

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图 2 IFN-γ、IL-6、TNF-α诊断SAP的ROC曲线分析

Figure 2. ROC curve analysis of IFN-γ、IL-6、TNF-α in diagnosis of severe acute pancreatitis

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图 3 风险评分模型的构建及其对SAP的诊断价值

注：a，风险评分LASSO模型(黑线1：IL-6；红线2：TNF-α)；b，风险评分的比较；c，诊断SAP的ROC曲线及95%CI；d，SAP组风险评分与APACHE Ⅱ评分的相关性。

Figure 3. Construction of risk scoring model and its diagnostic value for SAP

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表 1 两组AP患者临床资料比较

Table 1. Comparison of clinical data between two groups of patients with AP

项目	非SAP组(n=57)	SAP组(n=73)	统计值	P值
年龄(岁)	56.04±15.70	57.25±16.90	t=-0.418	0.676
男[例(%)]	28(49.12)	40(54.79)	χ²=0.413	0.521
BMI(kg/m²)	22.44±3.56	23.31±3.14	t=-1.474	0.143
糖尿病[例(%)]	7(12.28)	15(20.55)	χ²=1.556	0.212
高血压[例(%)]	16(28.07)	23(31.51)	χ²=0.180	0.671
AP病因[例(%)]
胆源性	45(78.95)	51(69.86)	χ²=1.368	0.242
高脂血症性	4(7.02)	11(15.07)	χ²=2.033	0.154
酒精性	6(10.53)	7(9.59)	χ²=0.031	0.860
病因不明	2(3.51)	4(5.48)	χ²=0.282	0.595
APACHE Ⅱ评分	4(4~5)	8(6~12)	Z=-7.503	＜0.001
BISAP评分	2(1~2)	4(3~4)	Z=-9.818	＜0.001
CTSI评分	2(2~3)	6(5~7)	Z=-9.797	＜0.001
改良Marshall评分	1(0~2)	3(3~4)	Z=-8.555	＜0.001
循环衰竭[例(%)]	4(7.02)	25(34.25)
呼吸衰竭[例(%)]	10(17.54)	47(63.38)
肾衰竭[例(%)]	2(3.51)	8(10.96)

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表 2 两组AP患者细胞因子水平的比较

Table 2. Comparison of cytokines levels in patients with SAP group and non-SAP group

细胞因子	非SAP(n=57)	SAP(n=73)	统计值	P值
GM-CSF(pg/mL)	36.45±5.01	38.39±7.79	t=-1.638	0.104
IFN-γ(pg/mL)	38.69±7.11	42.26±11.96	t=-2.114	0.037
IL-1β(pg/mL)	12.93(10.09~16.60)	15.85(12.96~19.67)	Z=-3.207	0.001
IL-2(pg/mL)	1.13(0.80~1.72)	1.20(0.98~1.79)	Z=-1.185	0.236
IL-4(pg/mL)	38.76±7.22	40.12±5.65	t=-1.174	0.243
IL-5(pg/mL)	0.56(0.43~0.72)	0.59(0.47~0.76)	Z=-1.159	0.246
IL-6(pg/mL)	69.12±24.01	110.81±27.78	t=-9.004	＜0.001
IL-7(pg/mL)	5.16±2.55	5.33±2.39	t=-0.383	0.702
IL-8(pg/mL)	62.48±15.04	74.90±11.78	t=-5.279	＜0.001
IL-10(pg/mL)	31.29(28.21~34.41)	31.28(30.03~33.84)	Z=-1.370	0.171
IL-12(pg/mL)	14.66(11.78~18.06)	13.60(10.37~15.52)	Z=-1.985	0.047
IL-13(pg/mL)	0.86 (0.80~0.91)	0.89 (0.75~1.04)	Z=-1.234	0.217
TNF-α(pg/mL)	77.64±31.64	128.48±25.33	t=-10.177	＜0.001

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表 3 多因素Logistic回归分析SAP的影响因素

Table 3. Multivariate Logistic regression analysis of independent risk factors of SAP patients

因素	β值	SE	P值	OR值	95% CI
性别	0.037	1.046	0.972	1.038	0.134~1.228
年龄	0.117	0.045	0.880	1.170	0.184~10.110
BMI	-0.269	0.140	0.054	0.764	0.581~1.005
饮酒史	0.576	1.532	0.707	1.778	0.088~35.804
吸烟史	0.165	1.148	0.886	1.179	0.124~11.180
糖尿病	0.185	1.205	0.878	1.203	0.113~12.763
高血压	-0.598	1.133	0.598	0.550	0.060~5.070
GM-CSF	-0.023	0.100	0.818	0.977	0.803~1.189
IFN-γ	0.174	0.071	0.014	1.190	1.036~1.367
IL-1β	0.071	0.068	0.298	1.074	0.939~1.227
IL-2	0.393	0.540	0.466	1.482	0.514~4.271
IL-4	0.042	0.061	0.493	1.043	0.925~1.175
IL-5	4.655	2.411	0.054	105.074	0.932~11 851.669
IL-6	0.138	0.036	＜0.001	1.148	1.070~1.231
IL-7	-0.099	0.205	0.629	0.906	0.606~1.353
IL-8	-0.054	0.038	0.161	0.948	0.879~1.022
IL-10	0.097	0.051	0.057	1.101	0.997~1.216
IL-12	-0.099	0.111	0.373	0.906	0.729~1.126
IL-13	1.232	1.592	0.439	3.427	0.151~77.664
TNF-α	0.096	0.025	＜0.001	1.100	1.048~1.155

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