BCL11A rs243021多态性与代谢相关脂肪性肝病易感性的关系
Association of the BCL11A rs243021 Polymorphism with Susceptibility to Metabolic Dysfunction-Associated Fatty Liver Disease
DOI: 10.12677/acm.2025.15123624, PDF, HTML, XML,   
作者: 孟 馨:青岛大学青岛市市立医院感染性疾病科,山东 青岛;青岛大学青岛医学院,山东 青岛;赵真真*:青岛大学青岛市市立医院临床研究中心,山东 青岛
关键词: 代谢相关脂肪性肝病B细胞淋巴瘤/白血病11A基因多态性Metabolic Dysfunction-Associated Fatty Liver Disease B-Cell Lymphoma/Leukemia 11A Gene Polymorphism
摘要: 目的:探讨中国青岛地区B细胞淋巴瘤/白血病11A (BCL11A)基因rs243021位点多态性与代谢相关脂肪性肝病(MAFLD)疾病易感性的关系。方法:纳入于青岛市市立医院就诊的MAFLD患者215例、健康对照159例,收集所有受试者的临床信息和生化指标,进行生化指标的临床检测及基因组DNA的提取和BCL11A rs243021位点基因型测定。计数资料比较采用Pearson χ2检验,计量资料根据其符合正态分布与否分别采用独立样本t检验或Wilcoxon秩和检验。应用非条件logistic回归模型进行位点多态性与疾病风险分析。结果:MAFLD组和健康对照组之间比较BCL11A rs243021位点的基因型分布和等位基因分布差异均无统计学意义(P > 0.05)。在A等位基因携带者与非携带者之间进行比较,各临床资料和实验室指标差异均无统计学意义(P > 0.05)。结论:青岛地区汉族人群中BCL11A rs243021多态性与MAFLD易感性无显著相关。
Abstract: Objective: To investigate the association between the B-cell lymphoma/leukemia 11A(BCL11A) rs243021 polymorphism and susceptibility to metabolic dysfunction-associated fatty liver disease (MAFLD) in the Qingdao region of China. Methods: A total of 215 MAFLD patients and 159 healthy controls were recruited from Qingdao Municipal Hospital. Demographic and laboratory data were recorded for all subjects. Clinical biochemical testing, genomic DNA extraction, and genotyping of the BCL11A rs243021 locus were conducted. Categorical variables were compared using Pearson’s chi-square test, while continuous variables were tested for normality and then compared by either an independent samples t-test or a Wilcoxon rank-sum test, as appropriate. Unconditional logistic regression models were used to analyze the association between genetic polymorphisms and disease risk. Results: There were no significant differences in genotype or allele frequencies between the MAFLD and control groups (both P > 0.05). When comparing carriers and non-carriers of the A allele, no statistically significant differences were observed in clinical and laboratory parameters (both P > 0.05). Conclusion: In the Han Chinese population of Qingdao, the BCL11A rs243021 polymorphism was not significantly associated with MAFLD susceptibility.
文章引用:孟馨, 赵真真. BCL11A rs243021多态性与代谢相关脂肪性肝病易感性的关系[J]. 临床医学进展, 2025, 15(12): 2038-2046. https://doi.org/10.12677/acm.2025.15123624

1. 引言

代谢相关脂肪性肝病(metabolic dysfunction-associated fatty liver disease, MAFLD),曾称非酒精性脂肪性肝病(non-alcoholic fatty liver disease, NAFLD),是一种以肝脏脂肪积聚为特征的代谢性疾病,与肥胖、2型糖尿病、代谢综合征等多种代谢紊乱密切相关[1]。临床上,MAFLD可分为单纯性脂肪肝(non-alcoholic fatty liver, NAFL)和非酒精性脂肪性肝炎(non-alcoholic steatohepatitis, NASH),后者伴有肝细胞炎症和不同程度的纤维化,可进展为肝硬化及肝细胞癌[2] [3]。根据2024年全球系统评价和荟萃分析,MAFLD全球患病率为30.2%,其中亚洲人群为30.9% [4],中国成人MAFLD患病率为29.88%,且近年来呈持续上升趋势[5]。MAFLD的发生是多种因素共同作用的结果,其中代谢异常是核心驱动力。肥胖和胰岛素抵抗会促进肝细胞对脂肪酸的摄取与合成,从而导致肝内脂质过度积聚;而慢性低度炎症及脂质毒性则会引起肝细胞损伤、激活Kupffer细胞,并触发促纤维化反应[6] [7]。遗传因素在MAFLD易感性和进展中同样发挥关键作用。大规模全基因组关联研究(GWAS)和横断面队列分析确认PNPLA3、TM6SF2、MBOAT7、GCKR等位点显著与MAFLD风险和纤维化程度相关[8]-[11]

B细胞淋巴瘤/白血病11A (B-cell lymphoma/leukemia 11A, BCL11A)是位于人类2P 16.1染色体的一个编码C2H2型锌指转录因子的基因,参与淋巴细胞生成、神经发育等多个生物学过程,也在胚胎期血红蛋白转化和血细胞分化方面发挥重要作用[12] [13]。在胰岛β细胞中,BCL11A不仅参与胰岛素分泌的调节,还在胰腺发育过程中起着重要作用。近年来的研究表明,BCL11A基因与代谢相关疾病,如2型糖尿病(type 2 diabetes mellitus, T2DM)密切相关。BCL11A在胰岛β细胞的分泌功能中具有调节作用:T2DM患者胰岛中BCL11A表达显著升高,高糖环境可诱导其上调,而BCL11A的过表达与胰岛素分泌下降密切相关[14]。相反,敲低BCL11A则可显著改善胰岛素分泌和葡萄糖耐量,增强胰岛素释放,提示其可能通过负调控β细胞功能参与T2DM的病理进程[15]。目前,国内外没有关于BCL11A rs243021与MAFLD的研究。考虑MAFLD与T2DM同为代谢性疾病,互为因果,亦可能具有共同的易感性多态性位点。本研究旨在探讨BCL11A rs243021位点多态性与MAFLD的相关性,并为进一步了解MAFLD的发病机制和新型诊疗方法提供理论依据。

2. 资料与方法

2.1. 研究对象

本研究选取于2023年10月至2025年5月于青岛市市立医院就诊的MAFLD患者及健康体检者。参考《代谢相关(非酒精性)脂肪性肝病防治指南(2024年版)》[16],所有MAFLD患者均通过B型超声及肝脏瞬时弹性超声(Fibroscan)诊断。除外每周饮酒量 > 140 g者,同时排除乙型、丙型等病毒性肝炎、自身免疫性肝病、药物性肝炎等可导致脂肪肝的特定肝病,并除外药物如他莫昔芬、丙戊酸钠、甲氨蝶呤、糖皮质激素等、全胃肠外营养、炎症性肠病、库欣综合征等导致脂肪肝的特殊情况。本研究纳入的MAFLD患者均未被诊断为糖尿病。对照组为同期体检中心健康体检者。两组均为青岛地区长期居住汉族人群,无血缘关系。

2.2. 资料收集及标本采集

通过问卷调查的方式收集了所有受试者的性别、年龄、饮酒史、既往慢性病史等。测量身高、体质量,并计算体质量指数(BMI)。所有受试者在禁食12小时后于次日早晨抽取空腹静脉血4 ml,分别置于2个EDTA抗凝管,其中一管进行生化学指标检验,包括以下指标:空腹血糖(fasting plasma glucose, FPG)、丙氨酸氨基转移酶(alanine aminotransferase, ALT)、天门冬氨酸氨基转移酶(aspartate aminotransferase, AST)、γ-谷氨酰基转移酶(Gama-glutamyltransferase, GGT)、碱性磷酸酶(alkaline phosphatase, ALP)、甘油三酯(triacylglycerol, TG)、总胆固醇(total cholesterol, TC)、高密度脂蛋白(high-density lipoprotein, HDL)、低密度脂蛋白(low-density lipoprotein, LDL)、总胆红素(Total Bilirubin, TBiL)等。另一管置于−80℃冰箱保存,用于提取DNA及基因型鉴定。

2.3. 基因组DNA提取及检测

全血基因组DNA提取使用全血DNA提取试剂盒(博淼生物科技有限公司,北京)。采用聚合酶链式反应(polymerase chain-reaction, PCR)方法对BCL11A基因rs243021位点多态性进行检测。PCR引物由同一公司设计合成,序列为:上游引物:ACGTTGGATGGCTCCAATTCCCTAAGAAAG,下游引物:ACGTTGGATGATTAGAGAGAAGTGACCAGG。PCR扩增反应后,继续进行碱性磷酸酶(SAP)反应、单碱基延伸反应、树脂纯化及芯片点样,最后进行质谱检测分析基因型。

2.4. 统计学方法

采用SPSS 27软件进行统计学分析。符合正态分布的计量资料采用均数 ± 标准差( x ¯ ±s )表示,应用独立样本t检验分析各组间的差异,非正态分布的计量资料采用M(P25~P75)表示,应用秩和检验分析各组间的差异,计数资料采用例数表示,组间比较应用Pearson χ2检验。应用非条件logistic回归模型进行位点多态性与疾病风险分析,计算其比值比(OR)及95%可信区间(95%CI)。以P < 0.05认为差异具有统计学意义。

3. 结果

3.1. 临床资料及实验室指标

本次研究共纳入受试者374例,其中MAFLD组共215例,健康对照组共159例,对MAFLD组、健康对照组分别进行基线数据描述,并进行一般临床资料和相关实验室指标的比较,两组间年龄、BMI、SBP、DBP、ALT、AST、GGT、ALP、FPG、TG、HDL差异均具有统计学意义(P值均<0.05) (表1)。

Table 1. Comparison of general clinical date and related laboratory indicators between MAFLD and control group

1. MAFLD组与健康对照组一般临床资料及相关实验室指标比较

指标

MAFLD (n = 215)

健康对照(n = 159)

统计值

t/Ζ/χ2

P

性别(男/女)

107/108

94/65

3.216

0.073

年龄(岁)

53.00 (42.00, 63.00)

37.00 (30.00, 51.00)

−8.224

<0.001

BMI, kg/m2

27.00 (24.80, 29.40)

25.30 (22.60, 28.20)

−3.706

<0.001

SBP, mmHg

132.00 (121.00, 150.00)

126.00 (117.00, 137.00)

−4.284

<0.001

DBP, mmHg

79.00 (73.00, 89.00)

76.00 (69.00, 87.00)

−2.817

0.005

ALT, U/L

28.56 (18.00, 42.88)

22.00 (13.85, 34.00)

−3.207

0.001

AST, U/L

24.66 (19.58, 33.10)

21.00 (17.00, 27.31)

−3.863

<0.001

GGT, U/L

31.22 (21.73, 57.96)

21.00 (14.00, 41.72)

−5.119

<0.001

ALP, U/L

85.93 (71.34, 103.41)

74.66 (61.74, 94.00)

−3.877

<0.001

FPG, mmol/L

5.12 (4.60, 5.89)

5.00 (4.57, 5.36)

−2.201

0.028

TG, mmol/L

1.72 (1.18, 2.42)

1.19 (0.87, 1.71)

−5.927

<0.001

TC, mmol/L

5.09 (4.45, 5.78)

4.89 (4.25, 5.55)

−1.720

0.085

HDL, mmol/L

1.14 (1.00, 1.31)

1.24 (1.05, 1.42)

−2.923

0.003

LDL, mmol/L

3.13 (2.64, 3.57)

2.99 (2.52, 3.51)

−1.234

0.217

TBiL, μmol/L

12.70 (10.10, 16.80)

12.60 (10.00, 15.73)

−0.724

0.469

注:缩写:体质量指数(BMI)、收缩压(SBP)、舒张压(DBP)、丙氨酸氨基转移酶(ALT)、天门冬氨酸氨基转移酶(AST)、γ-谷氨酰基转移酶(GGT)、碱性磷酸酶(ALP)、空腹血糖(FPG)、甘油三酯(TG)、总胆固醇(TC)、高密度脂蛋白(HDL)、低密度脂蛋白(LDL)、总胆红素(TBIL);符合正态分布的计量资料采用 x ¯ ±s 表示,非正态分布的计量资料采用M(P25~P75)表示;以P < 0.05为差异有统计学意义。

3.2. Hardy-Weinberg遗传平衡检验

基因测序发现BCL11A rs243021位点共有GG、GA、AA三种基因型,进行Hardy-Weinberg (H-W)遗传平衡检验,结果显示BCL11A rs243021的基因型在NMAFLD组和健康对照组中均符合H-W遗传平衡(MAFLD组:χ2 = 0.000,P = 1.000;健康对照组:χ2 = 0.004,P = 0.998),表明受试者来自同一孟德尔人群,具有群体代表性。

3.3. 等位基因及基因型的频率分布

经统计学分析结果显示,在MAFLD组和健康对照组之间,BCL11A rs243021位点的基因型、等位基因频率、隐性基因模型和显性基因模型分布差异无统计学意义(表2)。

Table 2. Allele and genotype frequency distribution of the BCL11A rs243021 polymorphism

2. BCL11A rs243021等位基因和基因型频率分布

MAFLD (n = 215)

健康对照(n = 159)

χ²

P

基因型

AA

99 (46.1)

80 (50.3)

0.715

0.699

GG

22 (10.2)

14 (8.8)

GA

94 (43.7)

65 (40.9)

等位基因

A

292 (67.9)

225 (70.8)

0.695

0.405

G

138 (32.1)

93 (29.2)

显性模型

GG

22 (10.2)

14 (8.8)

0.214

0.644

AA + GA

193 (89.8)

145 (91.2)

隐性模型

AA

99 (46.0)

80 (50.3)

0.667

0.414

GG + GA

116 (54.0)

79 (49.7)

注:以P < 0.05为差异有统计学意义。

3.4. BCL11A rs243021位点不同基因型发病风险比较

应用二元logistic回归模型分析BCL11A基因rs243021位点与MAFLD易感性的关系,经过校正年龄、性别及BMI后结果无统计学意义(表3)。

Table 3. Results of logistic regression analysis for risk factors of MAFLD

3. MAFLD危险因素的logistic回归分析结果

OR

95%CI

P

ORa

95%CIa

Pa

等位基因

A

0.875

0.638, 1.199

0.405

G

隐性模型

GG

0.847

0.419, 1.712

0.644

0.777

0.349, 1.730

0.536

AA + GA

显性模型

AA

0.923

0.573, 1.303

0.812

0.894

0.561, 1.426

0.638

GG + GA

注:ORa、95%CIaPa值为校正年龄、性别及BMI后的OR、95%CIP值;以P < 0.05为差异有统计学意义。

3.5. 携带A等位基因者和未携带者的各项数据比较

在所有受试者中,各项定量资料均不符合正态分布。对BCL11A rs243021位点不同基因型之间生物化学指标进行比较,结果显示,在所有受试者中,BCL11A rs243021位点A等位基因携带者与非携带者之间在年龄、BMI、SBP、DBP、ALT、AST、GGT、ALP、FPG、TG、TC、HDL、LDL、TBiL之间两者没有统计学差异(P > 0.05) (表4)。

Table 4. Comparison of clinical characteristics and laboratory parameters between carriers and non-carriers of the A allele

4. 携带A等位基因和未携带A等位基因组临床资料和相关实验室指标比较

指标

GG

AA + GA

统计值

t/Ζ/χ2

P

性别(男/女)

19/17

182/156

0.015

0.903

年龄(岁)

44.50 (31.75, 55.75)

48.00 (35.00, 60.00)

−1.377

0.168

BMI, kg/m2

27.76 (23.56, 31.67)

26.22 (23.86, 28.70)

−1.444

0.149

SBP, mmHg

131.50 (119.50, 147.25)

128.00 (119.00, 143.00)

−1.177

0.239

DBP, mmHg

77.00 (73.25, 88.25)

78.00 (71.00, 88.00)

−0.477

0.633

ALT, U/L

26.51 (15.13, 64.72)

25.00 (16.00, 39.43)

−0.180

0.857

AST, U/L

22.38 (18.37, 34.00)

22.80 (18.51, 30.28)

−0.059

0.953

GGT, U/L

29.14 (14.96, 49.93)

27.78 (17.95, 46.96)

−0.071

0.943

ALP, U/L

86.17 (70.81, 97.92)

82.11 (66.87, 98.43)

−0.805

0.421

FPG, mmol/L

4.91 (4.25, 5.84)

5.07 (4.62, 5.74)

−0.805

0.421

TG, mmol/L

1.35 (1.03, 2.22)

1.45 (1.00, 2.03)

−0.169

0.866

TC, mmol/L

5.06 (4.68, 5.51)

4.99 (4.31, 5.69)

−0.224

0.823

HDL, mmol/L

1.16 (1.05, 1.32)

1.17 (1.03, 1.37)

−0.182

0.856

LDL, mmol/L

3.14 (2.68, 3.62)

3.10 (2.57, 3.53)

−0.589

0.556

TBiL, μmol/L

14.10 (11.97, 16.82)

12.45 (9.99, 16.42)

−1.748

0.080

4. 讨论

MAFLD是一种与遗传、环境、应激、代谢等多种因素共同作用的复杂疾病。大量研究已表明,代谢综合征、肥胖与高血糖状态,尤其是胰岛素抵抗,是推动MAFLD发生的重要危险因素,而MAFLD也常常与2型糖尿病高度共病[17] [18]。遗传调控因素在这一病理网络中具有关键作用,比如调控脂肪代谢、胰岛素分泌及炎症反应的基因[19]

BCL11A是一种典型的C2H2型锌指转录因子,具有多种可变剪接异构体,广泛参与细胞分化和发育过程。首先,其最为人所知的功能是通过与NuRD (nucleosome remodeling and deacetylase)复合物协同作用,在人类成人红细胞中沉默γ-珠蛋白基因,从而实现胚胎期向成人期血红蛋白的转化[20]。BCL11A在淋巴细胞发生过程中也扮演关键角色:其锌指2~3区和4~6区通过识别特异DNA序列,调控B细胞及T细胞的基因表达程序,影响免疫细胞的发育和功能[21]。此外,BCL11A在多种恶性肿瘤的形成和维持、细胞增殖和抵抗凋亡、侵袭和转移、复发和耐药等多过程中均发挥重要的调控作用[22]

除上述功能外,多项研究聚焦BCL11A在糖代谢中所起作用。既往有研究显示,BCL11A基因rs10490072风险携带者的第一阶段葡萄糖刺激的胰岛素分泌减低,推测BCL11A作为DNA序列特异性转录抑制因子作用于BCL6、coup-TF、SIRT1,可能主要影响胰岛素从胰岛素颗粒中快速募集和释放的过程[23] [24]。Hebbar等人发现,BCL11A通过导致胰岛β细胞功能受损影响T2DM [25]。另有研究证实BCL11A的变异与胰岛素分泌减少及空腹胰高血糖素升高相关[26]

而目前直接研究BCL11A在脂肪代谢中可能作用机制的文献较少,目前尚无直接证据表明BCL11A在肝脂质代谢中发挥功能。Tang等人的研究指出BCL11A的DNA甲基化水平与甘油三酯水平呈性别差异相关,在男性中可能直接增加T2DM风险,而在女性则可能通过影响脂质代谢间接增加风险[27]。Benitez等人的研究表明,在胰腺发育期间,编码BCL11A的mRNA在Neurog3+内分泌细胞中比在Sox9+胰腺祖细胞或β细胞中更丰富,表明BCL11A在胰岛发育中可能起作用,是胰腺发育的重要转录因子[28],其影响胰岛素分泌和调控胰岛β细胞功能,间接提示了其可能通过调节全身能量储存与脂肪合成途径,间接推动肝内脂质积累和慢性炎症,从而促进MAFLD发展。

2010年BCL11A rs243021作为T2DM的新易感位点由Voight等人首次提出[29],A等位基因为该位点风险等位基因。MAFLD与T2DM之间存在复杂且双向的相互作用。MAFLD患者由于肝脏脂质沉积和慢性炎症,可诱发或加重全身胰岛素抵抗,使其发生 T2DM的风险显著增加;反之,T2DM患者因高胰岛素血症和血糖控制不良,可加速肝脏脂肪变性、炎症反应及纤维化进程,从而促进MAFLD的发生与进展[30]-[32]。MAFLD与T2DM的双向关联不仅在流行病学上表现为高共病率,更在病理生理层面相互促进[33] [34]。本研究首次探讨了BCL11A rs243021位点多态性与MAFLD易感性的相关性。

经检验,BCL11A基因rs243021位点的基因型、隐性基因模型、显性基因模型与等位基因频率的分布差异在MAFLD组和健康对照组之间无统计学意义。也未见A等位基因携带者与非携带者在临床实验室指标上有显著不同。这提示在青岛地区汉族人群中,BCL11A rs243021位点多态性与MAFLD易感性关联不显著。尽管如此,结合已有文献,BCL11A在胰岛素分泌与脂质代谢中的潜在作用使其成为多种代谢性疾病(包括肥胖、代谢综合征和T2DM)中的关键调控基因。

首先,考虑BCL11A可能通过“胰腺–肝脏轴”发挥作用:即通过影响胰岛素的分泌水平或敏感性,间接导致肝脏脂质从头合成增加及脂肪酸氧化受损。此外,虽然目前关于BCL11A在肝细胞内的直接靶点研究较少,但不能排除其直接结合并调控肝脏脂质代谢关键酶(如FASN、SREBP-1c)启动子的可能性。若rs243021变异导致BCL11A转录活性改变,可能会打破肝脏脂质代谢的稳态。

其次,从基因组结构与调控功能的角度分析,rs243021位于BCL11A基因的内含子区域。尽管内含子变异不直接改变蛋白质的氨基酸序列,但近年来的全基因组关联研究(Genome-wide association study, GWAS)表明,内含子区域常富集增强子或沉默子等顺式调控元件,可能通过影响转录因子的结合亲和力或改变染色质构象,进而调控靶基因的转录水平[35]。利用基因型-组织表达数据库(Genotype-Tissue Expression, GTEx)分析显示,许多位于非编码区的单核苷酸多态性(single nucleotide polymorphisms, SNPs)实际上是表达数量性状基因座(expression quantitative trait loci, eQTL),能够显著影响特定组织中基因的 mRNA表达水平[36]。因此,rs243021或与其处于强连锁不平衡状态的位点,可能通过精细调控BCL11A在肝脏或脂肪组织中的表达量,而非改变蛋白质结构,来参与代谢表型的塑造。这种调控作用往往具有组织特异性,或许解释了为何在全血样本中未观察到显著差异,提示未来有必要在肝脏组织水平进一步验证其表达差异。

rs243021的表型效应可能因族群遗传背景、样本量或环境因素而表现不同,因此即使本研究未显示显著关联,不代表其对MAFLD无潜在生物学机制影响。鉴于本研究的局限性及上述潜在机制,未来的研究方向可以聚焦于扩大样本量与族群多样性,开展多中心、跨种族的大规模验证性研究、深入挖掘基因区域内的遗传结构、开展功能性实验验证如构建体外细胞模型或动物模型探究BCL11A在肝脏脂质代谢中的具体作用。综上所述,我们认为BCL11A基因仍是代谢性疾病研究中的重要候选靶点,其确切的分子机制有待通过更多样化的人群队列及深入的功能学实验进一步揭示。

伦理学声明

本研究遵守国家所有相关法规、机构政策和赫尔辛基宣言,并于2025年2月27日经由青岛市市立医院伦理委员会审批,批号:2025-KTLL-033。所有纳入患者均签署知情同意书。

NOTES

*通讯作者。

参考文献

[1] Eslam, M., Sanyal, A.J., George, J., Sanyal, A., Neuschwander-Tetri, B., Tiribelli, C., et al. (2020) MAFLD: A Consensus-Driven Proposed Nomenclature for Metabolic Associated Fatty Liver Disease. Gastroenterology, 158, 1999-2014.e1. [Google Scholar] [CrossRef] [PubMed]
[2] Chalasani, N., Younossi, Z., Lavine, J.E., Charlton, M., Cusi, K., Rinella, M., et al. (2018) The Diagnosis and Management of Nonalcoholic Fatty Liver Disease: Practice Guidance from the American Association for the Study of Liver Diseases. Hepatology, 67, 328-357. [Google Scholar] [CrossRef] [PubMed]
[3] Habibullah, M., Jemmieh, K., Ouda, A., Haider, M.Z., Malki, M.I. and Elzouki, A. (2024) Metabolic-Associated Fatty Liver Disease: A Selective Review of Pathogenesis, Diagnostic Approaches, and Therapeutic Strategies. Frontiers in Medicine, 11, Article ID: 1291501. [Google Scholar] [CrossRef] [PubMed]
[4] Amini-Salehi, E., Letafatkar, N., Norouzi, N., Joukar, F., Habibi, A., Javid, M., et al. (2024) Global Prevalence of Nonalcoholic Fatty Liver Disease: An Updated Review Meta-Analysis Comprising a Population of 78 Million from 38 Countries. Archives of Medical Research, 55, Article 103043. [Google Scholar] [CrossRef] [PubMed]
[5] Wu, Y., Zheng, Q., Zou, B., Yeo, Y.H., Li, X., Li, J., et al. (2020) The Epidemiology of NAFLD in Mainland China with Analysis by Adjusted Gross Regional Domestic Product: A Meta-Analysis. Hepatology International, 14, 259-269. [Google Scholar] [CrossRef] [PubMed]
[6] Streba, L.A.M., Vere, C., Rogoveanu, I., et al. (2015) Nonalcoholic Fatty Liver Disease, Metabolic Risk Factors, and Hepatocellular Carcinoma: An Open Question. World Journal of Gastroenterology, 21, 4103-4110. [Google Scholar] [CrossRef] [PubMed]
[7] Teng, T., Qiu, S., Zhao, Y., Zhao, S., Sun, D., Hou, L., et al. (2022) Pathogenesis and Therapeutic Strategies Related to Non-Alcoholic Fatty Liver Disease. International Journal of Molecular Sciences, 23, Article 7841. [Google Scholar] [CrossRef] [PubMed]
[8] Moretti, V., Romeo, S. and Valenti, L. (2024) The Contribution of Genetics and Epigenetics to MAFLD Susceptibility. Hepatology International, 18, 848-860. [Google Scholar] [CrossRef] [PubMed]
[9] Kozlitina, J., Smagris, E., Stender, S., Nordestgaard, B.G., Zhou, H.H., Tybjærg-Hansen, A., et al. (2014) Exome-Wide Association Study Identifies a TM6SF2 Variant that Confers Susceptibility to Nonalcoholic Fatty Liver Disease. Nature Genetics, 46, 352-356. [Google Scholar] [CrossRef] [PubMed]
[10] Chen, Y., Du, X., Kuppa, A., Feitosa, M.F., Bielak, L.F., O’Connell, J.R., et al. (2023) Genome-Wide Association Meta-Analysis Identifies 17 Loci Associated with Nonalcoholic Fatty Liver Disease. Nature Genetics, 55, 1640-1650. [Google Scholar] [CrossRef] [PubMed]
[11] Chen, V.L., Kuppa, A., Oliveri, A., Chen, Y., Ponnandy, P., Patel, P.B., et al. (2025) Human Genetics of Metabolic Dysfunction-Associated Steatotic Liver Disease: From Variants to Cause to Precision Treatment. Journal of Clinical Investigation, 135, e186424. [Google Scholar] [CrossRef] [PubMed]
[12] Satterwhite, E., Sonoki, T., Willis, T.G., Harder, L., Nowak, R., Arriola, E.L., et al. (2001) The BCL11 Gene Family: Involvement of BCL11A in Lymphoid Malignancies. Blood, 98, 3413-3420. [Google Scholar] [CrossRef] [PubMed]
[13] Huang, P., Peslak, S.A., Ren, R., Khandros, E., Qin, K., Keller, C.A., et al. (2022) HIC2 Controls Developmental Hemoglobin Switching by Repressing BCL11A Transcription. Nature Genetics, 54, 1417-1426. [Google Scholar] [CrossRef] [PubMed]
[14] Yin, J., Xie, X., Ye, Y., Wang, L. and Che, F. (2019) BCL11A: A Potential Diagnostic Biomarker and Therapeutic Target in Human Diseases. Bioscience Reports, 39, BSR20190604. [Google Scholar] [CrossRef] [PubMed]
[15] Peiris, H., Park, S., Louis, S., Gu, X., Lam, J.Y., Asplund, O., et al. (2018) Discovering Human Diabetes-Risk Gene Function with Genetics and Physiological Assays. Nature Communications, 9, Article No. 3855. [Google Scholar] [CrossRef] [PubMed]
[16] 范建高, 徐小元, 南月敏, 等. 代谢相关(非酒精性)脂肪性肝病防治指南(2024年版) [J]. 实用肝脏病杂志, 2024, 27(4): 494-510.
[17] Godoy-Matos, A.F., Silva Júnior, W.S. and Valerio, C.M. (2020) NAFLD as a Continuum: From Obesity to Metabolic Syndrome and Diabetes. Diabetology & Metabolic Syndrome, 12, Article No. 60. [Google Scholar] [CrossRef] [PubMed]
[18] Sakurai, Y., Kubota, N., Yamauchi, T. and Kadowaki, T. (2021) Role of Insulin Resistance in MAFLD. International Journal of Molecular Sciences, 22, Article 4156. [Google Scholar] [CrossRef] [PubMed]
[19] Carlsson, B., Lindén, D., Brolén, G., Liljeblad, M., Bjursell, M., Romeo, S., et al. (2020) Review Article: The Emerging Role of Genetics in Precision Medicine for Patients with Non‐Alcoholic Steatohepatitis. Alimentary Pharmacology & Therapeutics, 51, 1305-1320. [Google Scholar] [CrossRef] [PubMed]
[20] Shang, S., Li, X., Azzo, A., Truong, T., Dozmorov, M., Lyons, C., et al. (2023) MBD2a-NuRD Binds to the Methylated Γ-Globin Gene Promoter and Uniquely Forms a Complex Required for Silencing of HbF Expression. Proceedings of the National Academy of Sciences, 120, e2302254120. [Google Scholar] [CrossRef] [PubMed]
[21] Horton, J.R., Yu, M., Zhou, J., Tran, M., Anakal, R.R., Lu, Y., et al. (2025) Multimeric Transcription Factor BCL11A Utilizes Two Zinc-Finger Tandem Arrays to Bind Clustered Short Sequence Motifs. Nature Communications, 16, Article No. 3672. [Google Scholar] [CrossRef] [PubMed]
[22] 陈艳敏, 杜诗蓓, 金倩涯, 等. BCL11A基因在实体肿瘤恶性进程中的研究进展[J]. 中华转移性肿瘤杂志, 2021, 4(1): 66-69.
[23] Simonis-Bik, A.M., Nijpels, G., van Haeften, T.W., Houwing-Duistermaat, J.J., Boomsma, D.I., Reiling, E., et al. (2009) Gene Variants in the Novel Type 2 Diabetes Loci CDC123/CAMK1D, THADA, ADAMTS9, BCL11A, and MTNR1B Affect Different Aspects of Pancreatic β-Cell Function. Diabetes, 59, 293-301. [Google Scholar] [CrossRef] [PubMed]
[24] Liang, F., Kume, S. and Koya, D. (2009) SIRT1 and Insulin Resistance. Nature Reviews Endocrinology, 5, 367-373. [Google Scholar] [CrossRef] [PubMed]
[25] Hebbar, P., Abubaker, J.A., Abu-Farha, M., Tuomilehto, J., Al-Mulla, F. and Thanaraj, T.A. (2019) A Perception on Genome-Wide Genetic Analysis of Metabolic Traits in Arab Populations. Frontiers in Endocrinology, 10, Article ID: 8. [Google Scholar] [CrossRef] [PubMed]
[26] Jonsson, A., Ladenvall, C., Ahluwalia, T.S., Kravic, J., Krus, U., Taneera, J., et al. (2013) Effects of Common Genetic Variants Associated with Type 2 Diabetes and Glycemic Traits on α-and β-Cell Function and Insulin Action in Humans. Diabetes, 62, 2978-2983. [Google Scholar] [CrossRef] [PubMed]
[27] Tang, L., Wang, L., Ye, H., Xu, X., Hong, Q., Wang, H., et al. (2014) BCL11A Gene DNA Methylation Contributes to the Risk of Type 2 Diabetes in Males. Experimental and Therapeutic Medicine, 8, 459-463. [Google Scholar] [CrossRef] [PubMed]
[28] Benitez, C.M., Qu, K., Sugiyama, T., Pauerstein, P.T., Liu, Y., Tsai, J., et al. (2014) An Integrated Cell Purification and Genomics Strategy Reveals Multiple Regulators of Pancreas Development. PLOS Genetics, 10, e1004645. [Google Scholar] [CrossRef] [PubMed]
[29] Voight, B.F., Scott, L.J., Steinthorsdottir, V., Morris, A.P., Dina, C., Welch, R.P., et al. (2010) Twelve Type 2 Diabetes Susceptibility Loci Identified through Large-Scale Association Analysis. Nature Genetics, 42, 579-589. [Google Scholar] [CrossRef] [PubMed]
[30] Wang, M., Zhao, Y., He, Y., Zhang, L., Liu, J., Zheng, S., et al. (2023) The Bidirectional Relationship between NAFLD and Type 2 Diabetes: A Prospective Population-Based Cohort Study. Nutrition, Metabolism and Cardiovascular Diseases, 33, 1521-1528. [Google Scholar] [CrossRef] [PubMed]
[31] Cernea, S. (2024) NAFLD Fibrosis Progression and Type 2 Diabetes: The Hepatic-Metabolic Interplay. Life, 14, Article 272. [Google Scholar] [CrossRef] [PubMed]
[32] Marušić, M., Paić, M., Knobloch, M. and Liberati Pršo, A. (2021) NAFLD, Insulin Resistance, and Diabetes Mellitus Type 2. Canadian Journal of Gastroenterology and Hepatology, 2021, Article ID: 6613827. [Google Scholar] [CrossRef] [PubMed]
[33] Younossi, Z.M., Golabi, P., de Avila, L., Paik, J.M., Srishord, M., Fukui, N., et al. (2019) The Global Epidemiology of NAFLD and NASH in Patients with Type 2 Diabetes: A Systematic Review and Meta-Analysis. Journal of Hepatology, 71, 793-801. [Google Scholar] [CrossRef] [PubMed]
[34] Mantovani, A., Petracca, G., Beatrice, G., Tilg, H., Byrne, C.D. and Targher, G. (2020) Non-Alcoholic Fatty Liver Disease and Risk of Incident Diabetes Mellitus: An Updated Meta-Analysis of 501 022 Adult Individuals. Gut, 70, 962-969. [Google Scholar] [CrossRef] [PubMed]
[35] Maurano, M.T., Humbert, R., Rynes, E., Thurman, R.E., Haugen, E., Wang, H., et al. (2012) Systematic Localization of Common Disease-Associated Variation in Regulatory DNA. Science, 337, 1190-1195. [Google Scholar] [CrossRef] [PubMed]
[36] Matthew, T., Richard Humbert, E., Robert, E., et al. (2020) The GTEx Consortium Atlas of Genetic Regulatory Effects across Human Tissues. Science, 369, 1318-1330.