Background: Yin-deficiency constitution (YinDC) refers to a traditional Chinese medicine concept, characterized by an imbalance state that includes an imbalance in the gut microbiota, resulting from a relative deficiency of Yin fluids within the body. In recent years, it has become apparent that the composition and structure of the gut microbiota play a significant role in the aging process. The imbalance of gut microbiota in YinDC may accelerate the aging process. However, the specific gut microbiota compositions involved in the YinDC premature aging process remain unknown.
Methods: In this study, we conducted a cohort study including 60 women with YinDC and BC to analyze their gut microbiota composition. We integrated 16S rDNA sequencing with machine learning methods to reveal the association between gut microbiota and premature aging in YinDC women.
Results: We found a significant difference in the composition of gut microbiota between the YinDC and the BC group. At the phylum level, Cyanobacteria and Synerobacteria only emerged in the YinDC group. At the genus level, Bacteroides, Bifidobacterium, Haemophilus, Alistipes, and Dialister showed higher abundance in the YinDC group. Bilophila, Eubacterium, and Aeromonas were the most significant indicators influencing the YinDC premature aging. The YinDC group had the most functional gene pathways associated with the metabolism.
Conclusion: Our study demonstrated that the gut microbiota was associated with premature aging in women with YinDC, potentially providing preliminary evidence and guidance for personalized anti-aging strategies.
Keywords: Yin-deficiency constitution; gut microbiota; machine learning; premature aging; traditional Chinese medicine.
Copyright © 2025 Zhai, Li, Cao, Li, Bao, Liang, Liu, Xia and Yu.