Estimation of Human Cerebral Atrophy Based on Systemic Metabolic Status Using Machine Learning

Kaoru Sakatani; Katsunori Oyama; Lizhen Hu; Shin'ichi Warisawa

doi:10.3389/fneur.2022.869915

Estimation of Human Cerebral Atrophy Based on Systemic Metabolic Status Using Machine Learning

Front Neurol. 2022 May 2:13:869915. doi: 10.3389/fneur.2022.869915. eCollection 2022.

Authors

Kaoru Sakatani¹, Katsunori Oyama², Lizhen Hu¹, Shin'ichi Warisawa¹

Affiliations

¹ Department of Human and Engineered Environmental Studies, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Japan.
² Department of Computer Science, College of Engineering, Nihon University, Koriyama, Japan.

Abstract

Background: Based on the assumption that systemic metabolic disorders affect cognitive function, we have developed a deep neural network (DNN) model that can estimate cognitive function based on basic blood test data that do not contain dementia-specific biomarkers. In this study, we used the same DNN model to assess whether basic blood data can be used to estimate cerebral atrophy.

Methods: We used data from 1,310 subjects (58.32 ± 12.91years old) enrolled in the Brain Doc Bank. The average Mini Mental State Examination score was 28.6 ± 1.9. The degree of cerebral atrophy was determined using the MRI-based index (GM-BHQ). First, we evaluated the correlations between the subjects' age, blood data, and GM-BHQ. Next, we developed DNN models to assess the GM-BHQ: one used subjects' age and blood data, while the other used only blood data for input items.

Results: There was a negative correlation between age and GM-BHQ scores (r = -0.71). The subjects' age was positively correlated with blood urea nitrogen (BUN) (r = 0.40), alkaline phosphatase (ALP) (r = 0.22), glucose (GLU) (r = 0.22), and negative correlations with red blood cell counts (RBC) (r = -0.29) and platelet counts (PLT) (r = -0.26). GM-BHQ correlated with BUN (r = -0.30), GLU (r = -0.26), PLT (r = 0.26), and ALP (r = 0.22). The GM-BHQ estimated by the DNN model with subject age exhibited a positive correlation with the ground truth GM-BHQ (r = 0.70). Furthermore, even if the DNN model without subject age was used, the estimated GM-BHQ showed a significant positive correlation with ground truth GM-BHQ (r = 0.58). Age was the most important variable for estimating GM-BHQ.

Discussion: Aging had the greatest effect on cerebral atrophy. Aging also affects various organs, such as the kidney, and causes changes in systemic metabolic status, which may contribute to cerebral atrophy and cognitive impairment. The DNN model may serve as a new screening test for dementia using basic blood tests for health examinations. Finally, the blood data reflect systemic metabolic disorders in each subject-this method may thus contribute to personalized care.

Keywords: Alzheimer's disease; MRI DL-based estimation of cerebral atrophy; artificial intelligence; cerebral atrophy; deep learning; dementia; screening test.