Hidden Links Between Skin Microbiome and Skin Imaging Phenome

Area of Science:

• Microbiome research
• Dermatology
• Bioinformatics

Background:

• The skin microbiome's role in skin health and disease is recognized, but its impact on skin appearance is less understood.
• Skin aging is influenced by intrinsic factors, but extrinsic and microbial factors are increasingly investigated.

Purpose of the Study:

• To investigate the relationship between the skin microbiome and skin imaging phenotypes (SIPs).
• To develop machine learning-based indices for skin aging (phenotype, microbiota, and integrated).
• To explore the predictive potential of the skin microbiome for SIPs and identify underlying mechanisms.

Main Methods:

• Utilized 1244 face imaging phenomes and 246 cheek metagenomes.
• Developed three machine learning-based skin age indices: skin phenotype age (SPA), skin microbiota age (SMA), and skin integration age (SIA).
• Analyzed correlations between skin microbial diversity (taxonomic and functional alpha diversity) and various SIPs (melanin, pore, pigment, UV spots, sebum, lightening, porphyrin).

Main Results:

• Skin microbial diversity positively correlated with melanin, pore, pigment, and UV spot levels, while negatively correlating with sebum, lightening, and porphyrin.
• Specific bacterial species, including Corynebacterium matruchotii, Staphylococcus capitis, and Streptococcus sanguinis, were negatively correlated with sebum and lightening levels.
• The skin microbiome demonstrated predictive capability for SIPs, with lightening level prediction achieving an error of 1.8%.

Conclusions:

• The skin microbiome significantly shapes skin imaging phenotypes and contributes to skin appearance.
• Microbial metabolic pathways, such as cobalamin and heme synthesis (driven by Cutibacterium acnes), may mediate the effects of the microbiome on pore, wrinkle, and sebum levels.
• This study provides novel insights into the skin microbiome's role in healthy skin aging and appearance.