Wang Xiaochen, Li Fengjuan, Sun Yangyang, Meng Fan, Song Yaolin, Su Xiaoquan
College of Computer Science & Technology, Qingdao University, Qingdao, Shandong, China.
Department of Dermatology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China.
mSystems. 2025 Jun 17;10(6):e0054825. doi: 10.1128/msystems.00548-25. Epub 2025 May 28.
Androgenetic alopecia (AGA), the most common form of hair loss, has been linked to dysbiosis of the scalp microbiome. In this study, we collected microbiome samples from the frontal baldness and occipital regions of patients with varying stages of AGA and conducted a comprehensive analysis of bacterial and fungal communities using 16S rRNA and ITS1 sequencing. Our results revealed that although the scalp microbiome dynamics in healthy subjects correlated strongly with chronological age, this trend was disrupted in AGA patients due to severe microbial imbalances, emphasizing the significant impact of AGA on the scalp microecology. Notably, microbial dysbiosis was not confined to the localized areas of hair loss but extended across the entire scalp. Moreover, the degree of dysbiosis was consistent with the severity of AGA. Leveraging multi-kingdom microbial features and machine learning, we developed a microbial index of scalp health (MiSCH), which effectively detects AGA and stratifies its severity. More importantly, MiSCH was able to identify high-risk individuals, those with significantly disrupted microbiome structures but no overt AGA phenotypic characteristics, thereby offering opportunities for early diagnosis, risk assessment, and personalized treatment of AGA.IMPORTANCEBy analyzing the bacteria and fungi on the scalp, this study shows how androgenetic alopecia (AGA) disrupts the balance of microbes not just in the hair loss areas, but across the entire scalp. Thus, we introduce the microbial index of scalp health (MiSCH), which leverages microbiome data for the early detection and severity prediction of AGA. This method is especially valuable for identifying people at risk of developing more severe hair loss, even before visible symptoms appear. By combining microbiome analysis with machine learning, this research offers a potential breakthrough for early diagnosis and personalized treatments, changing how we approach hair loss and offering new hope for managing the condition more effectively.
雄激素性脱发（AGA）是最常见的脱发形式，与头皮微生物群失调有关。在本研究中，我们从不同阶段AGA患者的额秃区和枕区收集了微生物样本，并使用16S rRNA和ITS1测序对细菌和真菌群落进行了全面分析。我们的结果显示，尽管健康受试者的头皮微生物群动态与实际年龄密切相关，但由于严重的微生物失衡，这种趋势在AGA患者中被打破，这突出了AGA对头皮微生态的重大影响。值得注意的是，微生物失调不仅局限于脱发局部区域，而是扩展到整个头皮。此外，失调程度与AGA的严重程度一致。利用多界微生物特征和机器学习，我们开发了一种头皮健康微生物指数（MiSCH），它能有效检测AGA并对其严重程度进行分层。更重要的是，MiSCH能够识别高危个体，即那些微生物群结构明显紊乱但没有明显AGA表型特征的个体，从而为AGA的早期诊断、风险评估和个性化治疗提供了机会。重要性通过分析头皮上的细菌和真菌，本研究表明雄激素性脱发（AGA）如何不仅破坏脱发区域，而且破坏整个头皮的微生物平衡。因此，我们引入了头皮健康微生物指数（MiSCH），它利用微生物组数据对AGA进行早期检测和严重程度预测。这种方法对于识别有发展为更严重脱发风险的人特别有价值，甚至在出现明显症状之前。通过将微生物组分析与机器学习相结合，本研究为早期诊断和个性化治疗提供了潜在的突破，改变了我们处理脱发的方式，并为更有效地管理这种疾病带来了新的希望。
