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ISSN 1001-5256 (Print)
ISSN 2097-3497 (Online)
CN 22-1108/R

Abnormal liver biochemical parameters in pregnancy during the epidemic of coronavirus disease 2019

DOI: 10.12449/JCH240118
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  • Corresponding author: LIU Jianxiang, jxiangliu@sohu.com (ORCID: 0000-0003-0196-6459)
  • Received Date: 2023-03-28
  • Accepted Date: 2023-05-05
  • Published Date: 2024-01-23
  •   Objective  To investigate the differences in abnormal liver biochemical parameters in pregnant patients during the epidemic or non-epidemic period of coronavirus disease 2019 (COVID-19).  Methods  A retrospective analysis was performed for 539 pregnant women who were discharged from Department of Obstetrics, Peking University First Hospital, from October 2017 to March 2022 and had at least one abnormal liver biochemical parameter among alanine aminotransferase (ALT), aspartate aminotransferase (AST), gamma-glutamyl transpeptidase (GGT), total bilirubin (TBil), and total bile acid. The patients in the epidemic period of COVID-19 and those in the non-epidemic period of COVID-19 were compared in terms of etiology, coagulation parameters, aminotransferases, bile acid, and renal function. The independent samples t-test was used for comparison of continuous data between two groups, and the chi-square test was used for comparison of categorical data between two groups.  Results  Among the patients discharged from Department of Obstetrics during the non-epidemic period of COVID-19, 262 had abnormal liver biochemical parameters, accounting for 1.46%, while 277 patients had abnormal liver biochemical parameters during the epidemic period of COVID-19, accounting for 1.73% among the patients discharged from Department of Obstetrics during the same period of time, and there was a significant difference between these two groups (χ2=3.947, P=0.047). The etiological analysis of the patients with abnormal liver biochemical parameters during the two periods showed that there was no difference in the proportion of patients with four pregnancy-specific liver diseases (hyperemesis gravidarum, preeclampsia and eclampsia, intrahepatic cholestasis of pregnancy, and acute fatty liver of pregnancy). As for the patients with abnormal liver biochemical parameters in pregnancy, there was no significant difference in the proportion of patients with normal creatinine and stimated glomerular filtration rate (eGFR) between the epidemic period and the non-epidemic period (86.78% vs 87.90%, χ2=0.141, P=0.708). The patients with ALT≥5×upper limit of normal accounted for 7.94% in the epidemic period of COVID-19 and 9.54% in the non-epidemic period (χ2=0.433, P=0.511), and the patients with severe cholestasis accounted for 7.75% in the epidemic period of COVID-19 and 9.27% in the non-epidemic period (χ2=0.392, P=0.531). The proportion of patients with obstetric bleeding during the epidemic period of COVID-19 was significantly lower than that during the non-epidemic period (14.61% vs 24.19%, χ2=489.334, P<0.001).  Conclusion  There is no difference in the proportion of patients with pregnancy-specific liver diseases among the patients with abnormal liver biochemical parameters in pregnancy between the epidemic period and the non-epidemic period of COVID-19, and there is no change in the proportion of patients with normal creatinine and eGFR among these patients in the epidemic period of COVID-19.

     

  • 目前全球非酒精性脂肪性肝病(non-alcoholic fatty liver disease,NAFLD)患病率已达32.4%,是最为流行的慢性肝病之一1。最新数据2表明,近30年(1990—2019年)NAFLD患病率增长10.5%~16%,NAFLD相关死亡率由1.75%增长到2.18%。NAFLD的发生发展过程主要分为4个阶段:单纯性脂肪肝(simple fatty liver,SFL)、脂肪性肝炎(non-alcoholic steatohepatitis,NASH)、肝纤维化和肝硬化。其中,不健康的饮食和久坐的行为方式3、2型糖尿病4、腹型肥胖5是影响其发生发展的重要危险因素。然而,在行为方式中,除了饮食和运动外,睡眠作为重要环节亦不容忽视。近年来对于睡眠在各种慢性疾病中的作用研究逐渐增多,尤其是发现睡眠和昼夜节律对参与食欲调节及能量代谢的激素有较大影响,睡眠障碍可增加肥胖和2型糖尿病的患病风险6。NAFLD作为代谢紊乱相关疾病,睡眠是否影响以及如何影响NAFLD的发生发展,目前说法不一。本研究基于团队已建立的队列资源,拟分析不同睡眠情况与NAFLD发生发展的关系,探讨NAFLD的睡眠相关危险因素,旨在为NAFLD的预防和治疗提供思路。

    选取2022年6月—2023年6月首都医科大学附属北京友谊医院通州门诊部体检且问卷资料完整的人群(健康体检队列)1 893例,2022年6月—2023年6月于首都医科大学附属北京友谊医院就诊的脂肪肝患者(脂肪肝门诊队列)203例。根据《非酒精性脂肪性肝病防治指南(2018年更新版)》(以下简称《指南》)7,排除以下人群:(1)未行腹部B超检查者;(2)HBsAg阳性或在病史中填写既往有HBV感染史者;(3)酗酒者,折合乙醇量男性40 g/d,女性20 g/d。最终共纳入1 868例研究对象。

    将B超无脂肪肝表现的研究对象纳入非脂肪肝组(non-NAFLD组)(n=1 122),B超显示有脂肪肝且ALT<1.5倍正常值上限(ULN)的研究对象纳入SFL组(n=624),B超显示有脂肪肝且ALT1.5×ULN的研究对象纳入NASH组(n=122)。本队列NASH组的纳入标准主要参考《指南》认为血清ALT水平持续升高,提示NAFLD患者可能存在NASH及目前概念验证期至Ⅱa期临床试验新药研究采取的临床判断策略8-9

    收集纳入研究对象的年龄、性别、身高、体质量、既往病史等一般情况,以及于本院统一进行的实验检查指标。所有研究对象均需填写包含匹兹堡睡眠质量指数(PSQI)量表的调查问卷。

    PSQI是经过验证和使用最为广泛的睡眠障碍评估量表之一10,主要用于评定受试者最近1个月的睡眠质量,分为7项,分别是睡眠质量、入睡时间、睡眠时间、睡眠效率、睡眠紊乱、催眠药物、日间功能障碍,每项按0~3等级计分,等级越高,状况越差。累计各成分得分为PSQI总分,总分范围为0~21分,得分越高,表示睡眠质量越差,其中0~5分为睡眠情况很好(0级),6~10分为睡眠情况尚可(1级),11~15分为睡眠情况一般(2级),16~21分为睡眠情况很差(3级)。

    采用SAS 9.2软件进行统计学分析。计量资料符合正态分布的以x¯±s表示,3组间比较采用单因素方差分析;不符合正态分布的以MP25P75)表示,3组间比较采用Kruskal-Wallis H检验。计数资料3组间比较采用χ2检验。采用二元Logistic回归分析睡眠因素与NAFLD的关联性,采用多分类Logistic回归分析睡眠因素与NAFLD不同阶段的关联性,并构建两个多因素模型校正可能的混杂因素,包括年龄性别校正模型和多因素校正模型,多因素校正模型校正了年龄、性别、文化程度、吸烟、糖尿病、高血压、BMI、TG、HDL-C因素。P<0.05为差异有统计学意义。针对PSQI量表因子分的多重比较时,采用Bonferroni校正,P<0.007为差异有统计学意义11

    non-NAFLD、SFL和NASH组在年龄、性别、文化程度、吸烟、糖尿病、高血压等一般资料方面差异均有统计学意义(P值均<0.001)。3组在BMI、ALT、TG、HDL-C水平上均具有统计学差异(P值均<0.05)(表1)。

    表  1  3组间一般资料比较
    Table  1.  The comparison of general information in three groups
    指标 non-NAFLD组(n=1 122) SFL组(n=624) NASH组(n=122) 统计值 P
    年龄(岁) 39.58±7.71 42.78±7.74 38.94±10.04 F=35.88 <0.001
    男[例(%)] 550(49.02) 506(81.09) 82(67.21) χ2=175.40 <0.001
    本科以上学历[例(%)] 721(64.26) 335(53.69) 35(28.69) χ2=65.90 <0.001
    吸烟[例(%)] 87(7.75) 131(20.99) 25(20.49) χ2=68.57 <0.001
    糖尿病[例(%)] 12(1.07) 58(9.29) 22(18.03) χ2=105.83 <0.001
    高血压[例(%)] 72(6.42) 147(23.56) 36(29.51) χ2=127.79 <0.001
    BMI(kg/m2 22.88±2.74 27.11±3.19 31.94±10.04 F=639.40 <0.001
    ALT(U/L) 16.00(12.00~22.00) 27.00(19.00~39.00) 94.00(83.00~115.00) χ2=649.38 <0.001
    TG(mmol/L) 0.89(0.69~1.22) 1.65(1.18~2.36) 1.84(1.34~2.30) χ2=552.89 <0.001
    HDL-C(mmol/L) 1.41(1.21~1.63) 1.13(1.01~1.29) 1.15(0.97~1.29) χ2=383.33 0.002
    下载: 导出CSV 
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    non-NAFLD、SFL和NASH组在PSQI量表总评分分级以及其包含的7个睡眠因素的0~3分分级中的人数比例比较,差异均有统计学意义(P值均<0.05)(图1)。

    图  1  3组PSQI量表总分及因子分比较
    Figure  1.  The comparison of the PSQI total score and each factor scores in three groups

    在单因素分析中显示,与non-NAFLD组比较,除睡眠时间短可增加NAFLD的患病风险(P<0.007),其余各因子分比较,差异均无统计学意义(P值均>0.007)。通过年龄、性别校正后,睡眠总体情况差(P<0.05)、入睡时间长、睡眠时间短、睡眠紊乱严重(P值均<0.007)是NAFLD患病的危险因素。随后通过年龄、性别、文化程度、吸烟、糖尿病、高血压、BMI、TG、HDL-C多因素校正后,PSQI总分和各个因子分比较,差异均无统计学意义(P值均>0.007)(表2)。

    表  2  睡眠情况与NAFLD的关联分析
    Table  2.  The association between sleep condition and NAFLD
    变量 患病率 单因素模型 年龄性别校正模型 多因素校正模型
    OR(95%CI P OR(95%CI P OR(95%CI P
    PSQI总分
    11分 697/1 763 Ref Ref Ref
    > 11分 49/105 1.34(0.90~1.99) 0.148 1.67(1.09~2.56) 0.019 0.75(0.41~1.38) 0.360
    睡眠质量
    < 2分 590/1 435 Ref Ref Ref
    2分 156/433 0.81(0.65~1.01) 0.058 0.90(0.71~1.13) 0.357 0.67(0.48~0.93) 0.017
    入睡时间
    < 2分 580/1 497 Ref Ref Ref
    2分 166/371 1.28(1.02~1.61) 0.035 1.61(1.26~2.07) <0.001 1.02(1.00~1.04) 0.414
    睡眠时间
    < 2分 648/1 695 Ref Ref Ref
    2分 98/173 2.11(1.54~2.90) <0.001 2.19(1.56~3.08) <0.001 1.05(0.65~1.70) 0.834
    睡眠效率
    < 2分 670/1 671 Ref Ref Ref
    2分 76/197 0.94(0.69~1.27) 0.683 0.93(0.67~1.29) 0.655 0.71(0.46~1.11) 0.133
    睡眠紊乱
    < 2分 665/1 701 Ref Ref Ref
    2分 81/167 1.47(1.07~2.02) 0.018 1.69(1.20~2.39) 0.003 1.03(0.64~1.67) 0.903
    催眠药物使用
    < 2分 729/1 818 Ref Ref Ref
    2分 17/50 0.77(0.43~1.39) 0.386 0.92(0.49~1.74) 0.806 0.65(0.28~1.51) 0.317
    日间功能障碍
    < 2分 463/1 117 Ref Ref Ref
    2分 283/751 0.85(0.71~1.03) 0.103 1.02(0.83~1.25) 0.862 0.85(0.65~1.12) 0.241
    下载: 导出CSV 
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    在单因素分析中,SFL的睡眠质量与non-NAFLD组比较,差异有统计学意义(P<0.007),而在多因素校正模型中,与non-NAFLD组比较,SFL的PSQI总分和各因子分数均无统计学差异(P值均>0.007)。在单因素分析中,与non-NAFLD组比较,睡眠总体情况差(P<0.05)、入睡时间长、睡眠时间短、睡眠紊乱严重(P值均<0.007)与NASH患病密切相关。在年龄性别校正模型中显示,与non-NAFLD相比,睡眠总体情况差(P<0.05)、入睡时间长、睡眠时间短、睡眠紊乱严重(P值均<0.007)与NASH患病密切相关。在多因素校正模型中,与non-NAFLD组比较,入睡时间长、睡眠时间短和睡眠紊乱严重(P值均<0.007)是NASH患病的危险因素(表3)。

    表  3  睡眠情况与NAFLD不同程度的关联分析
    Table  3.  The association between sleep condition and different severity in NAFLD
    变量 患病率 单因素模型 年龄性别校正模型 多因素校正模型
    OR(95%CI P OR(95%CI P OR(95%CI P
    SFL vs non-NAFLD
    PSQI总分
    11分 594/1 763 Ref Ref Ref
    > 11分 30/105 0.96(0.61~1.52) 0.865 1.14(0.70~1.86) 0.600 0.67(0.36~1.25) 0.208
    睡眠质量
    < 2分 507/1 435 Ref Ref Ref
    2分 117/433 0.70(0.55~0.90) 0.005 0.78(0.60~1.00) 0.054 0.63(0.45~0.89) 0.008
    入睡时间
    < 2分 507/1 497 Ref Ref Ref
    2分 94/371 0.79(0.61~1.04) 0.089 0.96(0.72~1.28) 0.769 0.84(0.74~1.43) 0.059
    睡眠时间
    < 2分 568/1 695 Ref Ref Ref
    2分 56/173 1.38(0.96~1.98) 0.083 1.29(0.88~1.90) 0.199 0.83(0.50~1.37) 0.470
    睡眠效率
    < 2分 560/1 671 Ref Ref Ref
    2分 64/197 0.95(0.69~1.30) 0.731 0.91(0.64~1.29) 0.601 0.74(0.47~1.15) 0.181
    睡眠紊乱
    < 2分 577/1 701 Ref Ref Ref
    2分 47/167 0.98(0.68~1.42) 0.920 1.06(0.72~1.58) 0.760 0.83(0.50~1.38) 0.466
    催眠药物使用
    < 2分 610/1 818 Ref Ref Ref
    2分 14/50 0.76(0.40~1.43) 0.390 0.90(0.46~1.78) 0.769 0.68(0.29~1.59) 0.378
    日间功能障碍
    < 2分 401/1 117 Ref Ref Ref
    2分 223/751 0.78(0.64~0.95) 0.055 0.94(0.76~1.17) 0.562 0.81(0.61~1.07) 0.140
    NASH vs non-NAFLD
    PSQI总分
    11分 103/1 763 Ref Ref Ref
    > 11分 19/105 3.51(2.01~6.14) <0.001 4.16(2.35~7.37) <0.001 1.31(0.54~3.15) 0.547
    睡眠质量
    < 2分 83/1 435 Ref Ref Ref
    2分 39/433 1.43(0.96~2.15) 0.081 1.53(1.02~2.30) 0.042 1.02(0.59~1.78) 0.938
    入睡时间
    < 2分 50/1 497 Ref Ref Ref
    2分 72/371 6.44(4.36~9.53) <0.001 7.35(4.92~10.99) <0.001 4.04(2.33~7.03) <0.001
    睡眠时间
    < 2分 80/1 695 Ref Ref Ref
    2分 42/173 7.33(4.72~11.39) <0.001 8.54(5.40~13.52) <0.001 3.53(1.83~6.82) <0.001
    睡眠效率
    < 2分 110/1 671 Ref Ref Ref
    2分 12/197 0.90(0.48~1.69) 0.748 0.98(0.52~1.84) 0.953 0.42(0.17~1.03) 0.058
    睡眠紊乱
    < 2分 88/1 701 Ref Ref Ref
    2分 34/167 4.65(2.96~7.32) <0.001 5.06(3.19~8.03) <0.001 2.96(1.48~5.93) 0.002
    催眠药物使用
    < 2分 119/1 818 Ref Ref Ref
    2分 3/50 0.83(0.25~2.75) 0.763 1.05(0.31~3.52) 0.939 0.42(0.07~2.45) 0.335
    日间功能障碍
    < 2分 62/1 117 Ref Ref Ref
    2分 60/751 1.35(0.93~1.97) 0.114 1.45(0.99~2.11) 0.056 1.23(0.75~2.01) 0.412
    下载: 导出CSV 
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    我国近20年(1999—2018年)NAFLD患者数量快速增长,特别是在年轻人群中12,且NAFLD与肝外肿瘤、糖尿病、心血管疾病和代谢综合征的发病率呈正相关,提示需要提高认识并采取积极行动,以控制NAFLD在中国的大流行13。行为方式的改变是NAFLD目前最好的干预方法14,但更多关注集中于行为方式中的饮食和运动15

    近年来,随着对睡眠的广泛关注,很多学者发现睡眠障碍相关因素是NAFLD的危险因素。一项包含了18篇文献的Meta分析16显示,NAFLD患者可能有更严重的白天嗜睡和更短的睡眠时间。尤其是阻塞型呼吸睡眠暂停与NAFLD的发生和发展密切相关,且独立于肥胖等其他共同危险因素17。Wang等18调查了中国22 807例参与者,发现晚睡与NAFLD患病率增加有显著相关性。Zarean等19在伊朗包括9 151名参与者的队列研究中发现,在控制了社会人口学、生活方式、临床和生物学变量后,NAFLD患者与睡眠效率(RR=0.92,95%CI:0.88~0.96)和安眠药使用(RR=1.48,95%CI:1.17~1.88)存在较强相关性。而本研究与以上结论尚有不同,通过本团队建立的健康体检队列和脂肪肝门诊队列进行单因素分析发现,睡眠时间短增加了NAFLD的患病风险,而在进行一般情况、高血压、糖尿病、体质量、血脂等多因素校正后,睡眠障碍相关因素与NAFLD患病无密切关联,这与Liu等20近期发表的文献结论一致。

    现有针对NASH与睡眠关系的研究较少,本研究发现无论单因素、年龄性别校正模型还是多因素校正模型均显示,入睡时间长、睡眠时间短和严重的睡眠紊乱是NASH患病的危险因素,而与SFL无密切关联,说明睡眠障碍因素在SFL发展为NASH的过程中起到了较为重要的作用。

    然而,本研究仅为横断面研究,所得结论只能说明相关性而无明确因果关系,后续队列持续收集资料后将对纵向数据进行归纳整理,讨论睡眠与SFL和NASH的因果关系。其次,本研究作为观察性研究,基线中各因素的差异提示可能存在潜在混杂因素,笔者团队采用了多因素校正模型的结果作为主要评价模型,以避免混杂因素导致的虚假关联。再次,本研究根据《指南》提出当ALT水平升高时更倾向于出现了NASH的描述,将本研究中ALT1.5×ULN的患者纳入NASH组进行分析,然而NASH的诊断还应依据病理,这也是本文的局限性之一。

    本研究根据NAFLD不同严重程度分为两个阶段——SFL和NASH,分别讨论其睡眠相关的危险因素,发现睡眠问题对于SFL的发生可能不存在显著影响,而对于SFL发展为NASH发挥了重要作用,提示在未来“既病防变”中需要更加关注睡眠问题带来的危害,积极宣教,降低NAFLD进展的疾病负担。

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