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

Alice S. Tang; Tomiko Oskotsky; Shreyas Havaldar; William G. Mantyh; Mesude Bicak; Caroline Warly Solsberg; Sarah Woldemariam; Billy Zeng; Zicheng Hu; Boris Oskotsky; Dena Dubal; Isabel E. Allen; Benjamin S. Glicksberg; Marina Sirota

doi:10.1038/s41467-022-28273-0

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

Alice S. Tang, Tomiko Oskotsky, Shreyas Havaldar, William G. Mantyh, Mesude Bicak, Caroline Warly Solsberg, Sarah Woldemariam, Billy Zeng, Zicheng Hu, Boris Oskotsky, Dena Dubal, Isabel E. Allen, Benjamin S. Glicksberg, Marina Sirota

Research output: Contribution to journal › Article › peer-review

13 Scopus citations

Abstract

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.

Original language	English (US)
Article number	675
Journal	Nature communications
Volume	13
Issue number	1
DOIs	https://doi.org/10.1038/s41467-022-28273-0
State	Published - Dec 2022

Bibliographical note

Funding Information:
Primary support through Grant # NIA R01AG060393, R01AG057683 (A.T., T.O., C.W.S., M.S.). Additional support was provided by NIA RF1AG068325 (D.B.D) and Medical Scientist Training Program T32GM007618 (A.T.). B.G. and M.B. are supported by grant 1 RF1 AG059319-01. We?d like to acknowledge Zachary Cutts, Stella Belonwu, and other members of the Sirota Lab for their suggestions and help.

Funding Information:
Primary support through Grant # NIA R01AG060393, R01AG057683 (A.T., T.O., C.W.S., M.S.). Additional support was provided by NIA RF1AG068325 (D.B.D) and Medical Scientist Training Program T32GM007618 (A.T.). B.G. and M.B. are supported by grant 1 RF1 AG059319-01. We’d like to acknowledge Zachary Cutts, Stella Belonwu, and other members of the Sirota Lab for their suggestions and help.

Publisher Copyright:
© 2022, The Author(s).

Publisher link

10.1038/s41467-022-28273-0

Find It

Find It at U of M Libraries

Cite this

Tang, A. S., Oskotsky, T., Havaldar, S., Mantyh, W. G., Bicak, M., Solsberg, C. W., Woldemariam, S., Zeng, B., Hu, Z., Oskotsky, B., Dubal, D., Allen, I. E., Glicksberg, B. S., & Sirota, M. (2022). Deep phenotyping of Alzheimer’s disease leveraging electronic medical records identifies sex-specific clinical associations. Nature communications, 13(1), Article 675. https://doi.org/10.1038/s41467-022-28273-0

Tang, AS, Oskotsky, T, Havaldar, S, Mantyh, WG, Bicak, M, Solsberg, CW, Woldemariam, S, Zeng, B, Hu, Z, Oskotsky, B, Dubal, D, Allen, IE, Glicksberg, BS & Sirota, M 2022, 'Deep phenotyping of Alzheimer’s disease leveraging electronic medical records identifies sex-specific clinical associations', Nature communications, vol. 13, no. 1, 675. https://doi.org/10.1038/s41467-022-28273-0

@article{58862e01edd84eff9078390b195567ba,

title = "Deep phenotyping of Alzheimer{\textquoteright}s disease leveraging electronic medical records identifies sex-specific clinical associations",

abstract = "Alzheimer{\textquoteright}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.",

author = "Tang, {Alice S.} and Tomiko Oskotsky and Shreyas Havaldar and Mantyh, {William G.} and Mesude Bicak and Solsberg, {Caroline Warly} and Sarah Woldemariam and Billy Zeng and Zicheng Hu and Boris Oskotsky and Dena Dubal and Allen, {Isabel E.} and Glicksberg, {Benjamin S.} and Marina Sirota",

note = "Funding Information: Primary support through Grant # NIA R01AG060393, R01AG057683 (A.T., T.O., C.W.S., M.S.). Additional support was provided by NIA RF1AG068325 (D.B.D) and Medical Scientist Training Program T32GM007618 (A.T.). B.G. and M.B. are supported by grant 1 RF1 AG059319-01. We?d like to acknowledge Zachary Cutts, Stella Belonwu, and other members of the Sirota Lab for their suggestions and help. Funding Information: Primary support through Grant # NIA R01AG060393, R01AG057683 (A.T., T.O., C.W.S., M.S.). Additional support was provided by NIA RF1AG068325 (D.B.D) and Medical Scientist Training Program T32GM007618 (A.T.). B.G. and M.B. are supported by grant 1 RF1 AG059319-01. We{\textquoteright}d like to acknowledge Zachary Cutts, Stella Belonwu, and other members of the Sirota Lab for their suggestions and help. Publisher Copyright: {\textcopyright} 2022, The Author(s).",

year = "2022",

month = dec,

doi = "10.1038/s41467-022-28273-0",

language = "English (US)",

volume = "13",

journal = "Nature communications",

issn = "2041-1723",

publisher = "Nature Research",

number = "1",

}

TY - JOUR

T1 - Deep phenotyping of Alzheimer’s disease leveraging electronic medical records identifies sex-specific clinical associations

AU - Tang, Alice S.

AU - Oskotsky, Tomiko

AU - Havaldar, Shreyas

AU - Mantyh, William G.

AU - Bicak, Mesude

AU - Solsberg, Caroline Warly

AU - Woldemariam, Sarah

AU - Zeng, Billy

AU - Hu, Zicheng

AU - Oskotsky, Boris

AU - Dubal, Dena

AU - Allen, Isabel E.

AU - Glicksberg, Benjamin S.

AU - Sirota, Marina

N1 - Funding Information: Primary support through Grant # NIA R01AG060393, R01AG057683 (A.T., T.O., C.W.S., M.S.). Additional support was provided by NIA RF1AG068325 (D.B.D) and Medical Scientist Training Program T32GM007618 (A.T.). B.G. and M.B. are supported by grant 1 RF1 AG059319-01. We?d like to acknowledge Zachary Cutts, Stella Belonwu, and other members of the Sirota Lab for their suggestions and help. Funding Information: Primary support through Grant # NIA R01AG060393, R01AG057683 (A.T., T.O., C.W.S., M.S.). Additional support was provided by NIA RF1AG068325 (D.B.D) and Medical Scientist Training Program T32GM007618 (A.T.). B.G. and M.B. are supported by grant 1 RF1 AG059319-01. We’d like to acknowledge Zachary Cutts, Stella Belonwu, and other members of the Sirota Lab for their suggestions and help. Publisher Copyright: © 2022, The Author(s).

PY - 2022/12

Y1 - 2022/12

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85123974767&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85123974767&partnerID=8YFLogxK

U2 - 10.1038/s41467-022-28273-0

DO - 10.1038/s41467-022-28273-0

M3 - Article

C2 - 35115528

AN - SCOPUS:85123974767

SN - 2041-1723

VL - 13

JO - Nature communications

JF - Nature communications

IS - 1

M1 - 675

ER -

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

Abstract

Bibliographical note

Publisher link

Find It

Other files and links

Fingerprint

Cite this