儿童皮质下脑结构与儿童注意缺陷多动障碍的因果关系——一项孟德尔随机化研究
Causal Relationship between Subcortical Brain Structures and Attention Deficit Hyperactivity Disorder in Children—A Mendelian Randomization Study
DOI: 10.12677/acm.2024.14112919, PDF, HTML, XML,   
作者: 滕良莹:潍坊市妇幼保健院超声科,山东 潍坊;臧彩红*, 曲秀君, 王 潇:潍坊市妇幼保健院药学部,山东 潍坊;贺 莉:潍坊市妇幼保健院药物静脉配置中心,山东 潍坊
关键词: 儿童皮质下脑结构ADHD孟德尔随机化Child Subcortical Brain Structures ADHD Mendelian Randomization
摘要: 目的:采用两样本孟德尔随机化研究探讨儿童皮质下脑结构与儿童注意缺陷多动障碍之间的因果关系,为儿童缺陷多动障碍的发病机制提供遗传学证据支持。方法:通过提取全基因组关联研究(genome-wide association study, GWAS)公开数据中的儿童皮质下脑结构和儿童注意缺陷多动障碍的相关数据,以逆方差加权法为主要分析方法进行因果推断,应用一系列敏感性分析验证结果强度。结果:双侧颅内容积(IVW:OR = 0.953; 95%CI: 0.924~0.983; P = 0.003)、双侧杏仁核体积 × 产后应激水平(IVW:OR = 0.948; 95%CI: 0.904~0.994; P = 0.028)与ADHD呈负相关,表明与正常儿童相比,ADHD患儿患者的双侧颅内容积、双侧杏仁核体积以及产后应激水平较低。敏感性分析结果显示了孟德尔分析的可靠性。结论:双侧颅内容积、双侧杏仁核体积以及产后应激水平与ADHD的发病有潜在的因果关系,这一发现对于ADHD的前期诊断及治疗具有一定的临床意义。
Abstract: Objective: A two-sample Mendelian randomization study was undertaken to elucidate the causal link between subcortical brain structures in children and attention deficit hyperactivity disorder (ADHD). This research contributes genetic evidence supporting the etiological mechanisms underlying ADHD in the pediatric population. Methods: We leveraged publicly accessible genome-wide association study (GWAS) data to extract relevant information on subcortical brain structures in children and their association with attention deficit hyperactivity disorder (ADHD). Employing the inverse variance weighting method (IVW) as our primary analytical approach for causal inference, we further substantiated the strength of our findings through an array of sensitivity analyses. Results: After F-statistics as well as an initial significance test, a total of two child subcortical brain structure phenotypes were suggested to exhibit a causal association on ADHD, including bilateral intracranial volume (IVW:OR = 0.953; 95%CI: 0.924~0.983; P = 0.003), bilateral amygdala volume x postnatal stress interaction (IVW:OR =0.948; 95%CI: 0.904~0.994; P = 0.028). The results showed that both phenotypes were negatively correlated with the development of ADHD. Conclusion: Our results demonstrate a potential causal relationship between child subcortical brain structures and ADHD. Bilateral intracranial volume and bilateral amygdala volume × postnatal stress interaction phenotypes are associated with a decreased risk of ADHD. It may provide a new avenue for researchers to explore child subcortical brain structures and ADHD and can lead to exploration of earlier intervention and treatment.
文章引用:滕良莹, 臧彩红, 曲秀君, 贺莉, 王潇. 儿童皮质下脑结构与儿童注意缺陷多动障碍的因果关系——一项孟德尔随机化研究[J]. 临床医学进展, 2024, 14(11): 589-598. https://doi.org/10.12677/acm.2024.14112919

1. 引言

注意缺陷多动障碍(attention deficit hyperactivity disorder, ADHD)是以与发育水平不相符的注意力不集中、不分场合的多动或冲动以及学习、认知、语言、运动和精神情感障碍[1]为特征的神经障碍,尤其以儿童患者居多。目前,ADHD的病因和发病机制尚未明确,在流行病学特征、遗传和环境风险因素、神经认知机制等方面存在诸多异质性的报道[2]。研究显示,全球范围内ADHD患病率约为5% [3],中国ADHD儿童调查平均患病率约为6.3% [4],最低患病率为1.6% [5],最高为12.9% [6]。有研究表明,该病与较高的自杀和犯罪活动关系紧密,对儿童的学业、家庭和社会等多方面产生严重的影响[7] [8]。虽然随着神经影像学,如脑部磁共振成像(MRI)技术的应用,越来越多研究表明ADHD儿童的脑部结构异常可能是其症状表现的重要生理病因[9] [10],但是还未发现有关于儿童皮质下脑结构与ADHD儿童之间因果关系的孟德尔随机化研究。

孟德尔随机化(Mendelian randomization, MR)是遗传流行病学研究的有力工具[11],其主要使用单核苷酸多态位点(single nucleotide polymorphism, SNP)作为工具变量(instrumental variables, IVs),来评估暴露因素和结局因素之间的因果关系[12] [13]。与传统的观察性研究相比,其优点在于可有效克服混杂因素和反向因果的干扰,因此,孟德尔分析可增强暴露和特定疾病相关性的因果推断[14]。本研究将利用基因组范围关联研究(genome-wide association study, GWAS)数据库的两样本双向孟德尔随机化研究来探讨儿童皮质下脑结构与儿童注意力缺陷障碍之间的因果关系。

2. 材料与方法

2.1. 研究设计

本研究使用MR两样本分析方法,通过提取GWAS汇总数据,来评估儿童皮质下脑结构与ADHD之间的因果关系,最后进行一系列敏感性分析方法验证结果的可靠性。MR分析基于以下三个核心假设[14]:① IVs与暴露是直接相关的;② IVs与可能存在的混杂因素无关;③ IVs通过而且仅通过暴露因素影响结局。本研究基于公共GWAS数据库,所有参与者均在原始GWAS中给予书面知情同意。见图1

Figure 1. Schematic diagram of the core hypothesis of Mendelian randomization

1. 孟德尔随机化核心假设示意图

2.2. 数据来源

本研究中使用的两个数据集均来自公开可用的GWAS数据。48种儿童皮质下脑结构GWAS数据来源于GWAS Catalog (https://pubmed.ncbi.nlm.nih.gov/36777645),这是一项来自荷兰鹿特丹普通人群的前瞻性队列研究,荟萃分析纳入2257名受试者。在ABCD研究中,N = 10,749人被纳入分析,其中55.1%是欧洲遗传血统[15]

ADHD数据来自于IEU Open Gwas Project数据库(ieu-a-1183),该数据的研究对象均为欧洲人群,样本量为55,374,其中20,183名患者和35,191名对照人群,包括了8,047,420个SNP。

2.3. IVs的选择

为满足关联性假设,首先我们对SNP数据进行筛选,筛选的标准为(1) 以P < 5 × 105为标准筛选儿童皮质下脑结构和ADHD的SNP;(2) 以系数r2 = 0.001,Kb = 10,000为标准去除连锁不平衡[16]-[19];(3) 剔除F < 10的弱工具变量(F = (N − K − 1) × r2/[(1 − r2) × K];N:GWAS样本数;K:IVs数量) [20] [21]。(4) 删除与混杂因素(家族遗传)相关的SNP以降低多效性;(5) 另外使用更有效的MR水平多效性残差和异常值(MR pleiotropy residual sum and outlier, MR-PRESSO)作为补充方法来剔除离群值[22]

2.4. MR分析

为了探讨儿童皮质下脑结构与ADHD之间的因果关系,我们使用逆方差加权法(inverse variance weighting, IVW)作为两样本孟德尔随机化分析方法的主要分析方法,当P值小于0.05时,我们说暴露和结局有显著因果关系。但逆方差加权法的前提是假设所有的遗传变异都是有效的工具变量[23] [24],有一定的局限性。为消除这种局限性,我们还使用了加权中位法(weighted median)、MR-Egger回归法、和中位模型(weighted mode)来对结果进行补充。所有数据使用R4.3.3版本数据包进行分析。

敏感性分析包括异质性检验、水平多效性检验和逐个剔除留一检验。采用Cochran’s Q、MR-Egger、MR-PRESSO、留一法来检验敏感性。其中Cochran’s Q检验用于检测异质性,如果P > 0.05,则表明分析结果没有明显的异质性,反之,则存在异质性[25]。使用MR-Egger来评估工具变量的水平多效性,如果MR-Egger截距项越接近于0表明水平多效性越低,若P > 0.05,则表明不存在水平多效性[26]。同时,MR-PRESSO作为补充方法来检测水平多效性,当MR-PRESSO全局检验的P > 0.05,则表明不存在水平多效性。采用留一法(leave-one-out, LOO)来评估单个SNP对因果效应的影响程度。如果某个工具变量的排除对整体结果产生较大影响,那么这个工具变量可能是MR分析中关键工具变量,或者可能存在遗传偏倚等问题[27]。儿童皮质下脑结构与ADHD之间的关系以优势比(odds radio, OR)及其95%可信区间(confidence interval, CI)表示。

2.5. 共定位分析

采用R语言软件(R4.3.3版)的coloc包进行过共定位分析,来判断儿童皮质下脑结构与ADHD相关基因序列中是否存在同一SNP。共定位分析有四个假设[28]-[30]:(1) H0:儿童皮质下脑结构和ADHD与某个基因组区域的SNP位点无显著相关;(2) H1/H2:儿童皮质下脑结构和ADHD与某个基因区域的SNP位点显著相关;(3) H3:儿童皮质下脑结构和ADHD与某个基因区域的SNP位点显著相关,但由不同的因果变异位点驱动;(4) H4:儿童皮质下脑结构和ADHD与某个基因区域的SNP位点显著相关,且由同一个因果变异位点驱动。当H4 > 80%时,我们认为因果效应是由共同SNP引起的[31]

3. 结果

3.1. 工具变量

本研究对48种儿童皮质下脑结构表型的GWAS数据进行了工具变量筛选,所有工具变量的F值均大于10,不存在弱工具变量偏倚,最终筛选出来1596个SNP用于分析,无与结局相关的混淆因素的SNP存在。

3.2. MR分析结果

经过MR分析,共检测到2种儿童皮质下脑结构与ADHD相关,差异有统计学意义(P < 0.05),分别是双侧颅内容积、双侧杏仁核体积 × 产后应激相互作用。见图2

Figure 2. Forest map of the relationship between subcortical brain structure phenotype and ADHD in two children (IVW: inverse variance weighted method; weighted median; weighted mode)

2. 两种儿童皮质下脑结构表型与ADHD的关系的森林图(IVW:逆方差加权法;weighted median:加权中位法;weight mode:加权模式)

IVW法作为主要检测方法,发现双侧颅内容积对ADHD存在因果关系(IVW:OR = 0.953; 95%CI: 0.924~0.983; P = 0.003),指出较高的双侧颅内容积水平是ADHA的保护作用。其他三种检测方法,也保持了一致性。MR-Egger (OR = 0.962; 95%CI: 0.885~1.045; P = 0.361);weight median (OR = 0.949; 95%CI: 0.906~0.995; P = 0.025);weight mode (OR = 0.933; 95%CI: 0.839~1.037; P = 0.187)。同样,双侧杏仁核体积 × 产后应激相互作用也是对ADHD有保护作用IVW (OR = 0.948; 95%CI: 0.904~0.994; P = 0.028);MR-Egger (OR = 0.955; 95%CI: 0.866~1.053; P = 0.361);weight median (OR = 0.934; 95%CI: 0.877~0.996; P = 0.037);weight mode (OR = 0.914; 95%CI: 0.828~1.010; P = 0.087)。

经过敏感性分析,分析结果没有异质性及水平多效性。Cochran’s Q用于检测异质性,结果为:双侧颅内容积的Q-pval为0.603,双侧杏仁核体积 × 产后应激相互作用的Q-pval为0.112。MR-Egger截距分析用于检测水平多效性,其结果为:双侧颅内容积的MR-Egger截距为−0.001,P = 0.828;双侧杏仁核体积 × 产后应激相互作用的MR-Egger截距为−0.002,P = 0.876。MR-Presso分析表明,不存在水平多效性。双侧颅内容积的Global Test检测Pval = 0.601;双侧杏仁核体积 × 产后应激相互作用的Global Test检测Pval = 0.126。同时,散点图(见图3)和漏斗图(见图4)也显示了结果的稳健性,留一图(见图5)没有发现单个SNP对结果有严重偏倚影响。

Figure 3. Scatterplot of SNP for subcortical brain structure and ADHD in children ((a) is the scatter plot of bilateral intracranial volume for ADHD; (b) is the scatter plot of bilateral amygdala volume × postpartum stress interaction on ADHD)

3. SNP对儿童皮质下脑结构和ADHD的散点图((a)为双侧颅内容积对ADHD的散点图;(b)为双侧杏仁核体积 × 产后应激相互作用对ADHD的散点图)

Figure 4. Funnel plot of SNP on subcortical brain structure and ADHD in children ((a) is the funnel plot of bilateral intracranial volume for ADHD; (b) is the funnel plot of bilateral amygdala volume × postpartum stress interaction on ADHD)

4. SNP对儿童皮质下脑结构和ADHD的漏斗图((a)为双侧颅内容积对ADHD的漏斗图;(b)为双侧杏仁核体积 × 产后应激相互作用对ADHD的漏斗图)

Figure 5. SNP mapping of subcortical brain structure and ADHD in children ((a) is a figure of bilateral intracranial volume for ADHD; (b) is the residual graph of bilateral amygdala volume × postpartum stress interaction on ADHD)

5. SNP对儿童皮质下脑结构和ADHD的留一图((a)为双侧颅内容积对ADHD的留一图;(b)为双侧杏仁核体积 ×产后应激相互作用对ADHD的留一图)

3.3. 共定位分析结果

共定位结果显示,双侧颅内容积与ADHD以及双侧杏仁核体积 × 产后应激相互作用与ADHD的H4假设概率均小于80%,具体结果为,双侧颅内容积与ADHD的H4假设概率为6.57E−05,双侧杏仁核体积 × 产后应激相互作用与ADHD的H4假设概率为0.005。因此,双侧颅内容积与ADHD以及双侧杏仁核体积 × 产后应激相互作用与ADHD的因果效应不是由两者基因序列中相同的SNP引起的,而是由暴露引起的[31]

在反向分析中,当ADHD作为暴露,儿童皮质下脑结构作为结局时,并没有发现ADHD对儿童皮质下脑结构的风险有因果关系(P > 0.05)。见图6

Figure 6. SNP colocalization map of subcortical brain structure and ADHD in children ((a) is the colocalization of bilateral intracranial volume for ADHD; (b) is the co-localization of ADHD by bilateral amygdala volume × postpartum stress interaction)

6. SNP对儿童皮质下脑结构和ADHD的共定位图((a)为双侧颅内容积对ADHD的共定位;(b)为双侧杏仁核体积 × 产后应激相互作用对ADHD的共定位)

4. 讨论

儿童注意力缺陷多动障碍(ADHD)患者的临床表现主要为注意力、记忆力和语言等认知功能障碍。有研究证实,这些认知功能的正常执行是以正常的脑蛋白网络结构为基础的,对ADHD发生具有重要作用[32]。随着神经影像学,如脑部磁共振成像MRI技术的发展,越来越多的研究表明ADHD复杂的临床症状与脑部结构密切相关[33] [34]。本研究采用孟德尔随机化研究方法,系统地评估儿童皮下脑结构与ADHD发病风险之间的因果关系。基因预测显示,双侧颅内容积以及双侧杏仁核体积和产后应激相互作用与ADHD的发病有负相关关系。

一些研究表明,ADHD患儿的脑发育在结构和活动等方面与正常儿童之间存在差异。脑成像研究显示,ADHD患儿的大脑总体积和皮质下体积,如尾状核、小脑、脑核,均小于正常儿童[10] [33]。皮质体积是皮质厚度和表面积的产物,有研究结果表明ADHD患者的皮质体积小于正常儿童。LEE等[34]一些研究者对152名5~18岁的ADHD患者进行研究,发现ADHD患者大脑皮质体积比正常组小3.7%。另外,MOSTOFSKY等对24名ADHD患者脑部MRI扫描,结果发现ADHD儿童的总颅内容积比正常儿童小8.3%,主要表现为双侧额叶皮质体积均显著减少[35]。MU S等使用FreeSurfer分割法,测量ADHD患者与对照组患者MRI的皮质下体积,发现ADHD的皮质下体积明显减小[36]。我们通过孟德尔随机化方法也证实了,双侧颅内容积对ADHD存在负相关的关系(IVW:OR = 0.953; 95%CI: 0.924~0.983; P = 0.003),表明ADHD患者的颅内容积要小于正常儿童,更有助于使用MRI影像去鉴别ADHD患儿。杏仁核是一个呈杏仁状的皮层下结构,它位于前颞叶背内侧部、海马体和侧脑室下角顶端,主要负责产生、识别和调节情绪,以及控制学习和记忆的脑结构。杏仁核参与组成边缘系统,借终纹与隔区、下丘脑联系,参与机体多种情绪的表达,在与述情障碍相关的研究中被认为与感知情绪意识和分配情绪注意资源有关,杏仁核受损的个体在应对负面情绪如恐惧和悲伤时决策能力下降[37]。除此之外,杏仁核能通过调控海马结构的活动,进行情感、学习和记忆等高级活动。如在阿尔兹海默症患者杏仁核体积明显缩小,从而影响机体的记忆功能,并证实杏仁核在感情记忆中发挥重要的作用。对于ADHD患者,多数存在记忆障碍,日常生活中容易忘事,丢三落四,做事条理性差。有研究通过神经心理学测试评分比较发现ADHD患者的即刻记忆及延迟回忆评分明显低于正常人群[38]。同时提示,对于ADHD患儿,杏仁核体积或者功能与正常儿童相比,都会异常[38]。HOOGMAN M等人[39]做了一项较大研究样本研究,发现ADHD患者相比于对照组,杏仁核体积、颅内体积、尾状核体积均会变小。而本研究结果也提示,杏仁核体积与ADHD的发生呈负相关,与正常儿童相比,ADHD患儿的杏仁核体积会偏小,也影响了ADHD患者的述情表达以及学习、记忆等高级活动。多项研究显示产后应激水平会增加儿童患ADHD风险,其效果取决于暴露的时间[40] [41]。脑是各种应激源的直接作用器官,应激源可通过脑部结构和功能的改变影响其他生理功能。关于应激水平对脑影响的研究主要集中在海马、杏仁核和前额叶皮质等区域。Ahmed等人[42]通过统计分析发现对于儿童期经受虐待后患有创伤后应激障碍的成年患者与健康对照组比较,发现双侧杏仁核体积均有减小。Koen Bolhuis等人[15]通过研究发现,产后应激与多个大脑结构有关系,如产后应激能够预测杏仁核体积,同时也意味着,杏仁核体积和产后应激水平会共同预测ADHD的发生风险。本研究通过孟德尔随机化方法提示,杏仁核体积和产后应激共同作用与ADHD存在负相关的因果关系(IVW:OR = 0.948)。为临床儿童皮质下脑结构与ADHD的关系,提供更多参考意见。

本研究也存在局限性,研究使用的GWAS汇总数据均来自欧洲人群,不同种族人群的基因分布存在差异,未来的研究需要结合更广泛的遗传标记和临床参数,以全面了解儿童皮质下脑结构与ADHD的发病机制。

综上所述,与正常儿童相比,ADHD患者的双侧颅内容积、双侧杏仁核体积以及产后应激水平都较低。用MR的方法了解儿童皮质下脑结构和ADHD的关系,对ADHD的早期诊断和治疗具有一定的临床指导意义。

NOTES

*通讯作者。

参考文献

[1] Rajaprakash, M. and Leppert, M.L. (2022) Attention-Deficit/Hyperactivity Disorder. Pediatrics in Review, 43, 135-147.
https://doi.org/10.1542/pir.2020-000612
[2] 何丽, 张雨平. 儿童注意缺陷多动障碍循证研究进展[J]. 山东医药, 2020, 60(7): 94-97.
[3] Sayal, K., Prasad, V., Daley, D., Ford, T. and Coghill, D. (2018) ADHD in Children and Young People: Prevalence, Care Pathways, and Service Provision. The Lancet Psychiatry, 5, 175-186.
https://doi.org/10.1016/s2215-0366(17)30167-0
[4] Liu, A., Xu, Y., Yan, Q. and Tong, L. (2018) The Prevalence of Attention Deficit/Hyperactivity Disorder among Chinese Children and Adolescents. Scientific Reports, 8, Article No. 11169.
https://doi.org/10.1038/s41598-018-29488-2
[5] 黄永玲, 程进, 吴曙东, 等. 安徽省学龄前儿童多动行为筛查及影响因素分析[J]. 中国学校卫生, 2021, 42(12): 1855-1858.
[6] 任路忠, 王淑华, 童卫红, 等. 深圳市龙岗区学龄前儿童多动行为现况研究[J]. 疾病控制杂志, 2008, 12(3): 259-261.
[7] Sonuga‐Barke, E.J.S., Becker, S.P., Bölte, S., Castellanos, F.X., Franke, B., Newcorn, J.H., et al. (2022) Annual Research Review: Perspectives on Progress in ADHD Science—From Characterization to Cause. Journal of Child Psychology and Psychiatry, 64, 506-532.
https://doi.org/10.1111/jcpp.13696
[8] Nigg, J.T., Sibley, M.H., Thapar, A. and Karalunas, S.L. (2020) Development of ADHD: Etiology, Heterogeneity, and Early Life Course. Annual Review of Developmental Psychology, 2, 559-583.
https://doi.org/10.1146/annurev-devpsych-060320-093413
[9] Swanson, J.M., Kinsbourne, M., Nigg, J., Lanphear, B., Stefanatos, G.A., Volkow, N., et al. (2007) Etiologic Subtypes of Attention-Deficit/Hyperactivity Disorder: Brain Imaging, Molecular Genetic and Environmental Factors and the Dopamine Hypothesis. Neuropsychology Review, 17, 39-59.
https://doi.org/10.1007/s11065-007-9019-9
[10] 宾博林, 周小燕, 邓德茂. 注意缺陷多动障碍的MRI研究进展[J]. 磁共振成像, 2023, 14(3): 149-152, 169.
[11] Zheng, J., Baird, D., Borges, M., Bowden, J., Hemani, G., Haycock, P., et al. (2017) Recent Developments in Mendelian Randomization Studies. Current Epidemiology Reports, 4, 330-345.
https://doi.org/10.1007/s40471-017-0128-6
[12] Hu, X., Zhao, J., Lin, Z., Wang, Y., Peng, H., Zhao, H., et al. (2022) Mendelian Randomization for Causal Inference Accounting for Pleiotropy and Sample Structure Using Genome-Wide Summary Statistics. Proceedings of the National Academy of Sciences, 119, e2106858119.
https://doi.org/10.1073/pnas.2106858119
[13] Richmond, R.C. and Davey Smith, G. (2021) Mendelian Randomization: Concepts and Scope. Cold Spring Harbor Perspectives in Medicine, 12, a040501.
https://doi.org/10.1101/cshperspect.a040501
[14] Davey Smith, G. and Hemani, G. (2014) Mendelian Randomization: Genetic Anchors for Causal Inference in Epidemiological Studies. Human Molecular Genetics, 23, R89-R98.
https://doi.org/10.1093/hmg/ddu328
[15] Bolhuis, K., Mulder, R.H., de Mol, C.L., Defina, S., Warrier, V., White, T., et al. (2022) Mapping Gene by Early Life Stress Interactions on Child Subcortical Brain Structures: A Genome‐Wide Prospective Study. JCPP Advances, 2, e12113.
https://doi.org/10.1002/jcv2.12113
[16] Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M.A.R., Bender, D., et al. (2007) PLINK: A Tool Set for Whole-Genome Association and Population-Based Linkage Analyses. The American Journal of Human Genetics, 81, 559-575.
https://doi.org/10.1086/519795
[17] Burgess, S., Small, D.S. and Thompson, S.G. (2015) A Review of Instrumental Variable Estimators for Mendelian Randomization. Statistical Methods in Medical Research, 26, 2333-2355.
https://doi.org/10.1177/0962280215597579
[18] Miao, J., Gu, X. and Shi, R. (2022) COVID-19 Is Associated with the Risk of Cardiovascular Disease Death: A Two-Sample Mendelian Randomization Study. Frontiers in Cardiovascular Medicine, 9, Article 974944.
https://doi.org/10.3389/fcvm.2022.974944
[19] Cupido, A.J., Kraaijenhof, J.M., Burgess, S., Asselbergs, F.W., Hovingh, G.K. and Gill, D. (2022) Genetically Predicted Neutrophil-to-Lymphocyte Ratio and Coronary Artery Disease: Evidence from Mendelian Randomization. Circulation: Genomic and Precision Medicine, 15, e003553.
https://doi.org/10.1161/circgen.121.003553
[20] Brion, M.A., Shakhbazov, K. and Visscher, P.M. (2012) Calculating Statistical Power in Mendelian Randomization Studies. International Journal of Epidemiology, 42, 1497-1501.
https://doi.org/10.1093/ije/dyt179
[21] Pierce, B.L., Ahsan, H. and VanderWeele, T.J. (2010) Power and Instrument Strength Requirements for Mendelian Randomization Studies Using Multiple Genetic Variants. International Journal of Epidemiology, 40, 740-752.
https://doi.org/10.1093/ije/dyq151
[22] Verbanck, M., Chen, C., Neale, B. and Do, R. (2018) Detection of Widespread Horizontal Pleiotropy in Causal Relationships Inferred from Mendelian Randomization between Complex Traits and Diseases. Nature Genetics, 50, 693-698.
https://doi.org/10.1038/s41588-018-0099-7
[23] Burgess, S., Dudbridge, F. and Thompson, S.G. (2015) Combining Information on Multiple Instrumental Variables in Mendelian Randomization: Comparison of Allele Score and Summarized Data Methods. Statistics in Medicine, 35, 1880-1906.
https://doi.org/10.1002/sim.6835
[24] Burgess, S., Butterworth, A. and Thompson, S.G. (2013) Mendelian Randomization Analysis with Multiple Genetic Variants Using Summarized Data. Genetic Epidemiology, 37, 658-665.
https://doi.org/10.1002/gepi.21758
[25] Bowden, J., Davey Smith, G. and Burgess, S. (2015) Mendelian Randomization with Invalid Instruments: Effect Estimation and Bias Detection through Egger Regression. International Journal of Epidemiology, 44, 512-525.
https://doi.org/10.1093/ije/dyv080
[26] Bowden, J., Del Greco M, F., Minelli, C., Davey Smith, G., Sheehan, N. and Thompson, J. (2017) A Framework for the Investigation of Pleiotropy in Two‐Sample Summary Data Mendelian Randomization. Statistics in Medicine, 36, 1783-1802.
https://doi.org/10.1002/sim.7221
[27] Chen, X., Hong, X., Gao, W., Luo, S., Cai, J., Liu, G., et al. (2022) Causal Relationship between Physical Activity, Leisure Sedentary Behaviors and COVID-19 Risk: A Mendelian Randomization Study. Journal of Translational Medicine, 20, Article No. 216.
https://doi.org/10.1186/s12967-022-03407-6
[28] Foley, C.N., Staley, J.R., Breen, P.G., Sun, B.B., Kirk, P.D.W., Burgess, S., et al. (2021) A Fast and Efficient Colocalization Algorithm for Identifying Shared Genetic Risk Factors across Multiple Traits. Nature Communications, 12, Article No. 764.
https://doi.org/10.1038/s41467-020-20885-8
[29] Wallace, C. (2021) A More Accurate Method for Colocalisation Analysis Allowing for Multiple Causal Variants. PLOS Genetics, 17, e1009440.
https://doi.org/10.1371/journal.pgen.1009440
[30] Yun, Z., Guo, Z., Li, X., Shen, Y., Nan, M., Dong, Q., et al. (2023) Genetically Predicted 486 Blood Metabolites in Relation to Risk of Colorectal Cancer: A Mendelian Randomization Study. Cancer Medicine, 12, 13784-13799.
https://doi.org/10.1002/cam4.6022
[31] Yin, Q. and Zhu, L. (2023) Does Co-Localization Analysis Reinforce the Results of Mendelian Randomization? Brain, 147, e7-e8.
https://doi.org/10.1093/brain/awad295
[32] 祝雨, 罗翔升, 郭晓杰, 等. 注意缺陷多动障碍儿童选择性注意受损的脑影像学特征[J]. 中国心理卫生杂志, 2021, 35(11): 947-953.
[33] 张欢, 杨斌让. 注意缺陷多动障碍患儿默认网络脑功能磁共振成像研究进展[J]. 中华儿科杂志, 2021, 59(11): 981-984.
[34] Castellanos, F.X. (2002) Developmental Trajectories of Brain Volume Abnormalities in Children and Adolescents with Attention-Deficit/Hyperactivity Disorder. JAMA, 288, 1740-1748.
https://doi.org/10.1001/jama.288.14.1740
[35] Mostofsky, S.H., Cooper, K.L., Kates, W.R., Denckla, M.B. and Kaufmann, W.E. (2002) Smaller Prefrontal and Premotor Volumes in Boys with Attention-Deficit/Hyperactivity Disorder. Biological Psychiatry, 52, 785-794.
https://doi.org/10.1016/s0006-3223(02)01412-9
[36] Mu, S., Wu, H., Zhang, J. and Chang, C. (2021) Structural Brain Changes and Associated Symptoms of ADHD Subtypes in Children. Cerebral Cortex, 32, 1152-1158.
https://doi.org/10.1093/cercor/bhab276
[37] Killgore, W.D.S. and Yurgelun-Todd, D.A. (2004) Activation of the Amygdala and Anterior Cingulate during Nonconscious Processing of Sad versus Happy Faces. NeuroImage, 21, 1215-1223.
https://doi.org/10.1016/j.neuroimage.2003.12.033
[38] 史亚楠, 范松丽, 王立宁, 薛国丽. 基于3.0MRI的注意缺陷多动障碍儿童脑白质网络结构变化与执行功能的关系[J]. 卒中与神经疾病, 2024, 31(2): 137-141.
[39] Hoogman, M., Bralten, J., Hibar, D.P., Mennes, M., Zwiers, M.P., Schweren, L.S.J., et al. (2017) Subcortical Brain Volume Differences in Participants with Attention Deficit Hyperactivity Disorder in Children and Adults: A Cross-Sectional Mega-Analysis. The Lancet Psychiatry, 4, 310-319.
https://doi.org/10.1016/s2215-0366(17)30049-4
[40] Tomasz, H., Aleksandra, G., Natalia, N.S., et al. (2022) Prenatal and Early Postnatal Exposure to a Natural Disaster and Attention Deficit/Hyperactivity Disorder Symptoms in Indian Children. Scientific Reports, 2, Article No. 16235.
https://doi.org/10.1038/s41598-022-20609-6
[41] Bitsko, R.H., Holbrook, J.R., O’Masta, B., Maher, B., Cerles, A., Saadeh, K., et al. (2022) A Systematic Review and Meta-Analysis of Prenatal, Birth, and Postnatal Factors Associated with Attention-Deficit/Hyperactivity Disorder in Children. Prevention Science, 25, 203-224.
https://doi.org/10.1007/s11121-022-01359-3
[42] Ahmed-Leitao, F., Spies, G., van den Heuvel, L. and Seedat, S. (2016) Hippocampal and Amygdala Volumes in Adults with Posttraumatic Stress Disorder Secondary to Childhood Abuse or Maltreatment: A Systematic Review. Psychiatry Research: Neuroimaging, 256, 33-43.
https://doi.org/10.1016/j.pscychresns.2016.09.008