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Deep phenotyping of Alzheimer’s disease leveraging electronic medical records identifies sex-specific clinical associations

Alzheimer’s Disease (AD) is a neurodegenerative disorder that is still not fully understood. Sex modifies AD vulnerability, but the reasons for this are largely unknown. We utilize two independent electronic medical record (EMR) systems across 44,288 patients to perform deep clinical phenotyping and...

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Autores principales: Tang, Alice S., Oskotsky, Tomiko, Havaldar, Shreyas, Mantyh, William G., Bicak, Mesude, Solsberg, Caroline Warly, Woldemariam, Sarah, Zeng, Billy, Hu, Zicheng, Oskotsky, Boris, Dubal, Dena, Allen, Isabel E., Glicksberg, Benjamin S., Sirota, Marina
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8814236/
https://www.ncbi.nlm.nih.gov/pubmed/35115528
http://dx.doi.org/10.1038/s41467-022-28273-0
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author Tang, Alice S.
Oskotsky, Tomiko
Havaldar, Shreyas
Mantyh, William G.
Bicak, Mesude
Solsberg, Caroline Warly
Woldemariam, Sarah
Zeng, Billy
Hu, Zicheng
Oskotsky, Boris
Dubal, Dena
Allen, Isabel E.
Glicksberg, Benjamin S.
Sirota, Marina
author_facet Tang, Alice S.
Oskotsky, Tomiko
Havaldar, Shreyas
Mantyh, William G.
Bicak, Mesude
Solsberg, Caroline Warly
Woldemariam, Sarah
Zeng, Billy
Hu, Zicheng
Oskotsky, Boris
Dubal, Dena
Allen, Isabel E.
Glicksberg, Benjamin S.
Sirota, Marina
author_sort Tang, Alice S.
collection PubMed
description Alzheimer’s Disease (AD) is a neurodegenerative disorder that is still not fully understood. Sex modifies AD vulnerability, but the reasons for this are largely unknown. We utilize two independent electronic medical record (EMR) systems across 44,288 patients to perform deep clinical phenotyping and network analysis to gain insight into clinical characteristics and sex-specific clinical associations in AD. Embeddings and network representation of patient diagnoses demonstrate greater comorbidity interactions in AD in comparison to matched controls. Enrichment analysis identifies multiple known and new diagnostic, medication, and lab result associations across the whole cohort and in a sex-stratified analysis. With this data-driven method of phenotyping, we can represent AD complexity and generate hypotheses of clinical factors that can be followed-up for further diagnostic and predictive analyses, mechanistic understanding, or drug repurposing and therapeutic approaches.
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spelling pubmed-88142362022-02-16 Deep phenotyping of Alzheimer’s disease leveraging electronic medical records identifies sex-specific clinical associations Tang, Alice S. Oskotsky, Tomiko Havaldar, Shreyas Mantyh, William G. Bicak, Mesude Solsberg, Caroline Warly Woldemariam, Sarah Zeng, Billy Hu, Zicheng Oskotsky, Boris Dubal, Dena Allen, Isabel E. Glicksberg, Benjamin S. Sirota, Marina Nat Commun Article Alzheimer’s Disease (AD) is a neurodegenerative disorder that is still not fully understood. Sex modifies AD vulnerability, but the reasons for this are largely unknown. We utilize two independent electronic medical record (EMR) systems across 44,288 patients to perform deep clinical phenotyping and network analysis to gain insight into clinical characteristics and sex-specific clinical associations in AD. Embeddings and network representation of patient diagnoses demonstrate greater comorbidity interactions in AD in comparison to matched controls. Enrichment analysis identifies multiple known and new diagnostic, medication, and lab result associations across the whole cohort and in a sex-stratified analysis. With this data-driven method of phenotyping, we can represent AD complexity and generate hypotheses of clinical factors that can be followed-up for further diagnostic and predictive analyses, mechanistic understanding, or drug repurposing and therapeutic approaches. Nature Publishing Group UK 2022-02-03 /pmc/articles/PMC8814236/ /pubmed/35115528 http://dx.doi.org/10.1038/s41467-022-28273-0 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Tang, Alice S.
Oskotsky, Tomiko
Havaldar, Shreyas
Mantyh, William G.
Bicak, Mesude
Solsberg, Caroline Warly
Woldemariam, Sarah
Zeng, Billy
Hu, Zicheng
Oskotsky, Boris
Dubal, Dena
Allen, Isabel E.
Glicksberg, Benjamin S.
Sirota, Marina
Deep phenotyping of Alzheimer’s disease leveraging electronic medical records identifies sex-specific clinical associations
title Deep phenotyping of Alzheimer’s disease leveraging electronic medical records identifies sex-specific clinical associations
title_full Deep phenotyping of Alzheimer’s disease leveraging electronic medical records identifies sex-specific clinical associations
title_fullStr Deep phenotyping of Alzheimer’s disease leveraging electronic medical records identifies sex-specific clinical associations
title_full_unstemmed Deep phenotyping of Alzheimer’s disease leveraging electronic medical records identifies sex-specific clinical associations
title_short Deep phenotyping of Alzheimer’s disease leveraging electronic medical records identifies sex-specific clinical associations
title_sort deep phenotyping of alzheimer’s disease leveraging electronic medical records identifies sex-specific clinical associations
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8814236/
https://www.ncbi.nlm.nih.gov/pubmed/35115528
http://dx.doi.org/10.1038/s41467-022-28273-0
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