Association of periodontal diseases with inflammatory markers and gestational diabetes: a case-control study

Abstract

Background

Gestational diabetes mellitus (GDM) is a common pregnancy complication that may trigger systemic inflammatory responses and potentially affect oral health. However, studies that simultaneously evaluate both systemic and oral health parameters in individuals with GDM remain scarce in the current literature. This study aimed to investigate the relationship between maternal oral health, hematological inflammatory markers, and GDM.

Methods

This case–control study included 40 pregnant women diagnosed with GDM and 40 healthy pregnant women with uncomplicated pregnancies. Maternal sociodemographic and clinical data, along with blood parameters, were obtained from medical records. Oral health was assessed using the Plaque Index (PI), Gingival Index (GI), Probing Depth (PD), Clinical Attachment Level (CAL), and the Decayed, Missing, and Filled Teeth (DMFT) index. Statistical analyses included the Mann–Whitney U test, chi-square test, and hierarchical binomial logistic regression. A p-value < 0.05 was considered statistically significant.

Results

PI, GI, and PD scores were significantly higher, and DMFT scores were significantly elevated in the GDM group compared to controls (p < 0.01 and p = 0.007, respectively), indicating greater periodontal inflammation and poorer overall oral health. Among the inflammatory markers, only eosinophil levels were significantly lower in the GDM group (p = 0.011). Logistic regression analysis identified weight gain during pregnancy as the only significant predictor of GDM (p < 0.05, OR = 1.117).

Conclusions

Pregnant women with GDM demonstrated significantly poorer periodontal health compared to healthy controls, suggesting a potential association between periodontal inflammation and GDM. Additionally, pregnancy weight gain was the strongest predictor of GDM, highlighting the influence of metabolic factors.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12903-025-06929-9.

Introduction

The periodontium comprises several components, including the gingiva, periodontal ligament, cementum, and alveolar bone. In periodontal disease, these structures are progressively affected by inflammation, leading to tissue destruction, pocket formation, and alveolar bone loss [1, 2]. Periodontal disease has been associated with adverse pregnancy outcomes such as preterm birth, low birth weight, as well as with various systemic diseases [3, 4].

In recent years, they have gained increasing attention in scientific research, particularly due to their potential systemic effects in relation to gestational diabetes mellitus (GDM) [5]. Both conditions share common inflammatory pathways, suggesting a possible bidirectional relationship wherein periodontal inflammation may contribute to the pathogenesis of GDM. Research indicates that periodontal disease leads to elevated levels of inflammatory markers, including interleukin-6 (IL-6) and C-reactive protein (CRP), which are also implicated in insülin resistance and the development of GDM [2, 3, 6]. This highlights the need to explore periodontal health as a modifiable risk factor in pregnancy.

Pregnancy is characterized by profound hormonal and immunological changes that not only alter metabolic processes but also increase susceptibility to infections, including periodontal diseases [7]. One of the most common oral health issues observed during pregnancy is gestational gingivitis, a condition marked by heightened gingival inflammation despite relatively unchanged plaque levels. This increased susceptibility is largely attributed to elevated estrogen and progesterone levels, which enhance vascular permeability and blood flow to the gingival tissues, making them more reactive to bacterial biofilms [7]. Given that systemic inflammation is a known contributor to insulin resistance, the interplay between periodontal disease and GDM warrants further investigation. Women with periodontal disease have been shown to exhibit higher systemic levels of inflammatory mediators, such as CRP and IL-6, both of which are associated with an increased risk of insulin resistance and hyperglycemia during pregnancy [8, 9].

Inflammatory markers derived from complete blood count (CBC) parameters stand out as practical and valuable biomarkers for assessing systemic inflammation during pregnancy, due to their non-invasive nature, low cost, and easy accessibility in routine clinical practice [4, 10].

The existing evidence on the association between periodontal disease and GDM is inconsistent, and there is no consensus on a causal relationship [5, 11]. While some studies suggest that periodontal inflammation exacerbates placental inflammatory responses, potentially leading to adverse pregnancy outcomes, definitive mechanistic pathways have yet to be fully elucidated [12]. Additionally, women with GDM face increased risks of complications such as preeclampsia and neonatal morbidity, further underscoring the complexity of these interactions [13, 14]. Although the relationship between periodontal diseases and GDM has been investigated from various perspectives in the current literature, the impact of this association on systemic inflammatory response has not been sufficiently explored. In particular, the potential of accessible and widely used clinical markers such as complete blood count has been largely overlooked. Therefore, this study aimed to evaluate the possible associations between maternal oral health status, inflammatory blood markers, and GDM. The null hypothesis posits that periodontal disease does not influence systemic inflammatory markers and, consequently, does not contribute to the development of GDM. Investigating this hypothesis could provide valuable insights into preventive strategies aimed at improving both maternal metabolic health and pregnancy outcomes.

Materials and methods

Study design and ethical approval

This case-control study was conducted between September 2022 and April 2023 at the Obstetrics and Gynecology Department of a university hospital. Ethical approval was obtained from the Non-Interventional Ethics Committee of Niğde Ömer Halisdemir University Faculty of Medicine (Decision No: 2022/72), and all procedures were performed in accordance with the principles outlined in the Declaration of Helsinki. Written informed consent was obtained from all participants prior to their inclusion in the study.

The case group comprised pregnant women diagnosed with GDM between the 24th and 28th weeks of gestation, based on standardized diagnostic criteria. The control group included healthy pregnant women without any complications who underwent routine GDM screening within the same gestational period. Participants in both groups were recruited during their antenatal visits and were evaluated using identical clinical and laboratory protocols to ensure consistency across groups.

A priori power analysis was conducted to determine the required sample size for the study. The analysis was based on the findings of the study by Damante et al. accordingly, an effect size of d = 0.98, a Type I error rate of 5%, and a statistical power of 80% were assumed. Based on these parameters, the minimum required sample size was calculated to be n1 = n2 = 25, resulting in a total of 50 participants. The analysis was performed using the G*Power 3.1.9.7 software. Therefore, the sample size used in the present study (n = 80; 40 GDM and 40 controls) provides adequate statistical power to support the analysis [15]. A total of 80 participants were included in the study based on the inclusion and exclusion criteria; the case group consisted of 40 patients who were diagnosed with GDM between the 24th and 28th weeks of gestation, while the control group consisted of 40 healthy patients without any pregnancy complications who underwent GDM screening during the same gestational weeks.

Inclusion and exclusion criteria

The study included systemically healthy individuals, except for those diagnosed with GDM, ensuring that other underlying conditions did not influence the results. Only single pregnancies were included to eliminate confounding factors associated with multiple gestations, and participants were restricted to an age range of 18–35 years to minimize variability related to maternal age.

Exclusion criteria were established to control for factors that could affect periodontal health and inflammatory Markers. Participants were excluded if they had used antibiotics or mouthwash within the last six months or had received periodontal treatment within the past three months. Individuals with infectious diseases were also excluded due to their potential impact on both periodontal and systemic health. To ensure consistency in periodontal assessments, women with fewer than 20 teeth were not included. Moreover, to Maintain a homogenous study population, individuals under 18 or over 35 years of age, those with multiple pregnancies, and those with a history of diabetes prior to pregnancy whether insulin-dependent or non insülin dependent were excluded. Finally, participants who had not undergone laboratory testing for GDM screening were not considered eligible for inclusion.

Data collection and assessment

The diagnosis of GDM was established based on the results of a 75-gram Oral Glucose Tolerance Test (OGTT), conducted between the 24th and 28th weeks of pregnancy as part of routine prenatal screening. The test involved measuring fasting plasma glucose, 1- hour, and 2-hour post-load glucose levels, with diagnostic thresholds defined by the International Association of Diabetes and Pregnancy Study Groups (IADPSG) [16]. According to these criteria, GDM was diagnosed if at least one of the following values was met or exceeded: fasting plasma glucose ≥ 92 mg/dL (5.1 mmol/L), 1-hour glucose ≥ 180 mg/dL (10.0 mmol/L), or 2-hour glucose ≥ 153 mg/dL (8.5 mmol/L).

All participants answered a set of structured questions devised by the study investigators exclusively for this research, using risk factors identified in the literature as a guide. These questions were designed to collect information on sociodemographic characteristics (e.g., age, educational status), obstetric data (e.g., pre-pregnancy weight, body mass index [BMI], number of pregnancies, and history of previous preterm birth), and lifestyle factors before and during pregnancy (e.g., coffee consumption), which were recorded using binary “yes” or “no” responses. Additionally, oral health behaviors, including toothbrushing frequency and other oral hygiene practices, were evaluated using categorical response options (Additional file 1). Blood samples were collected between the 24th and 28th weeks of gestation.

The following hematological parameters were analyzed to assess inflammation and immune response between the GDM group and the control group: Platelet Count (PLT), Mean Platelet Volume/Platelet Count (MPV/PLT), Platelet-to-Lymphocyte Ratio (PLR), Monocyte-to-Lymphocyte Ratio (MLR), Neutrophil-to-Lymphocyte Ratio (NLR), White Blood Cell Count (WBC), Red Blood Cell Count (RBC), Hemoglobin (HGB), Hematocrit (HCT), Mean Corpuscular Volume (MCV), Mean Corpuscular Hemoglobin (MCH), Mean Corpuscular Hemoglobin Concentration (MCHC), Red Cell Distribution Width (RDW), Platelet Distribution Width (PDW), Mean Platelet Volume (MPV), Plateletcrit (PCT), Neutrophil Count (NC), Lymphocyte Count (LYM), Monocyte Count (Mono), Eosinophil Count (EOS), and Basophil Count (Baso).

The inflammatory markers evaluated in this study (NLR, PLR, MLR, MPV/PLT), along with the hematological parameters used in their calculation (such as neutrophil, lymphocyte, monocyte, and platelet counts), were obtained from routine CBC tests. Due to their non-invasive nature and accessibility, these markers are considered practical tools for clinical assessment and may be utilized during pregnancy [4, 10].

In this study, oral health assessments including comprehensive dental and periodontal examinations were performed in pregnant women. The first evaluation was conducted within 24 h of recruitment, and a second assessment was carried out 24 h later. All examinations followed a standardized protocol to ensure procedural consistency and minimize inter-examiner variability. Importantly, these assessments were conducted independently of the participants’ medical records to avoid potential bias.

For the dental examination, caries experience was assessed using the Decayed, Missing, and Filled Teeth index (DMFT), and all evaluations were conducted by a single calibrated dentist (ADD). Periodontal parameters, including the Plaque Index (PI), Gingival Index (GI), Probing Depth (PD), and Clinical Attachment Level (CAL), were recorded at six sites per tooth using a Williams periodontal probe. Periodontal examinations were performed by a second calibrated dentist (SND), ensuring consistency and examiner reliability across all measurements.

Throughout all examinations, participants were positioned in a supine posture, and verbal instructions were provided to ensure appropriate head alignment. Assessments were conducted under standardized portable lighting conditions. To evaluate the reliability and consistency of the measurements, inter-examiner correlation analysis was performed based on data collected by the two examining dentists (ADD and SND).

Periodontal status was classified based on mean PD and CAL values recorded for each participant. Individuals with both PD < 3 mm and CAL < 3 mm were categorized as periodontally healthy. Mild periodontitis was defined as having either PD or CAL between 3.0 and 4.4 mm. Moderate periodontitis included PD or CAL values between 4.5 and 5.4 mm, while values ≥ 5.5 mm in either parameter were classified as severe periodontitis. This simplified classification was adapted to approximate clinical staging in the absence of site-specific measurements, following general thresholds used in epidemiological studies on periodontal disease [17].

Obstetric characteristics and oral examination data were then combined and statistically analyzed. The severity of tooth and gum disease and inflammatory blood markers were compared between the two groups.

Statistical analysis

Statistical analyses were performed using SPSS software (version 2.3.18). The Kolmogorov-Smirnov test was used to assess the normality of data distribution. Since the data did not follow a normal distribution, the Mann-Whitney U test was applied to compare differences between groups. Chi-square tests were used to analyze categorical variables. Significant parameters were further evaluated using hierarchical binomial logistic regression analysis. To assess intra-reviewer reliability for quantitative data, the Intraclass Correlation Coefficient (ICC) was calculated. A p-value of < 0.05 was considered statistically significant.

Results

The ICC values ranged between 0.85 and 0.90 for all dental and periodontal parameters, indicating excellent reliability compared to the thresholds set for reproducibility.

A total of 80 participants, including 40 individuals with GDM and 40 controls, were included in the study. The mean age of individuals with GDM was 29.63 ± 6.72 years, while the mean age of the control group was 26.60 ± 5.96 years, with a statistically significant difference between the groups (p < 0.05) (Table 1).

Table 1.

Characteristics of the subjects included in the study and the relationship between GDM and control groups

		Control N = 40 (50%)	GDM N = 40 (50%)	p-value
Demographic characteristics
Age (years)		26.60 ± 5.92 1	29.62 ± 6.72 1	0.0383
Education				0.3652
None		3 (7.5%)	0 (0%)
Primary education		14 (35.0%)	18 (45.0%)
High school		14 (35.0%)	12 (30.0%)
University		9 (22.5%)	10 (25.0%)
Health insurance status				1.0002
Yes		18 (45.5%)	18 (45.0%)
No		22 (55.5%)	22 (55.0%)
Height (cm)		159.25 (158.5) 1	161.68 (160.0) 1	0.0963
Obstetric characteristics
Gestational week (wk), median (min–max)		39 (38–41)	37 (32–41)	0.0003
Type of delivery	Vaginal delivery	%62,5	%37,5	0.0122
	Cesarean section	%37,5	%62,5
Pre-pregnancy weight (kg)		63.03 (60.0) 1	69.45 (66.0) 1	0.0703
Pregnancy weight (kg)		77.98 (77.0) 1	80.15 (79.0) 1	0.6823
First BMI (kg/m²)		24.04 (24.00) 1	27.24 (26.00) 1	0.0133
Last BMI (kg/m²)		29.67 (28.95) 1	31.47 (30.4) 1	0.2783
Weight gain during pregnancy(kg)		14.95 (15.00) 1	10.68 (9.5) 1	0.0003
Number of pregnancies				0.0612
Primar		18 (45.0%)	10 (25.0%)
Multipar		22 (55.0%)	30 (75.0%)
Interval with previous birth				0.2612
>2 years and above		21 (52.5%)	15 (37.5%)
< 2 years less		19 (47.5%)	25 (62.5%)
Number of births				0.2322
Primar		30 (75.0%)	24 (60.0%)
Multipar		10 (25.0%)	16 (40.0%)
Presence of previous premature birth				0.7122
Yes		3 (7.5%)	5 (12.5%)
No		37 (92.5%)	35 (87.5%)
Habits
Coffee				0.0022
Yes		0 (0.0%)a	9 (22.5%)b
No		40 (100.0%)a	31 (77.5%)b
Cigarette/Alcohol				0.0552
Yes		0. (0.0%)	5 (12.5%)
No		40 (100.0%)	35 (87.5%)b
Oral Habits
Brushing teeth				0.0002
Seldom		36 (90.5%)a	16 (40.0%)b
1 in a day		2 (5.0%)a	12 (30.0%)b
2 in a day		2 (5.0%)a	12 (30.0%)b
Other oral hygiene habits				0.0002
Yes		28 (70.0%)a	5 (12.5%)b
No		12 (30.0%)a	35 (87.5%)

BMI Body Mass Index, GDM Gestational Diabetes Mellitus

n (%), ¹Mean ± Standard Deviation, ²Pearson’s Chi-squared test, ³Mann-Whitney U test

^{a, b}Values within the same row that do not share a common superscript letter differ significantly between groups (p < 0.05)

GDM and control groups showed no statistically significant differences in education level, health insurance status, height, pre-pregnancy weight, BMI at the end of pregnancy, number of pregnancies and births, years between previous births, history of previous preterm births, and smoking and alcohol use (p > 0.05) (Table 1). While initial BMI values were significantly higher in the GDM group compared to the control group, the amount of weight gained during pregnancy was lower in the GDM group (p < 0.05, p < 0.01) (Table 1). The mean gestational week at delivery was significantly lower in the GDM group compared to the control group (p = 0.000) (Table 1). Caesarean section rates were significantly higher among women with GDM (62.5%) than in the control group (37.5%) (p = 0.012) (Table 1). Mean birth weight did not differ significantly between groups (3208 ± 3332.50 g in controls vs. 3187 ± 3200.00 g in GDM, p = 0.248) (Table 1). Similarly, no significant differences were observed between the groups regarding neonatal head circumference (35.55 ± 35.00 cm vs. 34.95 ± 35.00 cm, p = 0.609) (Table 1).

In the control group, infrequent toothbrushing and maternal age were statistically higher (p < 0.01) (Table 1). On the other hand, in the GDM group, coffee consumption was higher, while the use of oral care products other than toothbrushing was lower (p < 0.01) (Table 1).

Regarding blood parameters, eosinophil levels were significantly lower in the GDM group (p = 0.011) (Table 2).

Table 2.

Blood counts of subjects and relationship between GDM and control groups

Blood Counts	Control N = 40 (50%)	GDM N = 40 (50%)	p-value
PLT	215400.00 ± 67.805.45	216650.00 ± 67.564.04	0.761 1
MVP/PLT	0.058 ± 0.02	0.060 ± 0.04	0.378 1
PLR	116.16 ± 36.28	117.20 ± 42.97	0.919 1
MLR	0.33 ± 0.09	0.33 ± 0.13	0.468 1
NLR	3.98 ± 1.50	4.37 ± 1.71	0.440 1
WBC	9886.00 ± 3154.49	10664.25 ± 3003.59	0.309 1
RBC	4.36 ± 0.33	4.28 ± 0.36	0.519 1
HGB	12.21 ± 1.15	12.01 ± 1.42	0.434 1
HCT	36.66 ± 2.57	36.07 ± 3.52	0.511 1
MCV	84.49 ± 6.30	84.20 ± 5.88	0.694 1
MCH	28.15 ± 2.76	28.01 ± 2.57	0.792 1
MCHC	33.27 ± 1.27	33.24 ± 1.41	0.909 1
RDW	14.56 ± 1.53	15.00 ± 1.87	0.327 1
PDW	14.22 ± 2.91	13.61 ± 2.97	0.301 1
MPV	11.34 ± 1.11	10.99 ± 1.02	0.189 1
PCT	0.24 ± 0.06	0.23 ± 0.06	0.893 1
NC	7.30 ± 2.11	7.97 ± 2.70	0.327 1
LYM	1.95 ± 0.06	1.93 ± 0.53	0.916 1
Mono	0.63 ± 0.18	0.63 ± 0.25	0.694 1
EOS	0.09 ± 0.06	0.06 ± 0.06	0.011 1
Baso	0.02 ± 0.01	0.01 ± 0.01	0.420 1

Mean ± Standard Deviation, 1Mann-Whitney U test

PLT Platelet, MVP/PLT Mean Platelet Volume/Platelet, PLR Platelet-to-Lymphocyte Ratio, MLR Monocyte-to-Lymphocyte Ratio, NLR Neutrophil-to-Lymphocyte Ratio, WBC White Blood Cell, RBC Red Blood Cell, HGB Hemoglobin, HCT Hematocrit, MCV Mean Corpuscular Volume, MCH Mean Corpuscular Hemoglobin, MCHC Mean Corpuscular Hemoglobin Concentration, RDW Red Cell Distribution Width, PDW Platelet Distribution Width, MPV Mean Platelet Volume, PCT Plateletcrit, NC Nucleated Cells, LYM Lymphocytes, Mono Monocytes, EOS Eosinophils, Baso Basophils, GDM Gestational Diabetes Mellitus

However, no significant differences were observed between the groups in terms of other hematological parameters including PLT, WBC, RBC, HGB, HCT, RDW, and MPV (p > 0.05) (Table 2).

When periodontitis severity was reclassified based on mean PD and CAL, no statistically significant association was found between periodontal status and GDM diagnosis (χ² = 3.13, p = 0.372; Table 3).

Table 3.

Oral indices of subjects and relationship between gestational diabetes mellitus (GDM) and control

Oral Indices	Control N = 40 (50%)	GDM N = 40 (50%)	p-value
DMFT	3.62 ± 3.06	6.95 ± 5.94	0.007 1
PI	1.17 ± 0.50	1.78 ± 0.47	< 0.001 1
GI	1.20 ± 0.57	1.88 ± 0.47	< 0.001 1
PD	2.00 ± 0.34	2.52 ± 0.34	< 0.001 1
CAL	0.50 ± 1.37	0.95 ± 1.82	0.144 1
Periodontal Stages
Healty	36 (90.0%)	33 (82.5%)	0.3722
Mild	2 (5.0%)	6 (15.0%)
Moderate	1 (2.5%)	0 (0.0%)
Severe	1 (2.5%)	1 (2.5%)

Mean ± Standard Deviation, ¹Mann-Whitney U test, ²Chi-square test 

DMFT Decayed, Missing, Filled Teeth, PI Plaque Index, GI Gingival Index, PD Probing Depth, CAL Clinical Attachment Loss

In the GDM group, 82.5% of participants were classified as periodontally healthy, while 15.0% had mild periodontitis and 2.5% had severe periodontitis. In the control group, 90.0% were classified as healthy, and mild, moderate, and severe periodontitis were observed in 5.0%, 2.5%, and 2.5% of participants, respectively. Although mild periodontitis was more frequently observed in the GDM group, this trend did not reach statistical significance.

Systemic inflammatory marker levels were analyzed according to periodontal disease severity within both the GDM and control groups (Table 4).

Table 4.

Systemic inflammatory marker levels (mean ± standard deviation) according to periodontal disease severity in GDM and control groups

	GDM			Control				H-value	p-value
	Healthy	Mild	Severe	Healthy	Mild	Moderate	Severe
Leukocyte	10693.64 ± 3225.85	10993.33 ± 1378.39	7720.0 ± none	10187.69 ± 3184.61	7205.0 ± 49.5	7580.0 ± none	6700.0 ± none	5.64	0.131 1
NC	8.01 ± 2.92	8.16 ± 1.05	5.22 ± none	7.56 ± 2.06	5.15 ± 0.25	5.64 ± none	3.90 ± none	4.60	0.204 1
NLR	4.46 ± 1.81	4.12 ± 1.19	2.88 ± none	4.06 ± 1.54	3.65 ± 0.68	4.21 ± none	1.75 ± none	4.29	0.232 1
MLR	0.34 ± 0.14	0.33 ± 0.08	0.34 ± none	0.33 ± 0.1	0.39 ± 0.02	0.38 ± none	0.21 ± none	2.34	0.505 1
PLR	119.28 ± 45.31	102.51 ± 30.0	137.01 ± none	116.76 ± 37.51	108.24 ± 17.95	141.79 ± none	84.75 ± none	1.51	0.679 1

NC Neutrophil Count, NLR Neutrophil-to-Lymphocyte Ratio, MLR Monocyte-to-Lymphocyte Ratio, PLR Platelet-to-Lymphocyte Ratio, GDM Gestational Diabetes Mellitus

¹Kruskal–Wallis test

In the GDM group, the mean NC and NLR tended to decrease as periodontal disease severity increased, with NC declining from 8.01 ± 2.92 in healthy participants to 5.22 in those with severe periodontitis. A similar trend was observed in NLR values, which decreased from 4.46 ± 1.81 in the healthy group to 2.88 in the severe group. Although these trends did not reach statistical significance, Kruskal-Wallis tests revealed a near-significant difference in NC across periodontal stages (H = 4.60, p = 0.204). Other inflammatory indices, such as MLR and PLR, did not show consistent patterns across groups or stages. Overall, no statistically significant differences were found between periodontitis stages and inflammatory marker levels in either the GDM or control groups (all p > 0.05), but the observed trends in neutrophil activity may suggest a potential inverse association between neutrophil response and periodontal breakdown in GDM patients.

As shown in Table 5, several hematological parameters demonstrated significant correlations with oral health indices in the control group.

Table 5.

Spearman’s correlation analysis compares blood counts and oral health indices in control groups

		DMFT	PI	GI	PD	CAL
PLT	Spearman’s rho	− 0.164	− 0.158	− 0.233	− 0.247	− 0.319*
	p-value	0.313	0.330	0.148	0.125	0.045
MVP/PLT	Spearman’s rho	0.126	0.181	0.253	0.297	0.274
	p-value	0.438	0.264	0.116	0.062	0.087
PLR	Spearman’s rho	0.185	− 0.073	− 0.134	− 0.227	0.032
	p-value	0.253	0.654	0.409	0.160	0.842
MLR	Spearman’s rho	0.142	− 0.070	− 0.071	− 0.184	0.150
	p-value	0.382	0.670	0.663	0.256	0.355
NLR	Spearman’s rho	− 0.015	− 0.136	− 0.196	− 0.278	− 0.060
	p-value	0.926	0.401	0.224	0.083	0.712
WBC	Spearman’s rho	− 0.385*	− 0.329*	− 0.346*	− 0.346*	− 0.570**
	p-value	0.014	0.038	0.029	0.029	0.000
RBC	Spearman’s rho	0.034	0.022	− 0.072	− 0.075	− 0.169
	p-value	0.836	0.892	0.660	0.644	0.298
HGB	Spearman’s rho	0.066	0.180	0.180	− 0.063	− 0.034
	p-value	0.685	0.266	0.266	0.700	0.833
HCT	Spearman’s rho	0.114	0.264	0.230	− 0.037	− 0.021
	p-value	0.483	0.100	0.154	0.819	0.896
MCV	Spearman’s rho	0.197	0.354*	0.416**	0.076	0.290
	p-value	0.223	0.025	0.008	0.643	0.069
MCH	Spearman’s rho	0.112	0.189	0.265	− 0.005	0.177
	p-value	0.492	0.242	0.098	0.976	0.273
MCHC	Spearman’s rho	− 0.129	− 0.138	− 0.060	− 0.093	0.022
	p-value	0.428	0.395	0.715	0.566	0.893
RDW	Spearman’s rho	− 0.084	0.137	0.042	0.153	− 0.076
	p-value	0.605	0.399	0.797	0.344	0.639
PDW	Spearman’s rho	− 0.022	0.069	0.082	0.231	− 0.024
	p-value	0.894	0.674	0.613	0.151	0.884
MPV	Spearman’s rho	0.022	0.105	0.124	0.252	0.063
	p-value	0.891	0.518	0.445	0.117	0.701
PCT	Spearman’s rho	− 0.139	− 0.166	− 0.239	− 0.204	− 0.318*
	p-value	0.392	0.306	0.137	0.207	0.045
NC	Spearman’s rho	− 0.379*	− 0.331*	− 0.368*	− 0.366*	− 0.509**
	p-value	0.016	0.037	0.020	0.020	0.001
LYM	Spearman’s rho	− 0.236	− 0.078	− 0.068	− 0.043	− 0.302
	p-value	0.142	0.632	0.676	0.790	0.058
Mono	Spearman’s rho	− 0.196	− 0.154	− 0.162	− 0.257	− 0.286
	p-value	0.227	0.342	0.318	0.109	0.073
EOS	Spearman’s rho	− 0.050	0.119	0.132	0.007	− 0.143
	p-value	0.760	0.463	0.418	0.964	0.378
Baso	Spearman’s rho	− 0.151	− 0.133	− 0.163	− 0.022	− 0.045
	p-value	0.353	0.412	0.313	0.893	0.785

PLT Platelet, MVP/PLT Mean Platelet Volume/Platelet, PLR Platelet-to-Lymphocyte Ratio, MLR Monocyte-to-Lymphocyte Ratio, NLR Neutrophil-to-Lymphocyte Ratio, WBC White Blood Cell, RBC Red Blood Cell, HGB Hemoglobin, HCT Hematocrit, MCV Mean Corpuscular Volume, MCH Mean Corpuscular Hemoglobin, MCHC Mean Corpuscular Hemoglobin Concentration, RDW Red Cell Distribution Width, PDW Platelet Distribution Width, MPV Mean Platelet Volume, PCT Plateletcrit, NC Nucleated Cells, LYM Lymphocytes, Mono Monocytes, EOS Eosinophils, Baso Basophils, DMFT Decayed, Missing. and Filled Teeth, PI Plaque Index, GI Gingival Index, PD Probing Depth, CAL Clinical Attachment Loss

Notably, WBC count and NC showed moderate and statistically significant negative correlations with all periodontal indices, including CAL (WBC: ρ = − 0.570, p < 0.001; NC: ρ = − 0.509, p = 0.001). PCT and PLT also negatively correlated with CAL (p = 0.045). Furthermore, MCV was positively correlated with gingival index (ρ = 0.416, p = 0.008). These findings suggest a potential inverse association between inflammatory cell counts and periodontal destruction in systemically healthy individuals. In the diabetic group, several statistically significant correlations were found between hematological markers and oral health indices (Table 6).

Table 6.

Spearman’s correlation analysis compares blood counts and oral health indices in GDM

		DMFT	PI	GI	PD	CAL
PLT	Spearman’s rho	0.366*	0.259	0.199	0.271	− 0.260
	p-value	0.020	0.107	0.218	0.090	0.105
MVP/PLT	Spearman’s rho	− 0.378*	− 0.312*	− 0.243	− 0.314*	0.290
	p-value	0.016	0.050	0.131	0.048	0.069
PLR	Spearman’s rho	0.201	0.338*	0.294	0.319*	− 0.298
	p-value	0.214	0.033	0.066	0.045	0.062
MLR	Spearman’s rho	0.018	− 0.158	− 0.099	− 0.146	0.118
	p-value	0.913	0.329	0.544	0.370	0.469
NLR	Spearman’s rho	− 0.185	0.150	0.165	0.148	− 0.128
	p-value	0.253	0.356	0.309	0.363	0.433
WBC	Spearman’s rho	− 0.032	− 0.097	− 0.072	− 0.003	− 0.055
	p-value	0.846	0.553	0.657	0.985	0.738
RBC	Spearman’s rho	0.027	− 0.068	0.048	0.013	0.154
	p-value	0.867	0.677	0.767	0.937	0.342
HGB	Spearman’s rho	− 0.188	− 0.167	− 0.103	− 0.102	0.108
	p-value	0.245	0.302	0.528	0.532	0.509
HCT	Spearman’s rho	− 0.172	− 0.296	− 0.238	− 0.204	0.113
	p-value	0.289	0.064	0.140	0.206	0.487
MCV	Spearman’s rho	− 0.289	− 0.398*	− 0.413**	− 0.362*	0.029
	p-value	0.070	0.011	0.008	0.022	0.861
MCH	Spearman’s rho	− 0.307	− 0.233	− 0.220	− 0.224	− 0.030
	p-value	0.054	0.147	0.172	0.165	0.852
MCHC	Spearman’s rho	− 0.139	0.240	0.277	0.238	0.017
	p-value	0.392	0.136	0.084	0.138	0.919
RDW	Spearman’s rho	− 0.002	− 0.089	− 0.094	0.021	0.038
	p-value	0.990	0.583	0.562	0.900	0.814
PDW	Spearman’s rho	− 0.304	− 0.366*	− 0.287	− 0.354*	0.275
	p-value	0.057	0.020	0.073	0.025	0.086
MPV	Spearman’s rho	− 0.310	− 0.373*	− 0.288	− 0.357*	0.255
	p-value	0.052	0.018	0.072	0.024	0.112
PCT	Spearman’s rho	0.327*	0.160	0.123	0.188	− 0.220
	p-value	0.040	0.323	0.450	0.247	0.173
NC	Spearman’s rho	− 0.165	− 0.076	− 0.047	0.013	− 0.026
	p-value	0.309	0.640	0.775	0.936	0.872
LYM	Spearman’s rho	0.274	− 0.215	− 0.242	− 0.130	− 0.020
	p-value	0.087	0.183	0.132	0.424	0.903
Mono	Spearman’s rho	0.189	− 0.343*	− 0.285	− 0.239	0.128
	p-value	0.244	0.030	0.074	0.138	0.430
EOS	Spearman’s rho	0.424**	− 0.079	− 0.038	− 0.018	0.038
	p-value	0.006	0.628	0.817	0.913	0.815
Baso	Spearman’s rho	0.091	− 0.438**	− 0.431**	− 0.472**	− 0.244
	p-value	0.575	0.005	0.006	0.002	0.129

PLT Platelet, MVP/PLT Mean Platelet Volume/Platelet, PLR Platelet-to-Lymphocyte Ratio, MLR Monocyte-to-Lymphocyte Ratio, NLR Neutrophil-to-Lymphocyte Ratio, WBC White Blood Cell, RBC Red Blood Cell, HGB Hemoglobin, HCT Hematocrit,

MCV Mean Corpuscular Volume, MCH Mean Corpuscular Hemoglobin, MCHC Mean Corpuscular Hemoglobin Concentration, RDW Red Cell Distribution Width, PDW Platelet Distribution Width, MPV Mean Platelet Volume, PCT Plateletcrit, NC Nucleated Cells, LYM Lymphocytes, Mono Monocytes, EOS Eosinophils, Baso Basophils, DMFT Decayed. Missing. and Filled Teeth, PI Plaque Index, GI Gingival Index, PD Probing Depth, CAL Clinical Attachment Loss, GDM Gestational Diabetes Mellitus

PLT was positively correlated with DMFT (ρ = 0.366, p = 0.020), while MVP/PLT showed negative correlations with DMFT, PI, and PD (p < 0.05). PLR correlated positively with PI (ρ = 0.338, p = 0.033) and PD (ρ = 0.319, p = 0.045). MCV was negatively associated with PI, GI, and PD, with the strongest correlation observed with GI (ρ = − 0.413, p = 0.008). Interestingly, EOS was positively correlated with DMFT (ρ = 0.424, p = 0.006), whereas Baso demonstrated significant negative correlations with all periodontal indices except CAL (p < 0.01). No significant associations were observed for NC, WBC, or NLR.

Hierarchical binomial logistic regression analysis was performed to identify predictors of GDM. As shown in Table 7, only weight gain during pregnancy was significantly associated with GDM (OR = 1.117, 95% CI: 1.008–1.240, p = 0.036) (Fig. 1). All other predictors, including demographic characteristics (age, first BMI), obstetric characteristics (gestational week, type of delivery), lifestyle factors (coffee consumption, tooth brushing frequency, other oral hygiene habits), hematological parameter (eosinophil), and oral indices (DMFT, PI, GI, PD), did not show statistically significant associations (p > 0.05).

Table 7.

Presentation of hierarchical binominal logistic regression analysis

Predictor	Estimate	SE	Z	p-value	Odds Ratio	Lower (95% Cl)	Upper (95% Cl)	Deviance	AIC	R 2 MCF
Intercept ᵃ	1354.63	314,878	0.00430	0.997	Inf	−615,794	618,504
Model 1								106.4	110.4	0.0405
Age	3.24	4376	7.41E-4	0.999	25.60622	−8574	8580
Model 2								76.6	88.6	0.309
First BMI	−0.1279	0.0741	−1.7264	0.084	0.88	0.761	1.02
Weight gain during pregnancy	0.1107	0.0527	2.10209	0.036	1.117	1.008	1.24
Gestational week (Ref. ≤36	18.0817	1691.856	0.01069	0.991	7.91e −5	0.00	Inf
Type of delivery (Ref: C/S)	0.822	0.578	1.42223	0.155	2.275	0.733	7.06
Model 3								67.7	81.7	0.3898
Coffee	19.4299	3013.3222	0.00645	0.995	2.76e + 57	0.00	Inf
Model 4								31.2	51.2	0.7191
Brushing teeth (Ref: Seldom)
Once a day- Seldom	−410.41	99,308	−0.00413	0.997	5.76e-179	−195,050	194,229
Twice a day- Seldom	−341.63	73,930	−0.00462	0.996	4.30e-149	−145,242	144,558
Other oral hygiene habits (Ref: No)	30.3	50,374	6.02E-04	1.000	1.45e + 13	−98,701	98,762
Model 5								29.9	51.9	0.7305
Eosinophil	977.23	402,190	0.00243	0.998	Inf	−787,301	789,255
Model 6								4.75e-8	30	1.00
DMFT	−6.24	9345	−6.67e − 4	0.999	0.00196	−18,322	18,310
PI	−77.39	28,855	−0.00268	0.998	2.46e −34	−56,632	56,478
GI	18.67	27,017	6.91E-04	0.999	1.29e +8	−52,934	52,972
PD	−107.11	28,250	−0.00379	0.997	3.03e −47	−55,477	55,262

ᵃ Ref Represents reference level, R²_MCF McFadden’s R²

Fig. 1.

Forest plot displaying the odds ratio (OR) and 95% confidence interval (CI) for weight gain during pregnancy, the only statistically significant predictor of gestational diabetes mellitus (GDM) identified in the hierarchical binomial logistic regression analysis. The dashed vertical line at OR = 1 represents no effect

Discussion

Periodontal disease, as a chronic inflammatory condition, may serve as a systemic source of proinflammatory cytokines such as IL-6, tumor necrosis factor-alpha (TNF-α), and CRP. These mediators can enter the systemic circulation and contribute to a low-grade chronic inflammatory state, which is known to impair insulin receptor signaling and promote insulin resistance [18, 19]. During pregnancy, this inflammatory burden may exacerbate the physiological insulin resistance of gestation, potentially tipping the balance toward glucose intolerance and the development of GDM [20]. Although our study did not directly assess circulating cytokines, the observed associations between periodontal status and immune cell–based markers support this proposed inflammatory–metabolic axis.

The findings from Table 1 highlight significant differences between the GDM and control groups concerning various demographic, obstetric, and lifestyle characteristics. Notably, the GDM group was older (p = 0.038) and had a higher initial BMI (p = 0.013), consistent with the body of literature indicating that both advanced maternal age and obesity are critical risk factors for the development of GDM [21, 22]. Additionally, our study identified that individuals with GDM exhibited significantly lower weight gain during pregnancy (p = 0.000), which could suggest an underlying metabolic imbalance impacting nutritional status [23]. In alignment with these findings, a systematic review by Li et al. [24] emphasizes that women of older age are at an elevated risk for gestational complications, including GDM [25]. The observed increase in cesarean delivery incidences among the GDM cohort (p = 0.012) supports prior research that indicates a higher likelihood of surgical births in this population, reinforcing the association between GDM and adverse pregnancy outcomes [26]. Moreover, the finding that coffee consumption was significantly higher in the GDM group (p = 0.002) aligns with emerging evidence linking dietary habits with GDM risk, as outlined by Ni et al. [27, 28]. Furthermore, the study revealed less frequent oral hygiene practices among GDM patients, with a notable reduction in the use of additional oral care products beyond basic tooth brushing (p = 0.000). This finding is consistent with literature that suggests poor oral hygiene habits may exacerbate periodontal disease, presenting an additional risk factor during pregnancy [29]. Collectively, these findings highlight the influence of lifestyle and metabolic risk factors on the relationship between periodontal disease and GDM. The statistical analysis indicated that weight gain during pregnancy was the most significant predictor of GDM. Participants in the GDM group reported higher initial BMI values and exhibited specific lifestyle behaviors, such as increased coffee consumption and lower use of additional dental care products. These factors emphasize the need for further research to evaluate the impact of nutritional intake and overall health management on GDM outcomes [30]. While coffee consumption was higher in the GDM cohort, the assessment of its specific contribution to GDM development, particularly concerning sugar content or accompanying dietary habits, requires further study.

Despite previous research suggesting a strong association between periodontal disease and GDM, findings in the literature remain inconsistent. While some meta-analyses confirm a significant link, others fail to establish a clear relationship [5, 11, 22, 31]. Variability in methodologies, sample sizes, and population characteristics may contribute to these discrepancies. Furthermore, systemic inflammation, a proposed mechanism linking periodontal disease to GDM, remains an area of debate. While our study identified correlations between PLR and periodontal indices in GDM patients, no significant association was observed for MPV, suggesting that inflammatory markers alone may not fully explain the relationship between periodontal disease and GDM [32, 33]. Prior studies have indicated that systemic inflammation can manifest locally within periodontal tissue, which may exacerbate periodontal disease during systemic conditions such as pregnancy [21, 30].

In our study, although the periodontal status was poor in the GDM group, no significant difference was observed in hematological inflammation indices. On the other hand, a study conducted among pregnant women in East Africa (Tanzania) investigated placental microbial profiles in relation to adverse pregnancy outcomes and periodontal status. In this study, placental samples were histologically examined for chorioamnionitis by a single pathologist blinded to the clinical data, and periodontitis was found to significantly affect the composition of the placental microbiota. Furthermore, Yang et al. (2021) reported that placental dysfunction in GDM is shaped by the interplay of multiple mechanisms, including molecular alterations in trophoblast subtypes, structural and functional differences in immune cells such as natural killer (NK) cells and macrophages, and impaired ligand–receptor interactions [34, 35]. Taken together, these findings suggest that periodontal status may represent only a limited component of the systemic inflammatory response in GDM, and that the pathogenesis of the disease has a multifactorial nature. This may partly explain the absence of significant differences in hematological inflammation indices in our study despite the poor periodontal status.

Contrary to expectations based on previous literature, our study found no statistically significant differences in most inflammatory markers when comparing the GDM and control groups, with the exception of eosinophil levels, which were lower in the GDM group. This finding is noteworthy compared to earlier studies reporting increased levels of certain inflammatory markers in women with GDM [36]. The absence of significant changes in inflammatory markers suggests that factors other than inflammation may play an important role in the etiopathogenesis of GDM [37]. Considering that GDM risk is multifactorial, variables such as age, obesity, and family history may influence the risk independently of inflammatory parameters derived from periodontal disease. In this context, literature findings on eosinophils are also noteworthy. In one study, the mean percentage of eosinophils appeared slightly higher in the GDM group compared to the control group; however, this difference was not statistically significant. The same study noted that eosinophil levels typically increase in parasitic infections and have no significant association with blood glucose regulation [38]. Furthermore, a recent meta-analysis indicated that the association between certain immune cell counts, such as eosinophils which are thought to be related to insulin resistance and GDM remains unclear, as available studies on this topic are limited [39]. These findings suggest that the role of eosinophils in the pathogenesis of GDM has not yet been fully elucidated and warrants further investigation.

The interplay between periodontal disease, placental inflammation, and GDM remains inadequately understood, with the mechanisms linking these entities representing an urgent area for investigation. Chronic subclinical inflammation prevalent in periodontal disease can activate localized immune responses and transient bacteremia, potentially affecting systemic health during pregnancy [40, 41]. Specific hypotheses suggest that the systemic inflammatory state created by periodontal disease could lead to other complications, including those related to GDM [31, 42]. Further exploration of these pathways could reveal critical targets for preventive interventions in maternal healthcare.

One notable strength of this study lies in its methodology, specifically the utilization of CBC tests to assess inflammatory markers, offering a reliable and non-invasive approach [43]. Additionally, the presence of a blinded periodontist conducting periodontal assessments enhances the study’s methodological rigor. The comprehensive analysis of blood parameters following standardized laboratory protocols also bolsters the reliability of our findings.

Several limitations must be acknowledged. Additionally, the clinical examinations were performed in a bedside setting, which may lack the diagnostic precision of advanced imaging techniques or molecular analyses [44]. The absence of inflammatory biomarkers specific to periodontal disease such as those found in gingival crevicular fluid further limits the depth of inflammatory profiling [36]. Another one, the use of conventional oral health indices (PI, GI, PD, CAL) rather than formal diagnostic criteria for periodontitis may constrain the clinical interpretation of disease severity. Finally, as this was a single-center study, generalizability to broader populations remains uncertain due to possible variations in geographic, cultural, and socioeconomic factors among pregnant women.

Future studies should aim to include direct measurements of systemic inflammatory biomarkers such as CRP and IL-6 to enhance mechanistic insight into the relationship between periodontal disease and GDM. Longitudinal study designs that monitor both periodontal and metabolic status throughout gestation would provide a clearer understanding of temporal dynamics and potential causality. Research efforts should also encompass larger, prospective cohorts to clarify the underlying mechanisms and determine whether improvements in periodontal health can reduce GDM risk. Such investigations may contribute to the development of novel preventive strategies in maternal care. Ultimately, a deeper understanding of this bidirectional relationship could enhance clinical guidelines and improve outcomes for both maternal and fetal health [31].

Conclusions

This study found poorer periodontal health in GDM patients. Apart from lower eosinophil levels, no significant differences in inflammatory markers were identified. Pregnancy weight gain emerged as the primary predictor of GDM, emphasizing metabolic factors. These findings suggest that integrating oral health assessments and weight management strategies into prenatal care programs may be beneficial in supporting maternal health and reducing the risk of GDM at the population level.

Abbreviations

Baso

Basophils

BMI

Body Mass Index

CAL

Clinical Attachment Level

CBC

Complete Blood Count

CRP

C-reactive protein

DMFT

Decayed, Missing, and Filled Teeth

EOS

Eosinophils

GDM

Gestational diabetes mellitus

GI

Gingival Index

HCT

Hematocrit

HGB

Hemoglobin

ICC

Intraclass Correlation Coefficient

IL-6

Interleukin-6

LYM

Lymphocytes

MCV

Mean Corpuscular Volume

MCH

Mean Corpuscular Hemoglobin

MCHC

Mean Corpuscular Hemoglobin Concentration

MLR

Monocyte-to-Lymphocyte Ratio

Mono

Monocytes

MPV

Mean Platelet Volume

NC

Neutrophil Count

NLR

Neutrophil-to-Lymphocyte Ratio

OGTT

Oral Glucose Tolerance Test

PCT

Plateletcrit

PD

Probing Depth

PDW

Platelet Distribution Width

PI

Plaque Index

PLR

Platelet-to-Lymphocyte Ratio

PLT

Platelet Count

RDW

Red Cell Distribution Width

RBC

Red Blood Cell

WBC

White Blood Cell

Authors’ contributions

Author Contributions: Conceptualization, Isa Temur and Katibe Tugce Temur; Data curation, Isa Temur, Safak Donertas, Aycan Dal Donertas, Samet Aslan Formal analysis, Guldane Magat and Katibe Tugce Temur; Investigation, Isa Temur; Methodology, Isa Temur and Safak Donertas; Visualization, Katibe Tugce Temur; Writing – original draft, Isa Temur and Safak Necati Donertas; Writing – review & editing, Guldane Magat.

Funding

This research received no external funding.

Data availability

Availability of data and materials: Our dataset is publicly available on the Figshare platform at this link: https://figshare.com/s/57ff5390aba070dc898b.

Declarations

Ethics approval and consent to participate

The study was conducted between September 2022 and April 2023 at the Obstetrics and Gynecology Department of a university hospital. Ethical approval was obtained from the Non-Interventional Ethics Committee of Niğde Ömer Halisdemir University Faculty of Medicine (Decision No: 2022/72), and all procedures complied with the principles of the Declaration of Helsinki. Written informed consent was obtained from all participants.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Clinical trial registration

Not applicable.