Plasma protein biomarkers have been considered promising tools for diagnosing dementia subtypes due to their low variability, cost-effectiveness, and minimal invasiveness in diagnostic procedures. Machine learning (ML) methods have been applied to enhance accuracy of the biomarker discovery. However, previous ML-based studies often overlook interactions between proteins, which are crucial in complex disorders like dementia. While protein-protein interactions (PPIs) have been used in network models, these models often fail to fully capture the diverse properties of PPIs due to their local awareness. This drawback increases the chance of neglecting critical components and magnifying the impact of noisy interactions. In this study, we propose a novel graph-based ML model for dementia subtype diagnosis, the graph propagational network (GPN). By propagating the independent effect of plasma proteins on PPI network, the GPN extracts the globally interactive effects between proteins. Experimental results showed that the interactive effect between proteins yielded to further clarify the differences between dementia subtype groups and contributed to the performance improvement where the GPN outperformed existing methods by 10.4% on average.
We thank the staff of the BICWALZS and Suwon Geriatric Mental Health Centre for their involvement in data acquisition. The data analysis operations were supported by KREONET (Korea Research Environment Open NET work),managed and operated by the Korea Institute of Science and Technology Information (KISTI). This study was conducted with biospecimens and data from the consortium of the Biobank Innovations for Chronic cere-brovascular disease With ALZheimer's disease Study (BICWALZS), which was funded by the Korea Disease Control and Prevention Agency for the Korea Biobank Project (#6637-303). This work was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (MOE), Republic of Korea (NRF-2022R1A6A3A01086784), the BK21 FOUR program of the NRF funded by the MOE (NRF5199991014091), and Ajou University Research Fund. This study was also supported by the NRF grants funded by the Ministry of Science and ICT (MSIT), Republic of Korea (NRF-2019R1A5A2026045, NRF-2021R1A2C2003474, and NRF-RS-2022-001653), the Institute of Information and communications Technology Planning and Evaluation (IITP) grants funded by the MSIT (IITP-2024-No.RS-2023-00255968 for the Artificial Intelligence Convergence Innovation Human Resources DevelopmentandNo.2022-0-00653),theKoreaHealthTechnology RD Project through the Korea Health Industry Development Institute (KHIDI) funded by Ministry of Health and Welfare (MOHW), Republic of Korea (HR21C1003), and a grant of Korea Government Grant Program for Education and Research in Medical AI through the KHIDI funded by the Korea government (MOE and MOHW).Participants were from the Biobank Innovations for chronic Cerebrovascular disease With ALZheimer\u2019s disease Study (BICWALZS) [] at Ajou University Hospital (Suwon, Republic of Korea), a disease-focused biobank supported and funded by the National Institute of Health of the Korea Disease Control and Prevention Agency. The participants were diagnosed with MCI, AD, and VD based on he expanded Mayo Clinic criteria [], the core clinical criteria proposed by the National Institute on Aging and the Alzheimer\u2019s Association working group in 2011 [], and the major vascular neurocognitive disorder criteria proposed in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders [], respectively. Therefrom, the 392 participants were categorized into 289 MCI (74%), 73 AD (19%), and 30 VD (8%) participants. Additionally, the participants were divided into the discovery and validation cohorts based on the time of recruitment. The discovery cohort included 271 participants recruited between November 2016 and August 2019. Of these, 191 (70%), 58 (21%), and 22 (8%) had MCI, AD, and VD, respectively. The validation cohort included 121 participants, recruited from September 2019 to September 2020, consisting of 98 (81%) participants with MCI, 15 (12%) with AD, and 8 (7%) with VD.This study was conducted with biospecimens and data from the consortium of the Biobank Innovations for Chronic cerebrovascular disease With ALZheimer\u2019s disease Study (BICWALZS), which was funded by the Korea Disease Control and Prevention Agency for the Korea Biobank Project (#6637-303). This work was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (MOE), Republic of Korea (NRF-2022R1A6A3A01086784), the BK21 FOUR program of the NRF funded by the MOE (NRF5199991014091), and Ajou University Research Fund. This study was also supported by the NRF grants funded by the Ministry of Science and ICT (MSIT), Republic of Korea (NRF-2019R1A5A2026045, NRF-2021R1A2C2003474, and NRF-RS-2022-001653), the Institute of Information & communications Technology Planning & Evaluation (IITP) grants funded by the MSIT (IITP-2024-No.RS-2023-00255968 for the Artificial Intelligence Convergence Innovation Human Resources Development and No. 2022-0-00653), the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI) funded by Ministry of Health and Welfare (MOHW), Republic of Korea (HR21C1003), and a grant of \u2018Korea Government Grant Program for Education and Research in Medical AI\u2019 through the KHIDI funded by the Korea government (MOE and MOHW).
