Genetic analysis of obstructive sleep apnoea discovers a strong association with cardiometabolic health

Strausz, Satu; Ruotsalainen, Sanni; Ollila, Hanna; Karjalainen, Juha; Kiiskinen, Tuomo; Reeve, Mary Pat; Kurki, Mitja; Mars, Nina; Havulinna, Aki S.; Luonsi, Elina; Aly, Dina Mansour; Ahlqvist, Emma; Teder‐Laving, Maris; Palta, Priit; Groop, Leif; Mägi, Reedik; Mäkitie, Antti; Salomaa, Veikko; Bachour, Adel; Tuomi, Tiinamaija; FinnGen,; Palotie, Aarno; Palotie, Tuula; Ripatti, Samuli

doi:10.1183/13993003.03091-2020

Cited by 110 publications

(72 citation statements)

References 51 publications

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“…This reflects a similar prevalence of OSA diagnoses in COVID-19 infected as in the normal population in FinnGen, where prevalence is 8%. 18 Ninety-one (20.4%) patients required hospitalisation (36.3% male, mean age 65.9 years) including 19 patients with OSA (table 1).…”

Section: Resultsmentioning

confidence: 99%

Sleep apnoea is a risk factor for severe COVID-19

et al. 2021

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BackgroundObstructive sleep apnoea (OSA) is associated with higher body mass index (BMI), diabetes, older age and male gender, which are all risk factors for severe COVID-19.We aimed to study if OSA is an independent risk factor for COVID-19 infection or for severe COVID-19.MethodsOSA diagnosis and COVID-19 infection were extracted from the hospital discharge, causes of death and infectious diseases registries in individuals who participated in the FinnGen study (n=260 405). Severe COVID-19 was defined as COVID-19 requiring hospitalisation. Multivariate logistic regression model was used to examine association. Comorbidities for either COVID-19 or OSA were selected as covariates. We performed a meta-analysis with previous studies.ResultsWe identified 445 individuals with COVID-19, and 38 (8.5%) of them with OSA of whom 19 out of 91 (20.9%) were hospitalised. OSA associated with COVID-19 hospitalisation independent from age, sex, BMI and comorbidities (p-unadjusted=5.13×10−5, OR-adjusted=2.93 (95% CI 1.02 to 8.39), p-adjusted=0.045). OSA was not associated with the risk of contracting COVID-19 (p=0.25). A meta-analysis of OSA and severe COVID-19 showed association across 15 835 COVID-19 positive controls, and n=1294 patients with OSA with severe COVID-19 (OR=2.37 (95% 1.14 to 4.95), p=0.021).ConclusionRisk for contracting COVID-19 was the same for patients with OSA and those without OSA. In contrast, among COVID-19 positive patients, OSA was associated with higher risk for hospitalisation. Our findings are in line with earlier works and suggest OSA as an independent risk factor for severe COVID-19.

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Section: Resultsmentioning

confidence: 99%

Sleep apnoea is a risk factor for severe COVID-19

et al. 2021

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“…Third, we attempted to examine the relationship between sleep apnoea and depression in a population using MR. However, studies have reported sleep apnoea heritability estimates of 8.3% (95% CI 6–11%), whereas there are only five SNPs associated with sleep apnoea (p<5.0×10 −8 ), including three SNPs in UK Biobank [ 43 ].…”

Section: Discussionmentioning

confidence: 99%

Association between depression and sleep apnoea: a Mendelian randomisation study

et al. 2021

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BackgroundStudies have reported a close relationship between depression and sleep apnoea, yet it is unknown whether these are causally related. Thus, we aimed to determine whether depression is associated with the aetiology of sleep apnoea.MethodsWe used publicly available genetic summary data from two large consortia, the Psychiatric Genomics Consortium, with data from 36 single-nucleotide polymorphisms (SNPs) closely associated with major depressive disorder (MDD) and UK Biobank, including 456 736 patients with sleep apnoea and 766 964 controls. For Mendelian randomisation (MR) analysis, we used the inverse-variance weighted method, weighted median method, MR-Egger regression, MR pleiotropy residual sum, and outlier test to retrieve summary data. Analyses were performed using the “TwoSampleMR” package in R.ResultsOf the 36 SNPs associated with MDD, we found statistically significant evidence of a potential causal effect of MDD on the risk of sleep apnoea (odds ratio 1.004, 95% confidence interval: 1.001–1.006, p=0.001). Similar results were obtained using the MR-Egger and weighted median methods. Additionally, we found no heterogeneity or pleiotropy.ConclusionsOur findings suggest that depression slightly increases the risk of sleep apnoea. Further investigation of the potential biological mechanisms is necessary.

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“…Early EHR analyses used International Classification of Disease (ICD) codes for sleep apnea phenotyping. 27 , 28 ICD data are largely collected for clinical and billing purposes. ICD-derived disease diagnoses are often used when ruling out a given disease through a billed procedure such as a sleep examination whether or not the patient is found to have that condition.…”

Section: Background and Significancementioning

confidence: 99%

Sleep apnea phenotyping and relationship to disease in a large clinical biobank

et al. 2022

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Objective Sleep apnea is associated with a broad range of pathophysiology. While electronic health record (EHR) information has the potential for revealing relationships between sleep apnea and associated risk factors and outcomes, practical challenges hinder its use. Our objectives were to develop a sleep apnea phenotyping algorithm that improves the precision of EHR case/control information using natural language processing (NLP); identify novel associations between sleep apnea and comorbidities in a large clinical biobank; and investigate the relationship between polysomnography statistics and comorbid disease using NLP phenotyping. Materials and Methods We performed clinical chart reviews on 300 participants putatively diagnosed with sleep apnea and applied International Classification of Sleep Disorders criteria to classify true cases and noncases. We evaluated 2 NLP and diagnosis code-only methods for their abilities to maximize phenotyping precision. The lead algorithm was used to identify incident and cross-sectional associations between sleep apnea and common comorbidities using 4876 NLP-defined sleep apnea cases and 3× matched controls. Results The optimal NLP phenotyping strategy had improved model precision (≥0.943) compared to the use of one diagnosis code (≤0.733). Of the tested diseases, 170 disorders had significant incidence odds ratios (ORs) between cases and controls, 8 of which were confirmed using polysomnography (n = 4544), and 281 disorders had significant prevalence OR between sleep apnea cases versus controls, 41 of which were confirmed using polysomnography data. Discussion and Conclusion An NLP-informed algorithm can improve the accuracy of case-control sleep apnea ascertainment and thus improve the performance of phenome-wide, genetic, and other EHR analyses of a highly prevalent disorder.

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Genetic analysis of obstructive sleep apnoea discovers a strong association with cardiometabolic health

Cited by 110 publications

References 51 publications

Sleep apnoea is a risk factor for severe COVID-19

Sleep apnoea is a risk factor for severe COVID-19

Association between depression and sleep apnoea: a Mendelian randomisation study

Sleep apnea phenotyping and relationship to disease in a large clinical biobank

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