Trends and Mechanisms Linking Pediatric Attention-Deficit/Hyperactivity Disorder and Atopic Disorders

Literature retrieval was conducted in the Web of Science Core Collection (WoSCC) and Scopus databases, covering the period from 2005 to 2025. Keyword selection was based on broad field definitions and relevant terminology, including medical terms, Medical Subject Headings (MeSH) vocabulary, and synonyms related to AD and ADHD. The search strategy comprised the following topic sets: (“Attention-deficit/hyperactivity disorder” OR “ADHD”) AND (“atopic disorders” OR “asthma” OR “atopic eczema” OR “allergic rhinitis”) AND (“child*”), using the TS field in WoSCC and the TITLE-ABS-KEY field in Scopus. The literature screening process (Figure 1) was performed independently by two researchers; disagreements were resolved with the involvement of a third researcher who verified the final results. To ensure the scientific quality and consistency of included records, only English-language original research articles and review articles were retained for analysis. Under this search scheme, 498 records from WoSCC and 697 records from Scopus were collected and downloaded. After removal of duplicates, 838 eligible publications were included for bibliometric analyses and molecular-mechanism exploration of the association between AD and ADHD. To systematically investigate research trends and interaction mechanisms between AD and ADHD, we employed a series of bibliometric and bioinformatic tools. Bibliometric analyses were performed using CiteSpace (6.3.R3), VOSviewer (1.6.20), and the bibliometrix R package (v5.2.1). Quantitative indicators of publication output and collaboration patterns were calculated using bibliometrix. VOSviewer was used for in-depth analyses of co-authorship networks and keyword co-occurrence. CiteSpace was applied to map and cluster countries, institutions, journals, and keywords, and to generate a dual-map overlay of citation trajectories. Parameter settings were as follows: the time span was set to 2001–2025 with a 1-year time slice; institutions and keywords were specified as node types. The path selection method was set to “pathfinder”, while all other parameters were kept at default values. The K value was set to 25, and keyword clustering was performed using the log-likelihood ratio (LLR) algorithm. For molecular mechanism exploration, GeneCards (https://www.genecards.org/) was used to screen potential gene targets associated with AD and ADHD, and corresponding target screening was also performed for the AD subtypes asthma, atopic eczema, and allergic rhinitis. Based on overlaps among disease-associated genes, Cytoscape (v3.10.0) with StringApp (v2.2.0) was further used to interrogate molecular interactions and construct protein–protein interaction (PPI) networks. These networks were visualized and analyzed in Cytoscape to identify hub genes and key modules. Enrichment analyses of shared gene targets were conducted to elucidate their roles in biological processes, molecular functions, and disease pathways. Multiple R packages—clusterProfiler, ggplot2, ComplexHeatmap, enrichplot, and DOSE—were used to perform Disease Ontology (DO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Ontology (GO) enrichment analyses.本研究在Web of Science核心合集（WoSCC）与Scopus数据库中开展文献检索，检索时间范围为2005年至2025年。关键词选取基于宽泛的领域定义与相关术语，涵盖医学术语、医学主题词表（Medical Subject Headings, MeSH）词汇，以及与特应性皮炎（AD）和注意缺陷与多动障碍（ADHD）相关的同义词。本次检索策略包含以下主题组：（“注意缺陷与多动障碍” OR “ADHD”）AND（“特应性疾病” OR “哮喘” OR “特应性湿疹” OR “变应性鼻炎”）AND("child*")，在WoSCC中使用TS字段，在Scopus中使用TITLE-ABS-KEY字段。文献筛选流程（图1）由2名研究者独立完成；若出现分歧，则由第三名研究者介入协调，最终结果需经其审核确认。为确保纳入文献的科学性与一致性，本次分析仅保留英文原创研究论文与综述类论文。按照本次检索方案，共从WoSCC中检索并下载498条记录，从Scopus中检索并下载697条记录。去除重复文献后，最终纳入838篇符合条件的出版物，用于开展AD与ADHD关联的文献计量分析及分子机制探索。 为系统探究AD与ADHD的研究趋势及相互作用机制，本研究采用了一系列文献计量学与生物信息学工具。文献计量分析借助CiteSpace（6.3.R3版本）、VOSviewer（1.6.20版本）及bibliometrix R包（v5.2.1版本）完成。通过bibliometrix计算论文产出与合作模式的量化指标。VOSviewer用于深入分析作者合作网络与关键词共现关系。CiteSpace用于绘制并聚类国家、机构、期刊及关键词，并生成引用轨迹的双图叠加图谱。参数设置如下：时间跨度设为2001-2025年，时间切片为1年；节点类型指定为机构与关键词。路径选择方法设为“路径寻找者（pathfinder）”，其余参数均保留默认值。K值设为25，关键词聚类采用对数似然比（log-likelihood ratio, LLR）算法。 针对分子机制探索部分，本研究借助GeneCards数据库（https://www.genecards.org/）筛选与AD及ADHD相关的潜在基因靶点，并针对AD亚型哮喘、特应性湿疹及变应性鼻炎开展了对应的靶点筛选。基于疾病相关基因的重叠部分，本研究进一步借助搭载StringApp（v2.2.0版本）的Cytoscape（v3.10.0版本）解析分子相互作用，并构建蛋白质相互作用（PPI）网络。通过Cytoscape对该网络进行可视化与分析，以识别核心基因与关键模块。对共享基因靶点开展富集分析，以阐明其在生物过程、分子功能及疾病通路中的作用。本研究采用多款R包——包括clusterProfiler、ggplot2、ComplexHeatmap、enrichplot及DOSE——开展疾病本体论（Disease Ontology, DO）、京都基因与基因组百科全书（Kyoto Encyclopedia of Genes and Genomes, KEGG）及基因本体论（Gene Ontology, GO）富集分析。