Zhang Jian, Hou Can, Chen Wenwen, Hu Yao, Xu Shishi, Liu Haowen, Yang Yao, Valdimarsdóttir Unnur A, Fang Fang, Song Huan
Mental Health Center and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.
Med-X Center for Informatics, Sichuan University, Chengdu, China.
PLoS One. 2025 Aug 22;20(8):e0329701. doi: 10.1371/journal.pone.0329701. eCollection 2025.
Pre-existing comorbidities are linked to increased risk of severe COVID-19, but comprehensive assessments of comorbidity patterns remain limited.
We used network analysis to identify pre-existing comorbidity modules (i.e., groups of diseases more densely interconnected with each other than with other diseases in the comorbidity network) in a cohort of 420,920 individuals from the UK Biobank who were in England. We defined cases requiring hospitalization or who died of COVID-19 as "severe COVID-19". Logistic regression was used to examine associations between comorbidity modules and severe COVID-19, and a module-based comorbidity index was developed to predict severe COVID-19, compared with existing indices.
Comorbidity network analysis identified 190 disease pairs with confirmed comorbidity associations, which were further divided into seven comorbidity modules. Among the 30,914 individuals diagnosed with COVID-19, 3,970 were identified as severe cases (median age of 73.6 years, 58.77% being male). Six of seven identified modules showed statistically significant associations with severe COVID-19, especially modules related to circulatory and respiratory diseases (odds ratio = 1.67 [95% confidence interval 1.54-1.81]) and age-related eye diseases (1.39 [1.27-1.52]). Associations did not differ by sex, age or vaccination status but were generally stronger during the first wave of COVID-19 pandemic (i.e., 31st January-1st October, 2020). Our newly developed module-based comorbidity index showed better performance in predicting severe COVID-19 (AUC = 0.779) compared to the existing Charlson Comorbidity Index (0.714) and the 16-comorbidity index (0.714).
Our study demonstrated that pre-existing comorbidity modules, particularly modules related to circulatory and respiratory diseases and age-related eye diseases, were associated with severe COVID-19. Moreover, the module-based comorbidity index provides better prediction of severe COVID-19 than existing prediction indices.
既往合并症与COVID-19重症风险增加相关，但对合并症模式的全面评估仍然有限。
我们使用网络分析，在来自英国生物银行的420,920名英格兰个体队列中识别既往合并症模块（即，在合并症网络中彼此之间联系比与其他疾病更紧密的疾病组）。我们将需要住院治疗或死于COVID-19的病例定义为“重症COVID-19”。使用逻辑回归分析来检验合并症模块与重症COVID-19之间的关联，并开发了基于模块的合并症指数来预测重症COVID-19，并与现有指数进行比较。
合并症网络分析确定了190对具有确诊合并症关联的疾病对，这些疾病对进一步分为七个合并症模块。在30,914例被诊断为COVID-19的个体中，3,970例被确定为重症病例（中位年龄73.6岁，男性占58.77%）。七个已识别模块中的六个显示出与重症COVID-19有统计学意义的关联，特别是与循环系统和呼吸系统疾病相关的模块（优势比=1.67[95%置信区间1.54-1.81]）和与年龄相关的眼部疾病（1.39[1.27-1.52]）。关联在性别、年龄或疫苗接种状态方面没有差异，但在COVID-19大流行的第一波期间（即2020年1月31日至10月1日）通常更强。与现有的查尔森合并症指数（0.714）和16种合并症指数（0.714）相比，我们新开发的基于模块的合并症指数在预测重症COVID-19方面表现更好（AUC=0.779）。
我们的研究表明，既往合并症模块，特别是与循环系统和呼吸系统疾病以及与年龄相关的眼部疾病相关的模块，与重症COVID-19有关。此外，基于模块的合并症指数在预测重症COVID-19方面比现有预测指数表现更好。
