BMC microbiology · 2026
Dynamic shifts in cutaneous bacterial and fungal communities throughout human aging: a pilot study.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Li Tingting, Ge Xin, Chen Ganglian, Huang Zhijian, Li Yuying, Wan Zhe, Li Ruoyu, Wang Ruojun, Liu Wenjie
Аннотация
BACKGROUND: The skin microbiome plays an important role in aging, yet most aging biomarkers predominantly focus on gut bacteria, overlooking the skin microbial communities, especially its fungal component.
OBJECTIVES: To comprehensively profile the skin bacterial and fungal microbiome across age, sex, and anatomical sites (sun-exposed forehead vs. non-sun-exposed back) and develop an integrated microbial model for age prediction.
METHODS: A total of 160 skin swabs from 80 healthy individuals stratified into four age groups (centered at 10, 30, 50, 70 years) were conducted by DNA sequencing for microbial analysis. An age-predictive model was built using a random forest classifier trained on bacterial and fungal composition data.
RESULTS: We found clear age- and sex-specific differences in the skin microbiome. Fungal diversity was significantly higher in females, while bacterial diversity decreased markedly around age 30 in both sexes. Malassezia dominated fungal communities; its abundance peaked at 30 years, declining with age, especially on female foreheads. Age-dependent shifts occurred in dominant Malassezia species (e.g., M. globosa in children, M. arunalokei in the elderly). Bacterial communities shifted from diverse childhood profiles (e.g., Pseudomonas, Streptococcus) to Cutibacterium dominance in young adulthood, which declined in older individuals. Correlation analysis revealed stronger age-microbe associations in males. Finally, we developed a predictive model using four key microbial markers-Lactarius (fungus), Chryseobacterium, Gordonia, and Psychrobacter-that showed good performance in age-group classification (AUC = 0.97).
CONCLUSION: Collectively, these findings reveal distinct age- and sex-related patterns in the skin microbiome, highlight the importance of including fungi in microbiome studies, and demonstrate the potential of microbial profiles as candidate age-associated signatures.
OBJECTIVES: To comprehensively profile the skin bacterial and fungal microbiome across age, sex, and anatomical sites (sun-exposed forehead vs. non-sun-exposed back) and develop an integrated microbial model for age prediction.
METHODS: A total of 160 skin swabs from 80 healthy individuals stratified into four age groups (centered at 10, 30, 50, 70 years) were conducted by DNA sequencing for microbial analysis. An age-predictive model was built using a random forest classifier trained on bacterial and fungal composition data.
RESULTS: We found clear age- and sex-specific differences in the skin microbiome. Fungal diversity was significantly higher in females, while bacterial diversity decreased markedly around age 30 in both sexes. Malassezia dominated fungal communities; its abundance peaked at 30 years, declining with age, especially on female foreheads. Age-dependent shifts occurred in dominant Malassezia species (e.g., M. globosa in children, M. arunalokei in the elderly). Bacterial communities shifted from diverse childhood profiles (e.g., Pseudomonas, Streptococcus) to Cutibacterium dominance in young adulthood, which declined in older individuals. Correlation analysis revealed stronger age-microbe associations in males. Finally, we developed a predictive model using four key microbial markers-Lactarius (fungus), Chryseobacterium, Gordonia, and Psychrobacter-that showed good performance in age-group classification (AUC = 0.97).
CONCLUSION: Collectively, these findings reveal distinct age- and sex-related patterns in the skin microbiome, highlight the importance of including fungi in microbiome studies, and demonstrate the potential of microbial profiles as candidate age-associated signatures.