Abstract

Background

In addition to conventional symptomatic treatment drugs, anti-amyloid beta antibody drugs are expected to benefit patients with Alzheimer’s disease (AD). However, issues such as side effects and high costs persist, and new preventive and therapeutic drugs are desired. Meanwhile, information on the diagnosis and symptomatic treatment of AD accumulated during daily clinical practice is stored as real-world data and is considered a powerful means of discovering unknown factors that could provide clues for new prevention and treatment approaches for AD through comprehensive exploration.

Methods

We used anonymized hospital information system data from a tertiary care and academic hospital in Japan, spanning from 1981 to 2016, to search for potential suppressive factors for AD onset and to verify the validity of the discovered factors. We initially conducted a comprehensive search for candidate suppressive factors for AD and verified them using the inverse probability weighting (IPW) method with propensity scores.

Results

From the comprehensive search, we identified glycyrrhizic acid (GA), a component of licorice, a traditional medicine with anti-inflammatory, antioxidant, antibacterial, and antiaging properties, as a candidate suppressing factor for AD. The IPW method showed that the odds ratio of developing AD in the GA group was 0.642 (95% confidence interval: 0.566–0.727) compared with the non-GA group after adjustment.

Conclusions

This is the first human study to suggest that GA may be a factor that can suppress the onset of AD. Additionally, our method could be a promising tool for drug repositioning that applies existing drugs already used in clinical settings with well-known side effects to diseases different from their original use.

Details

Title
Identifying suppressive factors of Alzheimer’s disease through comprehensive analysis of real-world data: a single-center retrospective study
Author
Shiotani, Mana; Hyohdoh, Yuki; Hatakeyama, Yutaka; Kazui, Hiroaki; Okuhara, Yoshiyasu
Pages
1-18
Section
Research
Publication year
2025
Publication date
2025
Publisher
BioMed Central
e-ISSN
14712318
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3216558943
Copyright
© 2025. This work is licensed under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.