代谢相关脂肪性肝病发生脂肪性肝炎的无创诊断模型构建及分析

陈智恒; 高博文; 桂蓓; 施梅姐; 萧焕明; 谢玉宝; 黎胜; 池晓玲

doi:10.3969/j.issn.1001-5256.2023.08.014

代谢相关脂肪性肝病发生脂肪性肝炎的无创诊断模型构建及分析

DOI: 10.3969/j.issn.1001-5256.2023.08.014

陈智恒¹,
高博文¹,
桂蓓¹,
施梅姐^{2, 3},
萧焕明^{2, 3},
谢玉宝^{2, 3},
黎胜^{2, 3, 4},
池晓玲^{2, 3, , ,}

1.
广州中医药大学第二临床医学院, 广州 510006
2.
广东省中医院肝病科, 广州 510006
3.
广州中医药大学第二附属医院, 广州 510006
4.
省部共建中医湿证国家重点实验室(广州中医药大学第二附属医院), 广州 510120

基金项目:

国家“十三五”重大传染病专项课题 (2018ZX10725506-003);

国家“十三五”重大传染病专项课题 (2018ZX10725505-004);

广东省中医院院内专项 (YN2022DB04);

广东省中医院院内专项 (YN10101903);

国家中医药管理局全国名老中医药专家池晓玲传承工作室建设项目 (Teaching Letter from State Traditional Chinese Medicine Office (2022-75));

省部共建中医湿证国家重点实验室开放课题 (SZ2021KF08)

伦理学声明：本研究方案于2022年2月14日经由广东省中医院伦理委员会审批，批号为YE2022-035-01。

利益冲突声明：本文不存在任何利益冲突。

作者贡献声明：陈智恒、施梅姐、池晓玲负责课题设计，拟定写作思路，资料分析，撰写论文；高博文、桂蓓、萧焕明、谢玉宝、黎胜参与收集并核对数据，修改论文；池晓玲指导撰写文章并最后定稿。

详细信息

通信作者:
池晓玲，chixiaolingqh@163.com (ORCID: 0000-0003-3193-1943)

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出版历程
- 收稿日期: 2022-12-30
- 录用日期: 2023-02-13
- 出版日期: 2023-08-20

Construction and analysis of a noninvasive diagnostic model for steatohepatitis in metabolic associated fatty liver disease

Zhiheng CHEN¹,
Bowen GAO¹,
Bei GUI¹,
Meijie SHI^{2, 3},
Huanming XIAO^{2, 3},
Yubao XIE^{2, 3},
Sheng LI^{2, 3, 4},
Xiaoling CHI^{2, 3
, ,
,}

1.
The Second Clinical Medical College of Guangzhou University of Chinese Medicine, Guangzhou 510006, China
2.
Department of Hepatology, Guangdong Provincial Hospital of Traditional Chinese Medicine, Guangzhou 510006, China
3.
The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou 510006, China
4.
State Key Laboratory of Dampness Syndrome of Traditional Chinese Medicine Jointly Built by Province and Ministry, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou 510120, China

Research funding:

The Thirteenth Five-Year Plan for Major and Special Programs of the National Science and Technology of China (2018ZX10725506-003);

The Thirteenth Five-Year Plan for Major and Special Programs of the National Science and Technology of China (2018ZX10725505-004);

The Specific Research Fund for TCM Science and Technology of Guangdong Provincial Hospital of Chinese Medicine (YN2022DB04);

The Specific Research Fund for TCM Science and Technology of Guangdong Provincial Hospital of Chinese Medicine (YN10101903);

Chi Xiaoling National Famous Traditional Chinese Medicine Expert Inheritance Studio (Teaching Letter from State Traditional Chinese Medicine Office (2022-75));

Open Project of State Key Laboratory of Dampness Syndrome of Chinese Medicine (SZ2021KF08)

More Information

Corresponding author: CHI Xiaoling, chixiaolingqh@163.com (ORCID: 0000-0003-3193-1943)

摘要

摘要: 目的通过临床一般资料、血清学指标及肝脏弹性成像无创检查手段，基于LASSO及Logistic回归建立代谢相关脂肪性肝病(MAFLD)发生脂肪性肝炎的诊断模型，并评估该模型的诊断价值。方法纳入2018年1月—2021年12月于广东省中医院诊断为MAFLD且完善肝病理活检的患者为研究对象299例，根据肝病理NAS评分将其分为脂肪性肝炎组(n=170)例和无脂肪性肝炎组(n=129)。先后通过LASSO回归及多因素Logistic回归筛选MAFLD发生脂肪性肝炎的影响因素，并建立无创诊断模型，利用列线图形式可视化，采用加强Bootstrap法进行内部验证，绘制ROC曲线及Calibration曲线，并在MAFLD+NAFLD和MAFLD+cHBVi两个亚组人群中观察模型的诊断效能，并与其他诊断模型进行比较分析。计数资料组间比较采用χ²检验；符合正态分布的计量资料组间比较采用成组t检验，不符合正态分布的计量资料组间比较采用Mann-Whitney U检验。采用多因素Logistic回归分析，筛选最佳诊断因素，构建列线图诊断模型，绘制受试者工作特征曲线(ROC曲线)，计算ROC曲线下面积(AUC)，并进一步采用加强Bootstrap法对模型进行内部验证，绘制Calibration曲线显示校准度。结果两组间BMI、ALT、AST、ADA、ALP、GGT、TBA、TCO₂、UA、HbA1c比较差异均有统计学意义(P值均 < 0.05)；FibroScan方面，两组LSM及CAP比较提示差异具有统计学意义(P值均 < 0.001)；病理学方面，两组的纤维化等级、脂肪变积分、小叶炎症积分、气球样变积分及NAS总分差异均有统计学意义(P值均 < 0.001)。亚组队列方面，MAFLD+NAFLD有、无脂肪性肝炎组分别为63、48例，MAFLD+cHBVi有、无脂肪性肝炎组分别为90、71例。通过LASSO回归及多因素Logistic回归筛选出LSM、CAP、BMI、AST是判断MAFLD患者是否发生脂肪性肝炎的最佳诊断因素，并以此构建LCBA模型。LCBA模型结果提示，总MAFLD、MAFLD+NAFLD和MAFLD+cHBVi人群的AUC分别为0.816、0.866、0.764(P值均 < 0.001)，ROC曲线对比显示均优于acNASH、HSI、NFS模型。结论 LCBA模型用于诊断MAFLD患者是否发生脂肪性肝炎的效能稳定，且优于acNASH、HSI、NFS，值得临床推广。
- 非酒精性脂肪性肝病 /
- 代谢相关脂肪性肝病 /
- 诊断
Abstract: Objective To establish a diagnostic model for steatohepatitis in metabolic associated fatty liver disease (MAFLD) based on LASSO and logistic regression analyses by using general clinical data, serological parameters, and noninvasive liver elastography, and to evaluate the diagnostic value of this model. Methods A total of 299 patients who were diagnosed with MAFLD and underwent liver biopsy in Guangdong Provincial Hospital of Traditional Chinese Medicine from January 2018 to December 2021 were enrolled as subjects, and according to NAS score, they were divided into steatohepatitis group with 170 patients and non-steatohepatitis group with 129 patients. The LASSO regression analysis and the multivariate logistic regression analysis were used to identify the influencing factors for steatohepatitis in MAFLD, and a noninvasive diagnostic model was established, visualized in the form of nomogram, and internally validated by the enhanced Bootstrap method. The receiver operating characteristic (ROC) curve and the calibration curve were plotted for the model, and its diagnostic efficacy was observed in the MAFLD+NAFLD and MAFLD+cHBVi subgroups, which was then compared with other diagnostic models. The chi-square test was used for comparison of categorical data between groups; the independent-samples t test was used for comparison of normally distributed continuous data between groups, and the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between groups. A multivariate logistic regression analysis was used to determine optimal diagnostic factors, and a nomogram diagnostic model was established; the ROC curve was plotted, and the area under the ROC curve (AUC) was calculated; the enhanced Bootstrap method was used for internal validation of the model, and the calibration curve was plotted to show the level of calibration. Results There were significant differences between the two groups in body mass index (BMI), alanine aminotransferase, aspartate aminotransferase (AST), adenosine deaminase, alkaline phosphatase, gamma-glutamyl transpeptidase, total bile acid, total carbon dioxide concentration, uric acid, HbA1c (all P < 0.05). As for FibroScan, there were significant differences between the two groups in liver stiffness measurement (LSM) and controlled attenuation parameter (CAP) (both P < 0.001); as for pathology, there were significant differences between the two groups in fibrosis degree, steatosis score, lobular inflammation score, ballooning degeneration score, and total NAS score (all P < 0.001). In the subgroup analysis, there were 63 patients with steatohepatitis and 48 patients without steatohepatitis in the MAFLD+NAFLD group, and there were 90 patients with steatohepatitis and 71 patients without steatohepatitis in the MAFLD+cHBVi group. The LASSO regression analysis showed that LSM, CAP, BMI, and AST were the best diagnostic factors for the presence or absence of steatohepatitis in MAFLD patients, and the LCBA model was established based on these indices. The LCBA model showed an AUC of 0.816 in the total MAFLD population, 0.866 in the MAFLD+NAFLD population, and 0.764 in the MAFLD+cHBVi population (all P < 0.001), and comparisons based on the ROC curve showed that they were superior to the acNASH, HSI, and NFS models. Conclusion The LCBA model has a stable performance in the diagnosis of steatohepatitis in patients with MAFLD and is superior to acNASH, HSI, and NFS. Therefore, it holds promise for clinical application.
- Non-alcoholic Fatty Liver Disease /
- Metabolic Associated Fatty Liver Disease /
- Diagnosis

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图 1 MAFLD脂肪性肝炎的LASSO分析

Figure 1. LASSO analysis of MAFLD with or without steatohepatitis

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图 2 LCBA模型列线图可视化

Figure 2. LCBA model nomogram visualization

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图 3 LCBA模型的ROC曲线与Calibration曲线

Figure 3. ROC Curve and Calibration Curve for LCBA Model

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图 4 LCBA模型与acNASH、HSI、NFS模型的ROC曲线对比

Figure 4. ROC curve comparison between LCBA model and acNASH, HSI, NFS model

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表 1 MAFLD患者有无发生脂肪性肝炎基线资料对比

Table 1. Baseline comparison of patients with or without steatohepatitis in MAFLD

项目	无脂肪性肝炎组 (n=129)	脂肪性肝炎组 (n=170)	统计值	P值
年龄(岁)	41.00(35.00~48.00)	40.00(34.25~48.00)	Z=-0.880	0.379
男[例(%)]	103(80)	125(74)	χ²=1.099	0.294
BMI(kg/m²)	25.35(24.31~26.93)	26.99(24.99~30.00)	Z=-5.047	< 0.001
吸烟[例(%)]	33(26)	46(27)	χ²=0.024	0.877
过量饮酒[例(%)]	12(9)	19(11)	χ²=0.112	0.738
高血压[例(%)]	10(8)	16(9)	χ²=0.088	0.766
糖尿病[例(%)]			χ²=5.767	0.056
无	69(53)	71(42)
糖尿病前期	46(36)	66(39)
糖尿病期	14(11)	33(19)
MAFLD+NAFLD[例(%)]	48(37)	63(37)	χ²=0	1.000
MAFLD+cHBVi[例(%)]	71(55)	90(53)	χ²=0.059	0.808
纤维化等级[例(%)]			χ²=56.276	< 0.001
0	10(8)	3(2)
1	87(67)	52(31)
2	29(22)	98(58)
3	3(2)	10(6)
4	0(0)	7(4)
脂肪变积分[例(%)]			χ²=90.836	< 0.001
0	15(12)	1(1)
1	68(53)	29(17)
2	43(33)	72(42)
3	3(2)	68(40)
小叶炎症积分[例(%)]			χ²=78.487	< 0.001
0	20(16)	7(4)
1	69(53)	28(16)
2	28(22)	55(32)
3	12(9)	80(47)
气球样变积分[例(%)]			χ²=140.666	< 0.001
0	62(48)	1(1)
1	62(48)	71(42)
2	5(4)	98(58)
NAS总分(分)	3.07±0.93	6.01±0.97	t=-26.654	< 0.001
LSM(kPa)	5.50(4.40~6.70)	7.70(5.82~10.50)	Z=-7.058	< 0.001
CAP(dB/m)	279(252~305)	312(277~343)	Z=-5.470	< 0.001
ALT(U/L)	27.00(19.00~41.00)	45.00(31.00~75.25)	Z=-6.443	< 0.001
AST(U/L)	21.00(17.00~28.00)	32.50(23.00~44.75)	Z=-6.909	< 0.001
ADA(U/L)	9.0(7.1~11.0)	10.0(8.0~13.0)	Z=-3.342	< 0.001
Alb(g/L)	46.63±3.42	46.16±3.17	t=1.212	0.227
ALP(U/L)	66.00(54.00~79.00)	71.50(61.25~85.00)	Z=-3.094	0.002
GGT(U/L)	32(22~44)	45(29~73)	Z=-4.683	< 0.001
TBil(μmol/L)	11.60(8.90~13.70)	11.45(8.90~14.85)	Z=-0.517	0.606
DBil(μmol/L)	4.10(3.50~5.00)	4.25(3.50~5.57)	Z=-1.028	0.305
TBA(μmol/L))	3.00(1.70~4.80)	3.80(2.10~6.27)	Z=-2.156	0.031
WBC(×10⁹/L)	6.03(5.25~6.94)	6.26(5.41~7.17)	Z=-1.110	0.267
RBC(×10¹²/L)	5.02(4.75~5.34)	4.97(4.65~5.30)	Z=-0.939	0.348
Hb(g/L)	152.00(143.00~158.00)	151.00(141.00~161.75)	Z=-0.109	0.913
PLT(×10⁹/L)	238.53±53.90	232.19±63.73	t=0.932	0.352
Urea(mmol/L)	4.50(3.90~5.20)	4.40(3.70~5.18)	Z=-1.391	0.164
Cr(μmol/L)	77(65~86)	74(64~83)	Z=-1.756	0.079
TCO₂(mmol/L)	24.20(22.50~25.90)	23.45(21.70~25.10)	Z=-2.511	0.012
UA(μmol/L)	393(339~456)	416(370~490)	Z=-2.812	0.005
eGFR(mL·min^-1·1.73 m^-2)	102.84±13.37	105.59±14.23	t=-1.715	0.087
GLU(mmol/L)	5.00(4.66~5.45)	5.14(4.74~5.51)	Z=-1.773	0.076
HbA1c(%)	5.50(5.30~5.80)	5.65(5.32~6.00)	Z=2.567	0.010
TG(mmol/L)	1.45(1.06~2.20)	1.65(1.17~2.42)	Z=-1.844	0.065
TC(mmol/L)	4.83(4.15~5.60)	4.89(4.27~5.84)	Z=-1.226	0.220
HDL-C(mmol/L)	1.08(0.94~1.28)	1.06(0.89~1.22)	Z=-1.834	0.067
LDL-C(mmol/L)	3.19(2.73~3.83)	3.26(2.62~3.98)	Z=-0.274	0.784
Apo-A1(g/L)	1.28(1.18~1.44)	1.24(1.13~1.44)	Z=-1.467	0.143
Apo-B(g/L)	1.03(0.86~1.23)	1.07(0.89~1.29)	Z=-1.735	0.083
CK-MB(U/L)	14.5(12.5~17.5)	15.0(13.0~18.9)	Z=-1.845	0.065
TSH(μIU/mL)	1.59(1.12~2.31)	1.57(1.13~2.22)	Z=-0.321	0.749

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表 2 MAFLD脂肪性肝炎的多因素Logistic回归分析

Table 2. Multivariate Logistic regression analysis of steatohepatitis in MAFLD

项目	β值	SE	Wald	P值	OR	95%CI
LSM	0.138	0.054	6.574	0.010	1.148	1.033~1.275
CAP	0.263	0.063	17.251	＜0.001	1.301	1.149~1.472
BMI	0.015	0.004	18.145	＜0.001	1.015	1.008~1.043
AST	0.023	0.010	4.927	0.026	1.023	1.003~1.043

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表 3 LCBA模型在MAFLD+NAFLD和MAFLD+cHBVi人群的表现

Table 3. Performance of the LCBA model in the MAFLD+NAFLD and MAFLD+cHBVi populations

组别	例数	敏感度(%)	特异度(%)	AUC(95%CI)	P值
MAFLD组	299	78.24	71.32	0.816(0.768~0.858)	< 0.001
MAFLD+NAFLD组	111	93.65	64.58	0.866(0.788~0.923)	< 0.001
MAFLD+CHB组	161	74.44	70.42	0.764(0.691~0.827)	< 0.001

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表 4 LCBA模型与acNASH、HSI、NFS模型比较

Table 4. LCBA model compared to acNASH, HSI, NFS models

项目	MAFLD组(n=299)		MAFLD+NAFLD组(n=111)		MAFLD+cHBVi组(n=161)
项目	AUC(95%CI)	P值	AUC(95%CI)	P值	AUC(95%CI)	P值
LCBA	0.82(0.77~0.86)	< 0.010	0.87(0.79~0.92)	< 0.010	0.76(0.69~0.83)	< 0.010
acNASH	0.75(0.69~0.80)	< 0.010	0.82(0.74~0.89)	< 0.010	0.69(0.61~0.76)	< 0.010
HSI	0.68(0.62~0.73)	< 0.010	0.74(0.65~0.82)	< 0.010	0.62(0.54~0.70)	0.010
NFS	0.57(0.51~0.63)	0.040	0.51(0.41~0.60)	0.920	0.61(0.53~0.69)	0.010

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