Characterisation, identification, clustering, and classification of disease

Abstract The importance of quantifying the distribution and determinants of multimorbidity has prompted novel data-driven classifications of disease. Applications have included improved statistical power and refined prognoses for a range of respiratory, infectious, autoimmune, and neurological disea...

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Autores principales:	A. J. Webster, K. Gaitskell, I. Turnbull, B. J. Cairns, R. Clarke
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/23dddf77bfbd46098aa99463ae48cafd
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spelling	oai:doaj.org-article:23dddf77bfbd46098aa99463ae48cafd2021-12-02T15:54:01ZCharacterisation, identification, clustering, and classification of disease10.1038/s41598-021-84860-z2045-2322https://doaj.org/article/23dddf77bfbd46098aa99463ae48cafd2021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-84860-zhttps://doaj.org/toc/2045-2322Abstract The importance of quantifying the distribution and determinants of multimorbidity has prompted novel data-driven classifications of disease. Applications have included improved statistical power and refined prognoses for a range of respiratory, infectious, autoimmune, and neurological diseases, with studies using molecular information, age of disease incidence, and sequences of disease onset (“disease trajectories”) to classify disease clusters. Here we consider whether easily measured risk factors such as height and BMI can effectively characterise diseases in UK Biobank data, combining established statistical methods in new but rigorous ways to provide clinically relevant comparisons and clusters of disease. Over 400 common diseases were selected for analysis using clinical and epidemiological criteria, and conventional proportional hazards models were used to estimate associations with 12 established risk factors. Several diseases had strongly sex-dependent associations of disease risk with BMI. Importantly, a large proportion of diseases affecting both sexes could be identified by their risk factors, and equivalent diseases tended to cluster adjacently. These included 10 diseases presently classified as “Symptoms, signs, and abnormal clinical and laboratory findings, not elsewhere classified”. Many clusters are associated with a shared, known pathogenesis, others suggest likely but presently unconfirmed causes. The specificity of associations and shared pathogenesis of many clustered diseases provide a new perspective on the interactions between biological pathways, risk factors, and patterns of disease such as multimorbidity.A. J. WebsterK. GaitskellI. TurnbullB. J. CairnsR. ClarkeNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q A. J. Webster K. Gaitskell I. Turnbull B. J. Cairns R. Clarke Characterisation, identification, clustering, and classification of disease
description	Abstract The importance of quantifying the distribution and determinants of multimorbidity has prompted novel data-driven classifications of disease. Applications have included improved statistical power and refined prognoses for a range of respiratory, infectious, autoimmune, and neurological diseases, with studies using molecular information, age of disease incidence, and sequences of disease onset (“disease trajectories”) to classify disease clusters. Here we consider whether easily measured risk factors such as height and BMI can effectively characterise diseases in UK Biobank data, combining established statistical methods in new but rigorous ways to provide clinically relevant comparisons and clusters of disease. Over 400 common diseases were selected for analysis using clinical and epidemiological criteria, and conventional proportional hazards models were used to estimate associations with 12 established risk factors. Several diseases had strongly sex-dependent associations of disease risk with BMI. Importantly, a large proportion of diseases affecting both sexes could be identified by their risk factors, and equivalent diseases tended to cluster adjacently. These included 10 diseases presently classified as “Symptoms, signs, and abnormal clinical and laboratory findings, not elsewhere classified”. Many clusters are associated with a shared, known pathogenesis, others suggest likely but presently unconfirmed causes. The specificity of associations and shared pathogenesis of many clustered diseases provide a new perspective on the interactions between biological pathways, risk factors, and patterns of disease such as multimorbidity.
format	article
author	A. J. Webster K. Gaitskell I. Turnbull B. J. Cairns R. Clarke
author_facet	A. J. Webster K. Gaitskell I. Turnbull B. J. Cairns R. Clarke
author_sort	A. J. Webster
title	Characterisation, identification, clustering, and classification of disease
title_short	Characterisation, identification, clustering, and classification of disease
title_full	Characterisation, identification, clustering, and classification of disease
title_fullStr	Characterisation, identification, clustering, and classification of disease
title_full_unstemmed	Characterisation, identification, clustering, and classification of disease
title_sort	characterisation, identification, clustering, and classification of disease
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/23dddf77bfbd46098aa99463ae48cafd
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Characterisation, identification, clustering, and classification of disease

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