An accurate aging clock developed from large-scale gut microbiome and human gene expression data

Area of Science:

• Genomics
• Microbiome research
• Computational biology
• Aging research

Background:

• Accurate biological age measurement is crucial for understanding aging.
• Quantifying lifestyle impacts on healthy aging requires reliable biomarkers.
• Existing aging clocks have limitations in scope and accuracy.

Purpose of the Study:

• To develop an accurate
• aging clock
• using machine learning techniques.
• To predict chronological age from gene expression in capillary blood and microbial genes in stool samples.
• To associate biological age with lifestyle and health factors.

Main Methods:

• Machine learning algorithms were applied to predict chronological age.
• Human gene expression data from capillary blood was analyzed.
• Microbial gene expression data from a large metatranscriptomic dataset (90,303 individuals) was utilized.
• Statistical associations between predicted biological age and lifestyle/health factors were investigated.

Main Results:

• Chronological age can be accurately predicted from both human gene expression and gut microbiome data.
• The microbiome-aging model, utilizing a large dataset, shows high quality.
• Associations were found between biological age and diet (paleo, vegetarian) and health conditions (IBS).
• Key pathways of biological decline related to aging were identified.

Conclusions:

• Gene expression in blood and the gut microbiome serve as reliable biomarkers for an
• aging clock
• .
• Lifestyle factors significantly influence biological aging.
• This approach enables precise quantification of aging and the impact of interventions on healthy aging.