This study was designed as a pilot cross-sectional observational investigation aimed at generating preliminary evidence on the association between salivary monosodium glutamate concentration and dental caries experience. Given the absence of prior human data evaluating MSG as a salivary biochemical marker in relation to caries status, an exploratory analytical framework was adopted to assess feasibility, direction, and strength of association.

The study was conducted at the Dental Institute with outpatient dental services, involving both clinical examination facilities and an associated biochemical analysis laboratory. Participant recruitment, clinical examination, saliva collection, and laboratory analysis were carried out over a defined period from March 2025 to April 2025.

A total of 20 participants were enrolled in the study using convenience-based sampling. Participants aged 18–35 years were selected to minimize age-related biological and behavioral confounding in salivary composition and dental caries experience. This age group represents a period of relative physiological stability, with fully erupted permanent dentition and without the pronounced age-associated changes in salivary flow rate, buffering capacity, and oral microbiome composition observed in older adults. Additionally, restricting the age range reduced the influence of cumulative tooth loss, extensive restorative history, and systemic comorbidities that could independently affect both salivary biomarkers and caries indices. From a dietary perspective, individuals in this age group exhibit relatively stable and independent food consumption patterns, allowing clearer interpretation of salivary MSG levels in relation to recent dietary exposure. This restriction was applied to enhance internal validity in this exploratory study. Participants with a history of systemic disease, under medication, active periodontal therapy, xerostomia, tobacco or alcohol use, antibiotic intake within the preceding three months, or pregnancy or lactation were excluded, as these conditions are known to influence saliva. All eligible participants were informed about the objectives and procedures of the study, and written informed consent was obtained prior to participation.

Participants were recruited using a convenience based sampling approach from individuals attending the outpatient dental services of the institution during the study period. The sample size was determined based on feasibility considerations, including logistical constraints and the exploratory objective of evaluating salivary MSG measurement and its association with dental caries experience.

Unstimulated whole saliva was chosen as the biological sample since it represents basal secretion as well as cumulative dietary exposure. To reduce diurnal variation in salivary composition, samples were taken between 9:00 AM and 11:00 AM under standardized conditions.

Participants were asked to abstain from food, beverages (with the exception of water), and oral hygiene procedures for a minimum of 90 minutes before saliva collection. They were seated comfortably and requested to let saliva pool on the floor of the mouth before saliva collection in sterile polypropylene tubes. This passive drool method is the most reproducible and least invasive manner of unstimulated saliva collection. The tubes were sealed, labeled, and stored under cold at 4°C as soon as collection was done to maintain biochemical integrity until analysis.

Dental caries status was assessed clinically using the Decayed, Missing, and Filled Teeth (DMFT) index in accordance with the WHO 2013 Oral Health Survey Methods.‌^[19] All participants underwent intraoral examination under standard operating conditions using a plain mouth mirror and ball-ended explorer, with examinations performed under adequate illumination and air drying when required. No radiographic investigations were used for caries detection.

Caries assessment was carried out independently by two calibrated examiners who were trained using standardized diagnostic criteria prior to the commencement of the study. Calibration was performed through joint examination of a subset of patients not included in the final analysis to ensure consistency in diagnostic thresholds. In cases of disagreement between examiners, a consensus decision was reached through joint re-examination, thereby minimizing assessment bias.

For DMFT scoring, teeth with untreated cavitated carious lesions were recorded as decayed (D), teeth missing due to caries were recorded as missing (M), and teeth restored due to caries were recorded as filled (F). The DMFT score for each participant was calculated as the sum of decayed, missing, and filled teeth, representing cumulative lifetime caries experience.

In addition to DMFT, a caries index (CI) was calculated to provide a proportional measure of caries involvement relative to total caries experience. The caries index was computed using the formula:

CI = D/{D+M+F}

where D represents the number of decayed teeth, M the number of teeth missing due to caries, and F the number of filled teeth. This index reflects the proportion of untreated carious lesions within the overall caries burden. The combined use of DMFT and CI enabled assessment of both cumulative caries experience and the relative distribution of active disease within the dentition (Fig. 1).

Figure 1.

Dental caries assessment and index calculation. Representative intraoral clinical photographs and schematic depiction illustrating dental caries assessment using the DMFT index and caries index (CI). The table summarizes individual components of the DMFT score (Decayed, Missing, Filled teeth), salivary monosodium glutamate concentration, and calculated caries index.

All the reagents applied for MSG estimation were of analytical grade unless otherwise stated. MSG standard (99%), 2,4-dinitro-1-fluorobenzene (DNFB), sodium bicarbonate, hydrochloric acid, diethyl ether from Fluka, and methanol (HPLC grade).

A stock solution of MSG was made by dissolving MSG standard in deionized water at the concentration of 5 mg/mL. Serial dilutions of this stock were made to generate working standards of 10, 20, 40, 60, 80, and 100 µg/mL. These ranges were selected to cover the anticipated physiological range of salivary MSG, thus allowing construction of a valid calibration curve. The pH of all working solutions was set to 7.8 with the help of 5% sodium bicarbonate solution since it mimics salivary pH and stabilizes amino acid salts during derivatization.

All saliva samples were centrifuged at 3000 rpm for 10 minutes at room temperature to remove cellular debris and particulate matter. Following centrifugation, 0.5 mL of the clear supernatant was carefully aspirated, and the residual pellet was discarded. The recovered supernatant was used for subsequent pre-column derivatization to facilitate detection of monosodium glutamate using high-performance liquid chromatography (HPLC). Derivatization was performed to enhance the chromophoric properties of amino acids and improve analytical sensitivity and resolution. For derivatization, 0.5 mL of the centrifuged salivary supernatant and standard solutions were transferred into clean test tubes, to which 10 µL of 2,4-dinitro-1-fluorobenzene (DNFB) was added. The reaction mixture was incubated in a water bath at 40°C for three hours under dark conditions to prevent photodegradation of DNFB derivatives. Excess DNFB was extracted using 0.5–1.0 mL of diethyl ether, after which the aqueous phase was acidified with 50 µL of 6 M hydrochloric acid until the ether layer showed no visible coloration, ensuring complete removal of unreacted derivatizing agent. Residual ether was allowed to evaporate at room temperature, and the dried residue containing DNP–amino acid derivatives was reconstituted in 500 µL of methanol. Finally, 20 µL of the reconstituted sample was injected into the HPLC system for chromatographic analysis.

Analysis by HPLC was done in the reversed-phase mode utilizing a C18 column (250 mm×4.6 mm, 5 µm particle size). The gradient combination of water and methanol was optimized for separation of DNP-MSG derivatives with good peak resolution. The flow rate was set to 1.0 mL/min, and detection was done at the wavelength of 360 nm, which is the absorbance λ_max of the DNP-derivatives. Retention times were taken for all standard concentrations, and peak areas were integrated with the system software.

Calibration curves were constructed from a plot of peak area against standard MSG concentration, giving a regression equation employed to determine salivary MSG concentrations.

Every analysis batch contained a set of working standards and duplicate samples for quality control purposes. Intra-assay variability was low with the same derivatization conditions for all samples. Recovery experiments showed higher than 95% recovery of MSG across concentrations.

Salivary MSG concentration was conceptualized as an exposure variable reflecting recent dietary intake of MSG-containing processed foods. Dental caries experience, assessed using the DMFT index and caries index, was considered the outcome of interest, representing cumulative and proportional measures of disease burden, respectively. Based on this framework, correlation analysis was employed to explore the strength and direction of association between salivary MSG levels and caries indices. Logistic regression analysis was applied as an exploratory tool to examine whether higher salivary MSG concentrations were associated with increased odds of belonging to a higher caries-risk category. Selection of covariates for adjustment was guided by a directed acyclic graph (DAG) approach, identifying age and sex as minimal confounders measurable within the scope of the present study.

A directed acyclic graph (DAG) was constructed to guide confounder identification and model adjustment by mapping hypothesized causal relationships between salivary monosodium glutamate concentration and dental caries outcomes. Variables considered included age, sex, dietary pattern, oral hygiene practices, and socioeconomic factors. Based on the DAG, age and sex were identified as the minimal sufficient adjustment set that could be reliably measured within the constraints of this pilot study. These variables were therefore included in the adjusted regression models to reduce confounding bias while avoiding overfitting in a small sample (Fig. 2).

Figure 2.

Directed acyclic graph (DAG) illustrating the conceptual framework for the association between salivary MSG concentration and dental caries experience.

The data were tabulated and analyzed statistically with the help of SPSS software (version 26). Descriptive statistics (mean, standard deviation, median, and interquartile range) were estimated for salivary MSG levels and DMFT scores. Normality of the distribution of the data was assessed by the Shapiro-Wilk test. As salivary MSG levels and DMFT scores did not follow a normal distribution, non-parametric techniques were used. The relationship between MSG level and DMFT index was assessed with Spearman’s rank correlation coefficient, which assesses monotonic relationships not depending on distribution assumptions. Logistic regression analysis was used to examine whether salivary MSG levels were associated with caries risk status after controlling for potential confounders like age and gender. Odds ratios (OR) at 95% confidence intervals (CI) were calculated. A p-value of <0.05 was used.

The analytical approach was selected to prioritize transparency and appropriateness for a small pilot dataset. Non-parametric methods were preferred due to the non-normal distribution of variables and limited sample size. Logistic regression results are presented as exploratory estimates to aid interpretation of potential trends rather than as definitive predictive models.

Unstimulated whole saliva analysis showed variability in MSG levels among the twenty subjects. The mean was 127.97 µg/mL, ranging between 95 and 165 µg/mL. Based on DMFT scores, divided into high-risk and low-risk caries experience groups (Fig. 3).

Figure 3.

Distribution of salivary monosodium glutamate concentration. Histogram showing the distribution of salivary MSG concentrations among study participants, with an overlaid normal curve indicating central tendency and dispersion.

The high caries experience group exhibited higher salivary MSG values than the low-risk group, which was statistically significant (p<0.01). The violin plots demonstrated a greater density of higher MSG values within the high caries experience group. This suggests that higher salivary MSG concentrations were more frequently observed among participants with greater caries experience, rather than being evenly distributed across groups (Fig. 4).

Figure 4.

Association between salivary MSG concentration and DMFT index. (A) Violin plot comparing salivary MSG concentrations between low- and high-DMFT groups. (B) Scatter plot illustrating the relationship between salivary MSG concentration and DMFT index, with fitted regression line and 95% confidence band.

Correlation analysis was also conducted between DMFT scores and salivary MSG concentration to further explore the association. Spearman correlation showed a positive association between salivary MSG concentration and DMFT (ρ=0.62, 95% CI: 0.25–0.83, p=0.003), which indicates that participants with higher MSG levels had greater quantities of decayed, missing, or filled teeth. Scatter plots illustrated this trend, with an upward slope observed across the range of MSG values. Within the high caries experience group, the relationship appeared steeper, whereas a similar but less pronounced trend was observed in the low caries experience group (Fig. 4).

The association between salivary MSG concentration and the caries index was weaker than that observed with DMFT, although the direction of association remained positive. The subjects with higher salivary MSG concentration had greater proportional involvement of caries-experiencing teeth. The scatter plot showed this linear increase, indicating that MSG level could be related to both cumulative measures like DMFT and proportional indexes indicative of total distribution of disease in the dentition. While the statistical power was less than that observed with DMFT, the consistency of direction across measures is in favor of MSG as an indicator of caries severity and not an outlier finding.

Heat map visualization was used to explore the interrelationships between salivary MSG concentration, DMFT scores, and the caries index. Glutamate along with DMFT, with darker nodes on the heat map indicating closer association. The ranking table comparison between glutamate groups strongly supported this trend. Mean and total ranks were greater in high MSG groups for DMFT and caries index. These trends validate that increased glutamate level is linked with greater burden of caries in various statistical and graphical representations (Fig. 5).

Figure 5.

Correlation matrix of salivary MSG, DMFT, and caries index. Bubble heatmap depicting pairwise correlations between salivary MSG concentration, DMFT index, and caries index. Circle size reflects correlation magnitude, while color intensity indicates direction and strength of association.

Exploratory logistic regression analysis was performed to examine whether salivary MSG concentration was associated with higher caries experience status (DMFT >4). Salivary MSG concentration was associated with higher odds of belonging to the high DMFT group (OR=1.05, 95% CI: 1.004–1.102, p=0.030). This indicates that each unit increase in MSG concentration was associated with a modest increase in the odds of belonging to the higher caries experience group. Given the exploratory nature of the analysis and limited sample size, these findings should be interpreted as indicative of an association rather than predictive capability (Fig. 6).

Figure 6.

Logistic regression analysis of caries risk. Predicted probability of high caries experience (DMFT > 4) across increasing salivary monosodium glutamate concentrations, based on an exploratory logistic regression model adjusted for age and sex. The solid line represents the estimated probability, and the shaded region indicates the 95% confidence interval. Model parameters and sample size are shown within the figure.