Learning vector quantization model for prediction of cerebral Aβ in non‐demented individuals using plasma biomarkers

Abstract

AbstractBackgroundCerebral Aβ is the hallmark of the AD in the early therapeutic intervention as well as diagnosis of the disease. Detecting in‐vivo Aβ deposition, however, still have limited accessibility. In this respect, the current study aims to identify plasma markers related to cerebral Aβ status via simple machine learning approach.MethodsPredictability of the candidate plasma markers for cerebral Aβ burden was examined by analyzing 303 non‐demented participants those who had rules based medicine (RMB) and amyloid PET data from the ADNI cohort at baseline visit (83 control and 220 with MCI). Demographic variables (age, gender, education, and APOE status), and 146 of 190 plasma analytes passed quality check were selected as input in learning vector quantization (LVQ) model. The cerebral Aβ burden was measured using PiB PET images. Predictability and importance of variable were calculated using LVQ model.ResultsLVQ model with 10‐cross validation predicted cerebral Aβ status with the 90.67% accuracy (balanced accuracy: 93.14%) in non‐demented people. Based on variable importance analysis, Top‐10 variables above importance ratio above 60 after rescaling were selected: APOE carrier status (100.00), Angiotensin‐Converting Enzyme (ACE, 92.77), Eotaxin‐3 (89.74), Chromogranin A (CgA, 83.61), Brain Natriuretic Peptide (BNP, 79.61), Creatine Kinase‐MB (CK‐MB, 69.61), Monokine Induced by Gamma Interferon (68.26), T Lymphocyte‐Secreted Protein I‐309 (I‐309, 66.89), Matrix Metalloproteinase‐9 (MMP‐9, 64.52), Matrix Metalloproteinase‐9 total (MMP‐9 total, 60.90).ConclusionsOur findings demonstrated that plasma based analytes can predict the cerebral Aβ positivity in non‐demented people. More specifically, ACE is can be a candidate marker for early detection of Aβ deposition in the asymptomatic stage of dementia. Plus, Eoxatain, and CgA can also play a role in AD pathology. To this end, this research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program(IITP‐2021‐2017‐0‐01630) supervised by the IITP (Institute for Information & communications Technology Promotion).