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International Journal of Transgender Health logoLink to International Journal of Transgender Health
. 2025 Sep 5;27(5):2544–2560. doi: 10.1080/26895269.2025.2549794

Shifting hormones, shaping health: Gender-affirming therapy & oral care

PE Cornejo Ulloa a,*, GR van der Ploeg b,*, A Heintz-Buschart b, JA Westerhuis b, AK Smilde b, E Zaura a, BW Brandt a, MJ Buijs a, N Bostanci c, A Silbereisen c, FJ Bikker d, K Nazmi d, M Tebbens e, M den Heijer e, BP Krom a, MH van der Veen a,✉
PMCID: PMC13618161  PMID: 42807896

Abstract

Background

Gender-affirming hormone therapy (GAHT) is a critical aspect of transgender healthcare. Little is known about the effects of GAHT on oral health, which may be influenced by immune modulation, salivary composition shifts, and microbial dynamics as a result of hormone use.

Aim

To clinically, biochemically and microbiologically assess changes in oral health of transgender persons during the first year of GAHT.

Methods

Twenty-one transmasculine and eighteen transfeminine persons took part in the study. Participants were followed for 12 months. Every 3 months, participants underwent an oral checkup, including full oral assessment and light and fluorescent photos of gums and tongue, donated saliva and filled in a questionnaire over oral health self-perception and dry mouth. Salivary samples were later analyzed for certain proteins and for microbial composition using 16S rRNA gene amplicon sequencing. N-way Partial Least Squares (NPLS) models were used to perform a supervised analysis of the variation in the data associated with GAHT.

Results

GAHT induces transient disruptions in salivary composition and microbial communities during the first six months, followed by stabilization by twelve months. Differences in microbial composition were observed between transmasculine and transfeminine persons, suggesting that GAHT exerts distinct effects on oral microbiota depending on hormonal regiments, with potential implications for periodontal health.

Discussion

This clinical longitudinal study investigated for the first time the oral health of transgender individuals undergoing GAHT during the first year of transition. These results underscore the importance of tailored dental care during early stages of GAHT and contribute to a more comprehensive understanding of how systemic hormonal treatments can impact oral physiology. This work supports the development of more inclusive healthcare approaches that address the unique needs of transgender populations.

Keywords: Gender affirming hormone therapy, oral health, oral microbiome, sex steroid hormones, transgender oral health

Introduction

Sexual and gender minorities (SGMs) continue to face significant barriers to equity, including systematic discrimination (Madrigal-Borloz, 2019; United Nations Human Rights, 2019), marginalization, and violence that profoundly affect various aspects of their lives, such as education, housing, employment, and healthcare (Denison et al. 2021; Madrigal-Borloz, 2019; Mara et al., 2021; McFadden, 2020). Transgender persons represent a growing yet underserved population within the SGM community, experiencing unique healthcare challenges (National Institutes of Health - Sexual & Gender Minority Research Office, 2024).

Recent estimates suggest 1.8% of the US population identifies as transgender (Herman et al., 2022), and there is evidence that there is an increasing number of transgender persons seeking hormonal therapy (Leinung & Joseph, 2020). However, scientific research addressing their specific healthcare needs, including oral health, remains disproportionately scarce compared to the cisgender population. Gender affirming hormone therapy (GAHT) is a critical aspect of transgender healthcare, aimed at aligning secondary sexual characteristics with gender identity through the administration of testosterone for transmasculine persons and antiandrogens plus estrogen for transfeminine persons (Cundill, 2020; Morssinkhof et al., 2023).

Sex steroid hormones (SSHs), such as testosterone and estrogen, play essential roles in mucosal wound healing (Engeland et al., 2006; 2009) and gingival tissue turnover (Mariotti, 2005), amongst others, and play a crucial role in sexual dimorphism (Kroon et al., 2020; Shepherd et al., 2021; Wells, 2007). Sexual dimorphism is understood as the influence of sex on the biological response of an individual to certain stimuli, including disease susceptibility and progression (Oxford & Duncan, 2021), and altered levels of SSHs affect males and females differently (Saranya et al., 2022; Shiau & Reynolds, 2010).

In the oral cavity, fluctuating hormone levels are associated with gingival inflammation, dry mouth, periodontal disease, and shifts in microbial composition (Bostanci et al., 2021; Cornejo Ulloa et al., 2021; Meriç et al., 2023; Qin et al., 2015; Raju & Berens, 2021; Steffens et al., 2015). However, it remains unclear how the hormonal changes induced by GAHT influence oral health: do individuals retain their baseline biological response, or do they adopt characteristics aligned with their gender identity? The interplay between SSH-driven dimorphism and the unique hormonal profiles resulting from GAHT provides an unprecedented opportunity to study these dynamics.

To address this knowledge gap, we conducted a longitudinal study integrating clinical, biochemical, and microbiological analyses of 39 transgender persons undergoing GAHT. Our objectives were to identify oral health risks associated with GAHT and to explore the broader role of SSHs in oral health and disease. We found that GAHT transiently influences oral physiology differently for transmasculine and transfeminine persons. Through this work, we aim to promote inclusive research practices and advance healthcare equity for transgender persons.

Materials and methods

For readability purposes, this section presents a summarized version of the materials and methods. For the complete information, we suggest the reader consult the Supplementary Materials and Methods section.

This study used data from the oral health status assessment for participants undergoing gender affirming hormone treatment (GOHTRANS) clinical study, conducted at the Academic Center for Dentistry Amsterdam (ACTA) in collaboration with the VU Medical Center (Amsterdam, Netherlands). The study was approved by the medical ethical committee of the VU Medical Center (2019.613) and is registered at the Central Committee on Research Involving Human Subjects (CCMO) under number NL002027.

Study participants

A total of 39 participants (21 transmasculine and 18 transfeminine), aged 18–35 and systemically healthy, were recruited between 2020 and 2022. Strict inclusion and exclusion criteria were applied to minimize confounding factors, particularly those affecting the oral microbiome such as antibiotic use, smoking, or existing oral disease.

Study design

Participants were followed over four time points: baseline (start of GAHT), and at 3, 6, and 12 months (Figure 1A). Each visit included a comprehensive oral examination, including Bleeding on Probing (BOP), Clinical Oral Dryness Score (CODS), salivary and tongue biofilm sample collection and a dry mouth experience questionnaire. Hospital records including medication type and circulating hormone levels were also collected. Saliva and tongue biofilm samples were further processed and stored for further analyses.

Figure 1.

Figure 1.

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Hormonal modulation and stability during GAHT. (a) Study design. Twenty-one transmasculine and eighteen transfeminine participants were followed during the first year of GAHT (timepoints: Baseline [blue], 3 months [green], 6 months [yellow] and 1 year [purple]). gingival bleeding on probing (BOP%), tongue, and saliva for microbiome analysis were assessed at all timepoints. Salivary biochemical analysis was performed at baseline, 3 months, and 1 year, with partial data available for the 6-month time point (half circle). Plasma estradiol and testosterone levels were measured at baseline, 3 months, and 1 year, (b) Plasma levels of free testosterone, (c) Plasma levels of estradiol, (d) Salivary levels of mucin 5B (MUC5B), (e) Salivary levels of secretory immunoglobulin A (S-IgA), (f) Salivary levels of lysozyme, (g) Gingival bleeding on probing (BOP%) as a percentage of all measured sites, (h) Spearman correlation between changes in testosterone levels and changes in BOP% from baseline to month 12. Only analytes with a significant disruption across all time points are shown. All other measured analytes are reported in Supplementary Figures 1 and 2. Lines are drawn to connect the same participant at the different time points. Lower and upper bounds of the boxes correspond to the first and third quartiles. Lower and upper whiskers extend no further than 1.5 * interquartile range. Data points beyond the whiskers are shown as outliers. Asterisks indicate p-values of friedman’s tests followed by nemenyi post-hoc analysis of baseline versus subsequent timepoints (*: p ≤ 0.05, **: p ≤ 0.01, ***: p ≤ 0.001, ****: p ≤ 0.0001, ns: not significant, see statistical analysis and Supplementary Tables 1 and 2 for sample sizes and exact p-values).

Sample processing

Biochemical analyses of salivary samples

Total protein content (BSA), chitinase, S-IgA, Lysozyme, Amylase and MUC5B were assessed separately. Also, a Cytokine Human Magnetic 30-Plex Panel (LHC6003M, Lifetechnologies, Darmstadt, Germany) was run for 27 of 39 participants at baseline, 3 months and 12 months.

DNA isolation, PCR amplification and 16S rRNA gene amplicon sequencing

Tongue biofilm and saliva samples underwent DNA isolation. Afterwards, the V4 hypervariable region of the 16S rRNA gene was amplified. Paired-end sequencing of the DNA was conducted on the MiSeq platform (Illumina, San Diego, CA, USA) at the AUMC Clinical Genetics Department (Amsterdam, the Netherlands).

Data processing

The sequenced data of the 16S rRNA gene reads was processed using two different pipelines: DADA2 and UNOISE3. All subsequent analyses gave similar results for both pipelines. For consistency, only the results of the DADA2 pipeline were used for the analyses.

Statistical analyses

Statistical information

All statistical analyses were performed in R (R Core Team, 2013) (v4.4.1). Longitudinal changes in clinical, biochemical and microbiological parameters were assessed using Friedman’s test with Nemenyi post-hoc analysis. Principal coordinate analysis of the microbiota data was performed using the Bray-Curtis distance metric. PERMANOVA tests of the sub cohorts or time points in the first and second principal coordinate axes were performed. Alpha diversity of the microbiota data was assessed with the Shannon diversity index. A significance threshold of p ≤ 0.05 was used unless otherwise stated. Area under the receiver-operator curves for the intergroup NPLS models were computed by comparing the predicted subject gender identity with the real gender identity.

Microbiota data processing

The zero-radius operational taxonomic unit (zOTU) data and taxonomic information of the tongue and salivary microbiota were processed in MATLAB (version 2023a).

N-way partial least Squares (NPLS) and post-hoc clustering of NPLS loadings

To explore how the oral microbiome responds to gender-affirming hormone therapy (GAHT), N-way Partial Least Squares (NPLS) was applied to salivary and tongue microbiome data using the change in testosterone levels between baseline and month 3 as dependent variable. This method allowed us to decompose a third-order array containing subjects in the rows, microbial taxa in the columns, and time points in the third axis to identify differences between the gender identity groups. Data was pre-processed to ensure comparability between participants and microbial features. For both the tongue and salivary microbiome models shown in this article, a 1-component model was sufficient. Following model construction, microbiota data was further filtered to perform clustering. The aim of this step was to identify microbial sub communities that could be connected to a particular time profile in the NPLS model. After filtering, K-medoids algorithm was used to perform the clustering. This approach enabled us to identify microbial subcommunities associated with gender identity. The total abundance of each cluster across groups was then compared and statistical testing was used to assess the significance of these differences. All modeling results, clustering outputs, and statistical analyses are detailed in the Supplementary Materials and Methods.

Results

Hormonal modulation and stability during GAHT

GAHT effectively modulated SSHs to align with the participants’ gender identities (Figure 1B,C). This study aimed to evaluate whether these changes contribute to changes in oral health, salivary biochemistry, and microbiological profiles. Tongue biofilm and salivary samples were collected at baseline (on the first day of GAHT) and at 3, 6 and 12 months of GAHT (Figure 1A). Microbiota analyses were conducted on both sample types, while salivary samples were also used for biochemical analyses. These data were compared against circulatory hormone levels measured at baseline, 3 and 12 months of GAHT. Given the established links between hormonal fluctuations and gingival inflammation or microbial composition, measurable changes were expected (Bostanci et al., 2021; Cornejo Ulloa et al., 2021).

Hormonal profiles stabilize within one year

Circulatory hormone levels underwent significant changes during the first year of GAHT (Figure 1B,C, Supplementary Table 1). In transmasculine participants, testosterone levels rose significantly at all measured time points compared to baseline, while estradiol levels remained stable. Conversely, transfeminine participants experienced a significant increase in estradiol levels and a decrease in testosterone levels. Circulatory hormone levels exhibited transiently higher variation at the three-month mark. By month 12, testosterone and estradiol levels successfully stabilized within the reference ranges corresponding to participants’ gender identities (England et al., 1974; Vesper et al., 2015; Yamamoto et al., 1995).

Clinical and biochemical changes during GAHT

Clinical and biochemical measurements revealed transient disruptions during the first six months of GAHT, with most parameters returning to baseline by month 12. Measurements were taken at 0, 3, 6, and 12 months to assess the effects of GAHT on oral tissues and saliva. Significant changes were observed in eight analytes across the time series per sub cohort (i.e. transfeminine or transmasculine persons only), indicating a period of adjustment during early GAHT (Figure 1D–G).

Mucin 5B MUC5B), a glycoprotein crucial for mucosal lubrication and bacterial adherence (Faruque et al., 2022), showed significant changes in both transmasculine and transfeminine participants across the time series (Figure 1D). While transient decreases in MUC5B level may indicate temporary dry mouth symptoms, we only observed significant changes in the frequency and intensity of dry mouth symptoms (xerostomia inventory) in the transmasculine participants (Supplementary Figure 3, Supplementary Table 1). Secretory Immunoglobulin A (S-IgA), an antibody critical for first-line antimicrobial defense (Brandtzaeg, 2013), exhibited significant changes in both groups across the time series, suggesting a potential increase in microbial load or inflammatory activity during early GAHT (Figure 1E). Lysozyme, a protein involved in oral microbial defense (Van Nieuw Amerongen et al., 2004), decreased significantly in transmasculine participants between baseline and 12 months (Figure 1F). Gingival bleeding upon probing (BOP%) increased transiently at 3 months in transmasculine participants, indicating a brief period of heightened gingival inflammation (Figure 1G). A significant negative correlation (p < 0.0001, r= −0.8412; Spearman correlation test) was observed in transfeminine participants between the change in testosterone levels and the change in BOP%. Participants with a higher difference in testosterone levels between baseline and 12 months presented more gingival inflammation. This suggests an association between hormonal shifts and gingival health (Figure 1L). No such correlation was observed in transmasculine participants or with changes in estrogen levels for either sub cohort. No significant changes were observed in other measured analytes including chitinase, amylase, total protein, salivary pH, cytokines, chemokines and growth factors (Supplementary Figures 1 and 2, Supplementary Tables 1 and 2). Several salivary cytokines varied significantly during GAHT (Figure 1H–K, Supplementary Table 2).

Initial analyses suggested fluctuations in salivary cytokines during GAHT, including Interleukin 1 Receptor Agonist (IL-1RA), Interleukin 8 (IL-8) and Interferon gamma induced protein 10 (IP-10). However, after multiple testing correction, none of these changes remained statistically significant. These data are presented in Supplementary Table 2 and Supplementary Figure 2 for transparency, but no strong conclusions can be drawn regarding their role in GAHT-induced immune modulation.

Taken together, these findings suggest that most clinical and biochemical parameters stabilize by month 12, following a period of disruption during the first six months of GAHT.

Stability of oral microbial composition during GAHT

Microbiological analyses of tongue biofilm and saliva samples revealed a generally stable microbial composition during the first year of GAHT. Tongue biofilm samples yielded 544 zero-radius operational taxonomic units (zOTUs), 325 of which could be classified up to genus level. Saliva samples yielded 574 zOTUs, 372 of which could be classified up to genus level.

In tongue biofilm samples, microbial dominance hardly differed by sub cohort (Figure 2A). Prevotella was the dominant genus across all time points for the transmasculine group, while it dominated as baseline, 6 months, and 12 months in the transfeminine group. At 3 months, a transient domination by Streptococcus was observed in the transfeminine group. In salivary samples, Prevotella was the dominant genus across all time points in both sub cohorts (Figure 2B).

Figure 2.

Figure 2.

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Tongue biofilm and salivary microbiota analysis. Relative abundance over time in the (a) tongue microbiota in the transmasculine and the transfeminine Sub cohort, (b) saliva microbiota in the transmasculine and transfeminine Sub cohort. Shannon diversity metrics of c. the tongue microbiota in the transmasculine and transfeminine Sub cohort, (d) the salivary microbiota in the transmasculine and transfeminine Sub cohort. Principal coordinate analysis (PCoA) using Bray–Curtis distance for the (e) tongue microbiota, (f) tongue microbiota of the transmasculine Sub cohort, (g) tongue microbiota of the transfeminine Sub cohort, (h) salivary microbiota, (i) salivary microbiota of the transmasculine Sub cohort and j. salivary microbiota of the transfeminine Sub cohort. R2 and p-values correspond to a PERMANOVA analysis of the groups as indicated in the PCoA plots.

Shannon diversity indices for both salivary and tongue biofilm samples exhibited overall stability throughout the study period Figure 2C,D). However, a small number of participants exhibited transient decreases in microbial diversity during the first year, reflecting potential individual variability in response to GAHT.

Principal coordinates analysis (PCoA) revealed significant differences between the sub cohorts using the full time series (Figure 2E, H). However no significant differences between time points were observed in a similar analysis per sub cohort, indicating a stable general composition during the first year of GAHT (Figure 2F, G, I, J). Additionally, no clear clustering of the samples in either sub cohort could be observed with this approach. However, as an unsupervised method, PCoA only detects broad compositional shifts and does not capture nuanced associations between specific microbiota and hormonal changes cannot be seen.

NPLS models describe hormonal influence on microbial communities

To explore whether transfeminine and transmasculine participants maintained their baseline biological response to hormonal changes or adopted the response of their transitioned gender, we applied N-way Partial Least Squares (NPLS) modeling. This supervised approach captured changes in the tongue microbiota, salivary microbiota and salivary cytokines related to GAHT (Figure 3A,B, Supplementary Figures 4–6). Three models were generated for each dataset: one describing intergroup variation (differences between transmasculine and transfeminine participants), and two describing intragroup variation within each sub cohort). This resulted in nine NPLS models, with dependent variables tailored to the study design: a binary factor for gender identity in the intergroup models and changes in testosterone or estradiol levels from baseline to month 3 in the intragroup models for the transmasculine and transfeminine sub cohort, respectively.

Figure 3.

Figure 3.

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Output of intergroup NPLS models. NPLS models were created to assess microbial differences between the transmasculine and transfeminine Sub cohorts. Feature loadings for (a) tongue and (b) salivary microbiota reflect associations with each Sub cohort. Positive loadings correspond to microbiota that are associated with enrichment in the transfeminine Sub cohort and with depletion in the transmasculine Sub cohort, while negative loadings indicate the opposite. Microbial subcommunities (clusters) enriched in each Sub cohort were identified (Supplementary Table 6). Cluster relative abundance sum is shown for each Sub cohort in (c). tongue and (d) salivary microbiomes. Feature loadings in a, b were filtered to an absolute value ≥ 0.08 for visualization purposes. The unfiltered version of these plots is reported in Supplementary data. Error bars in c, d correspond to the standard error of the mean (SEM) across all samples per gender identity group. The mean difference between the transmasculine and the transfeminine group per time point in c, d was tested using a Benjamini-Hochberg corrected permutation test of 999 iterations (*: p ≤ 0.05, **: p ≤ 0.01, ***: p ≤ 0.001; Supplementary Table 7).

A one-component model was selected for each case, as cross-validation metrics indicated that selecting additional components did not sufficiently improve explanatory power or predictive performance. The exception was the model describing the tongue microbiome for the transmasculine sub cohort, where a two-component model was more appropriate. The intergroup models captured 7.33%, 5.03% and 22.65% of variation in the tongue microbiome, salivary microbiome and salivary cytokine datasets, respectively, while explaining 13%–35% of the variation in their dependent variables (Supplementary Table 3). Intragroup models of the transmasculine sub cohort described 7%–20% of variation in the input datasets and 47%–78% of variation in the change in testosterone levels from baseline to month 3 (Supplementary Table 4). Intragroup models of the transfeminine sub cohort described 1%–43% of the variation in the input datasets and 6%–33% of variation in the change in estradiol levels from baseline to month 3.

Predictive validation of the generated NPLS models was performed by comparing predicted with real gender identity using sensitivity and specificity metrics for the intergroup models and with a correlation test between measured and predicted hormonal changes for the intragroup models. This approach revealed that the intergroup models for the tongue microbiome, salivary microbiome and salivary cytokine datasets reliably predicted gender identity. Gender identity was predicted with sensitivities of 0.66, 0.78, and 0.58, specificities of 0.71, 0.81, and 0.60, and area under the receiver-operator curve (AUROC) of 0.79, 0.84, and 0.67, respectively (Supplementary Table 3). The intragroup models of the transmasculine sub cohort describing these data predicted changes in testosterone levels with correlation values of 0.88, 0.82, and 0.69, respectively (Supplementary Table 4). Only the intragroup model of the transfeminine sub. cohort describing the tongue microbiome predicted changes in estradiol levels with a correlation value of 0.59. All other models were not predictive. As such, five out of nine models were deemed sufficiently descriptive to describe changes in the oral microbiome between both sub cohorts and internal changes in the transmasculine group and were used in subsequent analysis.

By comparing the identified subject loadings in each NPLS model with the circulatory hormone levels and salivary biochemical information, the components can be interpreted (Supplementary Table 5). The intergroup NPLS models of tongue and salivary microbiomes identified subject loadings that distinguish participant gender identity as expected (Supplementary Table 5). The intragroup NPLS model of the transmasculine sub cohort of the tongue microbiome identified subject loadings that correlated with chitinase and total protein levels in the first component, while the second component correlated with the testosterone change between baseline and month 3 as expected, as well as amylase and BOP%. The intragroup NPLS model of the transmasculine sub cohort of the salivary microbiome identified subject loadings that correlated with the testosterone difference between baseline and month 3 as expected, as well as chitinase, S-IgA and amylase levels. All intragroup NPLS models describing the transfeminine sub cohort as well as all inter- and intragroup NPLS models of the salivary cytokine data were not interpretable with the measured circulatory hormone levels and salivary biochemical information and were excluded from further analysis.

Examining the loadings of each NPLS model reveals the association of each microbiota with gender identity or the change in circulatory hormone levels (Figure 3A,B, Supplementary Figures 7–12). The NPLS model describing intragroup variation in the tongue microbiome revealed that Firmicutes members such as Streptococcus salivarius, Streptococcus vestibularis along with Rothia dentocariosa, Prevotella salivae and some Veillonella spp. were enriched in transfeminine participants and depleted in transmasculine participants (Figure 3A, Supplementary Figure 7). Conversely, the model revealed that some Fusobacteria members such as Fusobacterium periodonticum and Leptotrichia spp. along with Lachnoanaerobaculum umeaense, Haemophilus parainfluenzae and Neisseria flavescens/subflava were enriched in transmasculine participants and depleted in transfeminine participants. Likewise, the NPLS model describing intergroup variation in the salivary microbiome revealed that Prevotella oris, Alloprevotella tannerae, R. dentocariosa and various Streptococcus spp. were enriched in transfeminine participants and depleted in transmasculine participants (Figure 3B, Supplementary Figure 8). Conversely, the model revealed that some Fusobacteria members such as F. periodonticum and Leptotrichia spp. along with Oribacterium parvum, Peptostreptococcus stomatis, N. flavescens/subflava, Veillonella spp. and Capnocytophaga gingivalis were enriched in transmasculine participants and depleted in transfeminine participants. The NPLS model describing intragroup variation of the transmasculine sub cohort in the tongue microbiome dataset revealed that many Fusobacteria members such as Leptotrichia wadei, Leptotrichia spp. and various Fusobacterium spp. along with Veillonella parvula, Bergeyella sp., N. flavescens/subflava, Porphyromonas pasteri, Capnocytophaga leadbetteri, Aggregatibacter segnis and S. salivarius/vestibularis were enriched in participants with a large testosterone increase between month 0 and 3 (Supplementary Figure 9). Conversely, a diverse group of microbiota such as Prevotella pallens, Actinomyces odontolyticus, Oribacterium asaccharolyticum, Actinomyces odontolyticus, Actinomyces graevenitzii, Rothia mucilaginosa, Prevotella veroralis, and Prevotella nanceiensis were enriched in participants with a small testosterone increase between month 0 and 3. The NPLS model describing intragroup variation of the transmasculine sub cohort in the salivary microbiome dataset revealed that various Prevotella members such as Prevotella denticola, Prevotella oris, Prevotella nigrescens, and P. nanceiensis, along with a diverse set of microbiota such as Streptococcus anginosus, Actinomyces georgia, Dialister invisus, Kingella oralis and Streptococcus sanguinis, were enriched in participants with a large testosterone increase between month 0 and 3 (Supplementary Figure 10). Conversely, other Prevotella members such as Prevotella pallens, Prevotella melaninogenica and Prevotella shahii along with a diverse set of microbiota including Catonella morbi, Alloprevotella rava, A. odontolyticus, O. parvum and P. stomatis were enriched in participants with a small testosterone increase between month 0 and 3.

In conclusion, the NPLS models are suitable for interpretation of the time-resolved intra and intergroup variation in the tongue and salivary microbiomes as a result of GAHT.

NPLS models describe microbial subcommunities associated with gender identity

To validate whether microbiota strongly associated with intergroup differences in the NPLS models constituted a significant portion of the microbiome, we examined the relative abundances of these bacteria in the tongue and salivary microbiomes across all time points. Bacterial zOTUs that were well-explained by the NPLS models were clustered based on their modeled time trajectories, and the summed relative abundances of these clusters were compared between transmasculine and transfeminine participants (Figure 3C,D, Supplementary Table 7). In the tongue biofilm microbiome, the intergroup NPLS model identified two microbial clusters associated with gender identity. The cluster associated with transmasculine gender identity included a mix of microbiota favoring biofilm expansion, such as F. periodonticum, V. parvula and N. flavescens/subflava. This cluster was enriched in transmasculine participants and depleted in transfeminine participants at baseline through month 6 (Figure 3C). Conversely, the cluster associated with transfeminine gender identity contained microbiota associated with mucosal stability and oral health, such as P. salivae, R. dentocariosa and S. salivarius. This cluster was enriched in transfeminine participants and depleted in transmasculine participants at baseline through month 6. By the end of the study period, these clusters were no longer differentially abundant, suggesting a convergence in tongue microbial profiles of the two sub cohorts.

In the salivary microbiome, the intergroup NPLS model also identified two microbial clusters associated with gender identity. The cluster associated with transmasculine gender identity included microbiota with diverse roles, such as P. oris, H. parainfluenzae and V. parvula. This cluster was enriched in transmasculine participants and depleted in transfeminine participants at baseline through month 6 (Figure 3D). Conversely, the cluster associated with transfeminine gender identity contained microbiota associated with oral health, such as A.tannerae, Streptococcus intermedius and R. dentocariosa. This cluster was enriched in transfeminine participants and depleted in transmasculine participants at baseline through month 6. Similarly to the tongue microbiota, these clusters were no longer differentially abundant by the end of the study period.

Overall, the NPLS models describing intergroup differences identified microbial subcommunities associated with gender identity. These clusters constituted a meaningful portion of the microbiota, with their summed relative abundances differing significantly between the sub cohorts during the early months of GAHT.

Discussion

The present study investigated the impact of Gender-Affirming Hormone Therapy (GAHT) on oral health, addressing a critical gap in understanding how hormonal changes influence the oral microbiome and clinical parameters (Cornejo Ulloa et al., 2021; Kumar, 2013). By integrating clinical, biochemical and microbiological analyses, we identified significant transient changes in the oral environment during the first year of GAHT, contributing novel insights into the interplay between SSH, microbial dynamics, and oral health. These findings have implications for both clinical practice and future research.

Our findings indicate that GAHT induces transient disruptions in oral health parameters during the early months, followed by a stabilization by month 12. Changes in biomarkers such as MUC5B, S-IgA, and lysozyme suggest that SSHs exert an indirect influence on the oral microbiome, potentially mediated by altered salivary composition and immune modulation. The correlation between testosterone levels and reduced bleeding on probing in transfeminine participants provides insight into the impact of hormone therapy in oral inflammatory processes (Bostanci et al., 2021; Soliman et al., 2022).

SSHs are known to influence mucosal integrity, immune function and microbial composition, but the precise mechanisms remain unclear (Cornejo Ulloa et al., 2024). Our results partially align with prior studies showing hormonal effects on periodontal health during pregnancy, autogenic—or internal—changes such as the menstrual cycle and menopause, and allogenic—or external—changes like Hormone Replacement Therapy (HRT) (Chaves et al., 2020; Machtei et al., 2004; Wu et al., 2015; Yamazaki et al., 2023) (Figure 4A). This can be attributed to differences in hormonal levels achieved in varied biological states such as gestation. For example, estrogen levels reached during pregnancy are significantly higher than the hormone doses administered in HRT or GAHT (Flores et al., 2021; Hembree et al., 2017; Jayasena et al., 2022). Interestingly, these high estrogens levels have been associated with an increase in periodontal disease-associated bacteria (Borgo et al., 2014). On the contrary, lower estrogen levels used in HRT seem to suppress the abundance of these bacteria, suggesting a protective role (Soliman et al., 2022; Tarkkila et al., 2010). In our study, the observed enrichment of proteolytic bacteria in transmasculine participants and health-associated Streptococcus spp. in transfeminine participants may reflect differential responses to testosterone and estrogen, warranting further exploration of these microbial shifts.

Figure 4.

Figure 4.

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Summary of the existing literature on hormonal changes and oral health together with the findings from this study and a proposed mechanism of action of SSHs in the oral cavity. (a) Known effects during hormonal changes in oral health and the oral microbiome. Graphs indicate estrogen hormone levels achieved during different biological states or due to hormonal medication. Summary of the effects of GAHT on hormonal levels. Chaos theory and transient changes of the oral microbiome on the sub cohorts. Known and observed interactions between GAHT and oral health, (b) based on limited evidence, testosterone likely has a direct effect on the oral tissues and salivary biochemistry. Limited evidence suggests that testosterone does not affect the oral microbiome directly. In our study, we propose an indirect and transient effect of testosterone in the transmasculine sub cohort on clinical parameters by indirectly influencing salivary composition and the oral microbiome, (c) there is stronger evidence that estradiol has a direct effect on oral tissues. Limited evidence suggests that estradiol does not affect the oral microbiome directly. In our study, we propose an indirect and transient effect of estradiol on salivary biochemistry and the oral microbiome but not on clinical parameters. This might be due to a modulating effect of the oral tissues, salivary biochemistry or oral microbiome, (d) suggested framework through which SSHs influence the oral cavity based on existing literature and findings from the present study.

We propose the “Chaos Theory” to describe the transient period of instability observed during early GAHT (Figure 4A). This phase, characterized by increased variability in clinical, biochemical and microbial parameters, likely represents a critical window of susceptibility to external stressors, including diet, hygiene and systemic health conditions. Resilience mechanisms. (Rosier et al., 2018), such as autoregulation of the oral microbiome, and baseline-health status appear to mitigate the long-term impact of GAHT-induced changes, allowing most parameters to return to baseline by month 12. This hypothesis aligns with previous findings in other hormonal changes, such as pregnancy and the menstrual cycle, where temporary dysbiosis or inflammation resolves as hormones return to baseline levels (Machtei et al., 2004; Wu et al., 2015; Yamazaki et al., 2023).

The observed stabilization of the oral microbiome and clinical markers over time suggests a robust adaptive capacity. Notably, microbial shifts in transfeminine participants mirror the effects of estrogen in HRT (Soliman et al., 2022; Tarkkila et al., 2010). Conversely, the proteolytic bacteria enriched in transmasculine participants are often linked to periodontal risk (Cornejo Ulloa et al., 2021), underscoring the need for targeted monitoring and interventions for this group. This finding was unexpected, given that most bacteria associated with periodontal disease are known to thrive in estrogen-rich environments (Cornejo Ulloa et al., 2021; Kornman & Loesche, 1982). In contrast, the effects of testosterone on these bacteria remain poorly understood, highlighting the need for further research in this area (Cornejo Ulloa et al., 2021).

Our findings also contribute to the broader discussion on whether SSHs act directly on the microbiome or through host-mediated pathways (Figure 4B,C). Prior studies have shown minimal direct effects of SSHs on in vitro biofilms (Cornejo Ulloa et al., 2023), supporting the hypothesis that SSH-driven changes in host factors, such as immune regulation and salivary composition, may play a more significant role. The correlation between hormonal changes and microbial profiles further support the existence of a complex network where indirect effects via salivary biochemistry influence the oral microbiome (Figure 4D). The proposed mechanism of action of SSH on the oral microbiome is based on the existing literature and the findings of this study. Further research using mediation analysis (VanderWeele, 2016) is needed to investigate the direct and indirect interactions that occur in this system.

Clinical recommendations arising from this study emphasize the importance of regular dental evaluations during the first year of GAHT, particularly within the initial six months. Early monitoring should mainly focus on dry mouth signs and symptoms, and gingival inflammation. For transmasculine persons, the sustained increase in periodontal disease-associated bacteria suggests that additional oral hygiene measures and professional gingival care may be needed to minimize the risk of developing periodontal disease. Healthcare providers should also consider interdisciplinary approaches, integrating oral care with overall hormonal and systemic health monitoring. Tailored guidance on oral hygiene practices, diet, and stress management could further mitigate the risk of oral health complications during GAHT.

Limitations

This study is limited by its relatively small sample size and homogeneous cohort, which may not fully capture the diversity of the transgender population. Confounding factors such as diet, oral hygiene and lifestyle changes were not controlled for, representing an area for improvement in future research. Additionally, while N-way Partial Least Squares (NPLS) modeling provided valuable insights, advanced data integration methods like ACMTF could be adapted to enhance the analysis of complex longitudinal datasets.

Future research

Expanding the scope to include more participants, socio demographic data, participants with varying baseline health conditions, control groups of cisgender persons undergoing comparable hormone therapies, and longer follow-up periods are relevant aspects for future research. A more heterogeneous and representative cohort could help validate the presented hypothesis regarding this transient period of instability observed during early GAHT, by exploring predictive markers of resilience versus susceptibility undergoing GAHT.

The enrichment of proteolytic bacteria in transmasculine participants and health-associated Streptococcus spp. in transfeminine participants may indicate distinct microbial responses to testosterone and estrogen. However, since the sequencing targeted only the V4 region of the 16S rRNA gene, taxonomic resolution may be limited, making it difficult to accurately determine the pathogenic or pro-inflammatory potential of all detected zOTUs. Exploring the functional implications of specific microbial shifts, such as the roles of the identified Prevotella spp., Veillonella spp. and Streptococcus spp. could further elucidate the mechanisms linking GAHT to oral health. Also, to investigate and further understand the direct and indirect interactions that occur in this complex network involving hormonal changes and microbial profiles, mediation analysis (VanderWeele, 2016) would be a valuable tool.

Evidence on the effects of testosterone on oral bacteria remains limited and poorly understood (Cornejo Ulloa et al., 2021). Future clinical research on this area should also include in vitro models to provide insight into the mechanistic dynamics of testosterone, the host and the oral microbiota.

Future research should build on these findings, exploring the long-term effects of GAHT on oral and systemic health and refining strategies to promote resilience during periods of hormonal transition. This approach reflects a broader commitment to recognizing and addressing the health disparities faced by transgender populations and to advancing beyond the single-sex research approaches that have historically dominated the field, supporting diversity and inclusion.

Conclusion

This study represents an important step toward understanding the effects of GAHT on oral health, revealing transient disruptions during early treatment and subsequent stabilization. By identifying biomarkers and microbial subcommunities associated with GAHT, we provide a foundation for personalized oral healthcare tailored to transgender persons. The inclusion of gender diversity in oral health research is critical for advancing equitable healthcare and ensuring that the unique needs of all individuals are addressed.

Supplementary Material

Supplemental Material
WIJT_A_2549794_SM6730.docx (4.5MB, docx)
Supplemental Material
WIJT_A_2549794_SM6726.docx (35.3KB, docx)
Supplemental Material
WIJT_A_2549794_SM6725.docx (25.7KB, docx)

Acknowledgements

G.R. van der Ploeg was funded by a grant from the University of Amsterdam, Research Priority Area on Personal Microbiome Health. We would like to thank E. Deutekom-Mulder for her work processing the samples for sequencing, M. L. Laine for her help with data interpretation, B. Keijser for putting us in contact with the Centre for Gender Dysphoria from the Vrije Universiteit, S. van Eeghen for her assistance in the inclusion of participants for the study, S. Kroon for her assistance with the title, and R. van Nieuwenhuizen, O. de Nie, C. Blank, M. Versteeg-Krijnen, M. Al Beiramani, S. Breed for their help with data collection and preliminary analyses as part of their master theses.

Funding Statement

The author(s) reported there is no funding associated with the work featured in this article.

Author contributions

P.E. Cornejo Ulloa and G.R. van der Ploeg contributed equally to this manuscript; P.E. Cornejo Ulloa: research design, sample collection, laboratory analysis, data interpretation, writing; G.R. van der Ploeg: data processing, analysis and interpretation, writing; A. Heintz-Buschart, J.A. Westerhuis and A.K. Smilde: data analysis supervision, critical feedback of the manuscript; E. Zaura: data interpretation, critical feedback of the manuscript; B.W. Brandt: data processing and critical feedback of the manuscript; M.J. Buijs: laboratory analyses and critical feedback of the manuscript; N. Bostanci: data analysis supervision, critical feedback of the manuscript; A. Silbereisen: laboratory analyses, data analysis supervision and critical feedback of the manuscript; F.J. Bikker: laboratory analyses supervision and critical feedback of the manuscript; K. Nazmi: laboratory analyses and critical feedback of the manuscript; M. Tebbens: research design, critical feedback of the manuscript; M. den Heijer: research design, critical feedback of the manuscript; B.P. Krom: research design, data analysis supervision, data interpretation, critical feedback of the manuscript; M.H. van der Veen: research design, data analysis supervision, data interpretation, critical feedback of the manuscript

Code availability statement

The full code used for the analyses is available on https://doi.org/10.5281/zenodo.14893069.

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The microbiota data are available upon request. The clinical, biochemical and processed microbiota data are available on https://doi.org/10.5281/zenodo.14893069.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplemental Material
WIJT_A_2549794_SM6730.docx (4.5MB, docx)
Supplemental Material
WIJT_A_2549794_SM6726.docx (35.3KB, docx)
Supplemental Material
WIJT_A_2549794_SM6725.docx (25.7KB, docx)

Data Availability Statement

The microbiota data are available upon request. The clinical, biochemical and processed microbiota data are available on https://doi.org/10.5281/zenodo.14893069.

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