修回日期: 2026-06-08
接受日期: 2026-07-23
在线出版日期: 2026-08-28
早期识别肝硬化心肌病(cirrhotic cardiomyopathy, CCM)患者不良预后的高危因素, 对于临床实施针对性干预、改善临床结局具有重要意义, 肝实质细胞是铁蛋白(serum ferritin, SF)含量最丰富的细胞, 其水平异常升高与机体氧化应激、炎症反应关系密切, 且在心力衰竭、冠状动脉疾病等心血管疾病预后中有一定预测价值.
探讨SF与残余胆固醇(residual cholesterol, RC)在CCM中的交互作用及预测预后意义.
本研究为前瞻性队列研究, 选取2021-01至2023-06 CCM患者204例作为研究对象, 随访2年, 依据患者预后情况分为预后不良组(53例)、预后良好组(148例), 比较两组基线临床资料、血清SF、RC水平, 分析CCM患者预后的影响因素, 分析血清SF与RC的交互作用, 分析血清SF与RC单独及联合常规指标对CCM预后的预测价值.
预后不良组Child-Pugh分级、MELD-Na评分、血小板计数、总胆红素、肌酐、N-末端B型脑钠肽前体(N-terminal pro-B-type natriuretic peptide, NT-proBNP)、SF、RC高于预后良好组, 左心室射血分数(left ventricular ejection fraction, LVEF)低于预后良好组(P<0.05); 校正Child-Pugh分级、NT-proBNP、LVEF后, SF[比值比(odds ratio, OR) = 1.843, 95%CI: 1.661-2.045]、RC(OR = 1.736, 95%CI: 1.541-1.957)均为CCM患者预后的独立影响因素(P<0.05); SF与RC同时暴露在CCM患者预后不良中存在交互作用, 其二者同时暴露时CCM患者预后不良风险OR为7.042、相对超危险度比(relative excess risk due to interaction) = 3.339(95%CI: 1.440-7.742)、归因比(attributable proportion) = 0.474(95%CI: 0.175-0.774)、交互作用指数 = 2.719(95%CI: 1.021-7.239); 基于Logistic回归分析建立Child-Pugh分级、NT-proBNP、LVEF、SF、RC联合预测模型, 联合预测模型的曲线下面积为0.938, 敏感度为83.02%, 特异度为86.49%, 预测价值显著优于SF、RC单独预测价值(P<0.05); 校正曲线显示, 联合预测模型的校准度为0.828; 精准-召回率曲线显示, 该预测模型的平均精度为0.873(95%CI: 0.804-0.928), 决策曲线曲线分析, 联合预测模型的临床净获益阈概率范围为10%-80%.
血清SF、RC水平是CCM患者预后的独立影响因素, 二者在导致患者预后不良风险中具有显著的协同交互作用, 二者联合Child-Pugh分级、NT-proBNP、LVEF等常规指标构建的预测模型可有效评估患者预后风险, 为临床早期识别高危患者、开展个体化干预提供一定指导价值.
核心提要: 铁蛋白与残余胆固醇作为反映机体炎症/氧化应激及残余脂质风险的血清学指标, 不仅在肝硬化心肌病(cirrhotic cardiomyopathy, CCM)患者预后不良组中显著升高, 且二者间存在显著协同交互作用. 将二者与传统肝功能(Child-Pugh分级)及心功能指标(N-末端B型脑钠肽前体、左心室射血分数)联合构建的预测模型, 对CCM患者预后具有较高的预测价值, 可为临床早期识别高危患者、开展个体化干预提供实用工具.
引文著录: 雷根昌, 张芝婷. 铁蛋白与残余胆固醇在肝硬化心肌病中的交互作用及预测预后意义. 世界华人消化杂志 2026; 34(8): 654-664
Revised: June 8, 2026
Accepted: July 23, 2026
Published online: August 28, 2026
Early identification of high-risk factors for poor prognosis in patients with cirrhotic cardiomyopathy (CCM) is crucial for implementing targeted clinical interventions and improving clinical outcomes. Hepatocytes, the most abundant cells in liver parenchyma, exhibit abnormally elevated serum ferritin (SF) levels, which are closely associated with oxidative stress and inflammatory responses in the body. These levels also hold predictive value for the prognosis of cardiovascular diseases such as heart failure and coronary artery disease.
To investigate the interaction between SF and residual cholesterol (RC) in CCM and its prognostic significance.
This is a prospective cohort study. A total of 204 patients with CCM from January 2021 to June 2023 were enrolled and followed up for 2 years. They were divided into a poor prognosis group (53 cases) and a good prognosis group (148 cases) based on their clinical outcomes. Baseline clinical data and serum SF and RC levels were compared between the two groups. Factors influencing the prognosis of CCM patients were analyzed, and the interaction between serum SF and RC was examined. The predictive value of serum SF and RC alone and combined with conventional indicators for the prognosis of CCM was assessed.
The Child-Pugh classification, model for end-stage liver disease-sodium score, platelet count, total bilirubin, serum creatinine, N-terminal pro-B-type natriuretic peptide (NT-proBNP), SF, and RC in the poor prognosis group were higher than those in the good prognosis group, while left ventricular ejection fraction (LVEF) was lower (P < 0.05). After adjusting for Child-Pugh classification, NT-proBNP, LVEF, SF [odds ratio (OR) = 1.843, 95%CI: 1.661-2.045], and RC (OR = 1.736, 95%CI: 1.541-1.957) were identified as independent prognostic factors for CCM patients (P < 0.05). An interaction was observed between the simultaneous exposure of elevated SF and RC in the poor prognosis of CCM patients. The OR for poor prognosis in patients with both elevated SF and RC was 7.042. The relative excess risk due to interaction was 3.339 (95%CI: 1.440-7.742), the attributable proportion was 0.474 (95%CI: 0.175-0.774), and the interaction index was 2.719 (95%CI: 1.021-7.239). A combined prediction model incorporating Child-Pugh classification, NT-proBNP, LVEF, SF, and RC was established based on Logistic regression analysis. The area under the curve of the combined prediction model was 0.938, with a sensitivity of 83.02% and specificity of 86.49%. The predictive performance of the combined prediction model was significantly better than that of SF and RC alone (P < 0.05). The calibration curve indicated a calibration degree of 0.828 for the combined prediction model, and the precision-recall curve showed an average precision of 0.873 (95%CI: 0.804-0.928). Decision curve analysis revealed that the combined model yielded a positive clinical net benefit within the threshold probability range of 10%-80%.
Serum SF and RC levels are independent prognostic factors in patients with CCM, and they have a significant synergistic interaction in increasing the risk of poor prognosis. The prediction model incorporating these two markers along with Child-Pugh classification, NT-proBNP, LVEF, and other conventional indicators can effectively stratify prognostic risk in patients and provide valuable guidance for early identification of high-risk individuals and individualized intervention.
- Citation: Lei GC, Zhang ZT. Interaction between serum ferritin and residual cholesterol in cirrhotic cardiomyopathy: Prognostic implications. Shijie Huaren Xiaohua Zazhi 2026; 34(8): 654-664
- URL: https://www.wjgnet.com/1009-3079/full/v34/i8/654.htm
- DOI: https://dx.doi.org/10.11569/wcjd.v34.i8.654
核心提要: 铁蛋白与残余胆固醇作为反映机体炎症/氧化应激及残余脂质风险的血清学指标, 不仅在肝硬化心肌病(cirrhotic cardiomyopathy, CCM)患者预后不良组中显著升高, 且二者间存在显著协同交互作用. 将二者与传统肝功能(Child-Pugh分级)及心功能指标(N-末端B型脑钠肽前体、左心室射血分数)联合构建的预测模型, 对CCM患者预后具有较高的预测价值, 可为临床早期识别高危患者、开展个体化干预提供实用工具.
肝硬化是各种慢性肝病发展的晚期阶段, 机体循环系统处于高动力状态, 导致心脏容量负荷、压力负荷增加, 损伤心肌细胞, 造成心肌收缩力下降[1-3]. 其中, 肝硬化心肌病(cirrhotic cardiomyopathy, CCM)已成为临床管理的难点, 患者常因高动力循环状态、心脏储备功能下降及潜在的冠状动脉疾病等因素, 面临更高的心血管事件及肝脏不良事件风险, 导致患者预后不良[4-6]. 故早期识别CCM患者不良预后的高危因素, 对于临床实施针对性干预、改善临床结局具有重要意义. 铁蛋白(serum ferritin, SF)是含铁离子的水溶性蛋白, 人体所有细胞均可合成铁蛋白, 其也被视为一种急性期反应蛋白, 其中肝实质细胞是SF含量最丰富的细胞[7-9]. 有研究认为, 其水平异常升高与机体氧化应激、炎症反应关系密切, 且在心力衰竭、冠状动脉疾病等心血管疾病预后中有一定预测价值[10-12]. 残余胆固醇(residual cholesterol, RC)是富含甘油三酯的脂蛋白残粒主要胆固醇成分, 是驱动动脉粥样硬化的重要因素, 与冠心病、缺血性卒中的发生发展关系密切[13,14]. 但在肝硬化这一特殊背景下, 机体代谢紊乱、炎症激活、氧化应激状态均可能影响SF进而RC水平, 二者在CCM患者预后中的独立作用、潜在协同效应及对传统指标的增量预测价值, 目前仍缺乏系统性的研究[15-17]. 基于此, 本研究通过前瞻性分析探讨SF与RC在CCM患者预后评估中的独立影响, 并分析二者间的交互作用, 通过构建并验证二者与常规指标的联合预测模型, 以期为临床医师提供一个较为有效的临床辅助工具, 用于早期识别高危患者, 及时调整监测策略和治疗方案, 改善患者结局.
根据样本量估算公式: n = [Z1-α/2]2×P×(1-P)/∆2, 其中Z1-α/2取值为1.96, ∆取值为0.05, P依据预实验取值为0.13, 同时考虑10%失访率, 得出最小样本量为194. 选取2021-01至2023-06 CCM患者204例作为研究对象, 随访2年, 依据患者预后情况分为预后不良组(53例)、预后良好组(148例). 纳入标准: ①符合肝硬化相关诊断标准[18]; ②经心电图检查确诊为心肌病; ③无出血倾向; ④对本研究知情同意且自愿签署知情同意书. 排除标准: ①存在原发性心脏病; ②肾脏功能严重损伤或不全; ③甲状腺功能异常; ④合并严重感染性疾病; ⑤妊娠期、哺乳期女性.
1.2.1 资料收集: 由经过统一培训的医护人员, 收集患者性别、年龄、体重指数(body mass index, BMI)、高血压、糖尿病等资料, 评估收集患者肝硬化病因[乙型肝炎病毒(hepatitis B virus, HBV)、丙型肝炎病毒(hepatitis C virus, HCV)]、酒精性肝硬化、原发性胆汁性胆管炎、药物性肝损伤、Child-Pugh分级、MELD-Na评分、治疗药物(β受体阻滞剂、他汀类药物、利尿剂、抗血小板药物), 检测记录血小板计数(platelet count, PLT)、血红蛋白(hemoglobin, Hb)、白蛋白(albumin, Alb)、总胆红素(total bilirubin, TBIL)、肌酐(serum creatinine, Scr)、丙氨酸氨基转移酶(alanine aminotransferase, ALT)、天冬门氨酸氨基转移酶(aspartate aminotransferase, AST)、N-末端B型脑钠肽前体(N-terminal pro-B-type natriuretic peptide, NT-proBNP)、左心室收缩末期内径(left ventricular end-systolic diameter, LVESD)、左心室舒张末期内径(left ventricular end-diastolic diameter, LVEDD)、左心室射血分数(left ventricular ejection fraction, LVEF)水平. BMI = 体重/身高2(kg/m2). 高血压[19]、糖尿病[20]依据相关标准进行诊断, Child-Pugh分级[21]是依据患者一般状况、腹水、血清胆红素、血清白蛋白浓度及凝血酶原时间的不同状态分为三个层次, 每项分别计1分、2分、3分, 将5个指标积分进行相加, 总分范围为5-15分, 其中≤6分患者为A级, >6分且≤9分患者为B级, >9分患者为C级. MELD-Na评分[22]依据肌酐、总胆红素、国际标准化比值、血清钠4项客观实验室指标进行计分, 计分公式: MELD-Na = MELD+1.59×(1.37-血清钠浓度), 若血清钠浓度低于125 mmol/L则按照125 mmol/L计算, 若高于137 mmol/L则按137 mmol/L计算, MELD = 0.957×ln(肌酐)+0.378×ln(胆红素)+1.12×ln(国际标准化比值)+0.643×病因(酒精性和淤胆性肝硬化取0, 其他取1). LVESD、LVEDD、LVEF检测采用彩色多普勒超声诊断仪(南京左右脑医疗科技集团有限公司, 苏械注准20182060834, 型号Digi-Lite IP).
1.2.2 指标检测: 取患者入院时空腹肘静脉血3 mL于抗凝管中, 另取3 mL空腹肘静脉血3000 r/minute离心10 min, 半径10 cm, 取上层血清于-80 ℃保存待测. PLT、Hb检测采用全自动血细胞分析仪(济南希森美康医用电子有限公司, 鲁械注准20182220002, 型号XS-500ix).
Alb检测采用溴甲酚绿法, TBIL检测采用钒酸盐氧化法, Scr检测采用苦味酸法, ALT检测采用丙氨酸底物法, 试剂盒均购自潍坊三维生物工程集团有限公司(鲁械注准20192400237、鲁械注准20192400220、鲁械注准20192400229、鲁械注准20172401072); AST检测采用国际临床化学和实验室医学联盟推荐的标准化检测方法, 试剂盒购自山东博迈达生物科技有限公司(鲁械注准20162400341), NT-proBNP检测采用化学发光法, 试剂盒购自深圳市新产业生物医学工程股份有限公司(粤械注准20152401077). SF检测采用免疫比浊法, 试剂盒购自上海景源医疗器械有限公司(沪械注准20152400200). RC需基于Friedwald公式[23]进行RC水平测定, 即RC = 总胆固醇－(高密度脂蛋白胆固醇＋低密度脂蛋白胆固醇), 总胆固醇检测采用终点法, 高密度脂蛋白胆固醇、低密度脂蛋白胆固醇检测采用直接法, 试剂盒购自广西康柏莱科技有限公司(桂械注准20202400470、桂械注准20192400036、桂械注准20192400039).
1.2.3 预后评估: 依据病情和相关指南[24]对患者进行治疗. 治疗后随访1年内, 患者发生任意一种不良预后事件则纳入预后不良组, 反之纳入预后良好组. 预后不良事件包括心源性事件、肝脏相关事件和全因死亡, 其中心源性事件包括心源性死亡、非致死性心肌梗死、因心力衰竭或心绞痛再入院等; 肝脏相关事件包括肝脏相关死亡、肝功能急性失代偿、新发肝细胞癌.
(1)记录患者失访及预后情况; (2)比较两组临床资料和血清SF、RC水平, 其中临床资料包括性别、年龄、BMI、高血压、糖尿病、肝硬化病因(HBV、HCV)、酒精性肝硬化、原发性胆汁性胆管炎、药物性肝损伤、Child-Pugh分级、MELD-Na评分及PLT、Hb、Alb、TBIL、Scr、ALT、AST、NT-proBNP、LVESD、LVEDD、LVEF水平、治疗药物(β受体阻滞剂、他汀类药物、利尿剂、抗血小板药物); (3)采用Lasso-Logistic回归筛选并分析CCM患者预后的影响因素; (4)分析血清SF、RC水平对CCM患者预后的预测价值; (5)分析血清SF、RC水平的交互作用; (6)分析血清SF、RC水平联合常规指标对CCM患者预后的预测价值.
统计学处理 以SPSS 22.0分析, 计数资料以n(%)表示, 行χ2检验, 计量资料以(mean±SD)表示, 行t检验, 等级资料采用Mann-Whitney U检验; 采用Lasso-Logistic回归筛选并分析CCM患者预后的影响因素; 以受试者工作特征(receiver operating characteristic, ROC)曲线、曲线下面积(area under the curve, AUC)值分析指标单独及联合对患者预后的预测价值, 为控制因多重比较而增加的Ⅰ类错误(假阳性)风险, 对DeLong检验获得的原始P值采用错误发现率(false discovery rate, FDR)校正法进行校正, 以经过FDR校正后的P值<0.05为差异有统计学意义; 基于ROC曲线最佳截断值, 分层分析比较两指标的联合效应是否大于单独存在指标的效应之和, 以进行交互作用评价, 采用相对超危险度比(relative excess risk due to interaction, RERI)、归因比(attributable proportion, AP)、交互作用指数(synergy index, S)、比值比(odds ratio, OR)分析交互作用强度, 其中S = (OR11-1)/(OR10-1)+(OR01-1), RERI = OR11-OR10-OR01+1, AP = RERI/OR11, 在上述计算中, "1"代表因素存在, "0"代表不存在, 若RERI = 0、AP = 0, S = 1, 则判定两指标间不存在交互作用, 否则认为存在交互作用; 采用校正曲线评估模型的校准度; 采用精准-召回率(precision-recall, PR)曲线评估模型的平均精度; 采用决策曲线(decision curve analysis, DCA)分析模型的临床效用; P<0.05为差异有统计学意义.
随访2年, 失访3例, 201例CCM患者完成统计分析. 其中预后良好148例, 预后不良53例, 包括20例全因死亡, 12例肝功能急性失代偿, 10例心衰再住院, 5例心肌梗死, 6例新发肝癌.
预后不良组与预后良好组性别、年龄、BMI、高血压、糖尿病、肝硬化病因、治疗药物、Hb、Alb、ALT、AST、LVESD、LVEDD比较, 差异无统计学意义(P>0.05); 预后不良组Child-Pugh分级、MELD-Na评分、TBIL、Scr、NT-proBNP、SF、RC高于预后良好组, PLT、LVEF低于预后良好组(P<0.05).详见表1.
| 资料 | 预后不良组(n = 53) | 预后良好组(n = 148) | t/χ2/U | P值 |
| 性别 | 0.173 | 0.678 | ||
| 男 | 35(66.04) | 93(62.84) | ||
| 女 | 18(33.96) | 55(37.16) | ||
| 年龄(岁) | 58.42±5.16 | 57.86±5.03 | 0.691 | 0.491 |
| BMI(kg/m2) | 24.27±1.71 | 23.99±1.65 | 1.050 | 0.295 |
| 高血压 | 24(45.28) | 64(43.24) | 0.066 | 0.797 |
| 糖尿病 | 18(33.96) | 46(31.08) | 0.149 | 0.699 |
| 肝硬化病因 | 0.349 | 0.986 | ||
| HBV | 22(41.51) | 63(42.57) | ||
| HCV | 11(20.75) | 33(22.30) | ||
| 酒精性肝硬化 | 12(22.64) | 31(20.95) | ||
| 原发性胆汁性胆管炎 | 5(9.43) | 15(10.14) | ||
| 药物性肝损伤 | 3(5.66) | 6(4.05) | ||
| Child-Pugh分级 | 15.745 | <0.001 | ||
| A级 | 15(28.30) | 80(54.05) | ||
| B级 | 25(47.17) | 57(38.51) | ||
| C级 | 13(24.53) | 11(7.43) | ||
| MELD-Na评分(分) | 14.24±3.53 | 11.19±2.66 | 6.542 | <0.001 |
| PLT(×109/L) | 76.78±15.41 | 92.53±18.14 | 5.633 | <0.001 |
| Hb(g/L) | 103.16±25.64 | 105.82±28.75 | 0.594 | 0.553 |
| Alb(g/L) | 31.47±2.51 | 31.80±2.37 | 0.856 | 0.393 |
| TBIL(μmol/L) | 37.52±9.13 | 32.63±7.96 | 3.689 | <0.001 |
| Scr(μmol/L) | 85.40±13.95 | 76.24±15.02 | 3.880 | <0.001 |
| ALT(U/L) | 34.18±4.19 | 33.76±3.87 | 0.663 | 0.508 |
| AST(U/L) | 47.22±5.31 | 46.54±5.15 | 0.818 | 0.414 |
| NT-proBNP(pg/mL) | 816.29±272.09 | 554.38±184.73 | 7.751 | <0.001 |
| LVESD(mm) | 38.32±5.11 | 37.84±4.76 | 0.618 | 0.537 |
| LVEDD(mm) | 52.08±7.46 | 51.63±6.91 | 0.398 | 0.691 |
| LVEF(%) | 51.48±4.77 | 56.41±5.29 | 5.969 | <0.001 |
| 治疗药物 | ||||
| β受体阻滞剂 | 24(45.28) | 77(52.03) | 0.710 | 0.399 |
| 他汀类药物 | 13(24.53) | 45(30.41) | 0.657 | 0.418 |
| 利尿剂 | 40(75.47) | 107(72.53) | 0.200 | 0.655 |
| 抗血小板药物 | 28(52.83) | 83(56.08) | 0.167 | 0.683 |
| SF(ng/mL) | 442.16±155.80 | 307.39±101.55 | 7.125 | <0.001 |
| RC(mmol/L) | 0.87±0.29 | 0.62±0.20 | 6.880 | <0.001 |
以CCM患者预后作为结局变量(预后良好 = 0, 预后不良 = 1), 将Child-Pugh分级、MELD-Na评分、PLT、TBIL、Scr、NT-proBNP、LVEF、SF、RC纳入Lasso回归分析, 最终筛选出5个变量: Child-Pugh分级、PLT、NT-proBNP、SF、RC. 以上述5个变量作为自变量纳入Logistic回归分析, Child-Pugh分级、PLT、NT-proBNP、SF、RC是CCM患者预后的独立影响因素(P<0.05); 之后校正Child-Pugh分级、PLT、NT-proBNP, SF、RC仍是CCM患者预后的独立影响因素(P<0.05). 见表2.
| 变量 | β | S.E. | Wald χ2 | OR | 95%CI | P值 |
| 校正前 | ||||||
| Child-Pugh分级 | ||||||
| A级 | - | - | - | 1.000 | - | - |
| B级 | 0.847 | 0.102 | 68.923 | 2.332 | 1.910-2.848 | 0.005 |
| C级 | 1.109 | 0.096 | 133.372 | 3.030 | 2.511-3.658 | <0.001 |
| NT-proBNP | 0.298 | 0.048 | 38.590 | 1.347 | 1.226-1.480 | 0.021 |
| PLT | -1.001 | 0.084 | 141.999 | 0.368 | 0.312-0.433 | <0.001 |
| SF | 0.458 | 0.052 | 77.487 | 1.580 | 1.427-1.750 | <0.001 |
| RC | 0.658 | 0.071 | 85.885 | 1.931 | 1.680-2.219 | <0.001 |
| 常数项 | -1.423 | 0.116 | 129.404 | - | - | <0.001 |
| 校正后 | ||||||
| SF | 0.611 | 0.053 | 133.113 | 1.843 | 1.661-2.045 | <0.001 |
| RC | 0.552 | 0.061 | 81.798 | 1.736 | 1.541-1.957 | <0.001 |
| 常数项 | -0.817 | 0.084 | 102.614 | - | - | <0.001 |
ROC曲线分析, 血清SF预测CCM患者预后的AUC为0.749(95%CI: 0.683-0.807), 最佳截断值为392.76 ng/mL, 敏感度为62.26%, 特异度为83.11%; RC预测CCM患者预后的AUC为0.769(95%CI: 0.704-0.825), 最佳截断值为0.76 mmol/L, 敏感度为66.04%, 特异度为73.65%.见图1.
以ROC曲线获取血清SF、RC的最佳截断值为界分为高表达与低表达. 调整其他因素后, SF与RC同时暴露在CCM患者预后不良中存在交互作用, 两者同时暴露时CCM患者预后不良风险OR值为7.042, 其增加的预后不良风险中, 有3.339个单位的风险是超出两者独立效应简单相加的部分(RERI = 3.339, 95%CI: 1.440-7.742), 两者同时暴露时CCM患者预后不良风险中有47.40%归因于两者交互作用(AP = 0.474, 95%CI: 0.175-0.774), 交互效应为两者单独暴露产生效应之和的2.719倍(S = 2.719, 95%CI: 1.021-7.239). 详见表3.
| SF/RC | 变量 | OR(95%CI) | RERI(95%CI) | AP(95%CI) | S(95%CI) | |
| 预后不良 | 预后良好 | |||||
| 低表达/低表达 | 6 | 45 | 1.000 | 3.339(1.440-7.742) | 0.474(0.175-0.774) | 2.719(1.021-7.239) |
| 低表达/高表达 | 12 | 45 | 2.135(1.102-4.136) | |||
| 高表达/低表达 | 11 | 43 | 2.568(1.328-4.966) | |||
| 高表达/高表达 | 24 | 15 | 7.042(3.625-13.679) | |||
根据Logistic回归分析建立以Child-Pugh分级、NT-proBNP、PLT、SF、RC联合预测模型公式: Logit(P) = -1.423+0.847×Child-Pugh分级B级+1.109×Child-Pugh分级C级-1.001×PLT+0.298×NT-proBNP+0.458×SF+0.658×RC. ROC曲线分析, 联合预测模型的AUC为0.938(95%CI: 0.898-0.977), 敏感度为83.02%, 特异度为86.49%, 预测价值显著优于SF、RC单独预测价值(DeLong检验显示, Z = 3.517、2.903, P<0.001), 见图2; 校正曲线显示, 联合预测模型的校准度为0.828, 预测准确性较高, 见图3; PR曲线显示, 该预测模型的平均精度为0.873(95%CI: 0.804-0.928). 表明该预测模型在不平衡的数据集中仍能维持较高的精确率和召回率, 展现出优异的稳健性. 详见图4; DCA曲线分析, 联合预测模型的临床净获益阈概率范围为10%-80%, 提示联合模型具有稳定且显著的临床效用性. 详见图5.
CCM是肝硬化的严重并发症之一, 是在肝硬化的基础上发生的心肌血液灌注不足, 导致心肌缺血、代谢异常和功能障碍的状态, 患者临床常表现为胸闷、胸痛、心悸、呼吸困难等, 与单纯肝硬化患者相比, 伴随心肌缺血的肝硬化患者生存率相对更低[25-27]. 但CCM的发病机制仍不明确, 有学者推测其与心肌细胞结构和功能改变等关系密切, 但其具体机制仍需进一步分析[28-30]. 本研究通过对CCM患者进行随访分析, 以期为临床预测CCM患者预后提供指导, 并初步探究CCM患者预后不良相关机制.
本研究结果显示, 预后不良组SF、RC水平显著高于预后良好组, 且二者均独立于Child-Pugh分级、NT-proBNP、LVEF等传统评估系统外的独立预测因素, 最重要的是, SF、RC水平在导致患者预后不良风险中存在显著的协同交互效应, 二者同时升高会导致患者预后不良风险倍增. 在肝硬化患者机体内, SF水平的异常增高往往反映了患者存在全身性炎症反应与氧化应激加剧状态, 这种状态会直接损害心肌细胞功能, 甚至导致心肌纤维化, 通过影响冠状动脉微血管功能而加剧心肌病病情[31,32]. 而RC作为促进动脉粥样硬化性脂蛋白残粒的核心成分, 其水平异常升高提示在肝功能异常、传统血脂谱紊乱的肝硬化患者中, 残余的脂代谢异常仍持续驱动血管内皮功能障碍和斑块不稳定风险[33,34]. 本研究分析得出的SF、RC存在交互作用, 提示患者SF、RC同时升高时, 可能处于高炎症、氧化应激状态和活跃的动脉粥样化状态, 二者对于心脏的损害效应并非简单叠加, 而是存在协同放大作用. 临床应对SF、RC水平同时异常升高的患者进行重点关注, 及时干预患者病情, 尽可能降低患者肝脏不良事件、心血管不良事件及死亡事件的发生风险.
Huang等[35]学者研究指出, SF水平较高的患者发生主要心血管不良事件的风险显著增加, 是因为该指标可反映全身性炎症反应及代谢状况, 因此其能够更准确地对患者进行风险分级, 甚至预测效果显著高于传统肝纤维化评估指标. 而Song等[36]学者的研究报道中指出, 胆固醇代谢异常患者存在肝病, 也会显著升高不良心血管结局发生风险. 这均提示了SF、RC分别对CCM患者预后存在影响, 但目前尚未有研究将二者进行联合以探讨其对CCM患者预后的预测能力. 本研究结果表明, SF、RC单独检测对CCM患者预后具有预测价值, 二者通过与传统心功能指标NT-proBNP、LVEF和肝脏储备功能指标Child-Pugh分级联合, 可显著提高预测能力, 经校正曲线和PR曲线、DCA曲线分析显示, 该模型预测准确性较高, 且在不平衡的数据集中仍能保持较高的精准率和召回率, 展现出优异的临床效用和稳健性. 从临床使用角度而言, 本研究构建的联合预测模型为床旁决策提供了新思路. SF、RC均为临床常规可检测且成本相对较低的血清学指标, 将二者纳入风险评估框架有助于临床早期识别肝功能稳定但心脑血管风险显著升高的人群, 对于这类患者, 临床可考虑肝脏疾病管理联合心血管风险评估的监测模式, 通过考虑患者是否需要强化抗炎、调控脂质代谢或加强心肌病的监测治疗.
但本研究也存在一定局限性, 首先, 单中心、前瞻性的观察研究, 样本量有限, 故本研究结论仍需多中心、大样本队列进行进一步验证, 其次, 随访时间相对较短, 主要关注的是患者中期预后, 二者对于患者长期预后的影响需要更长时间的观察. 最后, 本研究初步探究了SF、RC对CCM的交互作用, 但二者提高患者预后不良风险的具体分子机制仍需后续基础研究深入探索.
综上所述, SF、RC是评估CCM患者预后的重要生物标志物, 二者存在交互作用, 同时升高提示患者预后不良风险升高, 应引起临床高度警惕. 将二者与传统评估工具进行结合, 构建了一个精准、实用的风险分层模型, 有助于临床早期预警和个体化干预, 以尽可能改善CCM患者临床结局.
肝硬化心肌病(cirrhotic cardiomyopathy, CCM)是肝硬化的严重并发症之一, 患者面临较高心血管事件及死亡风险. 铁蛋白(serum ferritin, SF)作为炎症与氧化应激标志物, 残余胆固醇(residual cholesterol, RC)作为致动脉粥样硬化脂蛋白残粒的核心成分, 均被证实与心血管疾病预后相关. 然而, 在肝硬化这一特殊病理背景下, SF与RC对CCM患者预后的独立作用、潜在协同交互效应及其增量预测价值尚缺乏系统性研究.
探讨SF与RC在CCM患者预后评估中的独立影响及二者间的交互作用, 并构建及验证将二者与常规临床指标相结合的联合预测模型, 以期提升对CCM患者不良预后的早期识别能力, 为临床实施个体化干预提供依据.
分析SF、RC水平与CCM患者预后的关系, 明确各指标是否为独立影响因素及是否存在协同交互作用; 评估二者单独及联合常规指标[Child-Pugh分级、N-末端B型脑钠肽前体(N-terminal pro-B-type natriuretic peptide, NT-proBNP)、左心室射血分数(left ventricular ejection fraction, LVEF)]对CCM患者预后的预测效能, 并验证联合预测模型的临床实用性及稳健性.
采用前瞻性队列研究设计, 纳入204例CCM患者, 随访2年, 根据预后分为预后不良组(53例)与预后良好组(148例). 收集患者基线临床资料及血清SF、RC水平. 采用Lasso-Logistic回归筛选预后影响因素, 通过相对超危险度比、归因比、交互作用指数分析SF与RC的交互作用; 绘制受试者工作特征曲线、校正曲线、精准-召回率(precision-recall, PR)曲线及决策曲线(decision curve analysis, DCA)曲线评估并验证联合预测模型的预测效能及临床效用.
预后不良组SF、RC水平显著高于预后良好组. 校正混杂因素后, SF、RC均为CCM患者预后的独立影响因素, 且二者同时暴露时存在显著协同交互作用. 基于Child-Pugh分级、NT-proBNP、LVEF、SF、RC构建的联合预测模型曲线下面积显著优于SF或RC单独使用. 校准度、PR曲线及DCA曲线均显示该模型具有良好的准确性和临床净获益.
血清SF与RC水平是CCM患者预后的独立影响因素, 二者在增加患者预后不良风险中具有显著的协同交互作用. 将SF、RC与常规心肝功能指标联合构建的预测模型, 可有效评估患者预后风险, 具有较高的预测准确性和临床实用价值, 有助于临床早期识别高危患者并指导个体化干预.
本研究成果为CCM的危险分层提供了新的血清学生物标志物组合. 未来需通过多中心、大样本研究进一步验证该模型的普适性, 并深入探索SF与RC协同加重心肌损伤的具体分子机制. 此外, 基于该模型的高危人群能否从强化抗炎、调脂或靶向心肌保护治疗中获益, 是后续临床干预研究的重要方向.
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学科分类: 胃肠病学和肝病学
手稿来源地: 浙江省
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科学编辑: 刘继红 制作编辑:张砚梁