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<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Microbiol.</journal-id>
<journal-title>Frontiers in Microbiology</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Microbiol.</abbrev-journal-title>
<issn pub-type="epub">1664-302X</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/fmicb.2024.1360445</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Microbiology</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Roles of gut microbiota in androgenetic alopecia: insights from Mendelian randomization analysis</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name><surname>Fu</surname> <given-names>Haijing</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
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<contrib contrib-type="author">
<name><surname>Xu</surname> <given-names>Tianyi</given-names></name>
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<contrib contrib-type="author">
<name><surname>Zhao</surname> <given-names>Wumei</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
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<contrib contrib-type="author" corresp="yes">
<name><surname>Jiang</surname> <given-names>Leiwei</given-names></name>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
<xref ref-type="corresp" rid="c001"><sup>&#x002A;</sup></xref>
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<contrib contrib-type="author" corresp="yes">
<name><surname>Shan</surname> <given-names>Shijun</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
<xref ref-type="corresp" rid="c002"><sup>&#x002A;</sup></xref>
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<aff id="aff1"><sup>1</sup><institution>Department of Dermatology, Xiang&#x2019;an Hospital of Xiamen University, School of Medicine, Xiamen University</institution>, <addr-line>Xiamen</addr-line>, <country>China</country></aff>
<aff id="aff2"><sup>2</sup><institution>Department of Dermatology, Guizhou Provincial People&#x2019;s Hospital</institution>, <addr-line>Guiyang</addr-line>, <country>China</country></aff>
<aff id="aff3"><sup>3</sup><institution>Hangzhou Third People&#x2019;s Hospital, Affiliated Hangzhou Dermatology Hospital, Zhejiang University School of Medicine</institution>, <addr-line>Hangzhou</addr-line>, <country>China</country></aff>
<author-notes>
<fn fn-type="edited-by"><p>Edited by: Qi Zhao, University of Science and Technology Liaoning, China</p></fn>
<fn fn-type="edited-by"><p>Reviewed by: Chi-Yang Chiu, The University of Tennessee Health Science Center (UTHSC), United States</p>
<p>Georgia Damoraki, National and Kapodistrian University of Athens, Greece</p>
<p>Yuchen Li, Stanford University, United States</p></fn>
<corresp id="c001">&#x002A;Correspondence: Shijun Shan, <email>shanshijun2023@163.com</email></corresp>
<corresp id="c002">Leiwei Jiang, <email>leiweijiang@163.com</email></corresp>
</author-notes>
<pub-date pub-type="epub">
<day>02</day>
<month>04</month>
<year>2024</year>
</pub-date>
<pub-date pub-type="collection">
<year>2024</year>
</pub-date>
<volume>15</volume>
<elocation-id>1360445</elocation-id>
<history>
<date date-type="received">
<day>23</day>
<month>12</month>
<year>2023</year>
</date>
<date date-type="accepted">
<day>13</day>
<month>03</month>
<year>2024</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#x00A9; 2024 Fu, Xu, Zhao, Jiang and Shan.</copyright-statement>
<copyright-year>2024</copyright-year>
<copyright-holder>Fu, Xu, Zhao, Jiang and Shan</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/"><p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p></license>
</permissions>
<abstract>
<sec>
<title>Background</title>
<p>Androgenetic alopecia (AGA) is the most common type of androgen-associated hair loss. Previous studies have indicated an association between the gut microbiota and AGA. To delve deeper, we executed a two-sample Mendelian randomization (MR) analysis to investigate the potential causal relationship between the gut microbiota and AGA.</p>
</sec>
<sec>
<title>Methods</title>
<p>A two-sample MR investigation was utilized to delve into the intricate interplay between gut microbiota and AGA. Information regarding 211 gut microbial taxa was sourced from the MiBioGen consortium. The summary statistics of the genome-wide association studies (GWAS) for AGA were obtained from the FinnGen biobank, which included 195 cases and 201,019 controls. Various analytical approaches, including Inverse Variance Weighting (IVW), Weighted Median, MR-Egger, Weighted Mode, and Simple Mode were employed to evaluate the causal impact of gut microbiota on AGA. Sensitivity analyses were subsequently conducted to affirm the robustness of the findings.</p>
</sec>
<sec>
<title>Results</title>
<p>A two-sample MR investigation unveiled the genus <italic>Olsenella</italic>, genus <italic>Ruminococcaceae UCG-004</italic>, and genus <italic>Ruminococcaceae UCG-010</italic> were identified as risk factors associated with AGA. In contrast, the family <italic>Acidaminococcaceae</italic> and genus <italic>Anaerofilum</italic>, along with the genus <italic>Ruminiclostridium</italic> 9, demonstrated a protective effect. The sensitivity analyses provided additional assurance that the findings of the current study were less susceptible to the influence of confounding variables and biases.</p>
</sec>
<sec>
<title>Conclusion</title>
<p>The MR study has established a link between specific gut microbiota and AGA, offering evidence for the identification of more precisely targeted probiotics. This discovery has the potential to aid in the prevention, control, and reversal of AGA progression.</p>
</sec>
</abstract>
<kwd-group>
<kwd>androgenetic alopecia</kwd>
<kwd>causal relationship</kwd>
<kwd>genetic</kwd>
<kwd>gut microbiota</kwd>
<kwd>Mendelian randomization</kwd>
</kwd-group>
<counts>
<fig-count count="4"/>
<table-count count="0"/>
<equation-count count="0"/>
<ref-count count="63"/>
<page-count count="9"/>
<word-count count="5853"/>
</counts>
<custom-meta-wrap>
<custom-meta>
<meta-name>section-at-acceptance</meta-name>
<meta-value>Microorganisms in Vertebrate Digestive Systems</meta-value>
</custom-meta>
</custom-meta-wrap>
</article-meta>
</front>
<body>
<sec id="S1" sec-type="intro">
<title>Introduction</title>
<p>Male Pattern Hair Loss (MPHL) and Female Pattern Hair Loss (FPHL) also referred to as Androgenetic alopecia (AGA) stand out as the most prevalent form of hair loss, impacting a substantial portion of the population. It is estimated that by the age of 70, at least 80% of men and 50% of women experience AGA (<xref ref-type="bibr" rid="B16">Devjani et al., 2023</xref>). AGA is marked by the gradual miniaturization of hair follicles, resulting in hair loss (<xref ref-type="bibr" rid="B57">Tr&#x00FC;eb, 2002</xref>). Dealing with alopecia is a challenging and time-consuming process. Individuals experiencing hair loss often suffer from a diminished quality of life, including reduced self-confidence and heightened feelings of depression (<xref ref-type="bibr" rid="B35">Lee et al., 2002</xref>; <xref ref-type="bibr" rid="B62">Yeo et al., 2014</xref>; <xref ref-type="bibr" rid="B43">Marks et al., 2019</xref>). Consequently, the effective management of hair loss plays a crucial role in improving people&#x2019;s overall wellbeing. To enhance our ability to prevent and treat AGA effectively, it is essential to acquire a more comprehensive understanding of the underlying mechanisms that contribute to its development. Nevertheless, the precise cause behind the escalating incidence rate of AGA has yet to be fully elucidated.</p>
<p>Numerous factors influence the initiation and progression of AGA, the interaction of endocrine factors and genetic predisposition is one of the primary factors (<xref ref-type="bibr" rid="B42">Lolli et al., 2017</xref>). Studies have confirmed that a range of external factors, such as metabolism, psychological changes, environmental exposure, dietary intake, and microorganisms, can have adverse effects on the lifespan of hair (<xref ref-type="bibr" rid="B32">Lai et al., 2013</xref>; <xref ref-type="bibr" rid="B47">Phillips et al., 2017</xref>; <xref ref-type="bibr" rid="B25">Ho et al., 2019</xref>; <xref ref-type="bibr" rid="B55">Suzuki et al., 2021</xref>). Recently, there is a study has shown that gut microbiota also is an essential factor in the development of AGA (<xref ref-type="bibr" rid="B28">Jung et al., 2022</xref>). However, in research related to AGA, there is a relatively limited study on the specific role of the gut microbiome in AGA. The conventional observational study is susceptible to the impact of numerous potential factors, including lifestyle and socioeconomic status, during the implementation process, rendering it prone to biases. Hence, we examined existing summary data from the results of genome-wide association studies (GWAS) to investigate the influence of gut microbiota on AGA.</p>
<p>Genome-wide association studies with large sample sizes has revealed some single nucleotide polymorphisms (SNPs) correlated with both AGA and gut microbiota (<xref ref-type="bibr" rid="B59">Wang et al., 2019</xref>). Mendelian randomization (MR) is a method that employs genetic variants linked to a hypothesized risk factor as proxies to ascertain the causal impact of that exposure on a specific outcome (<xref ref-type="bibr" rid="B5">Birney, 2022</xref>). In this study, we evaluated the causal effects of gut microbiota and AGA using a two-sample MR study design. Our results demonstrated a potential causal association between specific gut microbiota and AGA.</p>
</sec>
<sec id="S2" sec-type="materials|methods">
<title>Materials and methods</title>
<sec id="S2.SS1">
<title>Study design</title>
<p>To explore the interaction between gut microbiota and AGA, we designated gut microbiota as the exposure variable, with AGA considered as outcomes. The MR study (<xref ref-type="bibr" rid="B18">Evans and Davey Smith, 2015</xref>) adhered to three crucial assumptions: (1) Instrumental variables (IVs) chosen from datasets were connected to the exposure variable; (2) they were independent of any unknown confounders related to the exposure; and (3) they exclusively influenced outcomes through exposure pathways (<xref ref-type="bibr" rid="B15">Davey Smith and Hemani, 2014</xref>). SNPs were employed as valid IVs in the MR study to assess the bidirectional causal relationship between the exposure and the outcome. The comprehensive flowchart for this MR study is illustrated in <xref ref-type="fig" rid="F1">Figure 1</xref>.</p>
<fig id="F1" position="float">
<label>FIGURE 1</label>
<caption><p>Comprehensive flowchart of study.</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fmicb-15-1360445-g001.tif"/>
</fig>
</sec>
<sec id="S2.SS2">
<title>Data sources</title>
<p>Instrumental variables (IVs) for investigating the correlation between human genetic variants and the composition of the gut microbiome were identified from a GWAS dataset within the global collaborative project MiBioGen (<xref ref-type="bibr" rid="B30">Kurilshikov et al., 2021</xref>). This extensive, multi-ethnic GWAS involved the coordination of 16S ribosomal RNA gene sequencing profiles and genotyping data from 18,340 participants. A total of 211 taxa, comprising 131 genera, 35 families, 20 orders, 16 classes, and 9 phyla, were encompassed in the analysis (<xref ref-type="bibr" rid="B30">Kurilshikov et al., 2021</xref>; <xref ref-type="bibr" rid="B12">Cui et al., 2023</xref>). The dataset for AGA included 201,214 European participants sourced from the freely accessible website FinnGen biobank.<sup><xref ref-type="fn" rid="footnote1">1</xref></sup> The specific has been placed in the <xref ref-type="supplementary-material" rid="TS1">Supplementary Table 4</xref>.</p>
</sec>
<sec id="S2.SS3">
<title>Instrument variable selection</title>
<p>We implemented a set of criteria to carefully select eligible genetic IVs:(1) Significance Threshold: Due to the limited number of IVs meeting the genome-wide significance threshold (<italic>p</italic> &#x003C; 5 &#x00D7; 10<sup>&#x2013;8</sup>) (<xref ref-type="bibr" rid="B30">Kurilshikov et al., 2021</xref>; <xref ref-type="bibr" rid="B24">Herbert et al., 2022</xref>), we opted for a relatively less stringent threshold (<italic>p</italic> &#x003C; 1 &#x00D7; 10<sup>&#x2013;5</sup>) based on previous research (<xref ref-type="bibr" rid="B12">Cui et al., 2023</xref>; <xref ref-type="bibr" rid="B13">Dai et al., 2023</xref>; <xref ref-type="bibr" rid="B41">Liu et al., 2023</xref>; <xref ref-type="bibr" rid="B61">Xiao et al., 2023</xref>). This less stringent threshold was selected to identify potential sets of variants that are likely to be enriched for association, allowing for a more comprehensive assessment and exploration of results. (2) Clumping Procedure: A clumping procedure was executed (<italic>R</italic><sup>2</sup> &#x003C; 0.001, clumping distance = 10,000 kb) to eliminate variants in strong linkage disequilibrium (LD). This step ensured the independence of each SNP in our selection. (3) To mitigate bias stemming from weak IVs, it was deemed necessary to ensure that the F-statistic surpassed a threshold of 10. This threshold helps assess the strength of the instruments and provides confidence in the instrumental variable analysis results (<xref ref-type="bibr" rid="B3">Ardissino et al., 2024</xref>).</p>
</sec>
<sec id="S2.SS4">
<title>Assessment and diagnostic criteria for AGA</title>
<p>Clinical manifestations vary between genders. Males experience frontotemporal recession and vertex loss, while females retain the frontal hairline, with hair loss more uniformly across the frontal region post-hairline. Gender-atypical patterns can occur. Assessment of AGA commonly employs the Hamilton-Norwood scale (12 degrees), and the Ludwig scale (3 stages). Diagnostic criteria include miniaturized follicles (hair diameter &#x003C;0.03 mm), decreased terminal and anagen hairs, increased vellus and telogen hairs, and perifollicular lymphohistiocytic inflammatory infiltrate around the infundibulum (<xref ref-type="bibr" rid="B2">Alessandrini et al., 2021</xref>).</p>
</sec>
<sec id="S2.SS5">
<title>MR analysis</title>
<p>We utilized five extensively employed Mendelian Randomization (MR) techniques to identify bidirectional causal connections between exposure and outcome. These methods include inverse variance weighting (IVW), weighted median, MR-Egger, weighted mode, and simple mode (<xref ref-type="bibr" rid="B7">Bowden et al., 2016</xref>; <xref ref-type="bibr" rid="B21">Hartwig et al., 2017</xref>; <xref ref-type="bibr" rid="B41">Liu et al., 2023</xref>). The IVW method calculates the causal impact of the exposure on the outcome by aggregating ratio estimates for each SNP. It was selected as the primary method due to its capacity to offer a robust and unbiased causal effect, especially in scenarios where no polymorphism or heterogeneity is identified (<xref ref-type="bibr" rid="B46">Mounier and Kutalik, 2023</xref>). A positive causal effect was confirmed if the IVW results were significant (<italic>p</italic> &#x003C; 0.05), and the beta values from other methods were consistent in direction. Subsequently, we proceeded to visually represent the outcomes derived from the five Mendelian Randomization (MR) methods. The bidirectional causal effect was quantified as an odds ratio (OR) computed through MR analysis.</p>
<p>To enhance the robustness of our analysis regarding causality, we employed the Bonferroni&#x2019;s method to establish distinct significance thresholds for multiple testing across various taxonomic levels. These thresholds were determined based on the number of bacteria within each taxonomic level. Specifically, we set the thresholds at 1.6 &#x00D7; 10<sup>&#x2013;3</sup> (0.05 divided by 32) for the family level and 4.2 &#x00D7; 10<sup>&#x2013;4</sup> (0.05 divided by 119) for the genus level. <italic>P</italic>-values falling below these thresholds were considered to indicate nominal significance (<italic>p</italic> &#x003C; 0.05), suggesting potential causal effects (<xref ref-type="bibr" rid="B54">Su et al., 2023</xref>). This study is reported following the Strengthening the Reporting of Observational Studies in Epidemiology Using Mendelian Randomization guidelines (STROBE-MR) (<xref ref-type="bibr" rid="B53">Skrivankova et al., 2021</xref>).</p>
</sec>
<sec id="S2.SS6">
<title>Sensitivity analyses</title>
<p>Use some basic sensitivity analyses to validate the results. Cochran&#x2019;s Q statistic is employed to assess heterogeneity among IVs (<xref ref-type="bibr" rid="B8">Bowden et al., 2018</xref>). A significance level of <italic>p</italic> &#x003C; 0.05 was deemed indicative of the presence of heterogeneity. MR pleiotropy residuals and outlier analysis (MR-PRESSO) are also used to validate the potential pleiotropy of the selected IVs and the direct effects on outcomes. Subsequently, we apply the leave-one-out method to exclude each SNP from the IVs and use the IVW method to assess whether individual SNPs significantly affect the causal effect. All the aforementioned analyses are conducted using the MR and MR-PRESSO R packages with two-sample MR (<xref ref-type="bibr" rid="B23">Hemani et al., 2018</xref>; <xref ref-type="bibr" rid="B58">Verbanck et al., 2018</xref>).</p>
<p>We utilized GWAS Catalog<sup><xref ref-type="fn" rid="footnote2">2</xref></sup> to investigate whether SNPs were linked to potential risk factors. The factors encompass body mass index, obesity, alcohol consumption and neuropsychiatric diseases (<xref ref-type="bibr" rid="B51">Severi et al., 2003</xref>). We excluded SNPs associated with any of these potential confounders at genome-wide significance.</p>
</sec>
<sec id="S2.SS7">
<title>Power calculation</title>
<p>The power calculation for the IVW estimates was conducted using a web tool available at <ext-link ext-link-type="uri" xlink:href="https://shiny.cnsgenomics.com/mRnd/">https://shiny.cnsgenomics.com/mRnd/</ext-link>. A further explanation of power calculation can be seen in &#x201C;binary outcome derivations&#x201D; in the web tool and the type I error rate (alpha) is set at 0.05 (<xref ref-type="bibr" rid="B9">Brion et al., 2013</xref>). Specifically, it was recommended to achieve a statistical power of over 80% for the analysis (<xref ref-type="bibr" rid="B63">Zhong et al., 2023</xref>).</p>
</sec>
</sec>
<sec id="S3" sec-type="results">
<title>Results</title>
<sec id="S3.SS1">
<title>MR analysis of gut microbiota on AGA</title>
<p>In our analysis, 62 SNPs were used as IVs for AGA. The comprehensive characteristics of these IVs pertaining to microbial taxa were summarized in <xref ref-type="supplementary-material" rid="TS1">Supplementary Table 1</xref>. Notably, all SNPs included in our analysis exhibited F-statistics greater than 19 indicating the robustness of these instruments. Upon reviewing the GWAS Catalog, we observed that only one SNP exhibits pleiotropic effects, primarily linked to smoking. Previous research indicates no significant association between AGA and smoking (<xref ref-type="bibr" rid="B29">Kavadya and Mysore, 2022</xref>). Therefore, we exclude the confounded without removing the SNP. MR analysis was conducted to evaluate the potential causal relationship between specific categories of gut microbiota and the occurrence of AGA. There are 6 bacterial taxa had a correlated with AGA. According to the IVW estimate, the genus <italic>Olsenella</italic> (OR = 1.9663, 95% CI: 1.1245&#x2013;3.4380, <italic>p</italic> = 0.0177), genus <italic>Ruminococcaceae UCG-004</italic> (OR = 2.9609, 95% CI: 1.1504&#x2013;7.6217, <italic>p</italic> = 0.0244), and genus <italic>Ruminococcaceae UCG-010</italic> (OR = 3.9630, 95% CI: 1.1047&#x2013;14.2160, <italic>p</italic> = 0.0346) were identified as risk factors, (<xref ref-type="fig" rid="F2">Figure 2</xref>) suggesting a potential association with decreased hair growth function. On the other hand, the family <italic>Acidaminococcaceae</italic> (OR = 0.2105, 95% CI: 0.0696&#x2013;0.6367, <italic>p</italic> = 0.0058) and genus <italic>Anaerofilum</italic> (OR = 0.4633, 95% CI: 0.2356&#x2013;0.9110, <italic>p</italic> = 0.0257), along with the genus <italic>Ruminiclostridium 9</italic> (OR = 0.2459, 95% CI: 0.0608&#x2013;0.9955, <italic>p</italic> = 0.0493), demonstrated a protective effect, (<xref ref-type="fig" rid="F2">Figure 2</xref>) implying a potential association with a reduced risk of AGA. Additionally, the estimates of causal effects obtained from the weighted median, MR-Egger, weighted mode, and simple mode methods demonstrated magnitudes and directions that were comparable to those derived from the previously mentioned IVW method, as detailed in <xref ref-type="supplementary-material" rid="TS1">Supplementary Table 2</xref> and <xref ref-type="fig" rid="F3">Figure 3</xref>. These findings provide insights into the specific gut microbiota components that may play a role in the development of AGA and its potential link to AGA.</p>
<fig id="F2" position="float">
<label>FIGURE 2</label>
<caption><p>The forest plot illustrates the causal effect of gut microbiota on AGA using the IVW method in MR. The error bars in the plot represent the 95% confidence interval of the odds ratio. In the plot, the blue dots represent outcomes related to AGA, while the red dots represent outcomes associated with AGA positivity. OR, odds ratio; MR, Mendelian randomization; IVW, inverse variance weighted.</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fmicb-15-1360445-g002.tif"/>
</fig>
<fig id="F3" position="float">
<label>FIGURE 3</label>
<caption><p>Scatter plots for causal effect of gut microbiota on AGA.</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fmicb-15-1360445-g003.tif"/>
</fig>
</sec>
<sec id="S3.SS2">
<title>Sensitivity analysis</title>
<p>In the sensitivity analysis, we employed Cochran&#x2019;s Q statistics with both IVW and MR-Egger methodologies to evaluate heterogeneity. The results indicated no significant heterogeneity among the IVs, as evidenced by all <italic>p-</italic>values &#x003E; 0.05 (<xref ref-type="supplementary-material" rid="TS1">Supplementary Table 3</xref>). Furthermore, both the MR-Egger intercept and the MR-PRESSO global test provided supporting evidence for the absence of statistically significant directional horizontal pleiotropy (all <italic>p-</italic>values &#x003E; 0.05, <xref ref-type="supplementary-material" rid="TS1">Supplementary Table 3</xref>). Additionally, the leave-one-out analysis demonstrated the lack of influential IVs as illustrated in <xref ref-type="fig" rid="F4">Figure 4</xref>.</p>
<fig id="F4" position="float">
<label>FIGURE 4</label>
<caption><p>Leave-one-out diagrams for causal effects of gut microbiota on AGA.</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fmicb-15-1360445-g004.tif"/>
</fig>
<p>These sensitivity analyses, encompassing Cochran&#x2019;s Q statistics, MR-Egger intercept, MR-PRESSO global test, and leave-one-out analysis, collectively underscored the robustness of the Mendelian Randomization findings in the two-sample analysis. Furthermore, the presentation of the funnel plot and forest plots indicating the reliability of the results (see <xref ref-type="supplementary-material" rid="TS2">Supplementary Figures 1</xref>, <xref ref-type="supplementary-material" rid="TS2">2</xref>).</p>
</sec>
</sec>
<sec id="S4" sec-type="discussion">
<title>Discussion</title>
<p>Androgenetic alopecia is characterized by a progressive thinning of hair in specific areas, influenced by genetic and hormonal factors (<xref ref-type="bibr" rid="B31">Kwack et al., 2023</xref>). Prior research has hinted at a connection between AGA and the microbiome (<xref ref-type="bibr" rid="B28">Jung et al., 2022</xref>), but much remains unknown about the genetic factors driving this relationship. We employed a bidirectional two-sample MR analytical method, drawing data from GWAS databases encompassing both gut microbiota and AGA. Our inquiry delved into the intricate action of gut microbiota on AGA, unearthing significant insights into how gut microbiota influences AGA. Specifically, our findings spotlight the protective role of certain microbial&#x2014;such as family <italic>Acidaminococcaceae</italic>, genus <italic>Anaerofilum</italic>, and genus <italic>Ruminiclostridium 9</italic>&#x2014;in AGA. Conversely, genera like genus <italic>Olsenella</italic>, genus <italic>Ruminococcaceae UCG-004</italic>, and genus <italic>Ruminococcaceae UCG-010</italic> were identified as risk factors associated with AGA.</p>
<p>The human microbiota has evolved in tandem with its host and serves as an essential component of the human body. Acquired at birth, the microbiota matures alongside the host and maintains significance throughout life, impacting various bodily functions from infancy to old age (<xref ref-type="bibr" rid="B1">Adak and Khan, 2019</xref>). Earlier investigations have indicated that in individuals with AGA, both the scalp and gut microbiomes exhibit greater complexity and density, as reflected by elevated values of network topological statistics such as degree centrality, vertices, and edges (<xref ref-type="bibr" rid="B28">Jung et al., 2022</xref>). Two patients with alopecia universalis experienced hair regrowth after undergoing fecal microbiota transplantation as a treatment for recurrent <italic>Clostridioides difficile</italic> infections (<xref ref-type="bibr" rid="B49">Rebello et al., 2017</xref>). Imbalanced gut microbiota, notably the proliferation of Lactobacillus murinus, followed by biotin deficiency, constitutes crucial factors contributing to the onset of alopecia (<xref ref-type="bibr" rid="B22">Hayashi et al., 2017</xref>). The transplantation of fecal microbiota or the use of a specific probiotic (<italic>Bifidobacterium longum HK003</italic>) obtained from the feces of healthy individuals could potentially stimulate hair regrowth (<xref ref-type="bibr" rid="B34">Lam et al., 2022</xref>). Concerning <italic>Ruminiclostridium 9</italic>, it has demonstrated its regulatory effects on lipid metabolism, inflammation reduction, enhancement of intestinal barrier function, weight gain reduction, and improved insulin sensitivity in mice, effectively countering obesity development (<xref ref-type="bibr" rid="B60">Wang et al., 2020</xref>). However, its specific role in AGA remains unstudied. Further research on the effect of the family <italic>Acidaminococcaceae</italic>, genus <italic>Anaerofilum</italic>, and genus <italic>Ruminiclostridium 9</italic> on AGA may guide targeted probiotic applications. Our findings suggest that modulating the gut microbiota through probiotics could offer new avenues for the prevention and treatment of AGA.</p>
<p><italic>Ruminococcaceae</italic> is a family of bacteria that belongs to the <italic>Bacillota</italic> (formerly known as <italic>Firmicutes</italic>) which is one of the major bacterial phyla observed in the human microbiome. <italic>Ruminococcaceae</italic> is known to be a significant component of the gut microbiota and plays important roles in various physiological processes (<xref ref-type="bibr" rid="B26">Hou et al., 2022</xref>). <italic>Ruminococcaceae</italic> were good predictors of folliculitis decalvans (<xref ref-type="bibr" rid="B45">Moreno-Arrones et al., 2023</xref>). Patients undergoing finasteride treatment exhibited a reduction in <italic>Ruminococcaceae</italic> levels compared to a healthy control group (<xref ref-type="bibr" rid="B17">Diviccaro et al., 2019</xref>; <xref ref-type="bibr" rid="B6">Borgo et al., 2021</xref>). The You-gui pill could increasing <italic>Ruminococcaceae UCG-007</italic> and <italic>Ruminococcaceae UCG-010</italic> in the intestine, thereby aiding in the treatment of kidney-yang deficiency syndrome (<xref ref-type="bibr" rid="B10">Chen et al., 2019</xref>). In our research, we identified that genus <italic>Ruminococcaceae UCG004</italic> (with a statistical power of 98%) and genus <italic>Ruminococcaceae UCG010</italic> (with a statistical power of 100%) are implicated as risk factors in association with AGA. The power levels exceeding 80% for both underscore the reliability of the involvement of genus <italic>Ruminococcaceae UCG004</italic> and genus <italic>Ruminococcaceae UCG010</italic> in AGA. Additional investigation is required to delve into the role of <italic>Ruminococcaceae</italic> in AGA.</p>
<p>The gut microbiota comprises a unique blend of various organisms, including bacteria, viruses, archaea, protozoa, and fungi, which interact bidirectionally with the central nervous system, forming what is known as the microbiome-gut-brain axis (<xref ref-type="bibr" rid="B44">Mayer, 2011</xref>; <xref ref-type="bibr" rid="B52">Sharon et al., 2016</xref>). This axis encompasses immune, neural, endocrine, and metabolic pathways, with steroid hormones playing a significant role. Thus far, the steroidogenic capacity remains incompletely understood. However, microbial species such as <italic>Clostridium scindens</italic> have the potential to convert glucocorticoids into androgens (<xref ref-type="bibr" rid="B50">Ridlon et al., 2013</xref>; <xref ref-type="bibr" rid="B19">Ferreira-Halder et al., 2017</xref>), thereby fostering the development of AGA. Additionally, <italic>Faecalibacterium</italic> spp. is a notable producer of butyrate (<xref ref-type="bibr" rid="B19">Ferreira-Halder et al., 2017</xref>), a short-chain fatty acid that plays a crucial role in gut microbiota-brain communication (<xref ref-type="bibr" rid="B14">Dalile et al., 2019</xref>) and has recently been proposed to influence sleep modulation (<xref ref-type="bibr" rid="B56">Szentirmai et al., 2019</xref>). Consequently, <italic>Faecalibacterium</italic> spp. may be a potential target for therapy AGA. Interestingly, certain members of the human microbiota (e.g., <italic>Bifidobacterium</italic> spp. and <italic>Lactobacillus</italic> spp.) encode genes involved in GABA production, suggesting microbial involvement in the production of this neurotransmitter within the gut (<xref ref-type="bibr" rid="B4">Barrett et al., 2012</xref>).</p>
<p>The precise biological mechanisms by which microbiotas influence AGA development remain unclear. The microbiota plays a major role in the endocrine system by interacting with estrogen, androgens, insulin, and other hormones (<xref ref-type="bibr" rid="B48">Qi et al., 2021</xref>). The androgens are a critical factor that leads to the gradual conversion of terminal hairs into intermediate and vellus hairs, resulting in the gradual hair thinning and hair loss seen in patients with AGA (<xref ref-type="bibr" rid="B42">Lolli et al., 2017</xref>). Different types of gut microbiota can produce enzymes essential for androgen metabolism, facilitating the synthesis and alteration of androgens. The microbial-mediated degradation of testosterone has been witnessed across various environmental contexts. For instance, Actinobacteria and Proteobacteria possess the capability to break down androgens (<xref ref-type="bibr" rid="B48">Qi et al., 2021</xref>). Additionally, Clostridium scindens, harboring the 20&#x03B1;-hydroxysteroid dehydrogenase (HSDH) enzyme in its genome, is a human gut microbe with significant potential to convert glucocorticoids into androgens (<xref ref-type="bibr" rid="B50">Ridlon et al., 2013</xref>). Meanwhile, microbiota-host communication is primarily facilitated through secreted exosomes (<xref ref-type="bibr" rid="B36">Li Q. et al., 2019</xref>). Exosomes, extracellular vesicles involved in cell communication, homeostasis, differentiation, and organogenesis (<xref ref-type="bibr" rid="B37">Li et al., 2018</xref>, <xref ref-type="bibr" rid="B39">2021</xref>; <xref ref-type="bibr" rid="B33">Lai et al., 2022</xref>), are pivotal in hair morphogenesis and regeneration, holding potential for alopecia treatment (<xref ref-type="bibr" rid="B27">Hu et al., 2020</xref>). Plasma exosomes, originating from host cells or gut microbiota, mediate local or remote mutual regulation (<xref ref-type="bibr" rid="B40">Li Y. et al., 2019</xref>; <xref ref-type="bibr" rid="B38">Li et al., 2020</xref>). Intriguingly, exosomes have been observed carrying Wnt proteins on their surface, inducing &#x03B2;-catenin activation&#x2014;a crucial signaling pathway in hair morphogenesis and regeneration (<xref ref-type="bibr" rid="B20">Gross et al., 2012</xref>). Exosomes from adipose-derived stem cells positively impact hair regrowth by enhancing DPC proliferation via upregulating the Wnt/&#x03B2;-catenin, TNF-&#x03B1; signaling pathways, and vascular endothelial growth factor expression (<xref ref-type="bibr" rid="B27">Hu et al., 2020</xref>). Further investigation is required to explore the functions of the six microbiotas mentioned in our article on the composition and decomposition of androgens. Elucidating the potential biological mechanisms at microbiotas that might influence AGA development is crucial.</p>
<sec id="S4.SS1">
<title>Limitations</title>
<p>Following Bonferroni&#x2019;s correction, no distinct causal relationship between gut flora, metabolites, and the risk factors associated with heart failure was identified. This implies that further investigations are essential to validate and confirm the potential relationships between these elements. The recommended threshold for statistical power is over 80%. Notably, the power values for genus <italic>Olsenella</italic> (93%), genus <italic>Ruminococcaceae UCG004</italic> (98%), and genus <italic>Ruminococcaceae UCG010</italic> (100%) all exceed 80%, thereby reinforcing the validity of our findings. However, it is noteworthy that the overall power calculated by our research institute is not entirely satisfactory. The power values for family <italic>Acidaminococcaceae</italic> (29%), genus <italic>Anaerofilum</italic> (41%), and genus <italic>Ruminiclostridium 9</italic> (31%) fall below the 80% threshold. This discrepancy might be attributed to the relatively small rate of cases/controls (195/201,019) in the GWAS data for AGA. To enhance the statistical power and obtain more accurate results in future studies, it is advisable to consider increasing the sample size of cases.</p>
<p>The systemic androgens and genetic factors are the primary causes of AGA, various external or exogenous factors also play a role in its development (<xref ref-type="bibr" rid="B11">Choi et al., 2017</xref>). Limited further studies due to inadequate summary data for various alopecia types, lack of stratification statistics for AGA degree, and absence of sex-based stratification data. Several limitations of our study exist. In conclusion, the intricate physiological mechanisms underlying the relationship between gut microbiota and AGA extend beyond the scope of our simplistic models. Subsequent research efforts should focus on identifying potential mechanisms to deepen our understanding of AGA for preventive measures.</p>
</sec>
</sec>
<sec id="S5" sec-type="conclusion">
<title>Conclusion</title>
<p>Our study pioneered a two-sample MR analysis using GWAS summary statistics to probe a potential causal connection between gut microbiota and AGA. This analytical approach not only holds promise for developing effective prevention and intervention strategies for AGA but also offers innovative insights into the underlying mechanisms of AGA through the lens of gut microbiota.</p>
</sec>
<sec id="S6" sec-type="data-availability">
<title>Data availability statement</title>
<p>The original contributions presented in this study are included in the article/<xref ref-type="supplementary-material" rid="TS2">Supplementary material</xref>, further inquiries can be directed to the corresponding authors.</p>
</sec>
<sec id="S7" sec-type="author-contributions">
<title>Author contributions</title>
<p>HF: Conceptualization, Data curation, Investigation, Methodology, Project administration, Software, Validation, Visualization, Writing &#x2013; original draft, Writing &#x2013; review and editing. TX: Methodology, Writing &#x2013; original draft. WZ: Methodology, Writing &#x2013; original draft. LJ: Funding acquisition, Resources, Software, Writing &#x2013; review and editing. SS: Funding acquisition, Resources, Supervision, Writing &#x2013; review and editing.</p>
</sec>
</body>
<back>
<sec id="S8" sec-type="funding-information">
<title>Funding</title>
<p>The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work is supported by the Natural Science Foundation Committee of China (Grant No. 81972953), Fund for Less Developed Regions of the National Natural Science Foundation of China (Grant No. 82160601), Guizhou Provincial Health Commission Science and Technology Fund Program (Grant No. 2021XMSB00029829), and Guizhou Provincial Science and Technology Projects, ZK (2022) general 250.</p>
</sec>
<ack><p>We would like to thank the MiBioGen and FinnGen consortium for making summary level data from this consortium publicly available.</p>
</ack>
<sec id="S9" sec-type="COI-statement">
<title>Conflict of interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec id="S10" sec-type="disclaimer">
<title>Publisher&#x2019;s note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
<sec id="S11" sec-type="supplementary-material">
<title>Supplementary material</title>
<p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type="uri" xlink:href="https://www.frontiersin.org/articles/10.3389/fmicb.2024.1360445/full#supplementary-material">https://www.frontiersin.org/articles/10.3389/fmicb.2024.1360445/full#supplementary-material</ext-link></p>
<supplementary-material xlink:href="Table_1.XLSX" id="TS1" mimetype="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet" xmlns:xlink="http://www.w3.org/1999/xlink"/>
<supplementary-material xlink:href="Table_2.DOCX" id="TS2" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document" xmlns:xlink="http://www.w3.org/1999/xlink"/>
</sec>
<fn-group>
<fn id="footnote1">
<label>1</label>
<p><ext-link ext-link-type="uri" xlink:href="https://r9.finngen.fi/">https://r9.finngen.fi/</ext-link></p></fn>
<fn id="footnote2">
<label>2</label>
<p><ext-link ext-link-type="uri" xlink:href="https://www.ebi.ac.uk/gwas/">https://www.ebi.ac.uk/gwas/</ext-link></p></fn>
</fn-group>
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