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The interplay between gut microbiota composition and dementia

  • Rad Ghannadzadeh Kermani pour , Sara Kamali Zounouzi , Melina Farshbafnadi und Nima Rezaei EMAIL logo
Veröffentlicht/Copyright: 21. Januar 2025

Abstract

Recently, researchers have been interested in the potential connection between gut microbiota composition and various neuropsychological disorders. Dementia significantly affects the socioeconomics of families. Gut microbiota is considered as a probable factor in its pathogenesis. Multiple bacterial metabolites such as short-chain fatty acids, lipopolysaccharides, and various neurotransmitters that are responsible for the incidence and progression of dementia can be produced by gut microbiota. Various bacterial species such as Bifidobacterium breve, Akkermansia muciniphila, Streptococcus thermophilus, Escherichia coli, Blautia hydrogenotrophica, etc. are implicated in the pathogenesis of dementia. Gut microbiota can be a great target for imitating or inhibiting their metabolites as an adjunctive therapy based on their role in its pathogenesis. Therefore, some diets can prevent or decelerate dementia by altering the gut microbiota composition. Moreover, probiotics can modulate gut microbiota composition by increasing beneficial bacteria and reducing detrimental species. These therapeutic modalities are considered novel methods that are probably safe and effective. They can enhance the efficacy of traditional medications and improve cognitive function.


Corresponding author: Nima Rezaei, Clinical Immunology, Tehran University of Medical Sciences Children’s Medical Center Hospital, Dr. Qarib St, Keshavarz Blvd, Tehran 14194, Iran; Universal Scientific Education and Research Network (USERN), Tehran, 1416634793, Iran; Research Center for Immunodeficiencies, Children’s Medical Center, Tehran University of Medical Sciences, Tehran, 1416634793, Iran; and Department of Immunology, School of Medicine, Tehran University of Medical Sciences, Tehran, 1416634793, Iran, E-mail:

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The authors state no conflict of interest.

  6. Research funding: None declared.

  7. Data availability: Not applicable.

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Received: 2024-08-20
Accepted: 2025-01-03
Published Online: 2025-01-21
Published in Print: 2025-07-28

© 2025 Walter de Gruyter GmbH, Berlin/Boston

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