Background: Alzheimer's disease (AD) is characterized by amyloid-β (Aβ) plaque accumulation and neurofibrillary tangles in the brain. Emerging evidence has suggested potential interactions between the brain and periphery, particularly the liver, in regulating Aβ homeostasis.
Objective: This study aimed to investigate the association of serum liver enzymes with brain amyloidopathy and cognitive performance in patients with complaints of cognitive decline.
Methods: A total of 1,036 patients (mean age 74 years, 66.2% female) with subjective cognitive decline, mild cognitive impairment, AD dementia, and other neurodegenerative diseases were included using the Smart Clinical Data Warehouse. Amyloid positron emission tomography (PET) imaging, comprehensive neuropsychological evaluations, and measurements of liver enzymes, including aspartate aminotransferase (AST), alanine aminotransferase (ALT), alkaline phosphatase, total bilirubin, and albumin, were assessed. After propensity score matching, logistic and linear regression analyses were used to investigate the associations between liver enzymes, amyloid status, and cognitive performance. Additionally, a machine learning approach was used to assess the classification performance of liver enzymes in predicting amyloid PET positivity.
Results: Lower ALT levels and higher AST-to-ALT ratios were significantly associated with amyloid PET positivity and AD diagnosis. The AST-to-ALT ratio was also significantly associated with poor memory function. Machine learning analysis revealed that the classification performance of amyloid status (AUC = 0.642) for age, sex, and apolipoprotein E ɛ4 carrier status significantly improved by 6.2% by integrating the AST-to-ALT ratio.
Conclusions: These findings highlight the potential association of liver function on AD and its potential as a diagnostic and therapeutic implications.
Keywords: Alzheimer’s disease; amyloid PET; cognition; liver enzymes; machine learning.
© 2023 – The authors. Published by IOS Press.