Evidence map›Paper›PMID 40434156›Full record

ArticlemSystems2025

Microbial dysbiosis and its diagnostic potential in androgenetic alopecia: insights from multi-kingdom sequencing and machine learning.

Xiaochen Wang, Fengjuan Li, Yangyang Sun, Fan Meng, Yaolin Song, Xiaoquan Su

Abstract read
In one paragraph

Article in mSystems, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Xiaochen WangCollege of Computer Science & Technology, Qingdao University, Qingdao, Shandong, China.ORCID 0009-0008-7326-429X
Fengjuan LiDepartment of Dermatology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China.
Yangyang SunCollege of Computer Science & Technology, Qingdao University, Qingdao, Shandong, China.
Fan MengCollege of Computer Science & Technology, Qingdao University, Qingdao, Shandong, China.
Yaolin SongDepartment of Pathology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China.ORCID 0009-0001-2600-7601
Xiaoquan SuCollege of Computer Science & Technology, Qingdao University, Qingdao, Shandong, China.ORCID 0000-0003-2144-1991

Funding

National Key Research and Development Program of China 2021YFF0704500National Natural Science Foundation of China 32070086National Natural Science Foundation of China 82003374Natural Science Foundation of Shandong Province ZR2020QH139| Science and Technology Support Plan for Youth Innovation of Colleges and Universities of Shandong Province of China ()Taishan Scholar Project of Shandong Province
6 · The paper itself

Abstract

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.

Indexed as

AlopeciaDysbiosisMachine LearningMicrobiotaAdultBacteriaFemaleHumansMaleMiddle AgedRNA, Ribosomal, 16SScalpYoung AdultRNA, Ribosomal, 16Sandrogenetic alopeciabioinformaticsmicrobiomescalpskin

Identifiers

PMID40434156
PMCPMC12172500

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LicenceCC BY
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.