Precision diagnosis of neurodegenerative diseases based on salivary microbiome profiles
| Dates: | 1 March 2024 |
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| Times: | 14:00 - 17:30 |
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| What is it: | Seminar |
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| Organiser: | Department of Electrical and Electronic Engineering |
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| Who is it for: | University staff, Current University students |
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| Speaker: | Prof Chihiro Akazawa |
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Welcome to this EEE Biomedical electronics, circuits and systems research events
Background: The incidence of neurodegenerative diseases, including Alzheimer's disease (AD) and Parkinson's disease, is increasing worldwide. Specifically, more than 50 million people are diagnosed with dementia associated with or progressing to neurodegenerative diseases. Pathological changes in the brain develop several years before cognitive impairment appears, and early diagnosis is effective in considering therapeutic strategies. However, a non-invasive and precision approach to classifying neurodegenerative diseases has not been established. To this end, we performed a robust investigation and machine learning of the salivary microbiome of neurodegenerative disease patients.
Methods: We evaluated the cognitive scores, including mini-mental state examination (MMSE), Hasegawa dementia scale revised (HDS-R), Alzheimer's disease assessment scale–cognitive subscale (ADAS-Cog), and Wechsler memory scale (WMS), in elderly people and classified the population as the healthy cognitive population, mild cognitive impairment (MCI), and dementia. We further investigated the confounders for salivary microbiome dysbiosis associated with cognitive impairment, such as anti-dementia drugs.
Results: Correlation analysis showed specific bacteria associated with cognitive scores and several confounders. Notably, we demonstrated that the populations with different cognitive functions could be classified by their salivary microbiome through machine learning.
Conclusion: Our results showed that profiling the microbiome by machine learning from non-invasively small amounts of saliva provides a classification of neurodegenerative diseases, including AD subjects. Furthermore, it is suggested that the technology develops into a precision diagnosis by considering the confounders.
Speaker
Prof Chihiro Akazawa
Organisation: Juntendo University, Graduate School of Medicine, Tokyo
Biography: Chihiro Akazawa, MD, PhD, is a Professor of Juntendo University, Graduate School of Medicine, Tokyo. He earned MD and PhD from Tokyo Medical and Dental University, TMDU. After a clinical training of Internal Medicine, he spent a post-doctoral training with Dr. Steve Heinemann at the Salk Institute for Biological Studies. Before moving to Juntendo University, he served as a Professor of TMDU for 12 years. He is currently serving as Program Supervisor and Program Officer of Japan Agency of Medical Research and Development (AMED), Technical Advisor of Ministry of Education, Science Technology, Sports and Culture, Japan (MEXT) and Political Advisor of Japanese Cabinet. His research explores regenerative medicine, stem cell research, neurological diseases, and cultured meat.
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Engineering A_4A.001
Engineering Building A
Booth St E
