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Article

Salivary Prevotella qPCR Signal as an Exploratory Non-Invasive Adjunct for Rotterdam Phenotype Stratification in Women with Polycystic Ovary Syndrome: A Proof-of-Concept Cross-Sectional Study

by
Arif Tunjungseto
1,2,
Oni As’ad Hadi
3,
Intan Winta Pratiwi
2,
Fakhriyah Iffatunnisa
2,
Fadhil Ahsan
4 and
Budi Santoso
2,*
1
Doctoral Program of Medical Science, Faculty of Medicine, Universitas Airlangga, Surabaya 60132, Indonesia
2
Department of Obstetrics and Gynecology, Faculty of Medicine, Universitas Airlangga, Surabaya 60132, Indonesia
3
Medical Program, Faculty of Medicine, Universitas Airlangga, Surabaya 60132, Indonesia
4
Department of Obstetrics and Gynecology, University Hospital of Würzburg, 97080 Würzburg, Germany
*
Author to whom correspondence should be addressed.
Diagnostics 2026, 16(13), 2041; https://doi.org/10.3390/diagnostics16132041
Submission received: 22 May 2026 / Revised: 17 June 2026 / Accepted: 18 June 2026 / Published: 30 June 2026
(This article belongs to the Section Clinical Laboratory Medicine)
Review Reports Versions Notes

Abstract

Background/Objectives: Polycystic ovary syndrome (PCOS) is a heterogeneous endocrine-metabolic disorder. Rotterdam-defined phenotypes may need adjunctive, non-invasive signals for research-level phenotypic stratification. Saliva deserves attention because it is easy to collect repeatedly, acceptable in outpatient settings, and may reflect oral-systemic inflammatory and endocrine-metabolic interactions. This study evaluated whether salivary qPCR signals for Lactobacillus, Prevotella, and Bifidobacterium differ across Rotterdam-defined PCOS phenotypes and controls, with emphasis on the exploratory relevance of Prevotella. Methods: This cross-sectional proof-of-concept study included 110 women: 87 with PCOS and 23 controls. PCOS phenotypes were classified according to the Rotterdam criteria. Salivary microbial targets were assessed using SYBR Green-based genus-specific qPCR. Available instrument-export spreadsheets were reviewed for standard-curve quality control. Because the available assay outputs did not support robust absolute quantification, inferential analyses used Cq-based microbial signals only. Lower Cq values indicate stronger target DNA signal. No universal bacterial 16S rRNA reference, exogenous spike-in, salivary flow correction, or fully validated copy-number conversion was available. Group differences were evaluated using non-parametric tests with Bonferroni-corrected post-hoc comparisons. ROC, regression, and correlation analyses were retained as hypothesis-generating analyses only. Results: Prevotella Cq values differed significantly across groups (p < 0.001), with lower median Cq values in phenotypes A, B, and C than in phenotype D and controls. Lactobacillus Cq values did not differ significantly (p = 0.249). Bifidobacterium showed an overall group difference (p < 0.001), but its pattern and assay performance were less consistent. Among women with PCOS, Prevotella Cq values were associated with Ferriman-Gallwey score and polycystic ovarian morphology. Conclusions: Salivary Prevotella showed the clearest exploratory genus-level qPCR signal across Rotterdam-defined PCOS phenotypes. The findings support further technical and clinical validation of saliva-based microbial profiling as a possible adjunct to conventional PCOS phenotyping. They do not validate Prevotella as a standalone diagnostic biomarker, do not define clinical cutoffs, and do not quantify absolute bacterial load.

1. Introduction

Polycystic ovary syndrome (PCOS) is a common endocrine disorder among reproductive-age women and is characterized by heterogeneous reproductive, endocrine, and metabolic manifestations. The Rotterdam criteria remain widely used for clinical classification and generate four major phenotypes based on combinations of hyperandrogenism, ovulatory dysfunction, and polycystic ovarian morphology [1,2]. This phenotypic heterogeneity is clinically important because different phenotypes may carry different reproductive, metabolic, and inflammatory implications [3,4,5,6,7,8].
Microbiome research has become increasingly relevant to PCOS because microbial ecosystems may interact with androgen exposure, insulin resistance, low-grade inflammation, bile acid metabolism, and immune regulation [9,10,11,12,13,14,15,16]. Most PCOS microbiome studies have focused on the gut or vaginal microbiome [10,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31]. Vaginal microbiome assessment is scientifically important and can be performed using non-invasive or minimally invasive sampling, but it represents a local reproductive-tract niche. Saliva addresses a different question. It captures oral microbial ecology and may provide a repeatable outpatient matrix for studying oral-systemic signals related to endocrine-metabolic disease.
Saliva specifically deserves evaluation in PCOS for three reasons. First, it can be collected repeatedly without pelvic sampling, which may improve acceptability for screening-oriented or longitudinal studies. Second, salivary microbiome studies in PCOS have reported altered oral microbial community features and disease-related associations, although the evidence remains limited and heterogeneous [9,32]. Third, oral and gut microbiota have been discussed in several female reproductive and gynecological disease contexts, including gynecological cancers, through immune, inflammatory, and hormone-related pathways [33]. These data do not prove diagnostic value in PCOS, but they justify saliva as a distinct exploratory matrix rather than a substitute for vaginal or gut microbiome profiling.
Among candidate genera, Lactobacillus, Bifidobacterium, and Prevotella are biologically plausible exploratory targets because prior studies, mainly in intestinal ecosystems, link these genera to barrier function, host metabolic regulation, dietary patterns, immune signaling, and inflammatory tone [34,35,36,37,38,39,40]. Prevotella is especially context-dependent: different species and strains may be associated with either eubiotic fiber-related profiles or dysbiotic inflammatory states depending on host and environmental conditions [37,38,39,40]. Genus-level qPCR therefore cannot identify whether a detected Prevotella signal reflects beneficial, commensal, or potentially pathogenic species.
Accordingly, this study analyzed qPCR-derived salivary Cq signals of Lactobacillus, Prevotella, and Bifidobacterium across Rotterdam-defined PCOS phenotypes and controls. The objective was not to replace hormonal assessment, ultrasound, clinical phenotyping, or vaginal microbiome assessment. The objective was to examine whether genus-specific salivary microbial signals, particularly Prevotella, show phenotype-stratified patterns that could justify future validation as a non-invasive adjunct to conventional PCOS phenotype assessment.

2. Materials and Methods

This study used a cross-sectional design. Participants were recruited from premenopausal and infertile women who regularly attended outpatient clinical services. PCOS was diagnosed according to the Rotterdam criteria. Phenotype A was defined as hyperandrogenism, ovulatory dysfunction, and polycystic ovarian morphology; phenotype B as hyperandrogenism and ovulatory dysfunction without polycystic ovarian morphology; phenotype C as hyperandrogenism and polycystic ovarian morphology without ovulatory dysfunction; and phenotype D as ovulatory dysfunction and polycystic ovarian morphology without hyperandrogenism [1,2]. Controls were women without clinical PCOS according to the same diagnostic framework.
Eligible participants were women aged 18–42 years, clinically diagnosed with PCOS according to the Rotterdam criteria or classified as controls, willing to participate in the study, able to provide written informed consent, and willing to undergo the required research procedures, including blood and saliva sampling. Participants with incomplete key clinical or salivary qPCR data were excluded from the final analysis.
The final analysis set included 110 women in total, comprising 87 women with PCOS and 23 controls. Baseline variables recorded for analysis included age, Ferriman-Gallwey score, menstrual irregularity or ovulatory dysfunction, and polycystic ovarian morphology on transvaginal ultrasonography. Ferriman-Gallwey scoring was used as the clinical measure of hirsutism [41]. Complete biochemical androgen, gonadotropin, anti-Mullerian hormone, fasting insulin, HOMA-IR, BMI, and periodontal datasets were not uniformly available in the analyzable dataset and therefore could not be incorporated into the main adjusted models.
Saliva was selected as an exploratory oral-systemic matrix, not as a replacement for vaginal microbiome sampling. Vaginal swabs were not collected because the study protocol focused on salivary qPCR targets and outpatient saliva sampling. Saliva samples were collected in the morning after 1–2 h of fasting. Participants were instructed not to brush their teeth, use mouthwash, or consume food immediately before sampling. Samples were collected into sterile containers and processed for DNA extraction. The sampling protocol did not include standardized salivary flow rate measurement, salivary pH, detailed periodontal examination, detailed oral hygiene scoring, exact dietary intake, or full adjustment for recent antibiotic or probiotic exposure. These missing variables are treated as major limitations.
Salivary microbial signals for Lactobacillus, Prevotella, and Bifidobacterium were assessed using genus-specific quantitative polymerase chain reaction (qPCR). DNA was extracted from saliva samples using the QIAamp DNA Stool Mini Kit (QIAGEN GmbH, Hilden, Germany). qPCR amplification was performed using GoTaq qPCR Master Mix (Promega Corporation, Madison, WI, USA) on a MyGo Mini S Real-Time PCR Cycler with MyGo Mini software version 3.5.6 (IT-IS Life Science Ltd., Mahon, Cork, Ireland). The reaction mixture consisted of 12.5 μL of master mix, 5 μL of nuclease-free water, 1 μL of forward primer, 1 μL of reverse primer, and 5.5 μL of DNA template, for a total reaction volume of 25 μL.
The thermal profile consisted of an initial denaturation at 95 °C for 120 s, followed by 40 amplification cycles of denaturation at 95 °C, target-specific annealing, and extension at 72 °C. Melting curve analysis was performed from 60 °C to 97 °C at 0.1 °C/s to assess amplification specificity. Instrument run files indicated an annealing temperature of 60 °C for Lactobacillus and Prevotella and 50 °C for Bifidobacterium. Genus-specific standard curves were generated using serially diluted standards ranging from 1.5 × 108 to 1.2 × 109 nominal units. For the present revision, the supplied instrument-export spreadsheets were reviewed to extract standard Cq values, calculated quantity outputs, slope, y-intercept, R2, amplification efficiency, duplicate standard Cq spread, and negative-control observations where available. Standard-curve QC was recalculated as the regression of Cq on log10 nominal standard concentration, and amplification efficiency was calculated as (10(−1/slope) − 1) × 100. The exports did not contain full fluorescence amplification traces, melt-peak image outputs, extraction blank results, LOD/LOQ experiments, universal bacterial 16S normalization, spike-in recovery, or inter-run reproducibility data. Therefore, the available run files were sufficient only for transparent exploratory Cq-based reporting and partial standard-curve QC. They were not sufficient to establish validated absolute quantification, lower limit of detection, lower limit of quantification, inter-assay reproducibility, or clinical assay performance.
The inferential analysis was based on Cq values rather than GAPDH-normalized Delta Ct/Delta Delta Ct expression analysis. GAPDH was not used as a bacterial reference gene because it is a host housekeeping gene and is not an appropriate denominator for genus-level bacterial abundance in saliva. In addition, no universal bacterial 16S rRNA reference, exogenous spike-in control, salivary flow normalization, total DNA yield normalization, or fully validated absolute copy-number conversion was available for all targets. Broad-coverage bacterial 16S qPCR has been proposed as a way to quantify total bacterial load and can complement community-level microbiome data [42]. Therefore, the present results are reported only as qPCR-derived Cq signals. Lower Cq values indicate stronger target DNA signal. Standard-curve-derived quantity outputs and curve-quality parameters are presented only as supplementary quality-control information. No claim of absolute CFU/mL bacterial load is made. qPCR reporting was revised in accordance with current MIQE 2.0 principles of transparent assay description, validation, and cautious interpretation [43].
Normality was assessed using the Shapiro–Wilk test [44]. Because the data were not normally distributed, continuous variables are reported as median with minimum and maximum values. Between-group comparisons were performed using the Kruskal–Wallis test [45]. Pairwise post-hoc comparisons were performed using Mann–Whitney U tests with Bonferroni correction [46]. Correlation analyses among women with PCOS were performed using Spearman rank correlation. False discovery rate correction was applied to exploratory correlation analyses using the Benjamini-Hochberg procedure [47].
Additional exploratory analyses were performed to support interpretation of the observed Prevotella-related findings. ROC analyses and age-adjusted regression models were retained as supplementary hypothesis-generating analyses only. These analyses were not used to establish diagnostic thresholds or clinical cutoffs because the study was cross-sectional, single-center, underpowered for biomarker validation, and lacked external validation and full adjustment for oral-health, metabolic, hormonal, dietary, and medication-related confounders.
The study protocol was reviewed and approved by the Ethics Committee of the Faculty of Medicine, Universitas Airlangga, Surabaya, Indonesia. Written informed consent was obtained from all participants before data and sample collection.

3. Results

3.1. Baseline Characteristics

A total of 110 participants were included: 23 women in phenotype A, 19 in phenotype B, 22 in phenotype C, 23 in phenotype D, and 23 controls. Baseline characteristics are summarized in Table 1.
Age did not differ significantly across groups (p = 0.811). Ferriman-Gallwey score differed markedly across groups (p < 0.001), as expected from phenotype definitions. Menstrual irregularity or ovulatory dysfunction and polycystic ovarian morphology on TVS also differed significantly across groups. In the corrected dataset, all participants in phenotype A had polycystic ovarian morphology on TVS, consistent with the Rotterdam phenotype A definition.
BMI, biochemical hormonal profiles, insulin-resistance markers, salivary flow rate, periodontal status, detailed diet, and recent antibiotic/probiotic exposure were not available as complete analyzable variables. Consequently, the reported group comparisons should be interpreted as unadjusted or minimally adjusted exploratory findings rather than confounder-controlled clinical biomarker estimates.

3.2. Salivary Microbial qPCR Signals Across PCOS Phenotypes and Controls

Salivary microbial Cq values across PCOS phenotypes and controls are shown in Table 2. These values should be read as qPCR-derived Cq signals; lower Cq values indicate higher target DNA signal.
Lactobacillus Cq values did not differ significantly across phenotypes and controls (p = 0.249). Prevotella Cq values differed significantly across groups (p < 0.001), with lower median Cq values in phenotypes A, B, and C than in phenotype D and controls. Because lower Cq values indicate stronger target DNA signal, these findings suggest a phenotype-associated Prevotella molecular signal in selected hyperandrogenic and/or PCOM-related PCOS phenotypes. This does not demonstrate absolute bacterial enrichment because total bacterial biomass and extraction efficiency were not normalized.

3.3. Post-Hoc Pairwise Comparisons and Exploratory Discriminatory Analyses

Post-hoc pairwise comparisons were performed for genera with significant overall between-group differences. Significant comparisons after Bonferroni correction are summarized in Table 3; the complete pairwise comparison table is provided in the Supplementary Materials.
For Prevotella, phenotype A, phenotype B, and phenotype C differed from phenotype D and/or controls after correction, reinforcing that the strongest phenotype-related salivary qPCR signal was observed for Prevotella. For Bifidobacterium, significant corrected differences were observed for phenotype A versus control and phenotype C versus control, but these findings were treated as secondary because the Bifidobacterium assay showed less robust available quality-control support than the Prevotella assay.

3.4. Exploratory Correlation Analysis Among Women with PCOS

Exploratory correlation analyses were performed among women with PCOS only (n = 87). The main findings are summarized in Table 4.
Ferriman-Gallwey score was not associated with Lactobacillus Cq values, but it correlated positively with Prevotella Cq values and negatively with Bifidobacterium Cq values. PCOM on TVS correlated negatively with Prevotella Cq values, indicating that PCOM was associated with a stronger Prevotella target DNA signal. Menstrual irregularity or ovulatory dysfunction was not significantly associated with any of the three microbial Cq signals after FDR correction. Because Cq direction is inverse to target DNA signal, all correlation directions should be interpreted cautiously and should not be read as causal mechanisms.

3.5. Standard-Curve Quality-Control Review of qPCR Instrument Exports

The supplied qPCR Excel exports were reviewed to address the remaining technical-QC comments. The review confirmed that standard-curve and Cq data could be summarized transparently, but it also confirmed that the available files did not support validated absolute bacterial quantification. Table 5 summarizes the standard-curve QC findings that were incorporated into the revised interpretation.

4. Discussion

This cross-sectional proof-of-concept study examined genus-specific salivary qPCR signals of Lactobacillus, Prevotella, and Bifidobacterium across Rotterdam-defined PCOS phenotypes and controls. The revised analysis deliberately reports Cq-based microbial signals rather than absolute bacterial abundance or GAPDH-normalized expression because the available assay files did not support uniformly robust absolute CFU/mL conversion. The additional review of the instrument-export spreadsheets improved technical transparency by adding standard-curve slopes, y-intercepts, R2 values, efficiency estimates, duplicate standard Cq spread, and available negative-control observations. However, these QC data reinforced rather than removed the need for cautious interpretation. Within this analytical frame, Prevotella showed the clearest and most interpretable phenotype-related pattern.
The main finding was that Prevotella Cq values differed significantly across PCOS phenotypes and controls. Phenotypes A, B, and C tended to show lower Prevotella Cq values than phenotype D and controls, indicating stronger Prevotella target DNA signal in selected PCOS phenotypes. This pattern is biologically plausible because these phenotypes include hyperandrogenism and/or PCOM-related features, which may reflect broader endocrine, inflammatory, and metabolic dysregulation [1,2,3,4,5,6,7,8,48,49,50]. However, the present data do not prove that Prevotella drives PCOS features, nor do they prove that PCOS causes salivary Prevotella changes.
The direction of the correlation findings requires careful interpretation. A positive correlation between Ferriman-Gallwey score and Prevotella Cq values means that higher hirsutism scores were associated with weaker Prevotella target signal, because higher Cq reflects lower target DNA signal. In contrast, the negative correlation between PCOM on TVS and Prevotella Cq values suggests that PCOM was associated with stronger Prevotella target DNA signal. These divergent associations reinforce the need to avoid simplistic claims that Prevotella is uniformly increased or decreased in PCOS.
Prevotella is biologically context-dependent. Depending on species, strain composition, diet, host metabolic status, periodontal ecology, and local mucosal conditions, Prevotella may be linked to fiber-rich dietary patterns, insulin sensitivity, or inflammatory dysbiosis [37,38,39,40]. Because the present study used genus-level qPCR rather than species-level sequencing, the data cannot determine whether the observed Prevotella signal reflects beneficial, commensal, or pathogenic Prevotella taxa. This is a key reason why the finding should be framed as an exploratory molecular signal rather than a mechanistic conclusion.
The Bifidobacterium results were statistically significant overall but less stable interpretively. Some corrected group differences were observed, particularly against controls. The supplied standard-curve export showed a positive slope (+1.766), very low R2 (0.058), and non-interpretable efficiency, indicating that the Bifidobacterium quantity output was not suitable for quantitative interpretation. Therefore, Bifidobacterium should be treated as a secondary exploratory finding requiring technical revalidation before biological or diagnostic interpretation.
A methodological strength of this study is the use of saliva as a non-invasive sample type, which is relevant for repeated outpatient research in reproductive endocrinology. Saliva was not chosen because it is superior to vaginal sampling. It was chosen because it addresses a different, more scalable oral-systemic diagnostic-adjunct question. Vaginal microbiome assessment remains important for local reproductive-tract ecology and should be included as a comparator in future studies. Another strength is explicit Rotterdam phenotype stratification rather than treating PCOS as a single homogeneous entity, because phenotype-level differences may be diluted when all PCOS cases are pooled.
Several limitations remain and should be read as central to the interpretation of this manuscript. First, the cross-sectional design prevents causal or temporal inference. Second, the microbiological analysis was genus-specific and qPCR-based, without 16S rRNA gene sequencing, shotgun metagenomics, species-level resolution, or functional profiling. Third, the newly incorporated qPCR spreadsheet review provided partial standard-curve QC, but the assays still did not meet the evidentiary standard required for validated absolute quantification. Lactobacillus showed low efficiency despite a moderate standard-curve fit, Prevotella had a discordant standard replicate and late negative-control amplification, and Bifidobacterium showed an uninterpretable standard curve. Fourth, the available files did not include complete amplification traces, melt-peak outputs, extraction blanks, LOD, LOQ, or inter-assay reproducibility for every target. Fifth, no universal bacterial 16S rRNA reference, exogenous spike-in, salivary flow correction, total DNA yield correction, or validated copy-number conversion was available. Sixth, BMI, insulin resistance, androgen biochemistry, diet, oral hygiene, periodontal status, recent antibiotic/probiotic exposure, medication use, and menstrual cycle phase were not fully included as covariates. Seventh, the study used a single-center sample with relatively small phenotype groups and lacked external validation. Eighth, exploratory ROC and regression analyses were not prespecified for clinical cutoff development and may be vulnerable to overfitting and type I error. Ninth, the salivary sampling protocol did not fully standardize diurnal variation or recent food intake beyond a 1–2 h fasting instruction.
Despite these limitations, the study provides a useful hypothesis-generating signal: salivary Prevotella Cq values differ across Rotterdam-defined PCOS phenotypes and show associations with selected diagnostic features. The findings justify larger, technically strengthened studies incorporating standardized oral examination, dietary and metabolic covariates, validated qPCR efficiency, universal bacterial reference targets or spike-in controls, absolute copy-number standards, species-level sequencing, vaginal and gut microbiome comparators, prespecified diagnostic thresholds, and independent validation cohorts.

5. Conclusions

In this cross-sectional proof-of-concept dataset, salivary Prevotella showed the clearest exploratory genus-level qPCR signal across Rotterdam-defined PCOS phenotypes. The revised interpretation is intentionally conservative: the study supports phenotype-associated variation in salivary microbial Cq signals as a candidate non-invasive adjunct for future research, but it does not establish Prevotella as a validated standalone diagnostic biomarker, does not quantify absolute bacterial load, and does not define clinical cutoffs. Future studies should combine saliva-based microbial profiling with robust qPCR validation, universal bacterial normalization or spike-in controls, species-level sequencing, metabolic and hormonal covariates, oral-health assessment, vaginal and gut microbiome comparators, prespecified diagnostic thresholds, and independent validation cohorts.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/diagnostics16132041/s1, Table S1. Complete pairwise post-hoc comparisons of salivary microbial Cq signals across PCOS phenotypes and controls; Table S2. Exploratory ROC analysis of Prevotella for selected phenotype and control comparisons; Table S3. Age-adjusted regression analysis of Prevotella signal among women with PCOS; Table S4. Primer sequences and qPCR conditions for salivary microbiota quantification; Table S5. qPCR standard-curve quality-control summary from available instrument files.

Author Contributions

Conceptualization, A.T. and B.S.; methodology, A.T., O.A.H., I.W.P., F.I., F.A. and B.S.; formal analysis, A.T. and O.A.H.; investigation, A.T., I.W.P. and F.I.; resources, B.S.; data curation, A.T.; writing—original draft preparation, A.T.; writing—review and editing, O.A.H., I.W.P., F.I., F.A. and B.S.; supervision, B.S.; project administration, A.T. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the Faculty of Medicine, Universitas Airlangga, Surabaya, Indonesia (No. 87/EC/KEPK/FKUA/2023) on 30 March 2023.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data supporting the findings of this study are available from the corresponding author upon reasonable request. The revised analysis incorporated available qPCR instrument-export spreadsheets for Lactobacillus, Prevotella, and Bifidobacterium standard-curve QC. These exports should be retained with the analytical dataset. Raw fluorescence amplification plots, melt-peak outputs, negative-control details, extraction blank results, replicate-level Cq files, LOD/LOQ experiments, normalization-control data, and clinical covariate files should also be retained for editorial or reviewer inspection where available.

Acknowledgments

The authors thank the participating clinics and all study participants. During the preparation of this manuscript/study, the authors used GEN AI for the purposes of language refinement, grammar correction, and improving clarity and readability of the text. The authors have reviewed and edited the output and take full responsibility for the content of this publication.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Rotterdam ESHRE/ASRM-Sponsored PCOS Consensus Workshop Group. Revised 2003 consensus on diagnostic criteria and long-term health risks related to polycystic ovary syndrome. Hum. Reprod. 2004, 19, 41–47. [Google Scholar] [CrossRef] [PubMed]
  2. Teede, H.J.; Tay, C.T.; Laven, J.J.E.; Dokras, A.; Moran, L.J.; Piltonen, T.T.; Costello, M.F.; Boivin, J.; Redman, L.M.; Boyle, J.A.; et al. Recommendations from the 2023 International Evidence-based Guideline for the Assessment and Management of Polycystic Ovary Syndrome. Hum. Reprod. 2023, 38, 1655–1679. [Google Scholar] [CrossRef] [PubMed]
  3. Azziz, R.; Carmina, E.; Chen, Z.; Dunaif, A.; Laven, J.S.E.; Legro, R.S.; Lizneva, D.; Natterson-Horowitz, B.; Teede, H.J.; Yildiz, B.O. Polycystic ovary syndrome. Nat. Rev. Dis. Primers 2016, 2, 16057. [Google Scholar] [CrossRef] [PubMed]
  4. Joham, A.E.; Norman, R.J.; Stener-Victorin, E.; Legro, R.S.; Franks, S.; Moran, L.J.; Boyle, J.; Teede, H.J. Polycystic ovary syndrome. Lancet Diabetes Endocrinol. 2022, 10, 668–680. [Google Scholar] [CrossRef] [PubMed]
  5. Stener-Victorin, E.; Teede, H.; Norman, R.J.; Legro, R.; Goodarzi, M.O.; Dokras, A.; Laven, J.; Hoeger, K.; Piltonen, T.T. Polycystic ovary syndrome. Nat. Rev. Dis. Primers 2024, 10, 27. [Google Scholar] [CrossRef] [PubMed]
  6. Mumusoglu, S.; Yildiz, B.O. Polycystic ovary syndrome phenotypes and prevalence: Differential impact of diagnostic criteria and clinical versus unselected population. Curr. Opin. Endocr. Metab. Res. 2020, 12, 66–71. [Google Scholar] [CrossRef]
  7. Bozdag, G.; Mumusoglu, S.; Zengin, D.; Karabulut, E.; Yildiz, B.O. The prevalence and phenotypic features of polycystic ovary syndrome: A systematic review and meta-analysis. Hum. Reprod. 2016, 31, 2841–2855. [Google Scholar] [CrossRef] [PubMed]
  8. Daan, N.M.P.; Louwers, Y.V.; Koster, M.P.H.; Eijkemans, M.J.C.; de Rijke, Y.B.; Lentjes, E.G.W.M.; Fauser, B.C.J.M.; Laven, J.S.E. Cardiovascular and metabolic profiles amongst different polycystic ovary syndrome phenotypes: Who is really at risk? Fertil. Steril. 2014, 102, 1444–1451.e3. [Google Scholar] [CrossRef] [PubMed]
  9. Lindheim, L.; Bashir, M.; Münzker, J.; Trummer, C.; Zachhuber, V.; Pieber, T.R.; Gorkiewicz, G.; Obermayer-Pietsch, B. The salivary microbiome in polycystic ovary syndrome (PCOS) and its association with disease-related parameters: A pilot study. Front. Microbiol. 2016, 7, 1270. [Google Scholar] [CrossRef] [PubMed]
  10. Sola-Leyva, A.; Pérez-Prieto, I.; Molina, N.M.; Vargas, E.; Ruiz-Durán, S.; Leonés-Baños, I.; Canha-Gouveia, A.; Altmäe, S. Microbial composition across body sites in polycystic ovary syndrome: A systematic review and meta-analysis. Reprod. Biomed. Online 2023, 47, 129–150. [Google Scholar] [CrossRef] [PubMed]
  11. Lindheim, L.; Bashir, M.; Münzker, J.; Trummer, C.; Zachhuber, V.; Leber, B.; Horvath, A.; Pieber, T.R.; Gorkiewicz, G.; Stadlbauer, V.; et al. Alterations in gut microbiome composition and barrier function are associated with reproductive and metabolic defects in women with polycystic ovary syndrome (PCOS): A pilot study. PLoS ONE 2017, 12, e0168390. [Google Scholar] [CrossRef] [PubMed]
  12. Torres, P.J.; Siakowska, M.; Banaszewska, B.; Pawelczyk, L.; Duleba, A.J.; Kelley, S.T.; Thackray, V.G. Gut microbial diversity in women with polycystic ovary syndrome correlates with hyperandrogenism. J. Clin. Endocrinol. Metab. 2018, 103, 1502–1511. [Google Scholar] [CrossRef] [PubMed]
  13. Guo, Y.; Qi, Y.; Yang, X.; Zhao, L.; Wen, S.; Liu, Y.; Tang, L. Association between polycystic ovary syndrome and gut microbiota. PLoS ONE 2016, 11, e0153196. [Google Scholar] [CrossRef] [PubMed]
  14. Qi, X.; Yun, C.; Sun, L.; Xia, J.; Wu, Q.; Wang, Y.; Wang, L.; Zhang, Y.; Liang, X.; Wang, L.; et al. Gut microbiota-bile acid-interleukin-22 axis orchestrates polycystic ovary syndrome. Nat. Med. 2019, 25, 1225–1233. [Google Scholar] [CrossRef] [PubMed]
  15. Parker, J.; O’Brien, C.; Hawrelak, J. A narrative review of the role of gastrointestinal dysbiosis in the pathogenesis of polycystic ovary syndrome. Obstet. Gynecol. Sci. 2022, 65, 14–28. [Google Scholar] [CrossRef] [PubMed]
  16. Thackray, V.G. Sex, microbes, and polycystic ovary syndrome. Trends Endocrinol. Metab. 2019, 30, 54–65. [Google Scholar] [CrossRef] [PubMed]
  17. Lu, C.; Wang, H.; Yang, J.; Zhang, X.; Chen, Y.; Feng, R.; Qian, Y. Changes in vaginal microbiome diversity in women with polycystic ovary syndrome. Front. Cell. Infect. Microbiol. 2021, 11, 755741. [Google Scholar] [CrossRef] [PubMed]
  18. He, F.; Li, Y. The gut microbial composition in polycystic ovary syndrome with insulin resistance: Findings from a normal-weight population. J. Ovarian Res. 2021, 14, 50. [Google Scholar] [CrossRef] [PubMed]
  19. Zhou, L.; Ni, Z.; Cheng, W.; Yu, J.; Sun, S.; Zhai, D.; Yu, C.; Cai, Z. Characteristic gut microbiota and predicted metabolic functions in women with PCOS. Endocr. Connect. 2020, 9, 63–73. [Google Scholar] [CrossRef] [PubMed]
  20. Yu, Z.; Qin, E.; Cheng, S.; Yang, H.; Liu, R.; Xu, T.; Liu, Y.; Yuan, J.; Yu, S.; Yang, J.; et al. Gut microbiome in PCOS associates to serum metabolomics: A cross-sectional study. Sci. Rep. 2022, 12, 22184. [Google Scholar] [CrossRef] [PubMed]
  21. Guo, J.; Shao, J.; Yang, Y.; Niu, X.; Liao, J.; Zhao, Q.; Wang, D.; Li, S.; Hu, J. Gut microbiota in patients with polycystic ovary syndrome: A systematic review. Reprod. Sci. 2022, 29, 69–83. [Google Scholar] [CrossRef] [PubMed]
  22. Min, Q.; Geng, H.; Gao, Q.; Xu, M. The association between gut microbiome and PCOS: Evidence from meta-analysis and two-sample Mendelian randomization. Front. Microbiol. 2023, 14, 1203902. [Google Scholar] [CrossRef] [PubMed]
  23. Suturina, L.; Belkova, N.; Igumnov, I.; Lazareva, L.; Danusevich, I.; Nadeiaeva, I.; Sholokhov, L.; Rashidova, M.; Belenkaya, L.; Belskikh, A.; et al. Polycystic ovary syndrome and gut microbiota: Phenotype matters. Life 2023, 13, 7. [Google Scholar] [CrossRef] [PubMed]
  24. Li, M.; Chang, Q.; Luo, Y.; Pan, J.; Hu, Y.; Liu, B.; Ma, M.; Wang, Q.; Guo, Y.; Wang, Q. The gut microbial composition in polycystic ovary syndrome with hyperandrogenemia and its association with steroid hormones. Front. Cell Dev. Biol. 2024, 12, 1384233. [Google Scholar] [CrossRef] [PubMed]
  25. Yang, Y.; Cheng, J.; Liu, C.; Zhang, X.; Ma, N.; Zhou, Z.; Lu, W.; Wu, C. Gut microbiota in women with polycystic ovary syndrome: An individual based analysis of publicly available data. eClinicalMedicine 2024, 77, 102884. [Google Scholar] [CrossRef] [PubMed]
  26. Sun, J.; Wang, M.; Kan, Z. Causal relationship between gut microbiota and polycystic ovary syndrome: A literature review and Mendelian randomization study. Front. Endocrinol. 2024, 15, 1280983. [Google Scholar] [CrossRef] [PubMed]
  27. Talebi, S.; Zeraattalab-Motlagh, S.; Jalilpiran, Y.; Payandeh, N.; Ansari, S.; Mohammadi, H.; Djafarian, K.; Ranjbar, M.; Sadeghi, S.; Taghizadeh, M.; et al. The effects of pro-, pre-, and synbiotics supplementation on polycystic ovary syndrome: An umbrella review of meta-analyses of randomized controlled trials. Front. Nutr. 2023, 10, 1178842. [Google Scholar] [CrossRef] [PubMed]
  28. Mehta, P.; Saha, D.; Das, A.; Das, B.K. Gut microbiota in diabetic-linked polycystic ovarian syndrome: Mechanisms and therapeutic insights. Tissue Cell 2025, 95, 102870. [Google Scholar] [CrossRef] [PubMed]
  29. Senthilkumar, H.; Arumugam, M. Gut microbiota: A hidden player in polycystic ovary syndrome. J. Transl. Med. 2025, 23, 443. [Google Scholar] [CrossRef] [PubMed]
  30. Li, J.; Qiao, J.; Li, Y.; Qin, G.; Xu, Y.; Lao, K.; Wang, Y.; Fan, Y.; Tang, P.; Han, L. Metabolic disorders in polycystic ovary syndrome: From gut microbiota biodiversity to clinical intervention. Front. Endocrinol. 2025, 16, 1526468. [Google Scholar] [CrossRef] [PubMed]
  31. Zhao, M.; Chen, D.; Hu, X.; Xie, C.; Xu, L.; Zhou, F. Gut-ovary axis in polycystic ovary syndrome: Mechanistic insights and gut microbiota-targeted therapeutic strategies. Front. Endocrinol. 2025, 16, 1684492. [Google Scholar] [CrossRef] [PubMed]
  32. Li, N.; Li, Y.; Qian, C.; Liu, Q.; Cao, W.; Ma, M.; He, R.; Chen, R.; Geng, R.; Liu, Y. Dysbiosis of the saliva microbiome in patients with polycystic ovary syndrome. Front. Cell. Infect. Microbiol. 2021, 10, 624504. [Google Scholar] [CrossRef] [PubMed]
  33. Wu, J.; Li, J.; Yan, M.; Xiang, Z. Gut and oral microbiota in gynecological cancers: Interaction, mechanism, and therapeutic value. npj Biofilms Microbiomes 2024, 10, 104. [Google Scholar] [CrossRef] [PubMed]
  34. Anderson, R.C.; Cookson, A.L.; McNabb, W.C.; Park, Z.; McCann, M.J.; Kelly, W.J.; Roy, N.C. Lactobacillus plantarum MB452 enhances the function of the intestinal barrier by increasing the expression levels of genes involved in tight junction formation. BMC Microbiol. 2010, 10, 316. [Google Scholar] [CrossRef] [PubMed]
  35. Mujagic, Z.; de Vos, P.; Boekschoten, M.V.; Govers, C.; Pieters, H.J.H.M.; de Wit, N.J.W.; Bron, P.A.; Masclee, A.A.M.; Troost, F.J. The effects of Lactobacillus plantarum on small intestinal barrier function and mucosal gene transcription: A randomized double-blind placebo controlled trial. Sci. Rep. 2017, 7, 40128. [Google Scholar] [CrossRef] [PubMed]
  36. O’Callaghan, A.; van Sinderen, D. Bifidobacteria and their role as members of the human gut microbiota. Front. Microbiol. 2016, 7, 925. [Google Scholar] [CrossRef] [PubMed]
  37. Kovatcheva-Datchary, P.; Nilsson, A.; Akrami, R.; Lee, Y.S.; De Vadder, F.; Arora, T.; Hallen, A.; Martens, E.; Björck, I.; Bäckhed, F. Dietary fiber-induced improvement in glucose metabolism is associated with increased abundance of Prevotella. Cell Metab. 2015, 22, 971–982. [Google Scholar] [CrossRef] [PubMed]
  38. Pedersen, H.K.; Gudmundsdottir, V.; Nielsen, H.B.; Hyötyläinen, T.; Nielsen, T.; Jensen, B.A.H.; Forslund, K.; Hildebrand, F.; Prifti, E.; Falony, G.; et al. Human gut microbes impact host serum metabolome and insulin sensitivity. Nature 2016, 535, 376–381. [Google Scholar] [CrossRef] [PubMed]
  39. De Filippis, F.; Pasolli, E.; Tett, A.; Tarallo, S.; Naccarati, A.; De Angelis, M.; Neviani, E.; Cocolin, L.; Gobbetti, M.; Segata, N.; et al. Distinct genetic and functional traits of human intestinal Prevotella copri strains are associated with different habitual diets. Cell Host Microbe 2019, 25, 444–453.e3. [Google Scholar] [CrossRef] [PubMed]
  40. Precup, G.; Vodnar, D.C. Gut Prevotella as a possible biomarker of diet and its eubiotic versus dysbiotic roles: A comprehensive literature review. Br. J. Nutr. 2019, 122, 131–140. [Google Scholar] [CrossRef] [PubMed]
  41. Ferriman, D.; Gallwey, J.D. Clinical assessment of body hair growth in women. J. Clin. Endocrinol. Metab. 1961, 21, 1440–1447. [Google Scholar] [CrossRef] [PubMed]
  42. Liu, C.M.; Aziz, M.; Kachur, S.; Hsueh, P.R.; Huang, Y.T.; Keim, P.; Price, L.B. BactQuant: An enhanced broad-coverage bacterial quantitative real-time PCR assay. BMC Microbiol. 2012, 12, 56. [Google Scholar] [CrossRef] [PubMed]
  43. Bustin, S.A.; Ruijter, J.M.; van den Hoff, M.J.B.; Kubista, M.; Pfaffl, M.W.; Shipley, G.L.; Tran, N.; Rödiger, S.; Untergasser, A.; Mueller, R.; et al. MIQE 2.0: Revision of the Minimum Information for Publication of Quantitative Real-Time PCR Experiments Guidelines. Clin. Chem. 2025, 71, 634–651. [Google Scholar] [CrossRef] [PubMed]
  44. Shapiro, S.S.; Wilk, M.B. An analysis of variance test for normality (complete samples). Biometrika 1965, 52, 591–611. [Google Scholar] [CrossRef]
  45. Kruskal, W.H.; Wallis, W.A. Use of ranks in one-criterion variance analysis. J. Am. Stat. Assoc. 1952, 47, 583–621. [Google Scholar] [CrossRef]
  46. Mann, H.B.; Whitney, D.R. On a test of whether one of two random variables is stochastically larger than the other. Ann. Math. Stat. 1947, 18, 50–60. [Google Scholar] [CrossRef]
  47. Benjamini, Y.; Hochberg, Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B Stat. Methodol. 1995, 57, 289–300. [Google Scholar] [CrossRef]
  48. Shabbir, S.; Khurram, E.; Moorthi, V.S.; Eissa, Y.T.H.; Kamal, M.A.; Butler, A.E. The interplay between androgens and the immune response in polycystic ovary syndrome. J. Transl. Med. 2023, 21, 259. [Google Scholar] [CrossRef] [PubMed]
  49. Schüler-Toprak, S.; Ortmann, O.; Buechler, C.; Treeck, O. The complex roles of adipokines in polycystic ovary syndrome and endometriosis. Biomedicines 2022, 10, 2503. [Google Scholar] [CrossRef] [PubMed]
  50. Liu, J.; Liu, Y.; Li, X. Effects of intestinal flora on polycystic ovary syndrome. Front. Endocrinol. 2023, 14, 1151723. [Google Scholar] [CrossRef] [PubMed]
Table 1. Baseline characteristics of study participants.
Table 1. Baseline characteristics of study participants.
CharacteristicPhenotype A
(n = 23)		Phenotype B
(n = 19)		Phenotype C
(n = 22)		Phenotype D
(n = 23)		Control
(n = 23)		p-Value
Sample size, n2319222323-
Age, years28 (19–40)29 (22–38)28 (18–42)30 (21–40)29 (20–38)0.811
Ferriman-Gallwey score8.0 (5.0–18.0)7.0 (5.0–12.0)6.5 (5.0–14.0)3.0 (0.0–4.0)0.0 (0.0–2.0)<0.001
Menstrual irregularity or ovulatory dysfunction, n (%)23 (100.0)19 (100.0)0 (0.0)23 (100.0)0 (0.0)<0.001
Polycystic ovarian morphology on TVS, n (%)23 (100.0)0 (0.0)22 (100.0)23 (100.0)0 (0.0)<0.001
Continuous data are presented as median (minimum–maximum), while categorical data are presented as n (%). p-values for continuous variables were obtained using the Kruskal–Wallis test, and categorical variables were compared using appropriate exact/non-parametric procedures. TVS, transvaginal sonography.
Table 2. qPCR-derived salivary microbial Cq values across PCOS phenotypes and controls.
Table 2. qPCR-derived salivary microbial Cq values across PCOS phenotypes and controls.
MicrobiotaPhenotype A
(n = 23)		Phenotype B
(n = 19)		Phenotype C
(n = 22)		Phenotype D
(n = 23)		Control
(n = 23)		p-Value
Lactobacillus32.50
(12.34–36.82)		34.46
(27.84–37.22)		35.42
(11.76–36.98)		34.46
(26.64–37.00)		35.59
(27.74–36.85)		0.249
Prevotella22.32
(19.11–35.68)		21.42
(18.70–35.95)		24.28
(18.21–32.20)		31.00
(19.44–37.03)		28.43
(20.54–36.77)		<0.001
Bifidobacterium36.77
(27.94–37.25)		36.44
(11.76–37.16)		36.64
(35.35–37.09)		35.71
(31.96–37.13)		35.71
(5.92–37.01)		<0.001
Values are presented as median Cq (minimum–maximum). Lower Cq values indicate higher target DNA signal. p-values were obtained using the Kruskal–Wallis test. The values are not reported as absolute CFU/mL bacterial loads.
Table 3. Significant post-hoc pairwise comparisons after Bonferroni correction.
Table 3. Significant post-hoc pairwise comparisons after Bonferroni correction.
MicrobiotaComparisonRaw p-ValueBonferroni-Adjusted p-ValueInterpretation
PrevotellaPhenotype A vs. Phenotype D<0.0010.010Significant
PrevotellaPhenotype A vs. Control<0.0010.010Significant
PrevotellaPhenotype B vs. Phenotype D<0.0010.010Significant
PrevotellaPhenotype B vs. Control<0.0010.010Significant
PrevotellaPhenotype C vs. Phenotype D0.0020.020Significant
PrevotellaPhenotype C vs. Control0.0040.040Significant
BifidobacteriumPhenotype A vs. Control<0.0010.010Significant
BifidobacteriumPhenotype C vs. Control<0.0010.010Significant
Pairwise comparisons were performed following the Kruskal–Wallis test. Only comparisons that remained significant after Bonferroni correction are shown. Complete post-hoc results are provided in Supplementary Table S1.
Table 4. Correlation between diagnostic PCOS features and salivary microbial Cq signals among women with PCOS.
Table 4. Correlation between diagnostic PCOS features and salivary microbial Cq signals among women with PCOS.
Clinical VariableMicrobiotanSpearman rhoRaw p-ValueFDR-Adjusted p-ValueInterpretation
Ferriman–Gallwey scoreLactobacillus870.0400.7120.937Not significant
Ferriman–Gallwey scorePrevotella870.423<0.001<0.001Significant
Ferriman–Gallwey scoreBifidobacterium87−0.2830.0080.023Significant
Menstrual irregularity/ovulatory dysfunctionLactobacillus870.1250.2470.557Not significant
Menstrual irregularity/ovulatory dysfunctionPrevotella87−0.0150.8920.937Not significant
Menstrual irregularity/ovulatory dysfunctionBifidobacterium870.1030.3420.615Not significant
PCOM on TVSLactobacillus870.0090.9370.937Not significant
PCOM on TVSPrevotella87−0.3020.0050.020Significant
PCOM on TVSBifidobacterium870.0190.8600.937Not significant
Correlation analysis was performed using Spearman rank correlation. Menstrual irregularity/ovulatory dysfunction and PCOM on TVS were coded as binary variables. Lower Cq values indicate higher target DNA signal; therefore, the direction of rho should be interpreted in relation to Cq, not absolute bacterial abundance.
Table 5. qPCR standard-curve quality-control summary derived from the supplied instrument-export spreadsheets.
Table 5. qPCR standard-curve quality-control summary derived from the supplied instrument-export spreadsheets.
QC ItemLactobacillusPrevotellaBifidobacterium
Standard concentration range1.5 × 108 to 1.2 × 109 nominal units1.5 × 108 to 1.2 × 109 nominal units1.5 × 108 to 1.2 × 109 nominal units
Standard replicate structureDuplicate levelsDuplicate levels; one 3.0 × 108 replicate was discordantDuplicate levels
Slope−5.438−5.714 with all standards; −4.021 after sensitivity exclusion of the discordant replicate+1.766
Y-intercept71.94773.823 with all standards; 57.941 after sensitivity exclusion20.386
R20.9660.249 with all standards; 0.938 after sensitivity exclusion0.058
Calculated efficiency52.7%49.6% with all standards; 77.3% after sensitivity exclusionNot interpretable because the slope was positive
Duplicate standard Cq spread0.056 to 0.318 Cq0.283 to 10.925 Cq, driven by the discordant standard replicate0.306 to 2.444 Cq
Negative-control statusNC amplified late at Cq 35.76Negative-control well amplified late at Cq 36.42No negative-control well was identifiable in the supplied export
Interpretive decisionCq signal retained for exploratory analysis only; absolute quantity output not treated as validatedCq signal retained as exploratory molecular signal only; no absolute load, assay validation, or clinical cutoff claimSecondary exploratory finding only; quantitative or biological interpretation requires technical revalidation
The standard range refers to nominal standard concentrations in the instrument-export files. Efficiency values outside common qPCR performance expectations, late negative-control amplification, absent extraction blanks, absent LOD/LOQ experiments, and absent inter-run reproducibility data prevented absolute CFU/mL or copy-number interpretation. Therefore, the inferential analysis remained restricted to Cq-based exploratory signals.
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Tunjungseto, A.; Hadi, O.A.; Pratiwi, I.W.; Iffatunnisa, F.; Ahsan, F.; Santoso, B. Salivary Prevotella qPCR Signal as an Exploratory Non-Invasive Adjunct for Rotterdam Phenotype Stratification in Women with Polycystic Ovary Syndrome: A Proof-of-Concept Cross-Sectional Study. Diagnostics 2026, 16, 2041. https://doi.org/10.3390/diagnostics16132041

AMA Style

Tunjungseto A, Hadi OA, Pratiwi IW, Iffatunnisa F, Ahsan F, Santoso B. Salivary Prevotella qPCR Signal as an Exploratory Non-Invasive Adjunct for Rotterdam Phenotype Stratification in Women with Polycystic Ovary Syndrome: A Proof-of-Concept Cross-Sectional Study. Diagnostics. 2026; 16(13):2041. https://doi.org/10.3390/diagnostics16132041

Chicago/Turabian Style

Tunjungseto, Arif, Oni As’ad Hadi, Intan Winta Pratiwi, Fakhriyah Iffatunnisa, Fadhil Ahsan, and Budi Santoso. 2026. "Salivary Prevotella qPCR Signal as an Exploratory Non-Invasive Adjunct for Rotterdam Phenotype Stratification in Women with Polycystic Ovary Syndrome: A Proof-of-Concept Cross-Sectional Study" Diagnostics 16, no. 13: 2041. https://doi.org/10.3390/diagnostics16132041

APA Style

Tunjungseto, A., Hadi, O. A., Pratiwi, I. W., Iffatunnisa, F., Ahsan, F., & Santoso, B. (2026). Salivary Prevotella qPCR Signal as an Exploratory Non-Invasive Adjunct for Rotterdam Phenotype Stratification in Women with Polycystic Ovary Syndrome: A Proof-of-Concept Cross-Sectional Study. Diagnostics, 16(13), 2041. https://doi.org/10.3390/diagnostics16132041

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