Nan Fang Yi Ke Da Xue Xue Bao.
2022 Nov 20; 42(11): 1720–1725.
Language:
Chinese
|
English
Wilson病脂代谢异常患者发生肝纤维化的列线图预测模型的建立与验证
Establishment and validation of a predictive nomogram for liver fibrosis in patients with Wilson disease and abnormal lipid metabolism
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1,
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,
2,
*
,
2
,
2
,
1
,
1
and
1
赵 晨玲
安徽中医药大学，安徽 合肥 230038,
Anhui University of Chinese Medicine, Hefei 230038, China
董 婷
安徽中医药大学第一附属医院，安徽 合肥 230031,
First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei
孙 伦燕
安徽中医药大学第一附属医院，安徽 合肥 230031,
First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei
胡 慧冰
安徽中医药大学第一附属医院，安徽 合肥 230031,
First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei
王 琼
安徽中医药大学，安徽 合肥 230038,
Anhui University of Chinese Medicine, Hefei 230038, China
田 丽伟
安徽中医药大学，安徽 合肥 230038,
Anhui University of Chinese Medicine, Hefei 230038, China
江 张胜
安徽中医药大学，安徽 合肥 230038,
Anhui University of Chinese Medicine, Hefei 230038, China
安徽中医药大学，安徽 合肥 230038,
Anhui University of Chinese Medicine, Hefei 230038, China
安徽中医药大学第一附属医院，安徽 合肥 230031,
First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei
TG: Triglycerides; TC: Total cholesterol; LDL-C: Low density lipoprotein cholesterol; HDL-C: High density lipoprotein cholesterol; Apo-A1: Apolipoprotein A1; Apo-B: Apolipoprotein B; Lpa: Lipoprotein a; Hcy: Homocysteine.Gender [
n
(%)]1.921
a
0.166 Male180 (51.43)67 (44.67) Female170 (48.57)83 (55.33)Age (year)28 (23, 36)27 (23, 31)1.9190.055TG (mmol/L)0.84 (0.67, 1.18)0.80 (0.62, 2.03)0.5850.559TC (mmol/L)4.92 (4.26, 5.37)4.84 (4.26, 5.39)0.0970.923LDL-C (mmol/L)2.90 (2.40, 3.65)2.83 (2.27, 4.22)0.0620.951HDL-C (mmol/L)1.18 (1.07, 1.31)1.24 (1.00, 1.38)0.9750.330Apo-A1 (g/L)1.37 (1.10, 1.77)1.38 (1.07, 1.70)1.2890.197Apo-B (g/L)0.99 (0.74, 1.30)1.07 (0.82, 1.28)1.2060.228Lpa (mg/L)140.8 (109.50, 238.03)159.55 (128.9, 236.2)1.6620.096Hcy (μmol/L)7.50 (6.10, 9.80)7.85 (5.60, 10.03)0.0750.940
2
肝纤维化组与非肝纤维化组的临床资料比较
Comparison of clinical data between the patients with and without hepatic fibrosis
|
Characteristics
|
HF group (
n
=62)
|
Non-HF group (
n
=288)
|
t
/
Z
/χ
2
|
|
|
HF: Hepatic fibrosis.
|
|
Gender [
n
(%)]
|
|
|
0.761a
|
0.383
|
|
Male
|
35 (56.45)
|
145 (50.35)
|
|
|
|
Female
|
27 (43.55)
|
143 (49.65)
|
|
|
|
Age (year)
|
31 (27, 36)
|
29 (23, 36)
|
1.852
|
0.064
|
|
TG (mmol/L)
|
1.71 (0.77, 3.27)
|
0.79 (0.66, 1.02)
|
5.267
|
＜0.001
|
|
TC (mmol/L)
|
6.24 (4.66, 6.68)
|
4.86 (4.15, 5.24)
|
4.708
|
＜0.001
|
|
LDL-C (mmol/L)
|
3.68 (2.42, 4.48)
|
2.83 (2.40, 3.55)
|
3.926
|
＜0.001
|
|
HDL-C (mmol/L)
|
1.04 (0.98, 1.23)
|
1.21 (1.09, 1.32)
|
4.361
|
＜0.001
|
|
Apo-A1 (g/L)
|
1.14 (0.76, 1.39)
|
1.42 (1.14, 1.79)
|
5.618
|
＜0.001
|
|
Apo-B (g/L)
|
1.40 (0.95, 2.12)
|
0.96 (0.72, 1.21)
|
5.829
|
＜0.001
|
|
Lpa (mg/L)
|
307.60 (136.98, 432.95)
|
135.4 (108, 219.5)
|
5.603
|
＜0.001
|
|
Hcy (μmol/L)
|
9.70 (6.60, 27.6)
|
7.35 (5.83, 8.98)
|
4.003
|
＜0.001
|
2.2. LASSO回归和多因素Logistic回归分析
在建模人群中，通过LASSO回归分析，共获得6个危险因素（非零系数），分别为TG、TC、LDL-C、HDL-C、Apo-A1和Apo-B（
）。应用多因素Logistic回归方法分析上述危险因素，结果显示TG、TC、LDL-C和Apo-B为WD脂代谢异常患者发生肝纤维化的独立危险因素（
）。
3
建模人群的多因素的Logistic回归分析
Result of multivariate logistic regression analysis in the modeling group
|
Intercept and variable
|
Prediction model
|
|
β
|
Odds ratio (95%
CI
)
|
|
|
β: Regression coefficient; CI: Confidence interval.
|
|
Intercept
|
-3.9212
|
0.0198 (0.0085-0.0404)
|
＜0.001
|
|
TG
|
1.7736
|
5.8918 (2.4113-14.7350)
|
＜0.001
|
|
TC
|
1.3468
|
3.8453 (1.5906-9.3648)
|
＜0.01
|
|
LDL-C
|
2.3217
|
10.1928 (4.6600-23.2254)
|
＜0.001
|
|
HDL-C
|
0.8843
|
2.4214 (0.9656-6.0081)
|
0.0564
|
|
Apo-A1
|
0.2794
|
1.3224 (0.4194-4.0726)
|
0.6282
|
|
Apo-B
|
1.7466
|
5.7352 (2.1642-15.6354)
|
＜0.001
|
2.3. 列线图预测模型的建立
将上述4个独立危险因素纳入，并成功建立WD脂代谢异常患者发生肝纤维化风险的个体化列线图预测模型（
）。通过模型上方的标尺，可以获得4个独立危险因素所对应的单项得分，将各单项得分相加即为总得分，与总得分相对应的预测概率就是WD脂代谢异常患者发生肝纤维化的风险。
2.4. 列线图预测模型的内外部验证
2.4.1. 区分度
通过绘制两组人群的ROC曲线，得到AUC分别为0.9055（95%
CI
：0.8614~0.9506）和0.9149（95%
CI
：0.8621~0.9679），AUC值均大于0.9，表明预测模型具有良好的区分度（
）。
2.4.2. 校准度
校准曲线结果显示，两组人群的平均绝对误差（MAE）分别为0.0220和0.0250，均方误差（MMSE）分别为0.0012和0.0011（MAE和MSE值越小，说明校准度越高），表明预测模型具有较高的校准度（
）。
2.4.3. 临床实用性
当两组人群决策曲线中的阈概率值分别在3%~90%和1%~99%范围内时，使用该列线图预测肝纤维化发生风险的净获益较高，表明预测模型具有良好的临床实用性（
）。
3. 讨论
流行病学研究结果显示，我国WD的发病率（1/30000）远高于世界平均水平（5.87/100 000）
[
13
-
15
]
。肝纤维化作为WD最重要的病理改变之一，若延误治疗，将导致约34%的患者进展为肝硬化，甚至出现严重并发症
[
16
,
17
]
，而对其进行早期识别及风险预测则有助于指导临床医生制订更为积极的防范措施和治疗策略。进一步研究发现，脂代谢异常是肝纤维化的关键驱动因素
[
18
]
，其相关指标的变化，甚至可以对WD患者的临床结局产生重要影响。
对于脂代谢异常患者发生肝纤维化的相关危险因素研究中，报道较多的是TG、TC、LDL-C、HDL-C、Apo-A1、Apo-B、Lpa和Hcy等
[
19
-
23
]
，但尚未达成统一共识，且目前尚未见关于WD脂代谢异常患者发生肝纤维化相关危险因素的报道。基于此，本研究通过LASSO回归、多因素Logistic回归分析筛选出WD脂代谢异常患者发生肝纤维化的独立危险因素包括TG、TC、LDL-C和Apo-B。研究表明，脂代谢异常是导致肝内脂质过度沉积的重要原因，其过程可能与TG、LDL-C合成分泌增高致胆固醇转运受限有关
[
24
]
。相较于传统的脂代谢指标，Apo-B因其独特的分子结构和理化特性不仅能够更加真实地反映出机体脂代谢的内在变化情况，还可以通过增加低密度脂蛋白的颗粒数量诱发氧化应激和炎症反应
[
25
]
。由此可见，TG、TC、LDL-C和Apo-B的高低，与机体脂代谢水平密切相关。
文献表明，WD脂代谢异常所造成的肝细胞脂滴沉积和肝脏脂肪变性不是一般性肝功能不全的结果，而是由肝细胞内铜过量蓄积所引起的
[
26
]
。在此基础上所诱发的炎症反应，可释放大量的炎症因子（如TNF-α、IL-6等）
[
27
]
，炎症因子不仅能够对存在于乳糜微粒和LDL中的TG水解过程产生影响，导致其清除受损，还能够抑制肝细胞内Apo-A1的合成和分泌，降低肝脏胆固醇的分解代谢，增加肝脏CH的含量。进一步研究发现，持续的炎症刺激还可活化肝星状细胞，进而促使细胞外基质分泌过多并沉积于肝脏
[
28
,
29
]
。HSC是肝纤维化发生的核心环节
[
30
]
，能够协同肝细胞代谢和转运脂质，当其发生异常时，便会导致TG、TC、LDL-C和Apo-B在肝内过度蓄积，诱发肝细胞线粒体损伤，进而导致肝纤维化乃至肝硬化。由此可见，脂代谢相关指标的异常变化在WD肝纤维化的形成和演变过程中发挥了极其重要的作用。
本研究所建立的列线图预测模型具有较高的准确性，可在WD脂代谢异常患者发生肝纤维化的早期识别及风险预测中发挥重要价值，并为临床医生和WD脂代谢异常患者在医疗措施干预和生活方式监测方面提供更加有利的临床指导。该预测模型虽具有较大的创新意义，但亦存在研究对象和观察变量纳入数量不足等局限，可对研究结果产生一定影响，因此有待于后续临床的进一步验证。
Funding Statement
安徽高校自然科学研究重点项目（KJ2021A0547）；国家中医药考试基金（TC2021023）；安徽省自然科学基金（2208085MH270）；安徽省西学中高级人才研修项目（2019qgxxzggrcpxxm20220104)；安徽中医药大学第一附属医院临床科学研究项目（2020yfyzc01）；新安医学教育部重点实验室；国家中医药管理局中医药循证能力建设项目：中医药脑病循证能力提升及平台建设（2019XZZX-NB001）
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