The human skin microbiome is the collection of microorganisms that inhabits the skin and carries out beneficial and necessary processes to support skin health (Byrd et al., 2018). Starting at birth, the skin microbiota play a critical role in the development and homeostatic maintenance of the immune system and skin physiology over one’s lifetime. For example, they influence key skin functions that include acidification, antimicrobial defense, lipid synthesis, and barrier integrity (Byrd et al., 2018; Almoughrabie et al., 2023; Zheng et al., 2022; Liu et al., 2023). Balance of the skin microbiome is crucial; an imbalance, or dysbiosis, can disrupt these functions and is associated with various conditions including atopic dermatitis and acne vulgaris (Nakatsuji and Gallo, 2018; Huang et al., 2023). Therefore, supporting the host-microbiome interactions at the skin surface is critical for maintaining the delicate balance between health and dermatological conditions.

Aging is a time-dependent process characterized by a decline in functional capacity that affects nearly all living organisms (López-Otín et al., 2013; Guo et al., 2022). It involves not only a decline in tissue and cellular function, but also broader deterioration of physiological processes, which can lead to increased inflammation and visible alterations in appearance, particularly in the skin (López-Otín et al., 2023; Tobin, 2017). The skin experiences significant transformations during aging due to intrinsic factors like reduced epidermal thickness, slower cell turnover, decreased collagen production, and changes in immune function, as well as extrinsic factors such as sun exposure, pollutants, and smoking (Farage et al., 2008). These factors collectively alter the physiological properties of the skin, leading to variations in pH, lipid and amino acid composition, and reduced sweat and sebum production (Choi, 2019; Schreiner et al., 2000; Waller and Maibach, 2006). Such differences affect the skin microbiota, which rely on the skin as a microbial niche and function to support a healthy skin barrier.

Aging skin undergoes significant alterations in its microbiome compared to that of younger individuals, with the skin microbiome reported to be an accurate predictor of chronological age, more so than the gut or oral microbiomes (Huang et al., 2020). With age, there is a notable rise in alpha diversity, which often coincides with a reduction in Cutibacterium (Shibagaki et al., 2017; Jugé et al., 2018; Howard et al., 2022; Ying et al., 2015; Kim et al., 2022; Larson et al., 2022). This shift is further marked by a rise in Corynebacterium and Streptococcus populations (Shibagaki et al., 2017; Jugé et al., 2018; Howard et al., 2022). The specific bacteria at the species level that differentiate young and aged skin can vary significantly from study to study, reflecting the substantial impact of population demographics, environmental factors, and methodological differences on the identification of age-related species of interest (Kim et al., 2022; Larson et al., 2022; Myers et al., 2023; Garlet et al., 2024). Despite this variability, numerous studies consistently identify Cutibacterium acnes and Corynebacterium kroppenstedtii as key markers of age-related microbiome changes (Larson et al., 2022; Garlet et al., 2024; Dimitriu et al., 2019; Zhou et al., 2023). C. acnes tends to decrease with age, while C. kroppenstedtii increases, underscoring their roles as key indicators of microbiome shifts that could influence skin health and the aging process. Furthermore, the skin microbiome is generally stable and resilient to environmental exposures during adulthood (Oh et al., 2016). However, in old age, there is an observed increase in interpersonal variation in the skin microbiome, indicating decline of microbiome stability and robustness (Jugé et al., 2018; Howard et al., 2022; Ying et al., 2015; Larson et al., 2022; Garlet et al., 2024).

While many studies have thoroughly evaluated the microbiome of the face as it ages (Huang et al., 2020; Shibagaki et al., 2017; Jugé et al., 2018; Howard et al., 2022; Kim et al., 2022; Larson et al., 2022; Myers et al., 2023; Garlet et al., 2024; Dimitriu et al., 2019; Zhou et al., 2023), there is a gap in comprehensive understanding of the aging microbiome of non-facial skin sites. Characterizing the aging skin microbiome across the body is crucial for uncovering insights into susceptibility to infections, skin conditions, and overall skin health. Moreover, as consumer interest in the “skinification” of beauty and personal care products grows, there is a demand for targeted solutions that address diverse aspects of skin health, including anti-aging treatments and personalized care. As the skin microbiome is a pivotal component of skin health, evaluating the microbiome variations with age across different body sites is essential. This broader understanding presents a unique opportunity for developing innovative interventions and products that can enhance skin vitality and wellbeing across the lifespan. Expanding this research not only contributes to a more holistic understanding of aging and skin health but also underscores the potential for microbiome-focused interventions to mitigate age-related skin changes.

In this study, we characterized the skin microbiome of the face and forearm in young and old aged individuals from a UK-based population using 16S rRNA gene sequencing. The objective of our study was to evaluate the differences in composition, species interactions, and predicted metabolic function of the bacterial community with age across two distinct skin sites. By focusing on a UK-based population, we aimed to provide insights into aging-related skin microbiome differences of this geographical region, which is relatively understudied compared to North America- and Asia-based populations. We employed comprehensive techniques including differential abundance testing, microbial association network analysis, microbiome clustering, and functional prediction to identify age-related patterns in the skin microbiome and to enhance our understanding of the skin microbiome’s interplay with the aging process.

A total of 59 participants from Birmingham, United Kingdom were enrolled in the study, including thirty young age (YA) (26.7 ± 4.45 years) and twenty-nine old age (OA) (72.3 ± 4.04 years) individuals (Supplementary Table S1). The gender distribution was similar for both age groups (YA: n = 12 male and n = 18 female; OA: n = 12 male and n = 17 female). Other participant demographics and clinical characteristics are summarized in Supplementary Table S1. Skin microbiome samples were collected from the antecubital fossa (arm) and cheek (face) of the participants and were sequenced using V1-V3 16S rRNA gene sequencing, yielding a median of 105,646 high-quality sequences per sample across both sites.

The aging process directly impacts skin appearance and function, resulting in distinct changes that range from visible signs of aging, such as fine lines and wrinkles, to increased susceptibility to skin conditions and infections (Tobin, 2017). These physiological changes are accompanied by shifts in the microbial populations that inhabit our skin, necessitating a better understanding of the consequences that these altered microbial communities have on skin health, particularly across different body sites. In our study, we find that the skin microbiome of the face and arm undergo significant yet distinct shifts with age, highlighting the site-specific variation in microbiome dynamics that are impacted by the aging process.

The face is a highly exposed skin region that is chronically exposed to external factors such as UV radiation and air pollution, both of which significantly contribute to signs of skin aging (Krutmann et al., 2017). We observed strong shifts in the microbiome of the face between young and old age groups, including increased alpha diversity, significant microbiome community composition shifts characterized by a large reduction in C. acnes, altered network topology, and predicted functional changes. The arm, while being a less exposed skin site than the face (Bulliard et al., 2007), also exhibited significant changes in the microbiome between age groups, albeit less pronounced. These included shifts in microbiome community composition characterized by a decrease in C. acnes and shifted Staphylococcus populations, as well as an altered network structure. Thus, skin sites with differential exposure to environmental aggressors show distinct skin microbiome shifts, suggesting that these differences could be driven in part by differences in intrinsic versus extrinsic skin aging factors. Indeed, differential exposure to polycyclic aromatic hydrocarbons (PAH), a class of organic pollutants in ambient air, has been shown to impact the skin microbiome, metabolome, and clinical skin parameters, with high exposure to PAH associated with biodegrading skin bacteria, dry skin, and hyperpigmentation (Leung et al., 2023; Leung et al., 2020; Misra et al., 2021). Similarly, exposure to UVA and UVB radiation, which have profound impacts on skin aging (Krutmann et al., 2017), has been observed to immediately alter microbiome composition (Burns et al., 2019). Therefore, the interplay between the skin, its microbiome, and aging are likely to be multi-faceted and dynamic, influenced by a combination of genetic factors, environmental exposures, and lifestyle choices.

A notable reduction in C. acnes was observed in old age for both skin sites (Figures 3C,D), supporting previous studies that similarly found this species to be a biomarker negatively associated with aging (Larson et al., 2022; Garlet et al., 2024; Zhou et al., 2023). Reduced sebum availability likely leads to a decrease in C. acnes, which plays a key role in maintaining skin pH through production of free fatty acids from sebum metabolism, contributing to the skin’s acidic nature and its protective resistance against unwanted microorganisms (Swaney and Kalan, 2021). While often associated as an etiological contributor to acne, C. acnes is a ubiquitous skin commensal that has key roles in immunomodulation, epithelial barrier regulation, lipid synthesis, and protection against pathogens (Almoughrabie et al., 2023; Claesen et al., 2020; Nagy et al., 2006; Agak et al., 2018; Nakamura et al., 2020; Sanford et al., 2016). As a result, depletion of C. acnes is likely to lead to hyperdiversification as a result of increased niche availability and reduced colonization resistance. This can provide rationale for the observed increase in interpersonal variation seen on the face with old age; diminished colonization resistance could allow for an increase in presence and abundance of non-specific taxa, particularly those that are transient and environmental, leading to high interpersonal variability of the microbiome. Indeed, predictive functional analysis of the face microbiome revealed increased abundances of functions involved in aromatic hydrocarbon/pollutant degradation (Figure 6), functions that are distributed among environmental microorganisms due to the widespread presence of these compounds in nature (Fuchs et al., 2011; Ke et al., 2023; Porter and Young, 2014; Parales et al., 2008). Increased abundance of bacteria with biodegradation capabilities has also been associated with high-level PAH exposure (Leung et al., 2023), providing a possible link in the present study between pollution exposure, the skin microbiome, and aging.

Our study also revealed gender-specific differences in how the microbiome differs with aging across different body sites. For the face, the microbiome changes observed in female participants largely mirrored the trends seen in the overall cohort results, characterized by a significant shift in community composition and diversity with age (Supplementary Figure S1). This may be attributed to the hormonal shifts experienced by women during menopause, which lead to a notable reduction in sebum production (Zouboulis and Boschnakow, 2001). Given that sebum availability influences growth of the skin microbiota (Swaney et al., 2023), these physiological changes are likely important drivers of the pronounced differences observed in the female microbiome with age. Conversely, the typically higher and more stable sebum production in males (Townsend and Kalan, 2023; Pochi et al., 1979) may provide a more consistent and robust microbial niche on the face. This could buffer against the age-related microbial changes seen in females, potentially explaining the more limited age-related differences observed in the male facial microbiome. However, on the arm, the aging-related microbial changes were similar across genders, suggesting that the less sebaceous environment of the arm is less influenced by gender-specific physiological factors. These findings underscore the importance of considering both skin site and gender in future studies on the skin microbiome and aging.

Our clustering analysis revealed the existence of age-driven microbiome profiles for both the face and arm. In particular, two facial microbiome types were identified using DMM, with high diversity/low C. acnes more frequent in old age and low diversity/high C. acnes more prevalent in young age (Figures 5A–D). These findings support previous efforts to assign facial skin microbiomes to cutotypes by applying Partitioning Around Medoids (PAM) clustering, which similarly identified a Cutibacterium-cutotype that exhibited low Shannon diversity and whose proportion declined with age across individuals (Sun et al., 2024). Therefore, C. acnes may play a significant role in maintaining a core, balanced microbial community in younger individuals, with its decline contributing to increased diversification and susceptibility to perturbation and dysbiosis with age (Larson et al., 2022). Interestingly, we observed two distinct old age-associated microbiome types for the arm; their compositional profiles were both characterized by a predominance of S. hominis, but differed in respect to their overall community diversity (Figures 5E–H). This divergence suggests that aging may not only shift the dominant taxa, but may also influence ecological stability and resilience of these communities. Indeed, microbial association analysis revealed a stark shift in structure of the microbial networks between young and old age for both skin sites (Figure 4), highlighting a less robust and more fragile microbiome with age. This has been observed in previous studies, which have similarly observed diminished microbial community stability and resilience in older populations (Kim et al., 2022; Kim et al., 2019). Overall, these findings suggest that aging not only disrupts the core, C. acnes-dominant microbiome, but also promotes a less predictable and more perturbation-susceptible microbial community that notably is skin-site specific.

Our findings highlight the potential role of S. capitis and S. hominis as key differentiating species between young and old age on the arm, with S. capitis more abundant in young age and S. hominis more abundant in old age (Figure 3D; Supplementary Figure S6). S. capitis, similar to C. acnes, thrives in lipid-rich skin regions (Chong et al., 2022), which may explain the observed decrease in abundance of this species with age. S. hominis is the second most frequently isolated coagulase-negative staphylococci from healthy human skin and is known for its ability to produce antimicrobial peptides as well as thioalcohol from apocrine gland secretions, contributing to body odor (Severn et al., 2022; Rudden et al., 2020). Interestingly, old-age individuals have a characteristic body odor (Mitro et al., 2012), suggesting a link between elevated levels of this bacterial species in old age and dermal body odor. While the effect of a S. capitis-to-S. hominis shift on skin health is unknown, it suggests that S. hominis may opportunistically colonize the skin in response to decreased competition and altered ecological niches resulting from aging, potentially exerting influence on microbial homeostasis and the skin’s local immune response. In support of this notion, S. hominis was previously found to be inversely correlated with sebum content and positively correlated with staphylococcal alpha diversity (Ahle et al., 2022), highlighting the potential importance of skin physiology and microbial community context in S. hominis expansion. S. hominis has also shown to have inhibitory properties against C. acnes (Ahle et al., 2022), suggesting interspecies competition may also play a role. Our study was limited to species-level interrogation of these bacteria, however, high strain heterogeneity and intra-species diversity has been well-documented for S. capitis, S. hominis, and C. acnes (Joglekar et al., 2023; Conwill et al., 2022), warranting future investigation into the role of strain diversity in the microbiome and aging.

Aging has been shown to impact not only the skin microbiome, but also the microbiome of other body sites such as the oral cavity and gastrointestinal tract (Larson et al., 2022; Huang et al., 2020; Sarafidou et al., 2024; Ghosh et al., 2022). As a part of this study, we also concurrently collected saliva samples for microbiome analysis from the same cohort (manuscript in progress). Characterizing the microbiota of these two distinct body sites will allow for even more in-depth insight into the impact of aging on the human microbiome, notably within the same population. Furthermore, this will provide the unique opportunity to integrate microbial data across body sites to investigate relationships between the skin and oral microbiota, particularly given the observation that oral bacteria have previously been associated with skin microbiome diversification in aging (Shibagaki et al., 2017).

While our study provided a detailed overview of how aging may modulate the bacterial members of the skin microbiome, it was limited by the use of 16S rRNA gene sequencing, which excludes non-bacterial microorganisms and does not provide strain-level resolution. Future efforts should employ metagenomic sequencing and additional techniques such as metatranscriptomics and metabolomics to gain a greater depth of understanding into the mechanisms of microbial function and strain specificity in skin aging. Furthermore, our study did not measure parameters of skin health such as moisture levels, elasticity, and the presence of visible signs of aging. Inclusion of these metrics may provide more defined relationships between microbiome shifts and aging. For example, relationships have been identified between the skin microbiota and clinical signs of aging such as grade of Crow’s feet wrinkles (Myers et al., 2023; Garlet et al., 2024) and dark spot area (Shibagaki et al., 2017; Li et al., 2020), as well as skin characteristics including sebum content, sebaceous gland area, transepidermal water loss (TEWL), natural moisturizing factor (NMF), antimicrobial peptides (AMPs), and lipids (Howard et al., 2022; Kim et al., 2022; Sun et al., 2024; Kim et al., 2019). An additional limitation of the study is the potential influence of confounding variables that we did not control for, which include lifestyle factors (e.g., cosmetic use, timing of last wash) and health-related factors (e.g., underlying health conditions, medication). Lastly, our study was limited by collection of the microbiome at a single time point. Future efforts should employ longitudinal studies to better understand temporal dynamics of the microbiome during the aging process, which may help to elucidate how changes in the skin microbial communities correlate with the progression of skin aging.

In summary, these findings provide comprehensive insights into the microbiome of aging skin in a UK-based population, highlighting the shift from a core, stable community driven by C. acnes in young individuals, to a more variable and less robust community in older individuals. This work also supports and complements previous studies of the aging skin microbiome carried out on North America- and Asia-based populations, highlighting the connection between the skin microbiota and the physiology of skin aging across ethnicities and geography. By comparing the microbiome of distinct skin sites, our study also underscores the importance of considering skin site specificity when studying the impact of aging on skin microbiome dynamics, with both intrinsic and extrinsic factors of aging likely playing significant roles. We also demonstrate the potential of microbiome typing techniques to unravel complex microbial interactions and microbiome profiles associated with age, emphasizing the opportunity for advanced techniques such as machine learning in generating more personalized and precision interventions tailored to the needs of an individual’s aging skin. Expanding these advanced methods to include other omics data, skin health parameters, and longitudinal studies has the potential to further our understanding of the intricate interplay between the skin microbiota, skin health, and the aging process. Ultimately, our findings highlight a significant opportunity for developing innovative and targeted next-generation anti-aging solutions, such as prebiotic or postbiotics treatments, to restore a more youthful, stable skin microbiome and thereby enhance overall skin health.