Disentangling the complex landscape of sleep–wake disorders with data‐driven phenotyping: A study of the Bernese center
Background and purpose The diagnosis of sleep–wake disorders (SWDs) is challenging because of the existence of only few accurate biomarkers and the frequent coexistence of multiple SWDs and/or other comorbidities. The aim of this study was to assess in a large cohort of well‐characterized SWD patien...
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Veröffentlicht in: | European journal of neurology 2024-01, Vol.31 (1), p.e16026-n/a |
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creator | Aellen, Florence M. Van der Meer, Julia Dietmann, Anelia Schmidt, Markus Bassetti, Claudio L. A. Tzovara, Athina |
description | Background and purpose
The diagnosis of sleep–wake disorders (SWDs) is challenging because of the existence of only few accurate biomarkers and the frequent coexistence of multiple SWDs and/or other comorbidities. The aim of this study was to assess in a large cohort of well‐characterized SWD patients the potential of a data‐driven approach for the identification of SWDs.
Methods
We included 6958 patients from the Bernese Sleep Registry and 300 variables/biomarkers including questionnaires, results of polysomnography/vigilance tests, and final clinical diagnoses. A pipeline, based on machine learning, was created to extract and cluster the clinical data. Our analysis was performed on three cohorts: patients with central disorders of hypersomnolence (CDHs), a full cohort of patients with SWDs, and a clean cohort without coexisting SWDs.
Results
A first analysis focused on the cohort of patients with CDHs and revealed four patient clusters: two clusters for narcolepsy type 1 (NT1) but not for narcolepsy type 2 or idiopathic hypersomnia. In the full cohort of SWDs, nine clusters were found: four contained patients with obstructive and central sleep apnea syndrome, one with NT1, and four with intermixed SWDs. In the cohort of patients without coexisting SWDs, an additional cluster of patients with chronic insomnia disorder was identified.
Conclusions
This study confirms the existence of clear clusters of NT1 in CDHs, but mainly intermixed groups in the full spectrum of SWDs, with the exception of sleep apnea syndromes and NT1. New biomarkers are needed for better phenotyping and diagnosis of SWDs. |
doi_str_mv | 10.1111/ene.16026 |
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The diagnosis of sleep–wake disorders (SWDs) is challenging because of the existence of only few accurate biomarkers and the frequent coexistence of multiple SWDs and/or other comorbidities. The aim of this study was to assess in a large cohort of well‐characterized SWD patients the potential of a data‐driven approach for the identification of SWDs.
Methods
We included 6958 patients from the Bernese Sleep Registry and 300 variables/biomarkers including questionnaires, results of polysomnography/vigilance tests, and final clinical diagnoses. A pipeline, based on machine learning, was created to extract and cluster the clinical data. Our analysis was performed on three cohorts: patients with central disorders of hypersomnolence (CDHs), a full cohort of patients with SWDs, and a clean cohort without coexisting SWDs.
Results
A first analysis focused on the cohort of patients with CDHs and revealed four patient clusters: two clusters for narcolepsy type 1 (NT1) but not for narcolepsy type 2 or idiopathic hypersomnia. In the full cohort of SWDs, nine clusters were found: four contained patients with obstructive and central sleep apnea syndrome, one with NT1, and four with intermixed SWDs. In the cohort of patients without coexisting SWDs, an additional cluster of patients with chronic insomnia disorder was identified.
Conclusions
This study confirms the existence of clear clusters of NT1 in CDHs, but mainly intermixed groups in the full spectrum of SWDs, with the exception of sleep apnea syndromes and NT1. New biomarkers are needed for better phenotyping and diagnosis of SWDs.</description><identifier>ISSN: 1351-5101</identifier><identifier>ISSN: 1468-1331</identifier><identifier>EISSN: 1468-1331</identifier><identifier>DOI: 10.1111/ene.16026</identifier><identifier>PMID: 37531449</identifier><language>eng</language><publisher>England: John Wiley & Sons, Inc</publisher><subject>Apnea ; Biomarkers ; central disorder of hypersomnia ; clustering ; Clusters ; Coexistence ; Comorbidity ; Diagnosis ; Disorders ; Disorders of Excessive Somnolence - diagnosis ; Disorders of Excessive Somnolence - epidemiology ; Humans ; Hypersomnia ; Insomnia ; insomnia disorder ; Machine learning ; Narcolepsy ; Neurotrophin 1 ; Phenotyping ; Polysomnography ; Sleep ; Sleep and wakefulness ; Sleep apnea ; Sleep disorders ; Sleep Wake Disorders - diagnosis ; sleep‐related breathing disorder ; sleep–wake disorders ; Vigilance</subject><ispartof>European journal of neurology, 2024-01, Vol.31 (1), p.e16026-n/a</ispartof><rights>2023 The Authors. published by John Wiley & Sons Ltd on behalf of European Academy of Neurology.</rights><rights>2023 The Authors. European Journal of Neurology published by John Wiley & Sons Ltd on behalf of European Academy of Neurology.</rights><rights>2024. This work is published under http://creativecommons.org/licenses/by-nc/4.0/ (the "License"). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3886-3b57e3c4b0ad930f478396427f1ad208d6c7dae09a8de7e302f15df4dfaed7d23</citedby><cites>FETCH-LOGICAL-c3886-3b57e3c4b0ad930f478396427f1ad208d6c7dae09a8de7e302f15df4dfaed7d23</cites><orcidid>0000-0001-9087-8359 ; 0000-0002-8949-0645</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1111%2Fene.16026$$EPDF$$P50$$Gwiley$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1111%2Fene.16026$$EHTML$$P50$$Gwiley$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,1417,11562,27924,27925,45574,45575,46052,46476</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/37531449$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Aellen, Florence M.</creatorcontrib><creatorcontrib>Van der Meer, Julia</creatorcontrib><creatorcontrib>Dietmann, Anelia</creatorcontrib><creatorcontrib>Schmidt, Markus</creatorcontrib><creatorcontrib>Bassetti, Claudio L. A.</creatorcontrib><creatorcontrib>Tzovara, Athina</creatorcontrib><title>Disentangling the complex landscape of sleep–wake disorders with data‐driven phenotyping: A study of the Bernese center</title><title>European journal of neurology</title><addtitle>Eur J Neurol</addtitle><description>Background and purpose
The diagnosis of sleep–wake disorders (SWDs) is challenging because of the existence of only few accurate biomarkers and the frequent coexistence of multiple SWDs and/or other comorbidities. The aim of this study was to assess in a large cohort of well‐characterized SWD patients the potential of a data‐driven approach for the identification of SWDs.
Methods
We included 6958 patients from the Bernese Sleep Registry and 300 variables/biomarkers including questionnaires, results of polysomnography/vigilance tests, and final clinical diagnoses. A pipeline, based on machine learning, was created to extract and cluster the clinical data. Our analysis was performed on three cohorts: patients with central disorders of hypersomnolence (CDHs), a full cohort of patients with SWDs, and a clean cohort without coexisting SWDs.
Results
A first analysis focused on the cohort of patients with CDHs and revealed four patient clusters: two clusters for narcolepsy type 1 (NT1) but not for narcolepsy type 2 or idiopathic hypersomnia. In the full cohort of SWDs, nine clusters were found: four contained patients with obstructive and central sleep apnea syndrome, one with NT1, and four with intermixed SWDs. In the cohort of patients without coexisting SWDs, an additional cluster of patients with chronic insomnia disorder was identified.
Conclusions
This study confirms the existence of clear clusters of NT1 in CDHs, but mainly intermixed groups in the full spectrum of SWDs, with the exception of sleep apnea syndromes and NT1. New biomarkers are needed for better phenotyping and diagnosis of SWDs.</description><subject>Apnea</subject><subject>Biomarkers</subject><subject>central disorder of hypersomnia</subject><subject>clustering</subject><subject>Clusters</subject><subject>Coexistence</subject><subject>Comorbidity</subject><subject>Diagnosis</subject><subject>Disorders</subject><subject>Disorders of Excessive Somnolence - diagnosis</subject><subject>Disorders of Excessive Somnolence - epidemiology</subject><subject>Humans</subject><subject>Hypersomnia</subject><subject>Insomnia</subject><subject>insomnia disorder</subject><subject>Machine learning</subject><subject>Narcolepsy</subject><subject>Neurotrophin 1</subject><subject>Phenotyping</subject><subject>Polysomnography</subject><subject>Sleep</subject><subject>Sleep and wakefulness</subject><subject>Sleep apnea</subject><subject>Sleep disorders</subject><subject>Sleep Wake Disorders - diagnosis</subject><subject>sleep‐related breathing disorder</subject><subject>sleep–wake disorders</subject><subject>Vigilance</subject><issn>1351-5101</issn><issn>1468-1331</issn><issn>1468-1331</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><sourceid>WIN</sourceid><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><recordid>eNp1kb1OwzAQxy0EoqUw8ALIEgsMae3Y-WIrpXxIFSwwR258aVNSJ9gJJWLpIyDxhn0SXFoYkLjlbvjpd3f6I3RMSZfa6oGCLvWJ6--gNuV-6FDG6K6dmUcdjxLaQgfGzAghbuCSfdRigcco51EbvV9lBlQl1CTP1ARXU8BJMS9zeMO5UNIkogRcpNjkAOVq-bkQz4BlZgotQRu8yKoplqISq-WH1NkrKFxOQRVVU1rdBe5jU9WyWRvW6kvQCoxdYVeCPkR7qcgNHG17Bz1dDx8Ht87o4eZu0B85CQtD32FjLwCW8DERMmIk5UHIIp-7QUqFdEko_SSQAkgkQgmWJG5KPZlymQqQgXRZB51tvKUuXmowVTzPTAK5fRCK2sRuyD3f45R7Fj39g86KWit7naWiKCQ8opGlzjdUogtjNKRxqbO50E1MSbxOJLaJxN-JWPZka6zHc5C_5E8EFuhtgEWWQ_O_KR7eDzfKLz8GmFM</recordid><startdate>202401</startdate><enddate>202401</enddate><creator>Aellen, Florence M.</creator><creator>Van der Meer, Julia</creator><creator>Dietmann, Anelia</creator><creator>Schmidt, Markus</creator><creator>Bassetti, Claudio L. A.</creator><creator>Tzovara, Athina</creator><general>John Wiley & Sons, Inc</general><scope>24P</scope><scope>WIN</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7TK</scope><scope>7U7</scope><scope>7X7</scope><scope>7XB</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>BENPR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>K9.</scope><scope>M0S</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0001-9087-8359</orcidid><orcidid>https://orcid.org/0000-0002-8949-0645</orcidid></search><sort><creationdate>202401</creationdate><title>Disentangling the complex landscape of sleep–wake disorders with data‐driven phenotyping: A study of the Bernese center</title><author>Aellen, Florence M. ; Van der Meer, Julia ; Dietmann, Anelia ; Schmidt, Markus ; Bassetti, Claudio L. A. ; Tzovara, Athina</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3886-3b57e3c4b0ad930f478396427f1ad208d6c7dae09a8de7e302f15df4dfaed7d23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Apnea</topic><topic>Biomarkers</topic><topic>central disorder of hypersomnia</topic><topic>clustering</topic><topic>Clusters</topic><topic>Coexistence</topic><topic>Comorbidity</topic><topic>Diagnosis</topic><topic>Disorders</topic><topic>Disorders of Excessive Somnolence - diagnosis</topic><topic>Disorders of Excessive Somnolence - epidemiology</topic><topic>Humans</topic><topic>Hypersomnia</topic><topic>Insomnia</topic><topic>insomnia disorder</topic><topic>Machine learning</topic><topic>Narcolepsy</topic><topic>Neurotrophin 1</topic><topic>Phenotyping</topic><topic>Polysomnography</topic><topic>Sleep</topic><topic>Sleep and wakefulness</topic><topic>Sleep apnea</topic><topic>Sleep disorders</topic><topic>Sleep Wake Disorders - diagnosis</topic><topic>sleep‐related breathing disorder</topic><topic>sleep–wake disorders</topic><topic>Vigilance</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Aellen, Florence M.</creatorcontrib><creatorcontrib>Van der Meer, Julia</creatorcontrib><creatorcontrib>Dietmann, Anelia</creatorcontrib><creatorcontrib>Schmidt, Markus</creatorcontrib><creatorcontrib>Bassetti, Claudio L. A.</creatorcontrib><creatorcontrib>Tzovara, Athina</creatorcontrib><collection>Wiley Online Library</collection><collection>Wiley Online Library</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Neurosciences Abstracts</collection><collection>Toxicology Abstracts</collection><collection>ProQuest Health and Medical</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>MEDLINE - Academic</collection><jtitle>European journal of neurology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Aellen, Florence M.</au><au>Van der Meer, Julia</au><au>Dietmann, Anelia</au><au>Schmidt, Markus</au><au>Bassetti, Claudio L. A.</au><au>Tzovara, Athina</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Disentangling the complex landscape of sleep–wake disorders with data‐driven phenotyping: A study of the Bernese center</atitle><jtitle>European journal of neurology</jtitle><addtitle>Eur J Neurol</addtitle><date>2024-01</date><risdate>2024</risdate><volume>31</volume><issue>1</issue><spage>e16026</spage><epage>n/a</epage><pages>e16026-n/a</pages><issn>1351-5101</issn><issn>1468-1331</issn><eissn>1468-1331</eissn><abstract>Background and purpose
The diagnosis of sleep–wake disorders (SWDs) is challenging because of the existence of only few accurate biomarkers and the frequent coexistence of multiple SWDs and/or other comorbidities. The aim of this study was to assess in a large cohort of well‐characterized SWD patients the potential of a data‐driven approach for the identification of SWDs.
Methods
We included 6958 patients from the Bernese Sleep Registry and 300 variables/biomarkers including questionnaires, results of polysomnography/vigilance tests, and final clinical diagnoses. A pipeline, based on machine learning, was created to extract and cluster the clinical data. Our analysis was performed on three cohorts: patients with central disorders of hypersomnolence (CDHs), a full cohort of patients with SWDs, and a clean cohort without coexisting SWDs.
Results
A first analysis focused on the cohort of patients with CDHs and revealed four patient clusters: two clusters for narcolepsy type 1 (NT1) but not for narcolepsy type 2 or idiopathic hypersomnia. In the full cohort of SWDs, nine clusters were found: four contained patients with obstructive and central sleep apnea syndrome, one with NT1, and four with intermixed SWDs. In the cohort of patients without coexisting SWDs, an additional cluster of patients with chronic insomnia disorder was identified.
Conclusions
This study confirms the existence of clear clusters of NT1 in CDHs, but mainly intermixed groups in the full spectrum of SWDs, with the exception of sleep apnea syndromes and NT1. New biomarkers are needed for better phenotyping and diagnosis of SWDs.</abstract><cop>England</cop><pub>John Wiley & Sons, Inc</pub><pmid>37531449</pmid><doi>10.1111/ene.16026</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0001-9087-8359</orcidid><orcidid>https://orcid.org/0000-0002-8949-0645</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Apnea Biomarkers central disorder of hypersomnia clustering Clusters Coexistence Comorbidity Diagnosis Disorders Disorders of Excessive Somnolence - diagnosis Disorders of Excessive Somnolence - epidemiology Humans Hypersomnia Insomnia insomnia disorder Machine learning Narcolepsy Neurotrophin 1 Phenotyping Polysomnography Sleep Sleep and wakefulness Sleep apnea Sleep disorders Sleep Wake Disorders - diagnosis sleep‐related breathing disorder sleep–wake disorders Vigilance |
title | Disentangling the complex landscape of sleep–wake disorders with data‐driven phenotyping: A study of the Bernese center |
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