Classifying migraine subtypes and their characteristics by latent class analysis using data of a nation-wide population-based study

Abstract Migraine neither presents with a definitive single symptom nor has a distinct biomarker; thus, its diagnosis is based on combinations of typical symptoms. We aimed to identify natural subgroups of migraine based on symptoms listed in the diagnostic criteria of the third edition of the Inter...

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Autores principales:	Wonwoo Lee, In Kyung Min, Kwang Ik Yang, Daeyoung Kim, Chang-Ho Yun, Min Kyung Chu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/595faa8d5c124a269d2b573657074895
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spelling	oai:doaj.org-article:595faa8d5c124a269d2b5736570748952021-11-08T10:53:59ZClassifying migraine subtypes and their characteristics by latent class analysis using data of a nation-wide population-based study10.1038/s41598-021-01107-72045-2322https://doaj.org/article/595faa8d5c124a269d2b5736570748952021-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-01107-7https://doaj.org/toc/2045-2322Abstract Migraine neither presents with a definitive single symptom nor has a distinct biomarker; thus, its diagnosis is based on combinations of typical symptoms. We aimed to identify natural subgroups of migraine based on symptoms listed in the diagnostic criteria of the third edition of the International Classification of Headache Disorders. Latent class analysis (LCA) was applied to the data of the Korean Sleep-Headache Study, a nationwide population-based survey. We selected a three-class model based on Akaike and Bayesian information criteria and characterized the three identified classes as “mild and low frequency,” “photophobia and phonophobia,” and “severe and high frequency.” In total, 52.0% (65/125) of the participants were classified as “mild and low frequency,” showing the highest frequency of mild headache intensity but the lowest overall headache frequency. Meanwhile, “photophobia and phonophobia” involved 33.6% (42/125) of the participants, who showed the highest frequency of photophobia and phonophobia. Finally, “severe and high frequency” included 14.4% (18/125) of the participants, and they presented the highest frequency of severe headache intensity and highest headache frequency. In conclusion, LCA is useful for analyzing the heterogeneity of migraine symptoms and identifying migraine subtypes. This approach may improve our understanding of the clinical characterization of migraine.Wonwoo LeeIn Kyung MinKwang Ik YangDaeyoung KimChang-Ho YunMin Kyung ChuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Wonwoo Lee In Kyung Min Kwang Ik Yang Daeyoung Kim Chang-Ho Yun Min Kyung Chu Classifying migraine subtypes and their characteristics by latent class analysis using data of a nation-wide population-based study
description	Abstract Migraine neither presents with a definitive single symptom nor has a distinct biomarker; thus, its diagnosis is based on combinations of typical symptoms. We aimed to identify natural subgroups of migraine based on symptoms listed in the diagnostic criteria of the third edition of the International Classification of Headache Disorders. Latent class analysis (LCA) was applied to the data of the Korean Sleep-Headache Study, a nationwide population-based survey. We selected a three-class model based on Akaike and Bayesian information criteria and characterized the three identified classes as “mild and low frequency,” “photophobia and phonophobia,” and “severe and high frequency.” In total, 52.0% (65/125) of the participants were classified as “mild and low frequency,” showing the highest frequency of mild headache intensity but the lowest overall headache frequency. Meanwhile, “photophobia and phonophobia” involved 33.6% (42/125) of the participants, who showed the highest frequency of photophobia and phonophobia. Finally, “severe and high frequency” included 14.4% (18/125) of the participants, and they presented the highest frequency of severe headache intensity and highest headache frequency. In conclusion, LCA is useful for analyzing the heterogeneity of migraine symptoms and identifying migraine subtypes. This approach may improve our understanding of the clinical characterization of migraine.
format	article
author	Wonwoo Lee In Kyung Min Kwang Ik Yang Daeyoung Kim Chang-Ho Yun Min Kyung Chu
author_facet	Wonwoo Lee In Kyung Min Kwang Ik Yang Daeyoung Kim Chang-Ho Yun Min Kyung Chu
author_sort	Wonwoo Lee
title	Classifying migraine subtypes and their characteristics by latent class analysis using data of a nation-wide population-based study
title_short	Classifying migraine subtypes and their characteristics by latent class analysis using data of a nation-wide population-based study
title_full	Classifying migraine subtypes and their characteristics by latent class analysis using data of a nation-wide population-based study
title_fullStr	Classifying migraine subtypes and their characteristics by latent class analysis using data of a nation-wide population-based study
title_full_unstemmed	Classifying migraine subtypes and their characteristics by latent class analysis using data of a nation-wide population-based study
title_sort	classifying migraine subtypes and their characteristics by latent class analysis using data of a nation-wide population-based study
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/595faa8d5c124a269d2b573657074895
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Classifying migraine subtypes and their characteristics by latent class analysis using data of a nation-wide population-based study

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