Background
The human microbiome refers to all the microorganisms that live on and inside us. These microorganisms are predominantly bacteria and can consist of both beneficial and harmful species that change throughout our lives due to different factors. Our facial skin is home to millions of these microscopic organisms, such as bacteria, fungi, and viruses; that influence the health of our skin (Byrd, A., Belkaid, Y. & Segre, J, 2018). These include the bacteria that peacefully coexist on our skin, as well as potentially harmful and dangerous invader bacteria (Scharschmidt TC, Fischbach MA, 2013). These facial microbes function in maintaining the physical barrier of our skin, protection against pathogens, working with the immune response, and the breakdown of natural products (Byrd, A., Belkaid, Y. & Segre, J, 2018).
Aging is a factor that influences the composition and function of the facial microbiome and is unavoidable for all humans. As we get older, our facial skin experiences a decrease in collagen, sebaceous/oil glands, and sweat, which can impact and influence the facial microbiome (Sun, C. et al. 2024). Skin aging is associated with physical changes such as wrinkles, loss of elasticity, and the appearance of rough texture (Kim, HJ. et al., 2022). It has been found that elderly individuals exhibit significantly higher microbiome diversity linked to reduced immune capabilities that are not as effective at eliminating certain bacteria and microbes (Jensen, J. M., & Proksch, E., 2009).
A study was recently conducted by Sun, C., that focused on the facial microbiome and in identifying distinct microorganisms that are associated with aging and skin properties, as well as assessing the impact of lifestyle habits on skin aging using a developed microbiome-based Facial Age Index (FAI).
Central Question:
What distinct microorganisms are associated with aging and how they can be impacted by aging, other skin characteristics, and lifestyle habits? How can the results of this study lead to the development and support of personalized skin interventions by targeting lifestyle, skin properties, and aging-related facial microbes?
Evidence:
For this study, 479 healthy Chinese individuals, both male and female, ranging from ages 18-64 located in the Northern, Central, and Southern regions of China had samples of their facial skin collected. To determine the composition of the facial microbiome and which bacteria had the highest levels of abundance, DNA sequencing was done on the samples collected from participants. These DNA sequence reads from the samples of the participants were then matched up and compared with known DNA sequence reads of species of bacteria found in the Facial Microbiome Genome Compendium (FMGC). Cutibacterium and Streptococcus were the two major types of bacteria found to make-up the facial microbiome and had the highest levels of abundance. Cutibacterium is a genera of bacteria that are a part of the normal skin microbiota and are commonly found in regions with many sebaceous/oil glands (Mayslich C, Grange PA, Dupin N, 2021). Some strains of Cutibacterium, such as Cutibacterium acnes, can become virulent and are linked to acne and other skin diseases (Mayslich C, Grange PA, Dupin N, 2021). Streptococci is also a genus of bacteria that are found to be part of the normal microbiota of humans, however, certain strains of it can cause a range of significant human diseases that can occur in the respiratory tract, bloodstream, or as skin infections (Patterson MJ, 1996). Using these findings of the two most abundant genera of bacteria, this study focused on how age, physio-optical, and lifestyle habits influences these two types of bacteria, along with other bacteria that make up the facial microbiome.
Figure 1: The proportion of Cutibacterium and Streptococcus observed, when age, moisture, and sebum are evenly grouped according to their distributions (Sun, C., et al. 2024).
The top factors that influence the facial microbiome were found to be age, moisture, sebum, and sensitivity. It was found that age had the most significant and direct impact on the community structure of the facial microbiome compared to other physio-optical factors using a mediation analysis. The mediation analysis tested the causal relationships between age, factors of the skin, and the microbiota of the facial skin. The relationships between age and skin physio-optical factors determined that age is an independent factor that is not influenced by any other physio-optical characteristics. Whereas physio-optical characteristics are influenced by age. Age had negative correlations with skin elasticity, sebum, and gloss, while having a positive correlation with sensitivity. When looking at how age, physio-optical factors, and bacteria of the skin are related, the study discovered that there was a decrease in the proportion of Cutibacterium bacteria with increasing age and higher levels of skin moisture, and an increase with elevated sebum levels (Figure 1). Streptococcus bacteria showed an opposite pattern where there was an increase in the proportion of Streptococcus within younger individuals and lower levels of skin moisture, and a significant decrease with elevated sebum levels (Figure 1).
Figure 2: Barplot showing the increase in Shannon diversity (number of different types of bacteria) with advancing age (Sun, C., et al. 2024).
This study also found that there was an increase in the number of different types of microorganisms on our facial skin with advancing age (Figure 2). The use of a Shannon index determined this, that measured the diversity of species in a community by looking at the number of individual microbial genomes sequenced. There was a higher diversity of different species of Streptococcus in older age groups. They determined that this increase in microbe diversity of the facial microbiome could be a result of a decrease in the effectiveness of the facial immune system of the skin barrier that is charged with eliminating harmful species of bacteria through the use of cytokines and cAMP (Wanke, I. et al. 2011). This decrease in effectiveness of the facial immune system in older age groups could have resulted from continuous exposure to environmental and/or chemical factors throughout their lives. This finding shows a link between increasing age, microbiome diversity, and an individual’s susceptibility to certain diseases. In total, the study found a total of 685 individual microbial genomes that are associated with age: 652 showed positive correlations with age, and 31 showed negative correlations with age. Some of the microbes that displayed a positive correlation with age (meaning that the older we get the more of these microbes are found on our facial skin) are bacteria of the genus Prevotella, Streptococcus, and Neisseria. Prevotella are a species of bacteria that are most commonly found at mucosal sites such as saliva, the respiratory system, and our gut (Larsen, JM, 2017). Neisseria is a genus of bacteria with many species that are a normal part of the human microbiome and are commonly found located at mucosal surfaces. Two species of Neisseria, N. gonorrhoeae and N. meningitidis, are pathogenic to humans and cause gonorrhea, meningitis, and septicemia (Humbert MV, Christodoulides M, 2019). Two genera that displayed negative correlation with age (meaning that the older we get the less of these microbes are found on our facial skin) were found to be Corynebacterium and Cutibacterium. Cutibacterium, as discussed above, is a genera of bacteria that are a part of our normal skin microbiota, but that has some pathogenic strains that can cause acne and other skin diseases (Mayslich C, Grange PA, Dupin N, 2021). Corynebacterium is a genera of bacteria that colonize the skin and mucous membranes in humans and that has certain strains such as C. striatum and C. jeikeium that can cause severe bloodstream infections, endocarditis, pneumonia, and skin infections (Yamamuro, R., Hosokawa, N., Otsuka, Y et al. 2021).
A Facial Aging Index (FAI) was composed in this study using identified age-related taxa and higher taxonomic levels along with age-related microbial genomes. In simpler terms, the FAI was created using bacterial species that significantly correlate with age, and the FAI represents the quantification of age through skin microbes. A high FAI score meant that there was a greater degree of skin aging in an individual. Using a Facial Aging Index (FAI), other factors are determined that can influence the prevalence of age-related microbes on our facial skin (Sun, C., et al. 2024).These factors include our lifestyle choices and behaviors. Certain lifestyles were discovered and tested to be associated with higher FAI scores. They found that age-related microbes and FAI scores are higher in individuals who wear a lot of makeup during the week (defined as three or more cosmetic applications per week) as well as someone who stays up late (goes to bed after 11 pm three or more times per week). Cutibacterium and Streptococcus species were found to be both significantly elevated in the group of individuals who wore heavy makeup. While in the group of individuals who stayed up late there was found to be a statistically significant increase in Cutibacterium. These findings conclude that we can enhance our skin microbiome health through modifying certain behaviors and that the species of microorganisms that make up our facial microbiome are susceptible to external influences caused by our lifestyles.
Next Step:
One idea for future studies is that instead of looking at different age groups of individuals and comparing them to each other, a study could be performed that tracked the same individuals over time throughout their lives. This could be a more effective alternative to looking at the impact of age on the facial microbiome. Another experiment that could be done is to use targeted interventions that can specifically target aging-related microbes and determine the effects that they have on skin health.
Through the knowledge that this study and potential future research studies can give us, it can result in the development of tailored skincare products and treatments that can cater to unique and specific requirements of individuals. This would be very important in providing effective treatments and preventative measures for skin diseases and conditions that can be caused by certain skin microbes.
Further Reading:
The Impact of Acne Treatment on Skin Bacterial Microbiota| Read for further information about Cutibacterium acnes and about the impact of acne treatments on the skin bacterial microbiome.
Human Skin Microbiome: Impact of Intrinsic and Extrinsic Factors on Skin Microbiota| Read for further information about the skin microbiota and the relationship between the microbiome and the body’s immune system, as well as the impacts of internal and external factors on the skin microbiome.
References:
Sun, C., Hu, G., Yi, L. et al. Integrated analysis of facial microbiome and skin physio-optical properties unveils cutotype-dependent aging effects. Microbiome 12, 163 (2024). https://doi.org/10.1186/s40168-024-01891-0
Scharschmidt TC, Fischbach MA. What lives on our skin: ecology, genomics and therapeutic opportunities of the skin microbiome. Drug Discovery Today: Disease Mechanisms. 2013;10(3):e83–9. https://doi.org/10.1016/j.ddmec.2012.12.003
Byrd, A., Belkaid, Y. & Segre, J. The human skin microbiome. Nat Rev Microbiol 16, 143–155 (2018). https://doi.org/10.1038/nrmicro.2017.157
Kim, HJ., Oh, H.N., Park, T. et al. Aged related human skin microbiome and mycobiome in Korean women. Sci Rep 12, 2351 (2022). https://doi.org/10.1038/s41598-022-06189-5
Wanke I, Steffen H, Christ C, Krismer B, Götz F, Peschel A, Schaller M, Schittek B. Skin Commensals amplify the innate immune response to pathogens by activation of distinct signaling pathways. J Investig Dermatol. 2011;131(2):382–90. DOI: https://doi.org/10.1038/jid.2010.328
Mayslich C, Grange PA, Dupin N. Cutibacterium acnes as an Opportunistic Pathogen: An Update of Its Virulence-Associated Factors. Microorganisms. 2021 Feb 2;9(2):303. doi: https://doi.org/10.3390/microorganisms9020303. PMID: 33540667; PMCID: PMC7913060.
Patterson MJ. Streptococcus. In: Baron S, editor. Medical Microbiology. 4th edition. Galveston (TX): University of Texas Medical Branch at Galveston; 1996. Chapter 13. Available from: https://www.ncbi.nlm.nih.gov/books/NBK7611/
Yamamuro, R., Hosokawa, N., Otsuka, Y., & Osawa, R. (2021). Clinical Characteristics of Corynebacterium Bacteremia Caused by Different Species, Japan, 2014–2020. Emerging Infectious Diseases, 27(12), 2981-2987. https://doi.org/10.3201/eid2712.210473.
Larsen JM. The immune response to Prevotella bacteria in chronic inflammatory disease. Immunology. 2017 Aug;151(4):363-374. doi: https://doi.org/10.1111/imm.12760. Epub 2017 Jun 20. PMID: 28542929; PMCID: PMC5506432.
Humbert MV, Christodoulides M. Atypical, Yet Not Infrequent, Infections with Neisseria Species. Pathogens. 2019 Dec 20;9(1):10. doi: https://doi.org/10.3390/pathogens9010010. PMID: 31861867; PMCID: PMC7168603.
Jensen, J. M., & Proksch, E. (2009). The skin’s barrier. Giornale italiano di dermatologia e venereologia : organo ufficiale, Societa italiana di dermatologia e sifilografia, 144(6), 689–700.