In a recent study published in Frontiers in Aging, researchers analyzed data from multiple studies and 13 microbiome datasets, including 16S ribosomal ribonucleic acid (rRNA) sequencing data to match skin clinical data from the face and identify microbial taxa related to skin aging.
Study: A multi-study analysis enables identification of potential microbial features associated with skin aging signs. Image Credit: Ground Picture/Shutterstock.com
Background
Human skin, the most exposed organ to the environment, includes a varied colony of microbes that can change dramatically throughout life.
The skin microbial composition predicts chronological age better than oral or gut microbial composition in adults.
The microbiome plays a role in aging since it contains most of the genes in the body. Understanding this link is critical for creating innovative microbiome-based skin texture and appearance treatments.
About the study
In the present study, researchers presented a method to identify microbial profiles related to skin aging indications.
The researchers used a three-step methodology to investigate the association between skin microbiota and aging indicators. They deposited sequencing data from 13 studies into Qiita, selected metadata to promote data harmonization, and processed and analyzed the data using Qiita's standardized bioinformatic workflow.
They performed a multi-study analysis using microbial sequencing data and information from 13 observational cohort-type studies.
The studies included female non-smokers aged between 18 and 70 years who did not consume systemic antifungals or antibiotics, did not suffer from acute cutaneous problems, and did not use exfoliating, whitening, or depigmenting treatments.
Participants were requested to wash their faces with non-antibacterial soap at least one day before testing. Soap and shampoo were used 24 and 48 hours before the sample, respectively, with no additional items permitted.
The team obtained microbiota samples in a climate-controlled chamber with 60% humidity and 21 degrees Celsius. Sterile cotton swabs were pre-moistened using 0.2M sodium chloride and 0.10% Tween 20 solutions.
The team rubbed the swabs across the participant’s cheeks for a minute before being stored at 80°C and filtered samples to obtain only one sample from each participant.
The team used three parameters to estimate skin quality, i.e., the grade of Crow's foot wrinkles (GCFW), transepidermal water loss (TEWL), and hydration.
They determined the GCFW by clinically scoring the Crow's feet wrinkles using a validated six-point scale; they measured hydration in the upper epidermis of cheek skin using a corneometer measuring changes in dielectric constants due to hydration; and the TEWL by measuring the extent of water evaporated from cheek skin.
The team extracted genomic deoxyribonucleic acid (DNA) from the swabs for polymerase chain reaction (PCR) and 16S rRNA sequencing.
The researchers performed linear mixed-effects modeling. They used the Bayesian Inferential Regression for Differential Microbiome Analysis (BIRDMAn) tool to identify species related to age and aging symptoms by differential abundance analysis.
Results
Microbial diversity was negatively associated with TEWL but positively associated with age, although the associations varied by substudies. Microbial diversity showed positive associations with Crow's foot wrinkles, a marker of skin aging, but negative associations with TEWEL.
Host age was strongly associated with GCFW but not with age, TEWL, or corneometer readings.
Collective data analysis without considering inter-study heterogeneity showed that host age and GCFW were positively associated with microbial diversity. Including the study variable as a random effect showed that host age remained significantly and positively associated with diversity, although GCFW was not.
The study variable most profoundly impacted microbiome composition variation, followed by age, GCFW, and TEWL. The corneometer did not explain microbiota variability appreciably.
Skin samples with lower levels of wrinkles showed associations with commensal microbial taxa like Kocuria, Staphylococcus, Lysobacter, and Peptostreptococcus.
Environmental bacteria, such as Kaistella and Brevibacterium, have also been related to skin changes and inflammatory disorders such as senile xerosis and psoriasis. These species were more abundant in samples from people with higher levels of wrinkles.
BIRDMAn analysis and centered log ratio plotting resulted in a smaller list of microbial taxa associated with TEWL and corneometer measurements. Some of the taxa related to reduced TEWL, such as Bacillus and Staphylococcus, were skin-specific; however, virtually all had a low prevalence.
Roseomonas, Janibacter, Lactobacillus, and Sphingomonas were the microbes related to high corneometer measurements.
Surprisingly, regardless of being the most prevalent genus in the cheek microbiome), Cutibacterium showed no significant association with age, trending negatively with increasing grade of Crow's foot wrinkles, and did not emerge as taxa strongly associated with skin aging and quality characteristics in the study.
Conclusion
Overall, the study findings highlighted the impact of the skin microbiome on aging. Microbial diversity on cheek skin was higher in older individuals, although Cutibacterium counts were low. The increased TEWL values indicated lowered microbial diversity with reduced skin barrier function.
The team identified taxa associated with age symptoms and skin quality metrics. Environmental bacteria, such as Kaistella, were related to high GCFW, whereas essential commensal gram-positive bacteria were related to low GCFW.
Future studies using different omics and experimental methods will be required to verify the findings and better understand the role of bacteria in aging lower skin layers.
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