Disease Modules Associated with Unfavorable Sleep Patterns and Their Genetic Determinants: A Prospective Cohort Study of the UK Biobank.

IF 3.7 Q2 GENETICS & HEREDITY
Phenomics (Cham, Switzerland) Pub Date : 2024-08-12 eCollection Date: 2024-10-01 DOI:10.1007/s43657-023-00144-8
Huazhen Yang, Can Hou, Wenwen Chen, Yu Zeng, Yuanyuan Qu, Yajing Sun, Yao Hu, Xiangdong Tang, Huan Song
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Abstract

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 (n = 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.

Supplementary information: The online version contains supplementary material available at 10.1007/s43657-023-00144-8.

与不良睡眠模式及其遗传决定因素相关的疾病模块:英国生物银行的前瞻性队列研究。
尽管睡眠相关特征与主要疾病之间存在既定的联系,但对受影响的疾病模块及其遗传决定因素的全面评估仍缺乏。采用多重对应分析和k-means聚类算法,将235,826名符合条件的参与者分为不同的不利睡眠模式组(短睡眠(n = 10,073)、打鼾(22,419)、失眠(102,771)、失眠和打鼾(62,909))和良好睡眠模式组(37,654)。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进行疾病模块识别。通过全基因组关联研究(GWAS)确定了与每种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“睡眠时间短”、“打鼾”、“失眠”和“失眠并打鼾”为特征的不良睡眠模式分别与0、9、10和19种疾病的风险增加有关。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,其特征是与消化系统,循环和内分泌系统(仅与打鼾相关的模式)以及肌肉骨骼系统(仅与失眠相关的模式)相关的主要疾病。使用受影响疾病的数量作为一个人对每个疾病模块的易感性指标[即易感评分(SS)], GWAS分析分别确定了与上述疾病模块的剩余SS相关的5个、1个和3个重要位点,这些位点映射了几种潜在的生物学途径,包括与激素调节和儿茶酚胺摄取相关的途径。总之,睡眠模式不好的人,尤其是打鼾和失眠的人,患多种疾病的风险会增加。确定三种主要疾病模块及其相关遗传决定因素可能有助于制定精确预防未来健康衰退的战略。补充信息:在线版本包含补充资料,下载地址:10.1007/s43657-023-00144-8。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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