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,   
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

参考文献

[1] Rajaprakash, M. and Leppert, M.L. (2022) Attention-Deficit/Hyperactivity Disorder. Pediatrics in Review, 43, 135-147. [Google Scholar] [CrossRef] [PubMed]
[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. [Google Scholar] [CrossRef] [PubMed]
[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. [Google Scholar] [CrossRef] [PubMed]
[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. [Google Scholar] [CrossRef] [PubMed]
[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. [Google Scholar] [CrossRef] [PubMed]
[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. [Google Scholar] [CrossRef] [PubMed]
[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. [Google Scholar] [CrossRef] [PubMed]
[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. [Google Scholar] [CrossRef] [PubMed]
[13] Richmond, R.C. and Davey Smith, G. (2021) Mendelian Randomization: Concepts and Scope. Cold Spring Harbor Perspectives in Medicine, 12, a040501. [Google Scholar] [CrossRef] [PubMed]
[14] Davey Smith, G. and Hemani, G. (2014) Mendelian Randomization: Genetic Anchors for Causal Inference in Epidemiological Studies. Human Molecular Genetics, 23, R89-R98. [Google Scholar] [CrossRef] [PubMed]
[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. [Google Scholar] [CrossRef] [PubMed]
[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. [Google Scholar] [CrossRef] [PubMed]
[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. [Google Scholar] [CrossRef] [PubMed]
[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. [Google Scholar] [CrossRef] [PubMed]
[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. [Google Scholar] [CrossRef] [PubMed]
[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. [Google Scholar] [CrossRef] [PubMed]
[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. [Google Scholar] [CrossRef] [PubMed]
[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. [Google Scholar] [CrossRef] [PubMed]
[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. [Google Scholar] [CrossRef] [PubMed]
[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. [Google Scholar] [CrossRef] [PubMed]
[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. [Google Scholar] [CrossRef] [PubMed]
[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. [Google Scholar] [CrossRef] [PubMed]
[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. [Google Scholar] [CrossRef] [PubMed]
[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. [Google Scholar] [CrossRef] [PubMed]
[29] Wallace, C. (2021) A More Accurate Method for Colocalisation Analysis Allowing for Multiple Causal Variants. PLOS Genetics, 17, e1009440. [Google Scholar] [CrossRef] [PubMed]
[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. [Google Scholar] [CrossRef] [PubMed]
[31] Yin, Q. and Zhu, L. (2023) Does Co-Localization Analysis Reinforce the Results of Mendelian Randomization? Brain, 147, e7-e8. [Google Scholar] [CrossRef] [PubMed]
[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. [Google Scholar] [CrossRef] [PubMed]
[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. [Google Scholar] [CrossRef] [PubMed]
[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. [Google Scholar] [CrossRef] [PubMed]
[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. [Google Scholar] [CrossRef] [PubMed]
[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. [Google Scholar] [CrossRef] [PubMed]
[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. [Google Scholar] [CrossRef] [PubMed]
[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. [Google Scholar] [CrossRef] [PubMed]
[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. [Google Scholar] [CrossRef] [PubMed]

为你推荐