血清肠型脂肪酸结合蛋白(I-FABP)在慢加急性肝衰竭发生发展中的预测价值
DOI: 10.12449/JCH240820
Value of intestinal fatty acid binding protein in predicting the development and progression of acute-on-chronic liver failure
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摘要:
目的 探讨血清肠型脂肪酸结合蛋白(I-FABP)对慢加急性肝衰竭(ACLF)发生发展的预测作用。 方法 回顾性分析2020年9月—2023年3月延边大学附属医院收治的168例肝硬化失代偿患者的临床资料,观察入院合并ACLF的患者情况和随访6个月新发ACLF事件。采用ELISA法测定患者入院血清I-FABP水平。非正态分布计量资料两组间比较采用Mann-Whitney U检验,多组间比较采用Kruskal-Wallis H秩和检验。计数资料组间比较采用χ2检验。趋势性分析采用Jonckheere-Terpstra检验。采用Spearman相关分析两变量间相关性。多变量Cox回归法分析随访期间新发ACLF的影响因素。Kaplan-Meier曲线分析不同组间ACLF发生情况,并采用Log-rank检验评估差异。采用受试者工作特征曲线(ROC曲线)和曲线下面积评估I-FABP对ACLF发生、发展的预测性能。 结果 入组168例患者中43例合并ACLF,125例无ACLF的患者在随访期间新发ACLF19例。纳入时合并ACLF患者的I-FABP水平高于无ACLF者,差异有统计学意义(Z=4.359,P<0.001)。新发ACLF患者的I-FABP水平均高于未发生ACLF者,差异有统计学意义(Z=3.414,P<0.001)。I-FABP随ACLF分级增加而升高(H=17.385,P<0.001,P趋势<0.001)。多因素分析显示I-FABP与随访期间新发ACLF独立相关(HR=2.138,95%CI:1.297~3.525,P=0.003),且I-FABP三分位数显示出良好的区分能力(χ2=12.16,P<0.001)。ROC曲线显示I-FABP对ACLF发生和发展具有较好的预测效能,ROC曲线下面积分别为0.854(95%CI:0.791~0.903)和0.747(95%CI:0.661~0.820),最佳截断值分别为2.07 μg/L和1.86 μg/L。 结论 I-FABP是预测ACLF发生和发展的生物标志物,有助于临床识别高危患者,改善临床管理。 -
关键词:
- 慢加急性肝功能衰竭 /
- 肠脂肪酸结合蛋白质类 /
- 生物标记
Abstract:Objective To investigate the value of intestinal fatty acid binding protein (I-FABP) in predicting the development and progression of acute-on-chronic liver failure (ACLF). Methods A retrospective analysis was performed for the clinical data of 168 patients with decompensated liver cirrhosis who were admitted to The Affiliated Hospital of Yanbian University from September 2020 to March 2023. The conditions of the patients with ACLF on admission were observed, and the patients were followed up for 6 months to identify new-onset ACLF cases. ELISA was used to measure the serum level of I-FABP on admission. The Mann-Whitney U test was used for comparison of non-normally distributed continuous data between two groups, and the Kruskal-Wallis H rank sum test was used for comparison between multiple groups; the chi-square test was used for comparison of categorical data between groups; the Jonckheere-Terpstra test was used for trend analysis. The Spearman correlation analysis was used to investigate the correlation between two variables, and the multivariate Cox regression analysis was used to investigate the influencing factors for new-onset ACLF during follow-up. The Kaplan-Meier curve was used to analyze the onset of ACLF in different groups, and the log-rank test was used for the analysis of such differences. The receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC) were used to investigate the performance of I-FABP in predicting the development and progression of ACLF. Results Among the 168 patients enrolled in this study, there were 43 patients with ACLF and 125 patients without ACLF, among whom 19 developed ACLF during follow-up. The patients with ACLF on admission had a significantly higher level of I-FABP than those without ACLF (Z=4.359, P<0.001). The patients with new-onset ACLF had a significantly higher level of I-FABP than those without new-onset ACLF (Z=3.414, P<0.001). The level of I-FABP increased with the increase in ACLF severity grade (H=17.385, P<0.001,Ptrend<0.001). The multivariate Cox regression analysis showed that I-FABP was independently associated with new-onset ACLF during follow-up (hazard ratio=2.138, 95% confidence interval [CI]: 1.297 — 3.525, P=0.003), and the tertile of I-FABP showed a good discriminatory ability (χ2=12.16, P<0.001). The ROC curve showed that I-FABP had a good performance in predicting the development and progression of ACLF, with an area under the ROC curve of 0.854 (95%CI: 0.791 — 0.903) and 0.747 (95%CI: 0.661 — 0.820), respectively, and an optimal cut-off value of 2.07 μg/L and 1.86 μg/L, respectively. Conclusion I-FABP can be used as a biomarker to predict the development and progression of ACLF, and it may help to identify high-risk patients and improve clinical management. -
Key words:
- Acute-On-Chronic Liver Failure /
- Fatty Acid-Binding Proteins /
- Biomarkers
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慢加急性肝衰竭(ACLF)是在慢性肝病基础上出现的以多器官功能衰竭为特征的重症肝病,病情凶险且进展迅速,短期病死率高[1]。因此,识别可用于ACLF诊断和危险分层的生物标志物具有重要的临床价值。目前已有多种模型应用于ACLF病情分层和预后评估[2-4],但预测ACLF发展的方法仍是临床亟待解决的问题,这对于改善患者病程和结局具有积极意义。
肝功能减退和门静脉高压等特征性改变可能导致肝硬化患者发生胃肠道功能障碍,并加重多器官衰竭[5-6]。胃肠道功能障碍指胃肠道功能性损伤,可能包括动力和/或吸收障碍、黏膜完整性破坏和微生物组变化等,其临床表现多变,对患者生活质量影响较大[7]。肠型脂肪酸结合蛋白(intestinal fatty acid-binding protein,I-FABP)被视为肠上皮细胞损伤标志物[8],与全身炎症、细菌移位和肠道功能障碍相关[9-11]。本课题组前期研究报道I-FABP能较好地预测肝硬化相关自发性细菌性腹膜炎发生以及患者预后[12]。在此背景下,本研究假设I-FABP可能与ACLF发生和发展相关,探讨I-FABP与ACLF发生及分级的关系;并评估I-FABP在预测ACLF发展中的应用价值。
1. 资料与方法
1.1 研究对象
回顾性分析2020年9月—2023年3月本院收治的肝硬化失代偿患者资料。纳入标准:(1)失代偿期肝硬化符合2019年《肝硬化诊治指南》[13]的相关诊断依据;(2)年龄>18周岁的住院患者。排除标准:(1)肝内外恶性肿瘤;(2)胃肠道手术或炎症性肠病患者;(3)主要研究数据不完整。
1.2 研究方法
通过查阅医院电子病历系统,记录患者的人口统计学、临床实验室结果及入院时的终末期肝病模型评分(MELD评分),计算公式为9.6×Ln(Cre,mg/dL)+3.8×Ln(TBil,mg/dL)+11.2×Ln(INR)+6.4×病因(胆汁淤积性和酒精性肝硬化为0,病毒等其他原因肝硬化为1)[2]。另外,收集患者入院血液样本,将离心后上清液储存在-80 ℃冰箱,集中检测分析。采用ELISA试剂盒(R&D Systems,USA)检测血清I-FABP浓度,标本预先稀释2倍再定量检测,参照说明书,建立标准曲线,检测范围为31.3~2 000 pg/mL,Eon酶标仪由美国Bio Tek公司提供。所有实验室指标均在本院临床实验室检测。研究人群进行6个月的随访,随访时间计为入院诊疗到随访期内ACLF发生。
1.3 ACLF分级标准
根据慢性肝衰竭联盟器官衰竭评分(CLIF-C OFs)对ACLF分级[3],(1)ACLF-1级:患者仅有肾衰竭;或1个非肾器官衰竭(包括肝衰竭、循环衰竭、凝血功能衰竭、呼吸衰竭),血清肌酐1.5~1.9 mg/dL伴或不伴轻中度肝性脑病;或脑衰竭同时血清肌酐1.5~1.9 mg/dL;(2)ACLF-2级:2个器官衰竭;(3)ACLF-3级:3个或以上器官衰竭。
1.4 统计学方法
采用SPSS 26.0、GraphPad prism 8.0、MedCalc软件进行数据处理与分析。非正态分布计量资料采用M(P25~P75)表示,两组间比较采用Mann-Whitney U检验,多组间比较采用Kruskal-Wallis H秩和检验。计数资料组间比较采用χ2检验。趋势性分析采用Jonckheere-Terpstra检验。采用Spearman相关分析两变量间相关性。多变量Cox回归法分析随访期间新发ACLF的影响因素。Kaplan-Meier曲线分析不同组间ACLF发生情况,并采用Log-rank检验评估差异。采用受试者工作特征曲线(ROC曲线)和曲线下面积(AUC)评估I-FABP对ACLF发生、发展的预测性能。P<0.05为差异有统计学意义。
2. 结果
2.1 一般情况
所纳入168例患者中男115例,女53例,中位年龄54(45~65)岁。有失代偿病史者81例,多数患者呈中重度肝损伤,MELD评分为19(12~27)分。常见的肝硬化病因是病毒性肝炎,HBV相关占60.7%,HCV相关占16.7%。
入组时,43例(25.6%)患者合并ACLF,其中多数患者未发现潜在诱因,13例与细菌感染诱因相关,6例与乙型肝炎再激活相关。相较于无ACLF患者,ACLF患者的基线WBC、CRP、Cre、TBil、INR、MELD评分和I-FABP水平均升高,Alb降低,差异均存在统计学意义(P值均<0.05)(表1)。
表 1 研究人群基线特征Table 1. Baseline characteristics of study population指标 非ACLF组(n=125) ACLF组(n=43) 统计值 P值 年龄(岁) 54(42~64) 55(48~67) Z=1.795 0.073 男性[例(%)] 83(66.4) 32(74.4) χ2=0.953 0.327 WBC(109/L) 6.39(5.20~9.35) 8.13(6.61~10.12) Z=3.140 0.002 CRP(mg/L) 13.9(8.3~40.6) 29.9(17.9~51.3) Z=3.800 <0.001 Alb(g/L) 33(29~37) 28(27~31) Z=5.054 <0.001 Cre(μmol/L) 85(75~106) 123(107~144) Z=6.066 <0.001 TBil(μmol/L) 39.8(27.2~67.8) 171.5(116.7~233.1) Z=8.915 <0.001 INR 1.30(1.04~1.74) 2.01(1.73~2.24) Z=6.749 <0.001 MELD评分 15(11~21) 29(25~33) Z=7.776 <0.001 I-FABP(μg/L) 1.51(0.87~2.55) 3.09(2.51~3.95) Z=4.359 <0.001 2.2 I-FABP和ACLF分级的相关性
将ACLF患者分为ACLF-1级17例,ACLF-2级17例,ACLF-3级9例。I-FABP随ACLF分级增加而升高(H=17.385,P<0.001,P趋势<0.001)。ACLF-3级和ACLF-2级的I-FABP水平均高于ACLF-1级(P值均<0.01)(图1)。
ROC曲线分析I-FABP对ACLF的诊断效能,显示其AUC为0.854(0.791~0.903),最佳截断值为2.07 μg/L,此时灵敏度为88.37%,特异度为68.00%(图2)。
2.3 I-FABP与ACLF器官功能衰竭的相关性
根据CLIF-C OFs标准,入组ACLF患者肝脏衰竭及凝血功能衰竭比例较高,分别为76.7%和37.2%,肾衰竭占16.3%,其他系统衰竭比例均小于10%。肝衰竭的I-FABP水平高于无肝衰竭患者[3.35(2.85~4.06)μg/L vs 2.48(1.96~2.90)μg/L,Z=2.372,P=0.018],肾衰竭患者的I-FABP水平高于无肾衰竭者[3.84(3.26~4.11)μg/L vs 2.84(2.27~3.47)μg/L,Z=2.877,P=0.004]。凝血衰竭患者的I-FABP水平与无凝血衰竭者比较,差异无统计学意义(Z=1.530,P=0.126)。
2.4 I-FABP与随访期间新发ACLF的相关性
本研究对125例在纳入时没有出现ACLF的肝硬化患者进行6个月随访,有19例患者在随访期间为新发ACLF,106例未进展为ACLF。两组在年龄和性别方面没有显著差异,随访期间新发ACLF患者的基线Cre、TBil、INR、I-FABP和MELD评分均高于未发生组(P值均<0.05)(表2)。
表 2 随访期间ACLF发展情况的临床资料比较Table 2. Comparison of clinical data on the development of ACLF during the follow-up指标 未发生ACLF组(n=106) 新发ACLF组(n=19) 统计值 P值 年龄(岁) 54(40~64) 53(47~62) Z=0.678 0.498 男性[例(%)] 70(66.0) 13(68.4) χ2=0.041 0.839 WBC(109/L) 6.35(5.20~9.35) 6.69(5.45~10.22) Z=0.589 0.556 CRP(mg/L) 13.2(7.7~40.7) 20.5(12.6~33.8) Z=1.241 0.214 Alb(g/L) 34(30~37) 31(25~35) Z=1.069 0.285 Cre(μmol/L) 84(75~105) 96(86~130) Z=2.156 0.031 TBil(μmol/L) 37.4(26.0~67.8) 48.0(40.5~61.1) Z=2.029 0.042 INR 1.26(1.02~1.61) 1.68(1.25~2.03) Z=2.219 0.027 MELD评分 14(11~19) 21(18~23) Z=3.867 <0.001 I-FABP(μg/L) 1.34(0.85~2.13) 2.69(1.91~3.14) Z=3.414 <0.001 Spearman相关分析显示,I-FABP与CRP和MELD评分呈正相关(r值分别为0.314、0.271,P值分别为<0.001、0.002)。多因素回归分析结果显示,基线Cre、I-FABP和MELD评分是随访期间发生ACLF的独立危险因素(P值均<0.05)(表3)。
表 3 多因素Cox回归分析随访期间发生ACLF的影响因素Table 3. Multivariate Cox regression analyzed the factors of developing ACLF during follow-up因素 β值 Wald HR(95%CI) P值 Cre 0.017 10.278 1.017(1.007~1.027) 0.001 I-FABP 0.760 8.880 2.138(1.297~3.525) 0.003 MELD评分 0.096 5.495 1.101(1.016~1.194) 0.019 2.5 I-FABP对随访期间新发ACLF的预测能力
根据I-FABP三分位数(Q1:<0.96,Q2:0.96~2.07,Q3:≥2.07)对125例在纳入中无ACLF者分层,6个月累积ACLF发生率分别为2.4%、11.4%和30.0%。Kaplan-Meier曲线分析显示,基线I-FABP水平越高,随访期间发生ACLF的概率也随之升高(Log-rank:χ2=12.16,P<0.001)(图3)。
ROC曲线评估Cre、I-FABP、MELD评分对随访期间新发ACLF的预测性能,结果显示I-FABP和MELD评分的AUC分别为0.747和0.779,两者的预测效能无显著差异(P=0.559)。I-FABP联合MELD评分的AUC为0.810,灵敏度为73.68%,特异度为80.19%(图4,表4)。
表 4 各指标对随访期间新发ACLF的预测性能Table 4. Predictive performance of the study parameters on new-onset ACLF during follow-up指标 AUC(95%CI) Cut-off值 灵敏度(%) 特异度(%) Z值 P值 Cre 0.656(0.565~0.738) 92.00 73.68 66.04 2.100 0.036 I-FABP 0.747(0.661~0.820) 1.86 78.95 62.26 4.275 <0.001 MELD评分 0.779(0.696~0.848) 16.00 89.47 61.32 6.617 <0.001 I-FABP+MELD评分 0.810(0.731~0.875) 73.68 80.19 5.955 <0.001 3. 讨论
ACLF病情凶险,短期病死率高,肝移植是唯一可能有效的治疗方式[1,14]。因此,早期识别ACLF患病风险较高的肝硬化失代偿具有重要的临床价值。目前,ACLF发病机制尚未明确,本研究假设“肠-肝互动异常”是该病的潜在机制之一,并在6个月的随访中证实肠屏障标志物I-FABP与ACLF发生发展相关。
既往研究[15-16]报道全身炎症反应引起的器官功能障碍是ACLF重要的病理生理机制,影响患者预后。肠上皮细胞损伤或坏死时,I-FABP释放入血,其水平反映肠屏障功能受损程度[8-9]。本研究中ACLF患者的I-FABP水平升高可能不仅与肝硬化门静脉高压及肠道黏膜通透性增加,使得肠道微生物通过肠-肝轴引起肝脏持续损伤有关[17-18],还可能与肝功能障碍加重肠道损伤和肾功能障碍影响I-FABP排泄有关[19-20]。此外,ACLF患者的I-FABP与炎症因子CRP存在显著正相关,提示ACLF系统性炎症反应可能参与并反映肠黏膜损伤,文献[10]也报道I-FABP水平受全身炎症反应综合征影响。而且,本研究中I-FABP与MELD评分相关性较高,并随ACLF严重度分级而升高。以上结果说明失代偿肝硬化患者的“肠-肝互动异常”与全身炎症反应相关,提示肠道功能障碍可能在ACLF病程进展中发挥作用。
由于ACLF短期病死率高,使得早期识别或预测ACLF的发生成为ACLF管理的关键因素之一。本研究结果显示I-FABP是肝硬化患者进展为ACLF的独立危险因素,且I-FABP三分位数显示出良好的区分能力,表明肠上皮损伤程度与ACLF密切相关。另外,本研究结果显示I-FABP对ACLF发生和发展具有较高的诊断和预测能力,弥补了MELD评分及其他预测模型因缺乏胃肠道功能障碍评估而存在的局限性。因此,将I-FABP引入现存的器官功能评估体系中,或能有助于临床识别高危患者,改善ACLF管理。
总之,肠道损伤标志物I-FABP与炎症反应、肝肾功能障碍的关系,表明肝硬化失代偿患者I-FABP水平升高与ACLF发生和发展紧密相关。对“肠-肝互动异常”在ACLF中的作用及其病理生理学机制,需要在未来的研究中进一步探索和验证。
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表 1 研究人群基线特征
Table 1. Baseline characteristics of study population
指标 非ACLF组(n=125) ACLF组(n=43) 统计值 P值 年龄(岁) 54(42~64) 55(48~67) Z=1.795 0.073 男性[例(%)] 83(66.4) 32(74.4) χ2=0.953 0.327 WBC(109/L) 6.39(5.20~9.35) 8.13(6.61~10.12) Z=3.140 0.002 CRP(mg/L) 13.9(8.3~40.6) 29.9(17.9~51.3) Z=3.800 <0.001 Alb(g/L) 33(29~37) 28(27~31) Z=5.054 <0.001 Cre(μmol/L) 85(75~106) 123(107~144) Z=6.066 <0.001 TBil(μmol/L) 39.8(27.2~67.8) 171.5(116.7~233.1) Z=8.915 <0.001 INR 1.30(1.04~1.74) 2.01(1.73~2.24) Z=6.749 <0.001 MELD评分 15(11~21) 29(25~33) Z=7.776 <0.001 I-FABP(μg/L) 1.51(0.87~2.55) 3.09(2.51~3.95) Z=4.359 <0.001 表 2 随访期间ACLF发展情况的临床资料比较
Table 2. Comparison of clinical data on the development of ACLF during the follow-up
指标 未发生ACLF组(n=106) 新发ACLF组(n=19) 统计值 P值 年龄(岁) 54(40~64) 53(47~62) Z=0.678 0.498 男性[例(%)] 70(66.0) 13(68.4) χ2=0.041 0.839 WBC(109/L) 6.35(5.20~9.35) 6.69(5.45~10.22) Z=0.589 0.556 CRP(mg/L) 13.2(7.7~40.7) 20.5(12.6~33.8) Z=1.241 0.214 Alb(g/L) 34(30~37) 31(25~35) Z=1.069 0.285 Cre(μmol/L) 84(75~105) 96(86~130) Z=2.156 0.031 TBil(μmol/L) 37.4(26.0~67.8) 48.0(40.5~61.1) Z=2.029 0.042 INR 1.26(1.02~1.61) 1.68(1.25~2.03) Z=2.219 0.027 MELD评分 14(11~19) 21(18~23) Z=3.867 <0.001 I-FABP(μg/L) 1.34(0.85~2.13) 2.69(1.91~3.14) Z=3.414 <0.001 表 3 多因素Cox回归分析随访期间发生ACLF的影响因素
Table 3. Multivariate Cox regression analyzed the factors of developing ACLF during follow-up
因素 β值 Wald HR(95%CI) P值 Cre 0.017 10.278 1.017(1.007~1.027) 0.001 I-FABP 0.760 8.880 2.138(1.297~3.525) 0.003 MELD评分 0.096 5.495 1.101(1.016~1.194) 0.019 表 4 各指标对随访期间新发ACLF的预测性能
Table 4. Predictive performance of the study parameters on new-onset ACLF during follow-up
指标 AUC(95%CI) Cut-off值 灵敏度(%) 特异度(%) Z值 P值 Cre 0.656(0.565~0.738) 92.00 73.68 66.04 2.100 0.036 I-FABP 0.747(0.661~0.820) 1.86 78.95 62.26 4.275 <0.001 MELD评分 0.779(0.696~0.848) 16.00 89.47 61.32 6.617 <0.001 I-FABP+MELD评分 0.810(0.731~0.875) 73.68 80.19 5.955 <0.001 -
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