Table1_Treating chronic atrophic gastritis: identifying sub-population based on real-world TCM electronic medical records.XLSX

Background and ObjectiveChronic atrophic gastritis (CAG) is a complex chronic disease caused by multiple factors that frequently occurs disease in the clinic. The worldwide prevalence of CAG is high. Interestingly, clinical CAG patients often present with a variety of symptom phenotypes, which makes it more difficult for clinicians to treat. Therefore, there is an urgent need to improve our understanding of the complexity of the clinical CAG population, obtain more accurate disease subtypes, and explore the relationship between clinical symptoms and medication. Therefore, based on the integrated platform of complex networks and clinical research, we classified the collected patients with CAG according to their different clinical characteristics and conducted correlation analysis on the classification results to identify more accurate disease subtypes to aid in personalized clinical treatment.
MethodTraditional Chinese medicine (TCM) offers an empirical understanding of the clinical subtypes of complicated disorders since TCM therapy is tailored to the patient’s symptom profile. We gathered 6,253 TCM clinical electronic medical records (EMRs) from CAG patients and manually annotated, extracted, and preprocessed the data. A shared symptom-patient similarity network (PSN) was created. CAG patient subgroups were established, and their clinical features were determined through enrichment analysis employing community identification methods. Different clinical features of relevant subgroups were correlated based on effectiveness to identify symptom–botanical botanical drugs correspondence. Moreover, network pharmacology was employed to identify possible biological relationships between screened symptoms and medications and to identify various clinical and molecular aspects of the key subtypes using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis.
Results5,132 patients were included in the study: 2,699 males (52.60%) and 2,433 females (47.41%). The population was divided into 176 modules. We selected the first 3 modules (M29, M3, and M0) to illustrate the characteristic phenotypes and genotypes of CAG disease subtypes. The M29 subgroup was characterized by gastric fullness disease and internal syndrome of turbidity and poison. The M3 subgroup was characterized by epigastric pain and disharmony between the liver and stomach. The M0 subgroup was characterized by epigastric pain and dampness-heat syndrome. In symptom analysis, The top symptoms for symptom improvement in all three subgroups were stomach pain, bloating, insomnia, poor appetite, and heartburn. However, the three groups were different. The M29 subgroup was more likely to have stomach distention, anorexia, and palpitations. Citrus medica, Solanum nigrum, Jiangcan, Shan ci mushrooms, and Dillon were the most popular botanical drugs. The M3 subgroup has a higher incidence of yellow urine, a bitter tongue, and stomachaches. Smilax glabra, Cyperus rotundus, Angelica sinensis, Conioselinum anthriscoides, and Paeonia lactiflora were the botanical drugs used. Vomiting, nausea, stomach pain, and appetite loss are common in the M0 subgroup. The primary medications are Scutellaria baicalensis, Smilax glabra, Picrorhiza kurroa, Lilium lancifolium, and Artemisia scoparia. Through GO and KEGG pathway analysis, We found that in the M29 subgroup, Citrus medica, Solanum nigrum, Jiangcan, Shan ci mushrooms, and Dillon may exert their therapeutic effects on the symptoms of gastric distension, anorexia, and palpitations by modulating apoptosis and NF-κB signaling pathways. In the M3 subgroup, Smilax glabra, Cyperus rotundus, Angelica sinensis, Conioselinum anthriscoides, and Paeonia lactiflora may be treated by NF-κB and JAK-STAT signaling pathway for the treatment of stomach pain, bitter mouth, and yellow urine. In the M0 subgroup, Scutellaria baicalensis, Smilax glabra, Picrorhiza kurroa, Lilium lancifolium, and Artemisia scoparia may exert their therapeutic effects on poor appetite, stomach pain, vomiting, and nausea through the PI3K-Akt signaling pathway.
ConclusionBased on PSN identification and community detection analysis, CAG population division can provide useful recommendations for clinical CAG treatment. This method is useful for CAG illness classification and genotyping investigations and can be used for other complicated chronic diseases.

背景与目的
慢性萎缩性胃炎（Chronic Atrophic Gastritis, CAG）是一种多因素致病的复杂慢性疾病，在临床中十分常见且全球患病率较高。值得注意的是，慢性萎缩性胃炎患者常表现出多样的症状表型，这为临床诊疗增加了难度。因此，亟需加深对慢性萎缩性胃炎临床人群复杂性的认知，获取更为精准的疾病亚型，并探索临床症状与用药之间的关联。为此，本研究基于复杂网络与临床研究整合平台，根据收集的慢性萎缩性胃炎患者的不同临床特征进行分组，并对分组结果开展相关性分析，以识别更精准的疾病亚型，助力临床个性化诊疗。

研究方法
中医药（Traditional Chinese Medicine, TCM）基于对患者症状特征的个体化诊疗，为复杂疾病的临床亚型提供了经验性认知。本研究收集了6253例慢性萎缩性胃炎患者的中医药临床电子病历（Electronic Medical Records, EMRs），并对数据进行人工标注、提取与预处理。本研究构建了症状-患者共享相似性网络（Patient Similarity Network, PSN），通过社区识别算法开展富集分析，对慢性萎缩性胃炎患者进行亚组划分，并明确各亚组的临床特征。基于疗效关联各亚组的不同临床特征，以明确症状与植物药的对应关系。此外，本研究采用网络药理学方法，结合基因本体（Gene Ontology, GO）与京都基因与基因组百科全书（Kyoto Encyclopedia of Genes and Genomes, KEGG）通路富集分析，明确筛选出的症状与药物间潜在的生物学关联，并解析关键亚型的临床与分子特征。

研究结果
本研究共纳入5132例患者，其中男性2699例（占比52.60%），女性2433例（占比47.41%）。该研究人群被划分为176个模块。本研究选取前3个模块（M29、M3与M0），用以阐释慢性萎缩性胃炎疾病亚型的特征性表型与基因型。M29亚组以胃脘胀满、浊毒内蕴为主要特征，更常出现胃脘胀闷、纳差与心悸症状，常用中药包括佛手（Citrus medica）、龙葵（Solanum nigrum）、僵蚕（Jiangcan）、山慈菇（Shan ci mushrooms）以及Dillon。M3亚组以上腹疼痛、肝胃不和为主要特征，患者尿黄、口苦与胃脘痛的发生率更高，常用中药包括土茯苓（Smilax glabra）、香附（Cyperus rotundus）、当归（Angelica sinensis）、山芎（Conioselinum anthriscoides）以及白芍（Paeonia lactiflora）。M0亚组以上腹疼痛、湿热证为主要特征，常见呕吐、恶心、胃脘痛与食欲减退症状，常用核心药物包括黄芩（Scutellaria baicalensis）、土茯苓（Smilax glabra）、胡黄连（Picrorhiza kurroa）、卷丹百合（Lilium lancifolium）以及茵陈（Artemisia scoparia）。
症状分析结果显示，三个亚组中与症状改善相关的核心症状均为胃脘痛、腹胀、失眠、食欲减退与烧心，但不同亚组的症状表现存在差异。通过GO与KEGG通路分析，本研究发现：M29亚组中，佛手、龙葵、僵蚕、山慈菇以及Dillon可能通过调控细胞凋亡与核因子κB（NF-κB）信号通路，改善胃脘胀闷、纳差与心悸症状；M3亚组中，土茯苓、香附、当归、山芎以及白芍可能通过核因子κB与JAK-STAT信号通路，治疗胃脘痛、口苦与尿黄症状；M0亚组中，黄芩、土茯苓、胡黄连、卷丹百合以及茵陈可能通过PI3K-Akt信号通路，改善食欲减退、胃脘痛、呕吐与恶心症状。

研究结论
基于症状-患者相似性网络识别与社区检测分析的慢性萎缩性胃炎人群分组方法，可为临床慢性萎缩性胃炎治疗提供有效参考。该方法可用于慢性萎缩性胃炎的疾病分型与基因分型研究，同时也可推广应用于其他复杂慢性疾病的相关研究。