Identifying candidate gut microbiota indicators for Alzheimer's disease through integrated data

Area of Science:

• Microbiome research
• Neurodegenerative disease biomarkers
• Computational biology and bioinformatics

Background:

• Alzheimer's disease (AD) is increasingly linked to gut microbiota dysbiosis.
• Identifying reliable gut microbiota biomarkers is crucial for early AD diagnosis.
• Previous studies often lack large-scale, multi-population data for robust biomarker discovery.

Purpose of the Study:

• To characterize gut microbiota composition and diversity in AD patients across diverse populations.
• To identify reliable gut microbiota indicators with diagnostic value for AD.
• To explore the potential of these indicators for non-invasive AD diagnosis and targeted therapies.

Main Methods:

• Integrated public data from AD dementia, mild cognitive impairment (MCI), and normal control (NC) groups.
• Conducted microbiota composition, diversity (α-diversity), and network analyses.
• Utilized seven common statistical methods to identify differential bacterial genera as candidate indicators.

Main Results:

• The AD dementia group exhibited significantly lower α-diversity and reduced microbial network density compared to MCI and NC groups.
• Identified 35 bacterial genera as candidate AD indicators, including novel findings for *RF39* and *Oligella*.
• *Faecalibacterium* emerged as the top indicator overall, with population-specific key indicators like *Akkermansia* (Chinese) and *Collinsella* (Turkish/Kazakh).

Conclusions:

• Gut microbiota alterations, including reduced diversity and network complexity, are characteristic of AD dementia.
• A panel of 35 bacterial genera shows promise as diagnostic biomarkers for AD.
• Findings support the development of non-invasive AD diagnostic tools and microbiota-targeted therapeutic strategies.