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Sleep modes based on objective measurement and diseases of the body systems: a cohort study of 87 617 participants from the UK Biobank dataset
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Yimeng WANG1, Qing CHEN1, Siwen LUO1, Fuquan SHI2, Mengchao HE1, Shengfeng WANG3, 4, Qiaorui WEN3, 4, Yingzhong DAI1, Hao QU1, Jia CAO1
Journal of Army Medical University | 2025, 47(4) : 318 - 325
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Journal of Army Medical University | 2025, 47(4): 318-325
• Monographic Reports •
Sleep modes based on objective measurement and diseases of the body systems: a cohort study of 87 617 participants from the UK Biobank dataset
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Yimeng WANG1, Qing CHEN1, Siwen LUO1, Fuquan SHI2, Mengchao HE1, Shengfeng WANG3, 4, Qiaorui WEN3, 4, Yingzhong DAI1, Hao QU1, Jia CAO1
Affiliations
  • 1Institute of Toxicology,Faculty of Military Preventive Medicine,Army Medical University (Third Military Medical University),Chongqing
  • 2Department of Emergency Response,Center for Disease Control and Prevention of Chinese PLA,Beijing
  • 3Department of Epidemiology and Biostatistics,School of Public Health,Peking University,Beijing
  • 4Key Laboratory of Epidemiology of Major Diseases (Peking University) of Ministry of Education,Beijing,China
Published: 2025-02-28 doi: 10.16016/j.2097-0927.202410006
Outline
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Objective

To investigate the impact of sleep modes on the risk for diseases of the body systems.

Methods

Based on a subset of UK Biobank dataset comprising 87 617 participants, 3 sleep dimensions including 6 sleep indicators were obtained through a wrist-worn accelerometer, that is sleep duration and onset, sleep rhythm (relative amplitude and stability), and sleep quality (sleep efficiency and number of awakenings). Latent profile analysis (LPA) was applied to identify and classify distinct sleep modes. Then their longitudinal medical records were the association between different sleep modes and the risk for 467 diseases.

Results‍

LPA identified 5 subgroups of unique sleep modes in the participants. Among the 5 subgroups, the subgroup 4 had relatively optimal levels in above sleep indicators. Compared to the subgroup 4, the other 4 subgroups exhibited variations in different sleep dimensions, with at least one indicator demonstrating an unfavorable trend. These subgroups also revealed differences in racial composition, shift work and social deprivation index. Moreover, there were notable differences in the risk of various system diseases among the subgroups (P<0.05). When compared to the subgroup 4, the other 4 subgroups exhibited an elevated risk for certain diseases (comprising a total of 126 diseases), with the diseases of the circulatory system, digestive system and musculoskeletal system most common. Among the 5 subgroups, the subgroup 2 (shorter sleep duration and later sleep onset) and the subgroup 5 (rhythm disorder) had the highest counts of associated risks, amounting to 85 and 91 types, respectively, but there was certain difference in their systematic composition.

Conclusion‍

There are different sleep modes within the participants, and the modes are potentially associated with an increased risk for diseases of body systems. Comprehensive interventions targeting overall sleep modes rather than single sleep indicator may yield obvious health benefits.

‍accelerometer  /  latent profile analysis  /  UK Biobank  /  disease risk
王艺蒙, 陈卿, 罗斯文, 史富全, 何孟超, 王圣峰, 温俏睿, 代应忠, 屈壕, 曹佳. 客观测量睡眠模式与各系统疾病谱系风险:英国生物银行87 617人队列研究. 陆军军医大学学报, 2025 , 47 (4) : 318 -325 . DOI: 10.16016/j.2097-0927.202410006
Yimeng WANG, Qing CHEN, Siwen LUO, Fuquan SHI, Mengchao HE, Shengfeng WANG, Qiaorui WEN, Yingzhong DAI, Hao QU, Jia CAO. Sleep modes based on objective measurement and diseases of the body systems: a cohort study of 87 617 participants from the UK Biobank dataset[J]. Journal of Army Medical University, 2025 , 47 (4) : 318 -325 . DOI: 10.16016/j.2097-0927.202410006
Year 2025 volume 47 Issue 4
15
6
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Article Info
doi: 10.16016/j.2097-0927.202410006
  • Receive Date:2024-10-08
  • Online Date:2026-10-30
  • Published:2025-02-28
Article Data
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History
  • Received:2024-10-08
  • Revised:2024-12-06
Affiliations
    1Institute of Toxicology,Faculty of Military Preventive Medicine,Army Medical University (Third Military Medical University),Chongqing
    2Department of Emergency Response,Center for Disease Control and Prevention of Chinese PLA,Beijing
    3Department of Epidemiology and Biostatistics,School of Public Health,Peking University,Beijing
    4Key Laboratory of Epidemiology of Major Diseases (Peking University) of Ministry of Education,Beijing,China
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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