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Association of Sleep Pattern and Genetic Susceptibility with Obstructive Sleep Apnea: A Prospective Analysis of the UK Biobank.
Zhou, Rong; Suo, Chen; Jiang, Yong; Yuan, Liyun; Zhang, Tiejun; Chen, Xingdong; Zhang, Guoqing.
Afiliación
  • Zhou R; Department of Epidemiology, School of Public Health, Fudan University, Shanghai, 200433, People's Republic of China.
  • Suo C; Shanghai Southgene Technology Co., Ltd., Shanghai, 201203, People's Republic of China.
  • Jiang Y; Department of Epidemiology, School of Public Health, Fudan University, Shanghai, 200433, People's Republic of China.
  • Yuan L; Taizhou Institute of Health Sciences, Fudan University, Taizhou, 225300, People's Republic of China.
  • Zhang T; China National Clinical Research Center for Neurological Diseases, Beijing, 100070, People's Republic of China.
  • Chen X; Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Science, Shanghai, 200031, People's Republic of China.
  • Zhang G; Department of Epidemiology, School of Public Health, Fudan University, Shanghai, 200433, People's Republic of China.
Nat Sci Sleep ; 16: 503-515, 2024.
Article en En | MEDLINE | ID: mdl-38803507
ABSTRACT

Purpose:

The prevalence of obstructive sleep apnea (OSA) is high worldwide. This study aimed to quantify the relationship between the incidence of OSA and sleep patterns and genetic susceptibility.

Methods:

A total of 355,133 white British participants enrolled in the UK Biobank between 2006 and 2010 with follow-up data until September 2021 were recruited. We evaluated sleep patterns using a customized sleep scoring method based on the low-risk sleep phenotype, defined as follows morning chronotype, 7-8 hours of sleep per day, never/rarely experience insomnia, no snoring, no frequent daytime sleepiness, never/rarely nap, and easily getting up early. The polygenic risk score was calculated to assess genetic susceptibility to OSA. Cox proportional hazard models were used to evaluate the associations between OSA and sleep patterns and genetic susceptibility.

Results:

During a mean follow-up of 12.57 years, 4618 participants were diagnosed with OSA (age 56.83 ± 7.69 years, women 31.3%). Compared with those with a poor sleep pattern, participants with a normal (HR 0.42, 95% CI 0.38-0.46), ideal (HR 0.21, 95% CI 0.19-0.24), or optimal (HR 0.15, 95% CI 0.12-0.18) sleep pattern were significantly more likely to have OSA. The genetic susceptibility of 173,239 participants was calculated, and the results showed that poor (HR 3.67, 95% CI 2.95-4.57) and normal (HR 1.89, 95% CI 1.66-2.16) sleep patterns with high genetic susceptibility can increase the risk for OSA.

Conclusion:

This large-scale prospective study provides evidence suggesting that sleep patterns across seven low-risk sleep phenotypes may protect against OSA in individuals with varying degrees of genetic susceptibility.
Palabras clave

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Nat Sci Sleep Año: 2024 Tipo del documento: Article

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Nat Sci Sleep Año: 2024 Tipo del documento: Article
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