Researchers recently published a study in Frontiers in Aging, in which they analyzed data from multiple studies and 13 microbiome datasets, including sequencing data of 16S ribosomal RNA (rRNA), to align clinical facial skin data and identify microbial taxa associated with skin aging.
Study: An analysis of multiple studies enables the identification of potential microbial features associated with skin aging. Photo credit: Ground Picture/Shutterstock.com
The human skin, the organ most exposed to the environment, contains a diverse microbial colony that can change dramatically throughout life.
The microbial composition of the skin predicts chronological age better than the microbial composition in the mouth or gut in adults.
The microbiome plays a role in aging as it contains the majority of genes in the body. Understanding this relationship is crucial for developing innovative microbiome-based treatments for skin structure and appearance.
About the Study
In the presented study, researchers introduced a method for identifying microbial profiles associated with signs of skin aging.
The researchers used a three-stage methodology to examine the relationship between skin microbiota and aging indicators. They deposited sequencing data from 13 studies in Qiita, selected metadata to promote data harmonization, and processed and analyzed the data using the standardized bioinformatic workflow of Qiita.
They conducted a multi-study analysis using microbial sequencing data and information from 13 cohort-style observational studies.
The studies involved non-smoking women aged 18 to 70 who were not taking systemic antifungals or antibiotics, did not suffer from acute skin problems, and did not use peeling, lightening, or depigmentation treatments.
Participants were asked to wash their faces with non-antibacterial soap at least one day before the test. Soap and shampoo were used 24 and 48 hours before the sample, respectively, with no other products allowed.
The team took microbiota samples in a climate chamber at 60% humidity and 21 degrees Celsius. Sterile swabs were premoistened with 0.2 M sodium chloride and 0.10% Tween 20 solutions.
The team rubbed the swabs on the participant’s cheeks for one minute before being stored at 80°C and filtered to obtain one sample per participant.
The team used three parameters to assess skin quality, namely the degree of crow’s feet wrinkles (GCFW), transepidermal water loss (TEWL), and hydration.
They assessed GCFW by clinically rating crow’s feet wrinkles on a validated six-point scale; they measured hydration in the upper epidermis of the cheek skin using a corneometer, which measures changes in dielectric constants due to hydration, and TEWL by measuring the extent of water evaporated from the 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 tool „Bayesian Inferential Regression for Differential Microbiome Analysis“ (BIRDMAn) to identify species associated with age and aging symptoms through differential frequency analysis.
The microbial diversity was negatively associated with TEWL, but positively associated with age, although the associations varied among sub-studies. Microbial diversity showed positive associations with crow’s feet wrinkles, a marker of skin aging, but negative associations with TEWEL.
The host’s age was strongly associated with GCFW, but not with age, TEWL, or corneometer measurements.
A collective data analysis without considering heterogeneity between the studies showed that the host’s age and GCFW were positively associated with microbial diversity. The inclusion of study variables as random effects showed that the host’s age continued to be significantly and positively associated with diversity, while this was not the case with GCFW.
The study variable had the greatest influence on the variation in microbiome composition, followed by age, GCFW, and TEWL. The corneometer could not significantly explain the variability of the microbiota.
Skin samples with lower wrinkle levels showed associations with commensal microbial taxa such as Kocuria, Staphylococcus, Lysobacter, and Peptostreptococcus.
Environmental bacteria such as Kaistella and Brevibacterium are also associated with skin changes and inflammatory conditions such as senile xerosis and psoriasis. These species were more common in samples from people with a higher degree of wrinkles.
The BIRDMAn analysis and the representation of the centered log-ratio led to a smaller list of microbial taxa associated with TEWL and corneometer measurements. Some of the taxa associated with reduced TEWL, such as Bacillus and Staphylococcus, were skin-specific; however, practically all had low prevalence.
Roseomonas, Janibacter, Lactobacillus, and Sphingomonas were the microbes associated with high corneometer measurements.
Surprisingly, Cutibacterium, despite being the most prevalent genus in cheek microbiome, did not show a significant association with age and showed a negative trend with increasing degree of crow’s feet wrinkles, and in the study, it was not identified as a taxon strongly associated with skin aging and quality characteristics.
Overall, the study results highlighted the influence of the skin microbiome on aging. Microbial diversity on cheek skin was higher in older individuals, although the numbers of Cutibacterium were low. The increased TEWL values suggested reduced microbial diversity with diminished skin barrier function.
The team identified taxa associated with age symptoms and skin quality metrics. Environmental bacteria such as Kaistella were associated with high GCFW, while essential commensal gram-positive bacteria were associated with low GCFW.
Future studies using different omics and experimental methods will be required to verify the results and better understand the role of bacteria in aging in the lower layers of the skin.