Yang Huazhen, Hou Can, Chen Wenwen, Zeng Yu, Qu Yuanyuan, Sun Yajing, Hu Yao, Tang Xiangdong, Song Huan
Mental Health Center and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610000 China.
Med-X Center for Informatics, Sichuan University, Chengdu, 610000 China.
Phenomics. 2024 Aug 12;4(5):415-429. doi: 10.1007/s43657-023-00144-8. eCollection 2024 Oct.
Despite the established associations between sleep-related traits and major diseases, comprehensive assessment on affected disease modules and their genetic determinants is lacking. Using multiple correspondence analysis and the k-means clustering algorithm, 235,826 eligible participants were clustered into distinct unfavorable sleep patterns [short sleep duration ( = 10,073), snoring (22,419), insomnia (102,771), insomnia and snoring (62,909)] and favorable sleep pattern groups (37,654). The associations of unfavorable sleep patterns with 134 diseases were estimated using Cox regression models; and comorbidity network analyses were applied for disease module identification. Genetic determinants associated with each disease module were identified by genome-wide association studies (GWAS). During an average follow-up of 10.80 years, unfavorable sleep patterns featured by 'short sleep duration', 'snoring', 'insomnia', and 'insomnia and snoring' were associated with increased risk of 0, 9, 10, and 19 diseases, respectively. Furthermore, comorbidity network analyses categorized these affected diseases into three disease modules, characterized by predominant diseases related to digestive system, circulatory and endocrine systems (snoring-related patterns only), and musculoskeletal system (insomnia-related patterns only). Using the number of affected diseases, as an index of a person's susceptibility to each disease module [i.e., susceptible score (SS)], GWAS analyses identified five, one, and three significant loci associated with the residual SS of these aforementioned disease modules, respectively, which mapped to several potential biological pathways, including those related to hormone regulation and catecholamine uptake. In conclusion, individuals with unfavorable sleep patterns, particularly snoring and insomnia, had increased risk of multiple diseases. The identification of three major disease modules with their relevant genetic determinants may facilitate strategy development for precision prevention of future health decline.
The online version contains supplementary material available at 10.1007/s43657-023-00144-8.
尽管睡眠相关特征与主要疾病之间的关联已得到证实,但缺乏对受影响疾病模块及其遗传决定因素的综合评估。使用多重对应分析和k均值聚类算法,将235,826名符合条件的参与者聚类为不同的不良睡眠模式组[短睡眠时间组(10,073人)、打鼾组(22,419人)、失眠组(102,771人)、失眠伴打鼾组(62,909人)]和良好睡眠模式组(37,654人)。使用Cox回归模型估计不良睡眠模式与134种疾病的关联;并应用共病网络分析来识别疾病模块。通过全基因组关联研究(GWAS)确定与每个疾病模块相关的遗传决定因素。在平均10.80年的随访期间,以“短睡眠时间”、“打鼾”、“失眠”和“失眠伴打鼾”为特征的不良睡眠模式分别与0、9、10和19种疾病风险增加相关。此外,共病网络分析将这些受影响的疾病分为三个疾病模块,其特征分别是与消化系统、循环和内分泌系统相关的主要疾病(仅打鼾相关模式)以及肌肉骨骼系统相关的主要疾病(仅失眠相关模式)。使用受影响疾病的数量作为一个人对每个疾病模块易感性的指标[即易感性评分(SS)],GWAS分析分别确定了与上述疾病模块的残余SS相关的5个、1个和3个显著位点,这些位点映射到几个潜在的生物学途径,包括与激素调节和儿茶酚胺摄取相关的途径。总之,具有不良睡眠模式的个体,尤其是打鼾和失眠者,患多种疾病的风险增加。识别三个主要疾病模块及其相关遗传决定因素可能有助于制定精准预防未来健康下降的策略。
在线版本包含可在10.1007/s43657-023-00144-8获取的补充材料。