Abstract
INTRODUCTION: As the number of biomarkers used to study Alzheimer's disease (AD) continues to increase, it is important to understand the utility of any given biomarker, as well as what additional information a biomarker provides when compared to others. METHODS: We used hierarchical clustering to group 19 cross-sectional biomarkers in autosomal dominant AD. Feature selection identified biomarkers that were the strongest predictors of mutation status and estimated years from symptom onset (EYO). Biomarkers identified included clinical assessments, neuroimaging, cerebrospinal fluid amyloid, and tau, and emerging biomarkers of neuronal integrity and inflammation. RESULTS: Three primary clusters were identified: neurodegeneration, amyloid/tau, and emerging biomarkers. Feature selection identified amyloid and tau measures as the primary predictors of mutation status and EYO. Emerging biomarkers of neuronal integrity and inflammation were relatively weak predictors. DISCUSSION: These results provide novel insight into our understanding of the relationships among biomarkers and the staging of biomarkers based on disease progression.
Original language | English (US) |
---|---|
Pages (from-to) | 274-284 |
Number of pages | 11 |
Journal | Alzheimer's and Dementia |
Volume | 19 |
Issue number | 1 |
DOIs | |
State | Published - Jan 2023 |
Keywords
- Autosomal dominant Alzheimer's disease
- biomarkers
- machine learning
ASJC Scopus subject areas
- Epidemiology
- Health Policy
- Developmental Neuroscience
- Clinical Neurology
- Geriatrics and Gerontology
- Cellular and Molecular Neuroscience
- Psychiatry and Mental health
Access to Document
Other files and links
Fingerprint
Dive into the research topics of 'Biomarker clustering in autosomal dominant Alzheimer's disease'. Together they form a unique fingerprint.Cite this
- APA
- Standard
- Harvard
- Vancouver
- Author
- BIBTEX
- RIS
Biomarker clustering in autosomal dominant Alzheimer's disease. / for the Dominantly Inherited Alzheimer Network (DIAN).
In: Alzheimer's and Dementia, Vol. 19, No. 1, 01.2023, p. 274-284.Research output: Contribution to journal › Article › peer-review
}
TY - JOUR
T1 - Biomarker clustering in autosomal dominant Alzheimer's disease
AU - for the Dominantly Inherited Alzheimer Network (DIAN)
AU - Luckett, Patrick H.
AU - Chen, Charlie
AU - Gordon, Brian A.
AU - Wisch, Julie
AU - Berman, Sarah B.
AU - Chhatwal, Jasmeer P.
AU - Cruchaga, Carlos
AU - Fagan, Anne M.
AU - Farlow, Martin R.
AU - Fox, Nick C.
AU - Jucker, Mathias
AU - Levin, Johannes
AU - Masters, Colin L.
AU - Mori, Hiroshi
AU - Noble, James M.
AU - Salloway, Stephen
AU - Schofield, Peter R.
AU - Brickman, Adam M.
AU - Brooks, William S.
AU - Cash, David M.
AU - Fulham, Michael J.
AU - Ghetti, Bernardino
AU - Jack, Clifford R.
AU - Vöglein, Jonathan
AU - Klunk, William E.
AU - Koeppe, Robert
AU - Su, Yi
AU - Weiner, Michael
AU - Wang, Qing
AU - Marcus, Daniel
AU - Koudelis, Deborah
AU - Joseph-Mathurin, Nelly
AU - Cash, Lisa
AU - Hornbeck, Russ
AU - Xiong, Chengjie
AU - Perrin, Richard J.
AU - Karch, Celeste M.
AU - Hassenstab, Jason
AU - McDade, Eric
AU - Morris, John C.
AU - Benzinger, Tammie L.S.
AU - Bateman, Randall J.
AU - Ances, Beau M.
N1 - Funding Information: We would like to acknowledge the participants and their families, without whom these studies would not be possible. In addition, we thank all the participating researchers and coordinators ( https://dian.wustl.edu/our‐research/observational‐study/dian‐observational‐study‐sites/ ) who support the studies. DIAN ClinicalTrials.gov identifier: NCT00869817. This research was funded by the National Institutes of Health (NIH) (grant numbers K01AG053474, K23AG046363, R01AG052550, UFAG 032438, UL1TR000448, P30NS098577, R01EB009352, P50AG05131, U01AG042791, U01AG042791‐S1 [FNIH and Accelerating Medicines Partnership], R1AG046179]; the German Center for Neurodegenerative Diseases (DZNE); the National Institute for Health Research (NIHR) Queen Square Dementia Biomedical Research Centre; and the Medical Research Council Dementias Platform United Kingdom (UK) (grant numbers MR/L023784/1, MR/009076/1), Alzheimer's Association International Research Grant Program #AARFD‐20‐681815, NSF DMS 156243, DIAN‐J by AMED, and an anonymous organization. Furthermore, we acknowledge the support of Fred Simmons and Olga Mohan, the Barnes‐Jewish Hospital Foundation, the Charles F. and Joanne Knight Alzheimer Research Initiative, the Hope Center for Neurological Disorders, the Mallinckrodt Institute of Radiology, the Paula and Rodger O. Riney fund, and the Daniel J. Brennan fund. Funding Information: The authors declare no competing interest. Anne Fagan has received research funding from the National Institute on Aging of the National Institutes of Health, Biogen, Centene, Fujirebio, and Roche Diagnostics. She is a member of the scientific advisory boards for Roche Diagnostics and Genentech and consults for Diadem, DiamiR, and Siemens Healthcare Diagnostics Inc. Carlos Cruchaga receives research support from Biogen, EISAI, Alector, and Parabon. The funders of the study had no role in the collection, analysis, or interpretation of data; in the writing of the report; or in the decision to submit the paper for publication. Dr. Cruchaga is also a member of the advisory board of ADx Healthcare, Halia Therapeutics, and Vivid Genomics. Jasmeer P. Chhatwal served on the medical advisory board for Otsuka Pharmaceuticals. Johannes Levin reports speaker's fees from Bayer Vital, speaker's fees from Willi Gross Foundation, consulting fees from Axon Neuroscience, consulting fees from Ionis Pharmaceuticals, author fees from Thieme medical publishers and W. Kohlhammer GmbH medical publishers, compensation for work as part‐time CMO from MODAG GmbH, and non‐financial support from AbbVie outside the submitted work. John Morris is funded by NIH grants numbers P50AG005681, P01AG003991, P01AG026276, and UF1AG032438. Dr. Jack serves on an independent data monitoring board for Roche and has served as a speaker for Eisai, but he receives no personal compensation from any commercial entity. He receives research support from NIH and the Alexander Family Alzheimer Disease Research Professorship of the Mayo Clinic. Eric McDade is involved in a clinical trial on AV‐1451 sponsored by Avid and serves on a data safety monitoring committee for Eli‐Lilly and Alector and is on the Scientific Advisory Board for Alzamend; and receives research support from Eli‐Lilly and Hoffman‐La Roche. Dr. McDade is a co‐inventor of the "Methods of diagnosing AD with phosphorylation changes" technology licensed by Washington University to C2N Diagnostics. Washington University also holds 5% equity in C2N. Through these relationships, Washington University and Dr. McDade are entitled to receive royalties from the license agreement with C2N. Dr. David Holtzman, Chair of Neurology at Washington University Medical School, is a co‐founder of C2N and serves on C2N's advisory board. Randall Bateman is on the scientific advisory board of C2N Diagnostics and reports research support from AbbVie, Biogen, Eisai, Eli Lilly, Co/Avid Radiopharmaceuticals, Roche, Janssen, and United Neuroscience. Dr. Weiner receives support for NIH grants: 5U19AG024904‐14; 1R01AG053798‐01A1; R01 MH098062; U24 AG057437‐01; 1U2CA060426‐01; 1R01AG058676‐01A1; and 1RF1AG059009‐01, DOD: W81XWH‐15‐2‐0070; 0W81XWH‐12‐2‐0012; W81XWH‐14‐1‐0462; W81XWH‐13‐1‐0259, PCORI: PPRN‐1501‐26817, California Dept. of Public Health: 16–10054, U. Michigan: 18‐PAF01312, Siemens: 444951–54249, Biogen: 174552, Hillblom Foundation: 2015‐A‐011‐NET, Alzheimer's Association: BHR‐16‐459161; The State of California: 18–109929. He also receives support from Johnson & Johnson, Kevin and Connie Shanahan, GE, VUmc, Australian Catholic University (HBI‐BHR), The Stroke Foundation, and the Veterans Administration. He has served on Advisory Boards for Eli Lilly, Cerecin/Accera, Roche, Alzheon, Inc., Merck Sharp & Dohme Corp., Nestle/Nestec, PCORI/PPRN, Dolby Family Ventures, National Institute on Aging (NIA), Brain Health Registry and ADNI. He serves on the Editorial Boards for Alzheimer's & Dementia, TMRI and MRI. He has provided consulting and/or acted as a speaker/lecturer to Cerecin/Accera, Inc., BioClinica, Nestle/Nestec, Roche, Genentech, NIH, The Buck Institute for Research on Aging, FUJIFILM‐Toyama Chemical (Japan), Garfield Weston, Baird Equity Capital, University of Southern California (USC), Cytox, and Japanese Organization for Medical Device Development, Inc. (JOMDD) and T3D Therapeutics. He holds stock options with Alzheon, Inc., Alzeca, and Anven. Funding Information: We would like to acknowledge the participants and their families, without whom these studies would not be possible. In addition, we thank all the participating researchers and coordinators (https://dian.wustl.edu/our-research/observational-study/dian-observational-study-sites/) who support the studies. DIAN ClinicalTrials.gov identifier: NCT00869817. This research was funded by the National Institutes of Health (NIH) (grant numbers K01AG053474, K23AG046363, R01AG052550, UFAG 032438, UL1TR000448, P30NS098577, R01EB009352, P50AG05131, U01AG042791, U01AG042791-S1 [FNIH and Accelerating Medicines Partnership], R1AG046179]; the German Center for Neurodegenerative Diseases (DZNE); the National Institute for Health Research (NIHR) Queen Square Dementia Biomedical Research Centre; and the Medical Research Council Dementias Platform United Kingdom (UK) (grant numbers MR/L023784/1, MR/009076/1), Alzheimer's Association International Research Grant Program #AARFD-20-681815, NSF DMS 156243, DIAN-J by AMED, and an anonymous organization. Furthermore, we acknowledge the support of Fred Simmons and Olga Mohan, the Barnes-Jewish Hospital Foundation, the Charles F. and Joanne Knight Alzheimer Research Initiative, the Hope Center for Neurological Disorders, the Mallinckrodt Institute of Radiology, the Paula and Rodger O. Riney fund, and the Daniel J. Brennan fund. Publisher Copyright: © 2022 the Alzheimer's Association.
PY - 2023/1
Y1 - 2023/1
N2 - INTRODUCTION: As the number of biomarkers used to study Alzheimer's disease (AD) continues to increase, it is important to understand the utility of any given biomarker, as well as what additional information a biomarker provides when compared to others. METHODS: We used hierarchical clustering to group 19 cross-sectional biomarkers in autosomal dominant AD. Feature selection identified biomarkers that were the strongest predictors of mutation status and estimated years from symptom onset (EYO). Biomarkers identified included clinical assessments, neuroimaging, cerebrospinal fluid amyloid, and tau, and emerging biomarkers of neuronal integrity and inflammation. RESULTS: Three primary clusters were identified: neurodegeneration, amyloid/tau, and emerging biomarkers. Feature selection identified amyloid and tau measures as the primary predictors of mutation status and EYO. Emerging biomarkers of neuronal integrity and inflammation were relatively weak predictors. DISCUSSION: These results provide novel insight into our understanding of the relationships among biomarkers and the staging of biomarkers based on disease progression.
AB - INTRODUCTION: As the number of biomarkers used to study Alzheimer's disease (AD) continues to increase, it is important to understand the utility of any given biomarker, as well as what additional information a biomarker provides when compared to others. METHODS: We used hierarchical clustering to group 19 cross-sectional biomarkers in autosomal dominant AD. Feature selection identified biomarkers that were the strongest predictors of mutation status and estimated years from symptom onset (EYO). Biomarkers identified included clinical assessments, neuroimaging, cerebrospinal fluid amyloid, and tau, and emerging biomarkers of neuronal integrity and inflammation. RESULTS: Three primary clusters were identified: neurodegeneration, amyloid/tau, and emerging biomarkers. Feature selection identified amyloid and tau measures as the primary predictors of mutation status and EYO. Emerging biomarkers of neuronal integrity and inflammation were relatively weak predictors. DISCUSSION: These results provide novel insight into our understanding of the relationships among biomarkers and the staging of biomarkers based on disease progression.
KW - Autosomal dominant Alzheimer's disease
KW - biomarkers
KW - machine learning
UR - http://www.scopus.com/inward/record.url?scp=85127456699&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85127456699&partnerID=8YFLogxK
U2 - 10.1002/alz.12661
DO - 10.1002/alz.12661
M3 - Article
AN - SCOPUS:85127456699
VL - 19
SP - 274
EP - 284
JO - Alzheimer's and Dementia
JF - Alzheimer's and Dementia
SN - 1552-5260
IS - 1
ER -