Abstract
Background
Gestational diabetes mellitus (GDM), characterized by pregnancy-induced insulin resistance, and periodontal disease, a chronic inflammatory condition of the periodontium, are intricately linked and may collectively amplify the risk of adverse maternal and fetal outcomes. This study aimed to evaluate periodontal microbial associations in periodontitis in relation to GDM.
Methods
An observational cross-sectional study was conducted among 80 pregnant women with periodontitis, stratified into GDM (Group A; n = 40) and non-GDM (Group B; n = 40) groups. Clinical parameters, including Plaque Index, Oral Hygiene Index–Simplified, Modified Sulcular Bleeding Index, and Probing Pocket Depth (PPD), along with glycemic parameters (OGTT), were recorded. Subgingival plaque samples were analyzed using real-time Polymerase Chain Reaction for key periodontal pathogens (Fusobacterium nucleatum, Porphyromonas gingivalis, Prevotella intermedia, Prevotella nigrescens, Selenomonas sputigena, Treponema denticola, and Tannerella forsythia). Pearson correlation analysis assessed associations between clinical and glycemic parameters and pathogens, followed by multiple linear regression to determine the independent effects of predictors on the outcomes (p < 0.05).
Results
Group A demonstrated significantly lower Ct values (higher bacterial load) for P. gingivalis, S. sputigena, P. nigrescens, T. denticola, and F. nucleatum (p ≤ 0.005). Strong positive correlations in the overall population were observed between P. gingivalis and probing pocket depth (r = 0.79, p = 0.001) and clinical attachment loss (r = 0.73, p = 0.010), followed by S. sputigena (r = 0.64, p = 0.01). Multiple regression analysis identified MSBI, PPD, and CAL as significant predictors of P. intermedia and P. gingivalis in both groups (p < 0.05), with OGTT-1h emerging as an additional predictor of P. gingivalis. In contrast, F. nucleatum and S. sputigena demonstrated significant inverse associations with these parameters (p < 0.05).
Conclusion
The results of the present study demonstrate that GDM is associated with an increased periodontal pathogenic burden, with clinical and glycemic parameters emerging as significant independent determinants, highlighting the intricate interplay between metabolic dysregulation and periodontal disease.
Keywords: Glycemic status, Gestational diabetes mellitus, Oral microbiome, Periodontal pathogens, Periodontitis, Pregnancy
A one-sentence summary: The coexistence of gestational diabetes mellitus and periodontal disease, accompanied by an increased periodontal pathogen burden, may adversely influence maternal and fetal health, highlighting the clinical relevance of the present investigation.
1. Introduction
Gestational diabetes mellitus (GDM) is a pregnancy-associated metabolic dysregulation and constitutes a major endocrine complication with an expanding global epidemiological prevalence.1 This disorder is exemplified by progressive insulin resistance and impaired glycaemia regulation resulting from placental hormonal and metabolic adaptations, which substantially elevate the risk of both maternal and foetal morbidity, including preeclampsia, preterm delivery, foetal macrosomia, neonatal hypoglycemia, and a heightened long-term predisposition to type 2 diabetes mellitus in both mother and offspring.2,3 Given the complexity and multifactorial nature of gestational diabetes mellitus, the established etiological and contributory determinants encompass maternal obesity, advanced maternal age, genetic susceptibility, prior history of GDM, polycystic ovarian syndrome, ethnicity, and adverse contemporary lifestyle factors, including physical inactivity and chronic psychosocial stress.4
Pregnancy is associated with profound modulation of host immune responsiveness, hormonal homeostasis, and microbial ecology.5 The oral microbiome, a highly dynamic polymicrobial ecosystem governed by local pH gradients, anaerobic microenvironments, nutritional substrates, and hormonal flux, demonstrates quantifiable compositional and functional restructuring during gestation.6 Notably, periodontal disease exhibits considerable prevalence among pregnant populations and has been independently correlated with adverse obstetric outcomes, including preterm birth and low birth weight.7,8
Emerging evidence indicates that gestational diabetes mellitus (GDM) is associated with microbial perturbations across multiple biological niches.9 While earlier research has predominantly emphasized gut microbial communities, recent studies demonstrate that pregnant women with GDM exhibit distinct oral microbial signatures compared with metabolically healthy pregnant controls.10,11 These alterations are typically characterized by enrichment of periodontal pathogens and depletion of health-associated microflora, accompanied by a pro-inflammatory cytokine milieu. Furthermore, localized oral inflammatory burden may amplify systemic inflammatory responses, thereby increasing susceptibility to GDM.12,13 The biological interplay underlying this bidirectional relationship is proposed to involve both direct and indirect mechanistic pathways, including systemic translocation of periodontal pathogens, their virulence determinants, and extracellular vesicles, as well as dissemination of pro-inflammatory mediators and vesicle-associated signalling molecules.14
Keystone periodontal pathogens, including Porphyromonas gingivalis, Treponema denticola, Tannerella forsythia, Fusobacterium nucleatum, and related obligate anaerobes, are well-recognized contributors to periodontal pathobiology and have also been implicated in adverse gestational outcomes.12,15,16 Nevertheless, their quantitative burden and differential enrichment profiles in the context of GDM-associated periodontitis remain inadequately defined. Moreover, emergent taxa within dysbiotic microbial consortia, including Selenomonas sputigena, have received limited investigative attention despite their increasing detection frequency in disease-associated biofilm communities.
Substantial evidence supports a significant association between periodontitis and gestational diabetes mellitus (GDM), with cross-sectional and meta-analytic studies demonstrating an increased risk of GDM in individuals with periodontal disease.17,18 Pregnant women with GDM show a higher prevalence of periodontitis,18,19 while pre-existing periodontitis may contribute to the onset and progression of GDM.20,21 However, the underlying molecular, microbial, and immunobiological mechanisms remain incompletely understood and warrant further investigation.
Accordingly, the aim of the present study was to investigate the association between gestational diabetes mellitus and periodontal disease by evaluating clinical periodontal status and the burden of periodontopathogens in pregnant women. The objective of the study was to quantify selected periodontopathogens in subgingival plaque samples using appropriate microbiological methods and to evaluate the influence of gestational diabetes mellitus as a systemic modifying factor on periodontal disease severity.
2. Materials and methods
2.1. Study design and population
This cross-sectional study was conducted across multidisciplinary clinical settings at Department of Periodontics, SRM Dental College, Ramapuram, and Department of Obstetrics and Gynecology, SRM Medical College and Hospital, Kattankulathur. Ethical approval was obtained from the Institutional Review Boards of SRM General Hospital (IRB No: 95/IEC) and SRM Dental College (IRB No: 205/SRMU/M&HS/SRMDC). The study was conducted in accordance with the Declaration of Helsinki (2013 revision) and STROBE guidelines, with written informed consent obtained from all participants prior to enrolment.
2.2. Sample size estimation
The sample size was determined using G∗Power version 3.1 based on the formula n = 2(Zα/2 + Zβ)2/d2. A confidence level of α = 0.05 (95%), a statistical power of β = 0.20 (80%), and an assumed large effect size of d = 0.9 were considered for the calculation. Based on these parameters, the required sample size was estimated at 40 participants per group, yielding a total of 80 subjects in the study.
2.3. Subject selection criteria and clinical examination
2.3.1. Inclusion criteria
Female participants aged ≥18 years in the second trimester of pregnancy (14–27 weeks) diagnosed with Stage II/III, Grade B periodontitis (2017 EPF/AAP Classification)22,23 and gestational diabetes mellitus (GDM) confirmed using the oral glucose tolerance test (OGTT) were enrolled.
GDM was diagnosed following the Carpenter and Coustan criteria (1982).24 Each participant received 100 g of oral glucose, and blood glucose levels were measured at four time points: fasting (baseline), 1 h, 2 h, and 3 h post-ingestion. A diagnosis of GDM was established when two or more of the following thresholds were met or exceeded: fasting glucose >95 mg/dL, 1-h glucose >180 mg/dL, 2-h glucose >155 mg/dL, or 3-h glucose >140 mg/dL.
2.3.2. Exclusion criteria
Participants were excluded if they had received periodontal therapy or antibiotic treatment within the previous six months or if they had systemic conditions such as hypertension, thyroid disorders, diagnosed hormonal abnormalities, or a history of Type 1 or Type 2 diabetes. Additionally, individuals with a history of tobacco use or alcohol consumption were excluded.
2.3.3. Clinical examination
Demographic and clinical parameters were recorded, including comprehensive medical, dental, and obstetric histories. Periodontal parameters were assessed at six sites per tooth using a UNC-15 periodontal probe. The recorded variables included full-mouth probing pocket depth (PPD), clinical attachment loss (CAL), Oral Hygiene Index–Simplified (OHI-S), Plaque Index (PI), and Modified Sulcular Bleeding Index (MSBI).
2.4. Study groups
Group A (Cases): Pregnant women aged ≥18 years diagnosed with Stage II/III, Grade B periodontitis and gestational diabetes mellitus (GDM). Periodontal criteria included PI ≥ 1, OHI-S ≥1, SBI ≥1, PPD: ≤ 5 mm (Stage II), ≥6 mm (Stage III) and interdental CAL at site of greatest loss 3-4 mm (stage II), ≥ 5 mm (stage III). Grade B: Moderate rate of disease progression (indirect evidence; systemic modifying factor considered).
GDM was confirmed based on OGTT thresholds: fasting >95 mg/dL, 1-h >180 mg/dL, 2-h >155 mg/dL, or 3-h >140 mg/dL.
Group B (Controls): Pregnant women aged ≥18 years with Stage II/III, Grade B periodontitis without GDM, meeting the same periodontal criteria as Group A.
2.5. Dental plaque sample collection
The site with the deepest periodontal pocket was identified, and the area was isolated with cotton rolls to prevent contamination. Subgingival plaque samples were collected using sterile paper points, which were gently inserted into the pockets and left in place for 10 - 15 seconds. The paper points containing the pooled plaque samples were then immediately transferred into a selective transport medium (Samples were placed in 0.1 mL TE buffer (50 mM Tris, 1 mM EDTA, pH 7.6) and transported to the laboratory for microbiological analysis. All samples were stored at −80 °C until further processing.
2.6. Processing of dental plaque samples
The samples were centrifuged at 7000 rpm for 5 min, and the supernatant was discarded. A lysis buffer containing lysozyme (1 g), Tris-HCl (1 ml), EDTA (200 μl), Triton (600 μl), and Milli-Q water (adjusted to a final volume of 40 ml) was prepared and vortexed. 0.8 μl of this buffer was added to each sample, followed by incubation at 37 °C for 30 min. Sodium dodecyl sulphate (20 μl) was then added, vortexed, and incubated again under the same conditions. An equal volume of phenol chloroform isoamyl alcohol was added, and the mixture was centrifuged at 10,000 rpm; the supernatant was transferred to a fresh Eppendorf tube. The extract was then treated with sodium acetate and ethanol, centrifuged, and washed with ethanol. The resulting DNA pellet was air-dried, reconstituted in sterile water, and subsequently frozen. Qualitative assessment of DNA integrity was performed using gel electrophoresis, and DNA concentration was determined using a NanoDrop spectrophotometer. The samples were then stored at −80 °C until further analysis.
2.6.1. Quantification of periodontal pathogens
Polymerase chain reaction (PCR) was carried out using a SYBR Green-based detection system to quantify key periodontal pathogens. For each reaction, 5 μl of SYBR Green master mix was combined with 5 μl of reaction solution containing forward primer (0.5 μl), reverse primer (0.5 μl), template DNA (2 μl), and sterile water (2 μl), making a total volume of 10 μl. Normalization was performed using a universal bacterial 16S rRNA gene as a reference for total bacterial load. The PCR mixture was then dispensed into microwells and amplified in a thermal cycler. Details of the primer sequences are displayed in Table 1.
Table 1.
Primer sequence of periodontal pathogens.
| Pathogen | Forward | Reverse |
|---|---|---|
| Prevotella intermedia | CGTGGACCAAAGATTCATCGGTGA | CCGCTTTCTTCCAAATCACA |
| Prevotella nigrescens | ATGAAACAAAGGTTTTCCGGTAAG | CCCACGTCTCTGTGGCGTGCGA |
| Tannerella forsythia | GCGTATGTAACCTGCCCGCA | TGCTTCAGT GTC AGT TAT ACC T |
| Treponema denticola | TAATACCGAATGTGCTCATTT ACAT | TCAAAGAAGCATTCCCTCTTCTTC TTA |
| Porphyromonas gingivalis | AGGCAGCTTGCCATACTGCG | ACTGTTAGCAACTACCGATGT |
| Fusobacterium nucleatum | CTGAACAATGGAAACTATATAGTAGAACAAACAAG | GTCCTTCATCGGCTTCTACTACCTAGGC |
| Selenomonas sputigena | AGAGTTTGATCCTGGCTCAG | CTCAATATTCTCAAGCTCGGTT |
| Universal primers | GATTAGATACCCTGGTAGTCCAC | CCCGGGAACGTATTCACCG |
Abbreviations: A, Adenine; T, Thymine; C, Cytosine; G,Guanine.
2.7. Statistical analysis
Statistical analysis was performed using IBM SPSS Statistics for Windows, Version 26.0 (IBM Corp., Armonk, NY, USA). Normality was assessed using the Shapiro–Wilk test. Normally distributed variables were expressed as mean ± standard deviation. Mean differences with 95% confidence intervals and effect sizes were calculated to quantify the magnitude and precision of intergroup differences. Intergroup comparisons were conducted using the Independent Samples t-test. Pearson correlation analysis evaluated associations between independent variables (clinical and glycemic parameters) and dependent variables (periodontal pathogens). Subsequently, multiple linear regression analysis was performed to assess the independent effects of these predictors on the outcome variables. A p-value <0.05 was considered statistically significant.
3. Results
3.1. Demographic, clinical, and glycemic parameters between the study groups
The groups were comparable in age (32.25 ± 6.92 vs 30.57 ± 6.84; p = 0.136). Group A exhibited significantly higher PI (2.63 ± 0.37 vs 2.37 ± 0.28; p = 0.001) and OHI-S (2.45 ± 0.33 vs 2.27 ± 0.21; p = 0.005). MSBI was significantly higher in Group B (2.20 ± 0.41 vs 1.70 ± 0.47; p < 0.001). Periodontal destruction was more pronounced in Group A, with greater PPD (7.20 ± 0.95 mm vs 5.75 ± 1.02 mm; p = 0.045) and higher, though borderline non-significant, CAL (7.01 ± 1.22 mm vs 5.88 ± 0.16 mm; p = 0.056). Glycemic parameters (OGTT) were markedly elevated in Group A across all time points (p < 0.05). The data are summarized in Table 2.
Table 2.
Comparison of demographic, clinical, and glycemic parameters between the study groups.
| Parameter | Group A (n = 40) | Group B (n = 40) | Mean Difference | 95% CI | Effect Size | †p-value | |
|---|---|---|---|---|---|---|---|
| Age | 32.25 ± 6.92 | 30.57 ± 6.84 | 1.68 | −1.40 - 4.76 | 0.24 | 0.136 | |
| PI | 2.63 ± 0.37 | 2.37 ± 0.28 | 0.26 | 0.12 - 0.40 | 0.79 | 0.001∗ | |
| OHI-S | 2.45 ± 0.33 | 2.27 ± 0.21 | 0.18 | 0.06 - 0.30 | 0.64 | 0.005∗ | |
| MSBI | 1.70 ± 0.47 | 2.20 ± 0.41 | −0.50 | −0.70 to −0.30 | 1.12 | < 0.001∗ | |
| PPD (mm) | 7.20 ± 0.95 | 5.75 ± 1.02 | 1.45 | 0.98 - 1.92 | 1.48 | 0.045∗ | |
| CAL(mm) | 7.01 ± 1.22 | 5.88 ± 0.16 | 1.13 | 0.72 - 1.54 | 1.20 | 0.05∗ | |
| Gestational Age | 23 weeks | 24 weeks | - | - | - | - | |
| OGTT (mmol/L) | OGTT-0h | 5.86 ± 0.42 | 2.28 ± 1.22 | 3.58 | 3.05 -4.11 | 3.10 | 0.101 |
| OGTT-1h | 10.35 ± 1.42 | 5.11 ± 1.15 | 5.24 | 4.67 - 5.81 | 4.10 | 0.05∗ | |
| OGTT-2h | 8.54 ± 1.44 | 3.43 ± 0.52 | 5.11 | 4.62 - 5.60 | 4.60 | 0.241 | |
Abbreviations: PI, Plaque Index; OHI-S, Oral Hygiene Index–S; MSBI, Modified Sulcular Bleeding Index; PPD, Probing Pocket Depth; CAL, Clinical Attachment Loss; mm, millimetre; OGTT, Oral Glucose Tolerance Test, CI, confidence interval.
† Independent t-test applied for comparison between groups. ∗p < 0.05 statistically significant.
3.2. Quantitative analysis of periodontal pathogens across study groups
Analysis of mean Ct values demonstrated a significantly higher bacterial load in Group A compared with Group B. The greatest differences were observed for P. gingivalis (26.91 ± 1.38 vs 30.43 ± 1.06; p ≤ 0.0001), followed by S. sputigena (27.11 ± 0.87 vs 30.76 ± 1.60; p ≤ 0.0001) and F. nucleatum (30.01 ± 1.11 vs 32.50 ± 0.30; p ≤ 0.0001). Significant differences were also noted for P. intermedia (p ≤ 0.0001) and T. denticola (p ≤ 0.005). In contrast, T. forsythia showed lower Ct values in Group B (p ≤ 0.0001). Findings are depicted in Table 3, Graph 1.
Table 3.
Comparison of mean cycle threshold (Ct) values of periodontal pathogens among the study groups.
| Periodontal Pathogens | Group A | Group B | Mean Difference | 95% CI | Effect Size | †p-value |
|---|---|---|---|---|---|---|
| P.intermedia | 29.991 ± 1.293 | 31.546 ± 1.926 | 1.555 | 0.829 – 2.229 | 0.93 | ≤ 0.0001∗ |
| P.nigrescens | 30.324 ± 0.727 | 33.278 ± 0.434 | 2.954 | 0.399 – 1.111 | 1.52 | ≤ 0.005∗ |
| T. forsythia | 34.188 ± 1.186 | 32.811 ± 1.331 | −1.377 | 0.815 – 1.938 | 1.08 | ≤ 0.0001∗ |
| T.denticola | 31.787 ± 0.701 | 34.629 ± 0.800 | 2.842 | 0.506 – 1.176 | 3.77 | ≤ 0.005∗ |
| P.gingivalis | 26.908 ± 1.377 | 30.428 ± 1.059 | 3.520 | 1.973 – 3.066 | 2.87 | ≤ 0.0001∗ |
| F.nucleatum | 30.007 ± 1.105 | 32.502 ± 0.304 | 2.495 | 0.211 – 3.982 | 2.96 | ≤ 0.0001∗ |
| S.sputigena | 27.113 ± 0.871 | 30.760 ± 1.603 | 3.647 | 0.211 – 3.982 | 2.79 | ≤ 0.0001∗ |
Abbreviations: CI, confidence interval.
† Independent t-test. ∗p < 0.05: statistically significant.
Graph 1.
Comparative distribution of Cycle Threshold (Ct) values of periodontal pathogens: Group A showed lower Ct values, indicating a higher bacterial load than Group B. Distinctly, P. gingivalis and S.sputigena were more abundant in Group A (periodontitis with gestational diabetes).
3.3. Correlation analysis of periodontal pathogens with clinical and glycemic parameters
In Group A, OHI-S and PI showed significant negative correlations with P. intermedia (r = −0.34,-0.32; p = 0.030, 0.041) and P. gingivalis (r = −0.45, −0.42; p = 0.004, 0.006). PPD was strongly negatively correlated with P. gingivalis (r = −0.69, p = 0.002) and associated with T. forsythia, F. nucleatum, and S. sputigena (p < 0.05). CAL showed negative correlation with P. gingivalis and positive correlation with T. forsythia. OGTT-1h and OGTT-2h were negatively correlated with P. gingivalis and positively associated with T. forsythia and T. denticola (p < 0.05).
Group B demonstrated fewer associations, including negative correlation of OHI-S with P. gingivalis (r = −0.18, p = 0.04) and positive association of PI with F. nucleatum (r = 0.15, p = 0.037). MSBI was negatively correlated with T. denticola and F. nucleatum, while PPD correlated positively with P. gingivalis and negatively with F. nucleatum and S. sputigena.
In the combined cohort, stronger associations were observed, with PPD positively correlating with P. gingivalis (r = 0.79, p = 0.001), F. nucleatum (r = 0.43, p = 0.002), and S. sputigena (r = 0.64, p = 0.01). CAL and MSBI also showed significant positive associations with key pathogens. The findings are illustrated in Graph 2.
Graph 2.
Correlation analysis of clinical parameters with periodontal pathogens in Group A, Group B, and the combined study population.
r = Pearson correlation coefficient (−1 to +1); positive values indicate direct and negative values indicate inverse correlations; strength classified as very low (0.00–0.19), low (0.20–0.39), moderate (0.40–0.59), high (0.60–0.79), and very high (0.80–1.00). Statistical significance: p < 0.05 (∗), p < 0.01 (∗∗).
3.4. Multiple linear regression analysis of determinants of periodontal pathogens
In Group A, MSBI, PPD, and CAL were significant predictors of P. intermedia (β = −0.23, −0.29, −0.25; p = 0.046, 0.024, 0.040) and P. gingivalis (β = −0.26, −0.33, −0.24; p = 0.027, 0.010, 0.049). OGTT-1h predicted both pathogens (p = 0.015, 0.004), and OGTT-2h predicted P. gingivalis (p = 0.020). T. forsythia and T. denticola showed positive associations with clinical and glycemic variables, whereas F. nucleatum and S. sputigena showed negative associations (p < 0.05).
In Group B, MSBI, PPD, and CAL remained significant predictors of P. intermedia and P. gingivalis (p < 0.05). PPD predicted T. forsythia and T. denticola, while F. nucleatum and S. sputigena were associated with MSBI, PPD, and CAL. Glycemic parameters showed limited predictive significance.
Overall, clinical parameters (MSBI, PPD, CAL) consistently emerged as strong independent predictors of key periodontal pathogens, with glycemic parameters contributing more prominently in Group A than in Group B (Table 4).
Table 4.
Multiple linear regression analysis of clinical and glycemic parameters (independent variables) with periodontal pathogens (dependent variables) in Study groups.
|
Pathogens |
GROUP A |
GROUP B |
|||||||
|---|---|---|---|---|---|---|---|---|---|
| Parameters | β | t | 95% CI | p Value | β | t | 95% CI | p Value | |
| Pia | OHI-Sb | −0.13 | −1.12 | −0.365 - 0.105 | 0.269 | −0.12 | −1.03 | −0.33 - 0.10 | 0.309 |
| PIb | −0.17 | −1.49 | −0.386 - 0.046 | 0.145 | −0.16 | −1.38 | −0.36 - 0.07 | 0.176 | |
| MSBIb | −0.23 | −2.07 | −0.426 to −0.034 | 0.046∗ | −0.24 | −2.05 | −0.44 to −0.01 | 0.047∗ | |
| PPDb | −0.29 | −2.36 | −0.466 to −0.114 | 0.024∗ | −0.29 | −2.36 | −0.48 to −0.04 | 0.024∗ | |
| CALb | −0.25 | −2.02 | −0.446 to −0.054 | 0.040∗ | −0.22 | −2.18 | −0.42 - 0.02 | 0.048∗ | |
| OGTT-0hb | −0.18 | −1.74 | −0.356 to −0.004 | 0.091 | −0.21 | −1.78 | −0.41 - 0.03 | 0.084 | |
| OGTT-1hb | −0.31 | −2.57 | −0.467 to −0.153 | 0.015∗ | −0.27 | −1.25 | −0.47 to −0.02 | 0.061 | |
| OGTT-2hb | −0.22 | −2.01 | −0.377 to −0.063 | 0.051 | −0.18 | −1.51 | −0.38 - 0.06 | 0.139 | |
| Pna | OHI-Sb | −0.14 | −1.28 | −0.20 – 0.09 | 0.115 | −0.11 | −0.96 | −0.31 - 0.11 | 0.344 |
| PIb | −0.13 | −1.6 | −0.33 – 0.09 | 0.218 | −0.14 | −1.21 | −0.34 - 0.08 | 0.233 | |
| MSBIb | −0.11 | −1.32 | −0.21 – 0.16 | 0.264 | −0.19 | −1.72 | −0.38 - 0.04 | 0.094 | |
| PPDb | −0.15 | −1.21 | −0.23 – 0.19 | 0.173 | −0.22 | −1.94 | −0.42 - 0.01 | 0.061 | |
| CALb | −0.11 | −0.64 | −0.21 – 0.21 | 0.231 | −0.18 | −1.57 | −0.37 - 0.06 | 0.125 | |
| OGTT-0hb | −0.16 | −1.61 | −0.28 – 0.07 | 0.053 | −0.18 | −1.55 | −0.36 - 0.05 | 0.129 | |
| OGTT-1hb | −0.10 | −1.36 | −0.22 – 0.04 | 0.118 | −0.24 | −2.03 | −0.44 to −0.01 | 0.069 | |
| OGTT-2hb | −0.18 | −1.07 | −0.19 – 0.11 | 0.104 | −0.16 | −1.39 | −0.35 - 0.07 | 0.173 | |
| Tfa | OHI-Sb | 0.14 | 1.18 | −0.095 - 0.375 | 0.246 | 0.16 | 1.34 | −0.11 - 0.43 | 0.188 |
| PIb | 0.17 | 1.47 | −0.046 - 0.386 | 0.150 | 0.19 | 1.58 | −0.07 - 0.45 | 0.122 | |
| MSBIb | 0.23 | 2.04 | 0.034 - 0.426 | 0.048∗ | 0.24 | 2.01 | −0.01 - 0.49 | 0.052 | |
| PPDb | 0.26 | 2.19 | 0.084 - 0.436 | 0.036∗ | 0.28 | 2.31 | 0.03 - 0.53 | 0.027∗ | |
| CALb | 0.21 | 1.85 | 0.014 - 0.406 | 0.072 | 0.21 | 1.78 | −0.03 - 0.45 | 0.084 | |
| OGTT-0hb | 0.19 | 1.81 | 0.014 - 0.366 | 0.078 | 0.22 | 1.87 | −0.02 - 0.46 | 0.070 | |
| OGTT-1hb | 0.30 | 2.55 | 0.143 - 0.457 | 0.016∗ | 0.30 | 1.49 | 0.05 - 0.55 | 0.078 | |
| OGTT-2hb | 0.27 | 2.28 | 0.113 - 0.427 | 0.029∗ | 0.27 | 1.19 | 0.02 - 0.52 | 0.065 | |
| Tda | OHI-Sb | 0.11 | 0.95 | −0.125 - 0.345 | 0.350 | 0.15 | 1.29 | −0.12 - 0.41 | 0.204 |
| PIb | 0.12 | 1.05 | −0.096 - 0.336 | 0.300 | 0.17 | 1.42 | −0.08 - 0.42 | 0.163 | |
| MSBIb | 0.24 | 2.12 | 0.044 - 0.436 | 0.041∗ | 0.22 | 1.90 | −0.01 - 0.46 | 0.066 | |
| PPDb | 0.28 | 2.33 | 0.104 - 0.456 | 0.026∗ | 0.26 | 2.14 | 0.02 - 0.51 | 0.039∗ | |
| CALb | 0.18 | 1.56 | −0.016 - 0.376 | 0.127 | 0.20 | 1.74 | −0.04 - 0.44 | 0.090 | |
| OGTT-0hb | 0.17 | 1.61 | −0.006 - 0.346 | 0.115 | 0.20 | 1.66 | −0.04 - 0.44 | 0.105 | |
| OGTT-1hb | 0.28 | 2.36 | 0.123 - 0.437 | 0.025∗ | 0.27 | 1.29 | 0.03 - 0.52 | 0.068 | |
| OGTT-2hb | 0.24 | 2.07 | 0.083 - 0.397 | 0.046∗ | 0.23 | 1.02 | 0.01 - 0.47 | 0.069 | |
| Pga | OHI-Sb | −0.14 | −1.22 | −0.39 - 0.08 | 0.229 | −0.15 | −1.27 | −0.36 - 0.10 | 0.212 |
| PIb | −0.18 | −1.62 | −0.41 - 0.04 | 0.114 | −0.21 | −1.79 | −0.41 - 0.03 | 0.081 | |
| MSBIb | −0.26 | −2.31 | −0.49 to −0.03 | 0.027∗ | −0.25 | −2.11 | −0.46 to −0.02 | 0.041∗ | |
| PPDb | −0.33 | −2.71 | −0.56 to −0.08 | 0.010∗ | −0.32 | −2.69 | −0.52 to −0.07 | 0.011∗ | |
| CALb | −0.24 | −2.02 | −0.48 to −0.01 | 0.049∗ | −0.27 | −2.23 | −0.49 to −0.03 | 0.033∗ | |
| OGTT-0hb | −0.21 | −2.01 | −0.43 to −0.01 | 0.049∗ | −0.19 | −1.62 | −0.39 - 0.05 | 0.114 | |
| OGTT-1hb | −0.37 | −3.02 | −0.61 to −0.11 | 0.004∗ | −0.26 | −2.18 | −0.46 to −0.02 | 0.036∗ | |
| OGTT-2hb | −0.29 | −2.44 | −0.55 to −0.05 | 0.020∗ | −0.23 | −1.04 | −0.44 to −0.01 | 0.068 | |
| Fna | OHI-Sb | −0.13 | −1.26 | −0.30 - 0.07 | 0.215 | −0.10 | −0.87 | −0.33 - 0.13 | 0.390 |
| PIb | −0.17 | −1.55 | −0.32 - 0.05 | 0.128 | −0.12 | −1.03 | −0.32 - 0.09 | 0.308 | |
| MSBIb | −0.15 | −1.42 | −0.31 - 0.06 | 0.164 | −0.16 | −2.41 | −0.36 - 0.07 | 0.016∗ | |
| PPDb | −0.12 | −2.11 | −0.33 - 0.09 | 0.021∗ | −0.18 | −2.56 | −0.38 - 0.05 | 0.028∗ | |
| CALb | −0.10 | −2.94 | −0.32 - 0.12 | 0.035∗ | −0.14 | −2.19 | −0.34 - 0.08 | 0.024∗ | |
| OGTT-0hb | −0.18 | −1.72 | −0.38 - 0.03 | 0.094 | −0.12 | −1.06 | −0.31 - 0.09 | 0.297 | |
| OGTT-1hb | −0.14 | −2.31 | −0.33 - 0.07 | 0.041∗ | −0.17 | −2.43 | −0.35 - 0.07 | 0.016∗ | |
| OGTT-2hb | −0.11 | −1.03 | −0.29 - 0.10 | 0.309 | −0.15 | −1.32 | −0.33 - 0.08 | 0.194 | |
| Ssa | OHI-Sb | −0.08 | −0.74 | −0.30 - 0.14 | 0.463 | −0.09 | −0.79 | −0.33 - 0.14 | 0.435 |
| PIb | −0.09 | −0.88 | −0.29 - 0.12 | 0.385 | −0.11 | −0.98 | −0.31 - 0.11 | 0.333 | |
| MSBIb | −0.14 | −1.29 | −0.34 - 0.07 | 0.205 | −0.15 | −2.33 | −0.35 - 0.09 | 0.019∗ | |
| PPDb | −0.16 | −2.46 | −0.36 - 0.05 | 0.015∗ | −0.17 | −2.45 | −0.37 - 0.07 | 0.015∗ | |
| CALb | −0.12 | −2.08 | −0.31 - 0.09 | 0.028∗ | −0.13 | −2.12 | −0.34 - 0.10 | 0.027∗ | |
| OGTT-0hb | −0.11 | −1.05 | −0.26 - 0.08 | 0.300 | −0.11 | −0.97 | −0.30 - 0.10 | 0.337 | |
| OGTT-1hb | −0.15 | −1.34 | −0.31 - 0.06 | 0.188 | −0.16 | −1.34 | −0.35 - 0.08 | 0.189 | |
| OGTT-2hb | −0.13 | −1.19 | −0.29 - 0.07 | 0.241 | −0.13 | −1.18 | −0.32 - 0.09 | 0.246 | |
Abbreviations: PI, Plaque Index; OHI-S, Oral Hygiene Index–Simplified; MSBI, Modified Sulcular Bleeding Index; PPD, Probing Pocket Depth; CAL, Clinical Attachment Loss; OGTT, Oral Glucose Tolerance Test; Pg, Porphyromonas gingivalis; Pi, Prevotella intermedia; Pn, Prevotella nigrescens; Tf, Tannerella forsythia; Td, Treponema denticola; Fn, Fusobacterium nucleatum; Ss, Selenomonas sputigena; CI, Confidence Interval.
β, regression coefficient.
t-value, test statistic assessing the significance of β.
p < 0.05 indicates statistical significance.
Dependent variables (periodontal pathogens).
Independent variables (clinical and glycemic parameters).
4. Discussion
Microbial diversity may remain relatively stable across gestation.25 Still, pregnancy is increasingly recognized to recalibrate the oral microbial ecosystem toward dysbiosis, with postpartum restitution toward a health-associated microbiome. This microbial plasticity is plausibly driven by gestational hormonal fluctuations, particularly progesterone and estrogen (Lin et al., 2018).26 Periodontal pathogens influence the maternal–foetoplacental unit through interconnected direct and indirect mechanisms, while gestational diabetes mellitus is concurrently associated with distinct oral microbiome that dysregulate glucose metabolism and foster disease-associated consortia.14 Accordingly, this study evaluated periodontal clinical parameters and quantified key periodontopathogens (P. gingivalis, T. forsythia, T. denticola, F. nucleatum, P.intermedia, P. nigrescens and S. sputigena) among women with periodontitis, comparing those with and without gestational diabetes mellitus (GDM).
The present study demonstrated that women with periodontitis and gestational diabetes (Group A) showed significantly higher Plaque Index, Oral Hygiene Index-S, Modified Sulcular Bleeding Index, and Probing Pocket Depth than those with periodontitis without gestational diabetes (Group B). The GDM group also exhibited a significantly greater periodontopathogen burden, with predominant enrichment of P. gingivalis, followed by S. sputigena and F. nucleatum. Interspecies comparisons within the GDM-associated periodontitis cohort revealed significant Ct value heterogeneity, with post hoc analyses identifying P. gingivalis and S. sputigena as the most abundant taxa. Notably, P. gingivalis demonstrated the strongest positive correlations with key clinical periodontal parameters. The increased prevalence of specific periodontopathogens in the GDM group may be associated with alterations in the oral microbial ecosystem observed in gestational diabetes mellitus.
These findings are consistent with prior evidence, support a biologically plausible association, and are in concordance with the observations of Tilakaratne et al. (2000),27 who reported higher gingival index scores and probing depths in pregnant women despite comparable plaque levels to non-pregnant controls. Maternal periodontal health appears to exhibit heightened susceptibility during gestation, with pregnancy acting as an inflammatory amplifier that can aggravate pre-existing gingivitis or periodontitis.25
Pearson correlation revealed significant associations between clinical and glycemic parameters and periodontal pathogens, stronger in Group A and the combined cohort, particularly for P. gingivalis. Multiple linear regression identified MSBI, PPD, and CAL as independent predictors, with OGTT-1h contributing in Group A, underscoring the synergistic interplay between periodontal inflammation and glycemic dysregulation in modulating microbial burden. The present findings are concordant with prior reports demonstrating an increased prevalence of Porphyromonas gingivalis among women diagnosed with gestational diabetes mellitus.28 Substantiating this association, Fusobacterium nucleatum has been implicated in mediating the translocation of Porphyromonas gingivalis into maternal tissues, thereby augmenting its systemic dissemination capacity.29 In addition, F. nucleatum functions as a critical ecological scaffold within polymicrobial biofilms, promoting interspecies co-aggregation and facilitating the hierarchical assembly and structural integration of multispecies microbial communities.30,31 Moreover, increased levels of Porphyromonas gingivalis, Treponema denticola, Prevotella intermedia, Tannerella forsythia, Campylobacter rectus, Aggregatibacter actinomycetemcomitans, and Fretibacterium species have been shown to correlate positively with gingival inflammation during pregnancy, even where the overall microbial load remains comparable, indicating a qualitative rather than merely quantitative microbial shift.15,25
The bidirectional association between gestational diabetes mellitus (GDM) and periodontitis is mediated by complex biological mechanisms, including the atopobiosis of Porphyromonas gingivalis, which contributes to adverse pregnancy outcomes. This occurs through invasion of the foeto-placental interface, persistence within maternal and foetal tissues with immune evasion, and induction of pro-inflammatory responses characterized by a Th2-to-Th1 shift and Th17/Treg imbalance. Additional mechanisms include activation of the acute-phase response, promotion of polymicrobial dysbiosis, increased oxidative stress, and stimulation of foetal cortisol production, leading to foetal stress.32
Direct pathways involve haematogenous or ascending dissemination of periodontal pathogens and their virulence factors to the maternal–foetal unit. Periodontal disease expands ecological niches that sustain a dysbiotic, inflammophilic microbiome, serving as a chronic reservoir of pathogens and inflammatory mediators.33 Concomitant junctional epithelial disruption and increased permeability further facilitate systemic microbial and endotoxin translocation to distant sites, including the maternal–foetal interface.34 Vaginal dysbiosis may additionally contribute via ascending infection routes.35 Indirect effects arise from the systemic dissemination of inflammatory mediators such as CRP, TNF-α, and IL-6, generated at inflamed periodontal sites, thereby disrupting placental and metabolic homeostasis.36,37
Another potential placental exposure route involves secondary vaginal colonization by oral pathogens via sexual transmission, a pathway not yet well defined in the literature.16 The detection of periodontal taxa such as Treponema denticola, Tannerella forsythia, and Prevotella intermedia in the urogenital microbial milieu supports this possibility.38 Comparative niche analyses further indicate that, among oral, gut, and vaginal ecosystems, the oral microbiome exhibits the most pronounced dysregulation in gestational diabetes mellitus.39 In aggregate, these data, along with our findings, underscore periodontal microbiome dysbiosis as a central associative link between GDM and periodontal disease.
The present study identified OGTT-1h as a significant predictor of periodontal pathogens in Group A, suggesting that glycemic fluctuations may influence microbial burden through immunometabolic pathways. Although hyperglycemia-related periodontal deterioration is well established in chronic diabetes, GDM represents a transient metabolic disturbance with limited glycemic exposure, likely insufficient alone to induce periodontal tissue destruction.40 Nevertheless, increased glucose availability may favor saccharolytic anaerobes and alter the subgingival ecological niche.41,42 Conversely, periodontal inflammation may exacerbate systemic immune activation and gestational insulin resistance, thereby impairing glycemic control.43
This study delineates GDM-associated oral dysbiosis marked by periodontal pathogen enrichment, particularly Selenomonas sputigena and alongside Porphyromonas gingivalis and Fusobacterium nucleatum, supports a dysbiosis-centred mechanistic axis in GDM-associated periodontitis, warranting focused translational and pathogen-specific mechanistic research. These findings provide a compelling clinical rationale for the structured integration of prenatal and periodontal care models, with the prospective capacity to enhance gestational well-being while concurrently elevating the standards of maternal oral healthcare during pregnancy.
While the present study provides meaningful insights, certain limitations warrant consideration. The absence of a non-GDM pregnant control cohort may have limited the strength of comparative inferences. Additionally, potential confounding variables, including body mass index, socioeconomic status, and genetic, environmental, and dietary factors, were not comprehensively accounted for and may have influenced the observed microbial profiles. Future longitudinal and multi-omic investigations are warranted to further elucidate underlying biological pathways and validate these associations across diverse populations.
5. Conclusion
In conclusion, the present study identifies a distinct dysbiosis-associated oral microbial signature in periodontitis with gestational diabetes mellitus, marked by an increased burden of key pathogens, particularly Porphyromonas gingivalis, Selenomonas sputigena, and Fusobacterium nucleatum. These microbial alterations, together with their clinical correlations, highlight the intricate host–microbiome–metabolic interplay in metabolically compromised pregnancy. Collectively, the findings provide a compelling biological framework linking GDM and periodontitis and reinforce the need for integrated prenatal–periodontal care. This evidence holds significant potential to enhance maternal health outcomes and supports future longitudinal and causal studies to enable targeted preventive and therapeutic strategies.
Informed consent
Written informed consent form were signed and taken from all the participants.
Ethical clearance
Ethical approval for the study was obtained from the Institutional Review Boards of SRM General Hospital (IRB No: 95/IEC) and SRM Dental College (IRB No: 205/SRMU/M&HS/SRMDC).
Data availability statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.
Contributions
1. Dr. DHATHRI PRIYA BANDI: Conceptualization, acquisition of data, analysis, and data curation; drafting the article; critically revising it for important intellectual content; and providing final approval of the version to be published.
2. Dr. BALASUBRAMANIAN KRISHNASWAMY: Conceptualization, drafting the article; critically revising it for important intellectual content; and providing final approval of the version to be published.
3. Dr.DHAYANAND JOHN VICTOR: Critically revised it for important intellectual content and provided final approval of the version to be published.
Sources of funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. The research was self funded and no other sources of funding are involved in any part of the project or manuscript writing.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
None.
Contributor Information
Dhathri Priya Bandi, Email: drdha3priya@gmail.com.
Balasubramanian Krishnaswamy, Email: dr.bala85@gmail.com.
Dhayanand John Victor, Email: dhayanadjohnvictor@gmail.com.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.