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Applying artificial intelligence for early risk forecasting of Alzheimer's disease

Date:
June 7, 2023
Source:
Hong Kong University of Science and Technology
Summary:
An international research team has developed an artificial intelligence (AI)-based model that uses genetic information to predict an individual's risk of developing Alzheimer's disease (AD) well before symptoms occur. This groundbreaking study paves the way for using deep learning methods to predict the risks of diseases and uncover their molecular mechanisms; this could revolutionize the diagnosis of, interventions for, and clinical research on AD and other common diseases such as cardiovascular diseases.
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FULL STORY

An international research team led by the Hong Kong University of Science and Technology (HKUST) has developed an artificial intelligence (AI)-based model that uses genetic information to predict an individual's risk of developing Alzheimer's disease (AD) well before symptoms occur. This groundbreaking study paves the way for using deep learning methods to predict the risks of diseases and uncover their molecular mechanisms; this could revolutionize the diagnosis of, interventions for, and clinical research on AD and other common diseases such as cardiovascular diseases.

Researchers led by HKUST's President, Prof. Nancy IP, in collaboration with the Chair Professor and Director of HKUST's Big Data Institute, Prof. CHEN Lei, investigated whether AI -- specifically deep learning models -- can model AD risk using genetic information. The team established one of the first deep learning models for estimating AD polygenic risks in both European-descent and Chinese populations. Compared to other models, these deep learning models more accurately classify patients with AD and stratify individuals into distinct groups based on disease risks associated with alterations of various biological processes.

In current daily practice, AD is diagnosed clinically, using various means including cognitive tests and brain imaging, but often when patients are showing symptoms, it is already well past the optimal intervention window. Therefore, early forecasting of AD risk can greatly aid diagnosis and the development of intervention strategies. By combining the new deep learning model with genetic testing, an individual's lifetime risk of developing AD can be estimated with more than 70% accuracy.

AD is a heritable disorder that can be attributed to genomic variants. As these variants are present from birth and remain constant throughout life, examining an individual's DNA information can help predict their relative risk of developing AD, thereby enabling early intervention and timely management. While FDA-approved genetic testing for the APOE-?4 genetic variant can estimate AD risk, it may be insufficient to identify high-risk individuals, because multiple genetic risks contribute to the disease. Therefore, it is essential to develop tests that integrate information from multiple AD risk genes to accurately determine an individual's relative risk of developing AD over their lifetime.

"Our study demonstrates the efficacy of deep learning methods for genetic research and risk prediction for Alzheimer's disease. This breakthrough will greatly accelerate population-scale screening and staging of Alzheimer's disease risk. Besides risk prediction, this approach supports the grouping of individuals according to their disease risk and provides insights into the mechanisms that contribute to the onset and progression of the disease," said Prof. Nancy Ip.

Meanwhile, Prof. Chen Lei remarked that, "this study exemplifies how the application of AI to the biological sciences can significantly benefit biomedical and disease-related studies. By utilizing a neural network, we effectively captured nonlinearity in high-dimensional genomic data, which improved the accuracy of Alzheimer's disease risk prediction. In addition, through AI-based data analysis without human supervision, we categorized at-risk individuals into subgroups, which revealed insights into the underlying disease mechanisms. Our research also highlights how AI can elegantly, efficiently, and effectively address interdisciplinary challenges. I firmly believe that AI will play a vital role in various healthcare fields in the near future."

The study was conducted in collaboration with researchers at the Shenzhen Institute of Advanced Technology and University College London as well as clinicians at local Hong Kong hospitals including Prince of Wales Hospital and Queen Elizabeth Hospital. The findings were recently published in Communications Medicine. The research team is now refining the model and aims to ultimately incorporate it into standard screening workflows.

AD, which affects over 50 million people worldwide, is a fatal disease that involves cognitive dysfunction and the loss of brain cells. Its symptoms include progressive memory loss as well as impaired movement, reasoning, and judgment.


Story Source:

Materials provided by Hong Kong University of Science and Technology. Note: Content may be edited for style and length.


Journal Reference:

  1. Xiaopu Zhou, Yu Chen, Fanny C. F. Ip, Yuanbing Jiang, Han Cao, Ge Lv, Huan Zhong, Jiahang Chen, Tao Ye, Yuewen Chen, Yulin Zhang, Shuangshuang Ma, Ronnie M. N. Lo, Estella P. S. Tong, Michael W. Weiner, Paul Aisen, Ronald Petersen, Clifford R. Jack, William Jagust, John Q. Trojanowski, Arthur W. Toga, Laurel Beckett, Robert C. Green, Andrew J. Saykin, John Morris, Leslie M. Shaw, Zaven Khachaturian, Greg Sorensen, Lew Kuller, Marcus Raichle, Steven Paul, Peter Davies, Howard Fillit, Franz Hefti, David Holtzman, Marek M. Mesulam, William Potter, Peter Snyder, Adam Schwartz, Tom Montine, Ronald G. Thomas, Michael Donohue, Sarah Walter, Devon Gessert, Tamie Sather, Gus Jiminez, Danielle Harvey, Matthew Bernstein, Paul Thompson, Norbert Schuff, Bret Borowski, Jeff Gunter, Matt Senjem, Prashanthi Vemuri, David Jones, Kejal Kantarci, Chad Ward, Robert A. Koeppe, Norm Foster, Eric M. Reiman, Kewei Chen, Chet Mathis, Susan Landau, Nigel J. Cairns, Erin Householder, Lisa Taylor-Reinwald, Virginia Lee, Magdalena Korecka, Michal Figurski, Karen Crawford, Scott Neu, Tatiana M. Foroud, Steven G. Potkin, Li Shen, Kelley Faber, Sungeun Kim, Kwangsik Nho, Leon Thal, Neil Buckholtz, Marylyn Albert, Richard Frank, John Hsiao, Jeffrey Kaye, Joseph Quinn, Betty Lind, Raina Carter, Sara Dolen, Lon S. Schneider, Sonia Pawluczyk, Mauricio Beccera, Liberty Teodoro, Bryan M. Spann, James Brewer, Helen Vanderswag, Adam Fleisher, Judith L. Heidebrink, Joanne L. Lord, Sara S. Mason, Colleen S. Albers, David Knopman, Kris Johnson, Rachelle S. Doody, Javier Villanueva-Meyer, Munir Chowdhury, Susan Rountree, Mimi Dang, Yaakov Stern, Lawrence S. Honig, Karen L. Bell, Beau Ances, Maria Carroll, Sue Leon, Mark A. Mintun, Stacy Schneider, Angela Oliver, Daniel Marson, Randall Griffith, David Clark, David Geldmacher, John Brockington, Erik Roberson, Hillel Grossman, Effie Mitsis, Leyla de Toledo-Morrell, Raj C. Shah, Ranjan Duara, Daniel Varon, Maria T. Greig, Peggy Roberts, Chiadi Onyike, Daniel D’Agostino, Stephanie Kielb, James E. Galvin, Brittany Cerbone, Christina A. Michel, Henry Rusinek, Mony J. de Leon, Lidia Glodzik, Susan De Santi, P. Murali Doraiswamy, Jeffrey R. Petrella, Terence Z. Wong, Steven E. Arnold, Jason H. Karlawish, David Wolk, Charles D. Smith, Greg Jicha, Peter Hardy, Partha Sinha, Elizabeth Oates, Gary Conrad, Oscar L. Lopez, MaryAnn Oakley, Donna M. Simpson, Anton P. Porsteinsson, Bonnie S. Goldstein, Kim Martin, Kelly M. Makino, M. Saleem Ismail, Connie Brand, Ruth A. Mulnard, Gaby Thai, Catherine McAdams-Ortiz, Kyle Womack, Dana Mathews, Mary Quiceno, Ramon Diaz-Arrastia, Richard King, Myron Weiner, Kristen Martin-Cook, Michael DeVous, Allan I. Levey, James J. Lah, Janet S. Cellar, Jeffrey M. Burns, Heather S. Anderson, Russell H. Swerdlow, Liana Apostolova, Kathleen Tingus, Ellen Woo, Daniel H. S. Silverman, Po H. Lu, George Bartzokis, Neill R. Graff-Radford, Francine Parfitt, Tracy Kendall, Heather Johnson, Martin R. Farlow, Ann Marie Hake, Brandy R. Matthews, Scott Herring, Cynthia Hunt, Christopher H. van Dyck, Richard E. Carson, Martha G. MacAvoy, Howard Chertkow, Howard Bergman, Chris Hosein, Ging-Yuek Robin Hsiung, Howard Feldman, Benita Mudge, Michele Assaly, Charles Bernick, Donna Munic, Andrew Kertesz, John Rogers, Dick Trost, Diana Kerwin, Kristine Lipowski, Chuang-Kuo Wu, Nancy Johnson, Carl Sadowsky, Walter Martinez, Teresa Villena, Raymond Scott Turner, Kathleen Johnson, Brigid Reynolds, Reisa A. Sperling, Keith A. Johnson, Gad Marshall, Meghan Frey, Barton Lane, Allyson Rosen, Jared Tinklenberg, Marwan N. Sabbagh, Christine M. Belden, Sandra A. Jacobson, Sherye A. Sirrel, Neil Kowall, Ronald Killiany, Andrew E. Budson, Alexander Norbash, Patricia Lynn Johnson, Joanne Allard, Alan Lerner, Paula Ogrocki, Leon Hudson, Evan Fletcher, Owen Carmichael, John Olichney, Charles DeCarli, Smita Kittur, Michael Borrie, T-Y. Lee, Rob Bartha, Sterling Johnson, Sanjay Asthana, Cynthia M. Carlsson, Adrian Preda, Dana Nguyen, Pierre Tariot, Stephanie Reeder, Vernice Bates, Horacio Capote, Michelle Rainka, Douglas W. Scharre, Maria Kataki, Anahita Adeli, Earl A. Zimmerman, Dzintra Celmins, Alice D. Brown, Godfrey D. Pearlson, Karen Blank, Karen Anderson, Robert B. Santulli, Tamar J. Kitzmiller, Eben S. Schwartz, Kaycee M. Sink, Jeff D. Williamson, Pradeep Garg, Franklin Watkins, Brian R. Ott, Henry Querfurth, Geoffrey Tremont, Stephen Salloway, Paul Malloy, Stephen Correia, Howard J. Rosen, Bruce L. Miller, Jacobo Mintzer, Kenneth Spicer, David Bachman, Stephen Pasternak, Irina Rachinsky, Dick Drost, Nunzio Pomara, Raymundo Hernando, Antero Sarrael, Susan K. Schultz, Laura L. Boles Ponto, Hyungsub Shim, Karen Elizabeth Smith, Norman Relkin, Gloria Chaing, Lisa Raudin, Amanda Smith, Kristin Fargher, Balebail Ashok Raj, Thomas Neylan, Jordan Grafman, Melissa Davis, Rosemary Morrison, Jacqueline Hayes, Shannon Finley, Karl Friedl, Debra Fleischman, Konstantinos Arfanakis, Olga James, Dino Massoglia, J. Jay Fruehling, Sandra Harding, Elaine R. Peskind, Eric C. Petrie, Gail Li, Jerome A. Yesavage, Joy L. Taylor, Ansgar J. Furst, Vincent C. T. Mok, Timothy C. Y. Kwok, Qihao Guo, Kin Y. Mok, Maryam Shoai, John Hardy, Lei Chen, Amy K. Y. Fu, Nancy Y. Ip. Deep learning-based polygenic risk analysis for Alzheimer’s disease prediction. Communications Medicine, 2023; 3 (1) DOI: 10.1038/s43856-023-00269-x

Cite This Page:

Hong Kong University of Science and Technology. "Applying artificial intelligence for early risk forecasting of Alzheimer's disease." ScienceDaily. ScienceDaily, 7 June 2023. <www.sciencedaily.com/releases/2023/06/230607124033.htm>.
Hong Kong University of Science and Technology. (2023, June 7). Applying artificial intelligence for early risk forecasting of Alzheimer's disease. ScienceDaily. Retrieved October 31, 2024 from www.sciencedaily.com/releases/2023/06/230607124033.htm
Hong Kong University of Science and Technology. "Applying artificial intelligence for early risk forecasting of Alzheimer's disease." ScienceDaily. www.sciencedaily.com/releases/2023/06/230607124033.htm (accessed October 31, 2024).

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