Data-driven identification of ageing-related diseases from electronic health records

Abstract Reducing the burden of late-life morbidity requires an understanding of the mechanisms of ageing-related diseases (ARDs), defined as diseases that accumulate with increasing age. This has been hampered by the lack of formal criteria to identify ARDs. Here, we present a framework to identify...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Valerie Kuan, Helen C. Fraser, Melanie Hingorani, Spiros Denaxas, Arturo Gonzalez-Izquierdo, Kenan Direk, Dorothea Nitsch, Rohini Mathur, Constantinos A. Parisinos, R. Thomas Lumbers, Reecha Sofat, Ian C. K. Wong, Juan P. Casas, Janet M. Thornton, Harry Hemingway, Linda Partridge, Aroon D. Hingorani
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a4cfa63aeff54b99ac644b906f56e8bb
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:a4cfa63aeff54b99ac644b906f56e8bb
record_format	dspace
spelling	oai:doaj.org-article:a4cfa63aeff54b99ac644b906f56e8bb2021-12-02T14:06:55ZData-driven identification of ageing-related diseases from electronic health records10.1038/s41598-021-82459-y2045-2322https://doaj.org/article/a4cfa63aeff54b99ac644b906f56e8bb2021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-82459-yhttps://doaj.org/toc/2045-2322Abstract Reducing the burden of late-life morbidity requires an understanding of the mechanisms of ageing-related diseases (ARDs), defined as diseases that accumulate with increasing age. This has been hampered by the lack of formal criteria to identify ARDs. Here, we present a framework to identify ARDs using two complementary methods consisting of unsupervised machine learning and actuarial techniques, which we applied to electronic health records (EHRs) from 3,009,048 individuals in England using primary care data from the Clinical Practice Research Datalink (CPRD) linked to the Hospital Episode Statistics admitted patient care dataset between 1 April 2010 and 31 March 2015 (mean age 49.7 years (s.d. 18.6), 51% female, 70% white ethnicity). We grouped 278 high-burden diseases into nine main clusters according to their patterns of disease onset, using a hierarchical agglomerative clustering algorithm. Four of these clusters, encompassing 207 diseases spanning diverse organ systems and clinical specialties, had rates of disease onset that clearly increased with chronological age. However, the ages of onset for these four clusters were strikingly different, with median age of onset 82 years (IQR 82–83) for Cluster 1, 77 years (IQR 75–77) for Cluster 2, 69 years (IQR 66–71) for Cluster 3 and 57 years (IQR 54–59) for Cluster 4. Fitting to ageing-related actuarial models confirmed that the vast majority of these 207 diseases had a high probability of being ageing-related. Cardiovascular diseases and cancers were highly represented, while benign neoplastic, skin and psychiatric conditions were largely absent from the four ageing-related clusters. Our framework identifies and clusters ARDs and can form the basis for fundamental and translational research into ageing pathways.Valerie KuanHelen C. FraserMelanie HingoraniSpiros DenaxasArturo Gonzalez-IzquierdoKenan DirekDorothea NitschRohini MathurConstantinos A. ParisinosR. Thomas LumbersReecha SofatIan C. K. WongJuan P. CasasJanet M. ThorntonHarry HemingwayLinda PartridgeAroon D. HingoraniNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-17 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Valerie Kuan Helen C. Fraser Melanie Hingorani Spiros Denaxas Arturo Gonzalez-Izquierdo Kenan Direk Dorothea Nitsch Rohini Mathur Constantinos A. Parisinos R. Thomas Lumbers Reecha Sofat Ian C. K. Wong Juan P. Casas Janet M. Thornton Harry Hemingway Linda Partridge Aroon D. Hingorani Data-driven identification of ageing-related diseases from electronic health records
description	Abstract Reducing the burden of late-life morbidity requires an understanding of the mechanisms of ageing-related diseases (ARDs), defined as diseases that accumulate with increasing age. This has been hampered by the lack of formal criteria to identify ARDs. Here, we present a framework to identify ARDs using two complementary methods consisting of unsupervised machine learning and actuarial techniques, which we applied to electronic health records (EHRs) from 3,009,048 individuals in England using primary care data from the Clinical Practice Research Datalink (CPRD) linked to the Hospital Episode Statistics admitted patient care dataset between 1 April 2010 and 31 March 2015 (mean age 49.7 years (s.d. 18.6), 51% female, 70% white ethnicity). We grouped 278 high-burden diseases into nine main clusters according to their patterns of disease onset, using a hierarchical agglomerative clustering algorithm. Four of these clusters, encompassing 207 diseases spanning diverse organ systems and clinical specialties, had rates of disease onset that clearly increased with chronological age. However, the ages of onset for these four clusters were strikingly different, with median age of onset 82 years (IQR 82–83) for Cluster 1, 77 years (IQR 75–77) for Cluster 2, 69 years (IQR 66–71) for Cluster 3 and 57 years (IQR 54–59) for Cluster 4. Fitting to ageing-related actuarial models confirmed that the vast majority of these 207 diseases had a high probability of being ageing-related. Cardiovascular diseases and cancers were highly represented, while benign neoplastic, skin and psychiatric conditions were largely absent from the four ageing-related clusters. Our framework identifies and clusters ARDs and can form the basis for fundamental and translational research into ageing pathways.
format	article
author	Valerie Kuan Helen C. Fraser Melanie Hingorani Spiros Denaxas Arturo Gonzalez-Izquierdo Kenan Direk Dorothea Nitsch Rohini Mathur Constantinos A. Parisinos R. Thomas Lumbers Reecha Sofat Ian C. K. Wong Juan P. Casas Janet M. Thornton Harry Hemingway Linda Partridge Aroon D. Hingorani
author_facet	Valerie Kuan Helen C. Fraser Melanie Hingorani Spiros Denaxas Arturo Gonzalez-Izquierdo Kenan Direk Dorothea Nitsch Rohini Mathur Constantinos A. Parisinos R. Thomas Lumbers Reecha Sofat Ian C. K. Wong Juan P. Casas Janet M. Thornton Harry Hemingway Linda Partridge Aroon D. Hingorani
author_sort	Valerie Kuan
title	Data-driven identification of ageing-related diseases from electronic health records
title_short	Data-driven identification of ageing-related diseases from electronic health records
title_full	Data-driven identification of ageing-related diseases from electronic health records
title_fullStr	Data-driven identification of ageing-related diseases from electronic health records
title_full_unstemmed	Data-driven identification of ageing-related diseases from electronic health records
title_sort	data-driven identification of ageing-related diseases from electronic health records
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/a4cfa63aeff54b99ac644b906f56e8bb
work_keys_str_mv	AT valeriekuan datadrivenidentificationofageingrelateddiseasesfromelectronichealthrecords AT helencfraser datadrivenidentificationofageingrelateddiseasesfromelectronichealthrecords AT melaniehingorani datadrivenidentificationofageingrelateddiseasesfromelectronichealthrecords AT spirosdenaxas datadrivenidentificationofageingrelateddiseasesfromelectronichealthrecords AT arturogonzalezizquierdo datadrivenidentificationofageingrelateddiseasesfromelectronichealthrecords AT kenandirek datadrivenidentificationofageingrelateddiseasesfromelectronichealthrecords AT dorotheanitsch datadrivenidentificationofageingrelateddiseasesfromelectronichealthrecords AT rohinimathur datadrivenidentificationofageingrelateddiseasesfromelectronichealthrecords AT constantinosaparisinos datadrivenidentificationofageingrelateddiseasesfromelectronichealthrecords AT rthomaslumbers datadrivenidentificationofageingrelateddiseasesfromelectronichealthrecords AT reechasofat datadrivenidentificationofageingrelateddiseasesfromelectronichealthrecords AT ianckwong datadrivenidentificationofageingrelateddiseasesfromelectronichealthrecords AT juanpcasas datadrivenidentificationofageingrelateddiseasesfromelectronichealthrecords AT janetmthornton datadrivenidentificationofageingrelateddiseasesfromelectronichealthrecords AT harryhemingway datadrivenidentificationofageingrelateddiseasesfromelectronichealthrecords AT lindapartridge datadrivenidentificationofageingrelateddiseasesfromelectronichealthrecords AT aroondhingorani datadrivenidentificationofageingrelateddiseasesfromelectronichealthrecords
_version_	1718391981201162240

Data-driven identification of ageing-related diseases from electronic health records

Ejemplares similares