Ultrasensitive Multiplex Salivary Cytokine Profiling Reveals Distinct Inflammatory Patterns in Dental Caries

Ultrasensitive Multiplex Salivary Cytokine Profiling Reveals Distinct Inflammatory Patterns in Dental Caries | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Ultrasensitive Multiplex Salivary Cytokine Profiling Reveals Distinct Inflammatory Patterns in Dental Caries Margaux Dubois, Morgane Ortis, Lilit Tonoyan, Emma Dubois, Marie-France Bertrand, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9267095/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract BACKGROUND Dental caries is a prevalent chronic disease characterized by complex inflammatory responses in the oral environment. This case-control pilot study evaluated the analytical performance of an ultra-sensitive electrochemiluminescence multiplex assay (MSD S-PLEX®) for salivary cytokine profiling and explored whether combined cytokine signatures can discriminate dental caries lesions (CL) from healthy (H) and non-carious gingival inflammation (GI). METHODS Unstimulated whole saliva from 79 individuals (H, CL, GI) was analyzed using the S-PLEX® assay to quantify nine salivary cytokines (IL-1β, TNF-α, IL-6, IL-4, IL-10, IL-12p70, IFN-γ, IL-17, and IL-2). Group comparisons were performed using Mann-Whitney tests, and diagnostic performance was assessed by logistic regression and the area under the receiver operating characteristic curve. RESULTS The assay showed high sensitivity and reproducibility. Cytokine profiling revealed selective elevation of certain cytokines in saliva from CL. Among the models tested, TNF-α + IL-17 provided the best discrimination between CL and H (AUC = 0.901, p < 0.001) and between CL and H + GI (AUC = 0.795, p = 0.0003). Inclusion of additional cytokines did not improve performance. However, discrimination between CL and GI was limited with TNF-α + IL-17, while IL-1β + IL-17 provided a modest but significant discrimination (AUC = 0.737, p = 0.004). CONCLUSION This study provides evidence that ultrasensitive multiplex salivary cytokine profiling can reveal caries-associated inflammatory patterns. These findings highlight the translational potential of salivary cytokine panels as complementary tools for improved discrimination of caries and underline their potential for advancing precision approaches in caries research and management. Health sciences/Health care/Dentistry/Dental conditions Health sciences/Diseases/Oral diseases Oral Health Dental Caries Cytokines Multiplex Analysis Saliva Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Dental caries is one of the most prevalent chronic diseases worldwide, with significant impact on quality of life and healthcare costs [ 1 ]. It is a multifactorial disease driven by dynamic interactions between the host, oral microbiota, and environmental factors [ 2 ], and is now recognized as a consequence of microbial dysbiosis, favoring acidogenic and aciduric bacteria that disrupt oral homeostasis and promote tooth demineralization [ 2 , 3 ]. The progression of carious lesions (CL) allows bacterial invasion of the dentin and pulp, triggering inflammation. Within this context, the dental pulp hosts various immune cells, including neutrophils, monocytes, dendritic cells, and lymphocytes, while odontoblasts and other resident pulp cells constitute a first line of defense by sensing microbial patterns and releasing pro-inflammatory cytokines and chemokines [ 4 , 5 ]. These responses involve mediators such as IL-1β, IL-6, IL-8, and TNF-α, which drive pulpal disease progression [ 6 ], while IL-17 has emerged as a contributor to mucosal defense and local inflammation [ 7 ]. Accordingly, elevated cytokines have been detected in pulpal fluids, gingival crevicular fluid, and dentinal exudates, supporting their potential as biomarkers of pulpal pathology [ 8 , 9 ]. Clinical studies further link IL-1β, IL-8, and IL-6 with pulpitis severity and oral inflammation [ 6 , 9 ]. However, despite this evidence at the tissue level, data on salivary cytokines in the context of caries remain limited, even though saliva represents a readily accessible, non-invasive diagnostic fluid. Importantly, biofilm dysbiosis and Streptococcus mutans -derived products are known to induce proinflammatory cytokines such as TNF-α, IL-1β and IL-6 [ 10 ], suggesting that caries-associated microbial activity may also be reflected in salivary inflammatory profiles. In this context, recent studies have proposed combinatorial immune signatures as discriminators of oral conditions, including oral cancer and periodontitis [ 11 ], with pilot data suggesting their potential to distinguish deep carious lesions from gingivitis and health [ 12 ]. As personalized medicine advances, identifying salivary immune signatures holds promise for improving the diagnosis and management of oral diseases [ 12 , 13 ]. While clinical examination remains the standard for caries diagnosis, complemented by radiographic and adjunctive methods, salivary cytokine analysis offers complementary information on the underlying inflammatory response of the disease and may contribute to improving early diagnosis, risk assessment, and disease monitoring. Traditional approaches for cytokine quantification, such as enzyme-linked immunosorbent assays (ELISA) and bead-based multiplex assays, provide reliable measurements but often lack the sensitivity needed to reliably detect low-abundance cytokines in saliva [ 14 – 17 ]. The S-PLEX® ultra-sensitive immunoassay technology, developed by Meso Scale Discovery (MSD), achieves femtogram-per-mL detection limits while maintaining a broad dynamic range and high specificity, which is particularly advantageous in oral and dental research where analyte concentrations are frequently low. Its multiplexing capability and high reproducibility enable the simultaneous quantification of multiple cytokines, supporting more comprehensive immunoprofiling and enhancing the translational potential of salivary biomarker studies. Despite these advantages, the application of S-PLEX® technology to unstimulated whole saliva has not yet been validated [ 18 , 19 ]. We hypothesized that salivary cytokine profiles reflect caries-associated inflammatory responses and that specific combinations of cytokines can discriminate caries from health and gingival inflammation. Accordingly, this proof of-concept study aimed to evaluate and validate S-PLEX® technology for salivary cytokine profiling among healthy individuals (H), patients with CL, and those with gingival inflammation (GI). Specifically, we tested the MSD S-PLEX® Proinflammatory panel 1 (human) kit, which enables detection of nine cytokines representing key immune pathways: innate immunity (IL-1β, TNF-α), Th1 (IL-12p70, IFN-γ), Th2 (IL-4), Th17 (IL-17), regulatory responses (IL-10), and general T-cell activation (IL-2). Material and methods Study design and participants This case-control ancillary study was conducted within the ORASPORT cohort, a retrospectively registered clinical trial (NCT05765422), at Université Côte D'Azur, Nice, France [ 20 ]. The present investigation used the ORASPORT cohort as a pre-existing, well-characterized population for saliva biobanking and clinical data collection. Data collected within the ORASPORT framework were subsequently reused for this ancillary research through secondary analysis, independent of the original study aim related to exercise and oral microbiota. Of the 200 participants planned for inclusion in the ORASPORT cohort, 79 were included in the present ancillary study, reflecting an interim analysis based on the data available at the time of analysis, as the ORASPORT cohort is still ongoing and has not yet reached its final recruitment target. Seventy-nine healthy young adults were recruited from university campuses, the Dental Department of CHU Nice, and local sports events. Inclusion criteria were age 18–30 years, documented written informed consent, and completion of a standardized questionnaire covering medical, dental, and lifestyle history. Exclusion criteria were use of anti-inflammatory medication within 48 hours, or antibiotic/probiotic treatment within three weeks prior to sampling. Demographic characteristics (age, sex, body mass index [BMI]) were recorded, and psychological stress levels were assessed using the standardized Likert scale. The study was approved by the Committee for the Protection of Persons (CPP; approval ID-RCB 2022-A01682-41, March 2023) and registered under RCB: 2022-A01682-41-22-PP-11. All procedures complied with the Declaration of Helsinki. Clinical examination Standardized clinical oral health assessments were conducted on the recruitment day, before saliva collection, by a single calibrated dental professional (MD), ensuring consistency across all evaluations. Given the design of the ORASPORT study, which involved recruitment in non-clinical settings (university campuses and community environments), comprehensive diagnostic procedures such as radiographic imaging and full periodontal probing were not feasible. Caries status was recorded using the Decayed, Missing, and Filled Teeth (DMFT) index following the World Health Organization (WHO) 5th edition criteria and International Caries Detection and Assessment System (ICDAS) guidelines [ 21 , 22 ]. Gingival status was evaluated using the Loe and Silness Gingival Index (LSI) [ 23 ]. Additional assessment included tooth wear and Plaque Index (PI) [ 24 ]. Participants were classified into four groups: healthy controls without oral inflammation or pathology (H, n = 31), carious lesions (CL, n = 17), gingival inflammation (GI, n = 17), and individuals presenting multiple conditions, mainly CL + GI (CL + GI, n = 14). GI status was determined for patients with LSI > 0 and without concomitant oral lesions. The 14 individuals presenting multiple conditions (CL + GI), were excluded from comparative cytokine analyses to ensure clear discrimination between groups and avoid potential confounding effects on biomarker profiles. Saliva sampling and salivary cytokine quantification Saliva samples were collected immediately after the oral health examination. Participants were instructed not to eat, drink, brush their teeth, or smoke for at least 30 minutes prior to sampling [ 20 ]. Unstimulated whole saliva sample (1 mL) was collected by passive drooling (spit method) into sterile tubes containing protease inhibitors (Pierce™ Protease Inhibitor Tablets, Thermo Fisher Scientific) to prevent protein degradation. The biological samples were transported and processed within 2 hours of collection. All the samples were subsequently stored at -80°C until analyses [ 20 ]. Cytokines were quantified using the ultrasensitive S-PLEX ® Proinflammatory Panel 1 (MSD), which detects nine cytokines at femtogram-per-mL sensitivity (IL-1β, IL-2, IL-4, IL-6, IL-10, IL-12p70, IL-17, IFN-γ, and TNF-α). The quantification process is based on a multiplex electrochemiluminescence sandwich immunoassay (MULTI-ARRAY® technology), in which analytes are captured by immobilized antibodies and detected using SULFO-TAG–labeled secondary antibodies coupled with signal amplification, enabling ultra-low detection limits. This method provides high sensitivity together with a broad dynamic detection range. Assays were performed in duplicate according to the manufacturer’s instructions and measured on a MESO® QuickPlex SQ 120 instrument. Briefly, saliva samples were incubated in precoated multi-spot plates, followed by sequential incubation with detection antibodies and electrochemiluminescent readout after application of an electrical stimulus. Analytical validation included assessments of sensitivity, linearity, reproducibility, and matrix effect. Calibration curves (8 points) were fitted using a 4-parameter logistic (4PL) model with Discovery Workbench 4.0 analysis software. All assays were performed using the same reagent lot to ensure consistency. Cytokine concentrations were expressed as fg/mL of whole saliva. An additional dataset normalized to total salivary protein (fg/µg) was also generated for comparison. Main analyses were conducted using the fg/mL dataset. Data management and statistical analysis Cytokine concentrations were log₁₀-transformed and standardized as z-scores prior to analysis. Z-scores were calculated by standardizing cytokine concentrations relative to the mean and standard deviation of the healthy reference group (H). Values near 0 indicate levels close to the healthy mean, while positive and negative values indicate higher or lower concentrations, respectively. Outlier filtering was applied only to the healthy group using the Tukey’s method (values Q3 + 1.5 × IQR), as part of a predefined data-cleaning strategy aimed at minimizing the impact of technical variability in baseline measurements. This strategy was restricted to the H group based on the assumption that extreme values in H more likely reflected technical noise or random variation rather than pathology [ 25 , 26 ]. A total of 23 data points were identified as outliers and set to missing (NaN), corresponding to 8.2% of the dataset (279 measurements derived from 31 participants across nine cytokines), while all participants were retained in the analysis. All subsequent analyses were performed using the remaining available data. Multiple group comparisons were performed using the Kruskal–Wallis test for overall effects, followed by pairwise comparisons using the Mann–Whitney U test based on data distribution assessed with Shapiro–Wilk test. The significance of single-, double, and triple-cytokine combinations between groups was assessed with Mann–Whitney U tests, and p-values were adjusted using the Benjamini–Krieger–Yekutieli procedure. Diagnostic performance of single-, bi-, and tri-cytokine models was evaluated using receiver operating characteristic (ROC). For each biomarker (or predefined composite score), ROC curves were generated and the area under the curve (AUC) was calculated together with 95% confidence intervals. When relevant, AUC values were statistically compared between groups to assess differences in discriminatory performance. Statistical analyses were conducted using GraphPad Prism V10.5. Results Characteristics of the cohort A total of 79 participants were included and classified as H (n = 31), CL (n = 17), GI (n = 17), or multiple oral pathologies (n = 14). Demographic variables (age, sex, BMI, and stress level) were comparable across groups (Table 1 ). In contrast, clinical indices demonstrated clear differences between groups, supporting their classification. The DMFT index was significantly higher in the CL group (4.59 ± 3.71) compared to both GI (0.76 ± 1.64, p < 0.0001) and H (0.84 ± 1.53, p < 0.0001). PI was significantly higher in the GI group (0.88 ± 0.69) relative to H (0.19 ± 0.59, p = 0.0002). Similarly, LSI was markedly higher in GI (1.29 ± 0.59) compared to CL (0 ± 0, p < 0.0001) and H (0.09 ± 0.53, p < 0.0001). Erosion scores did not differ significantly between groups. Table 1 Demographics and oral parameters of Carious lesion (CL), Gingival inflammation (GI), and Healthy (H) groups. Parameters a CL n = 17 GI n = 17 H n = 31 Overall p -value b CL vs GI p -value c CL vs H p -value c GI vs H p -value c General demographic characteristics Age 24.5 ± 3.10 24.5 ± 3.20 24.5 ± 3.65 n.s. n.s. n.s. n.s. Sex (M/F) 8 / 9 10 / 7 11 / 21 n.s. n.s. n.s. n.s. BMI (kg/m 2 ) 21.8 ± 3.04 22.5 ± 3.31 21.2 ± 2.35 n.s. n.s. n.s. n.s. Stress score 2.41 ± 1.23 2.29 ± 1.11 2.31 ± 0.99 n.s. n.s. n.s. n.s. Dental parameters DMFT 4.59 ± 3.71 0.76 ± 1.64 0.84 ± 1.53 0.0006 0.0001 0.0001 n.s. PI 0.47 ± 0.72 0.88 ± 0.69 0.19 ± 0.59 0.0002 n.s. n.s. 0.0002 LSI 0 ± 0 1.29 ± 0.59 0.09 ± 0.53 0.0001 0.0001 n.s. 0.0001 Erosion 0.29 ± 0.47 0.12 ± 0.33 0.19 ± 0.39 n.s. n.s. n.s. n.s. a Data are presented as mean ± SD; b Overall p-values were calculated using the Kruskal–Wallis test; c Post-hoc p-values were calculated using the Mann–Whitney test with Benjamini–Krieger–Yekutieli adjustment. n.s. = non-significant results. BMI, Body Mass Index; DMFT, Decayed, Missing and Filled Teeth; PI, Plaque Index; LSI, Löe–Silness Index. Validation of ultrasensitive multiplex cytokine detection in saliva To ensure the reliability of cytokine quantification in unstimulated saliva, we validated the performance of the S-PLEX® Proinflammatory Panel 1. Standard curves for all nine cytokines, generated from serial dilutions of recombinant standards, showed excellent 4PL fits, with R² values ranging from 0.998 to 0.999 (Supplementary Table S1 ). The assay covered a broad dynamic range (> 4 log 10 ) without signal saturation or hook effect at high concentrations. The limits of detection and quantification confirmed fg-level sensitivity. Across all saliva samples (n = 79) cytokine distributions on a log₁₀ scale (Fig. 1 ) demonstrated robust assay performance. Intra-assay coefficients of variation (CV, %) were calculated for each cytokine based on duplicate measurements, using either volume-normalized concentrations (fg/mL) or protein-normalized concentrations (fg/µg). Volume-based normalization consistently resulted in reduced variability, reflecting high intra-assay precision, with CV below 10% across all concentration ranges (Fig. 2 ). These findings support volume-based normalization as a more robust and relevant approach for mucosal immunoassays compared with protein-based normalization. Median concentrations typically ranged between 100 and 10000 fg/mL (Fig. 1 ), indicating physiological relevance and technical robustness. Notably IL-1β reached very high levels, with two samples exceeding the upper detection limit (> 1.70 ×10⁵ fg/mL). Together, these results confirm the high analytical sensitivity, reproducibility, and suitability of the S-PLEX® Proinflammatory Panel 1 multiplex immunoassay for comprehensive non-invasive profiling of salivary cytokines. Salivary cytokine combinations show distinct patterns in health, caries, and gingival inflammation To identify immune biomarkers distinguishing caries from other oral conditions, we compared salivary cytokine distributions in patients with CL vs H, GI, and combined controls (H + GI) (n = 65) (Fig. 3 , Table 2 ). From the initial cohort (n = 79), individuals presenting multiple conditions (CL + GI, n = 14), were excluded from cytokine analyses to maintain clear group distinctions and minimize potential confounding effects on biomarker profiles. Table 2 Significance of cytokine signatures discriminating carious lesions (CL) from healthy controls (H), gingival inflammation (GI), and combined controls (H + GI). Cytokine signature a,b Signature type CL vs H c CL vs GI c CL vs H + GI c TNF-α single < 0.001 n.s. 0.0003 IL-6 single 0.01 n.s. n.s. IL-17 + TNF-α Bi-cytokine < 0.001 n.s. 0.0002 TNF-α + IL-1β Bi-cytokine < 0.0001 n.s. 0.0005 IL-17 + IL-1β Bi-cytokine 0.0117 0.0162 0.0117 IL-1β + IL-17 + IFN- γ Tri-cytokine n.s. 0.05 n.s. IL-17 + TNF-α+ IL-1β Tri-cytokine < 0.0001 n.s. 0.0004 IL-12p70 + IL-17 + TNF-α Tri-cytokine 0.001 n.s. 0.007 a Only cytokine combinations with at least one significant comparison (p < 0.05) are shown. b Quantification of salivary cytokines was performed using the S-PLEX® Proinflammatory Panel 1 (MSD); c p-values were calculated using Mann-Whitney test, with p-values adjusted via the Benjamini–Krieger–Yekutieli test on log₁₀(x + 1)-transformed, z-score-normalized cytokine concentrations. n.s. = non-significant results. The discriminative performance of cytokine patterns was first assessed for individual cytokines, followed by pairwise combinations (bi-cytokine models) and tri-cytokine models obtained by adding a third marker to the best-performing pairs. Among single cytokines, TNF-α was the most robust biomarker, showing significantly higher levels in CL vs H ( p 0.05). IL-6 was also significantly higher in CL compared with H ( p = 0.01), whereas IL-1β alone was not significant (data not shown). Bi-cytokine models improved discrimination. The IL-1β + TNF-α combination showed the strongest significance for CL vs H ( p < 0.0001) and remained highly significant for CL vs H + GI ( p = 0.0005). Similarly, IL-17 + TNF-α was significantly higher in CL compared with both H and H + GI ( p < 0.001 and p = 0.0002, respectively), with a clear rightward shift in CL compared with H and minimal overlap between groups, supporting its specificity as a marker of caries-associated inflammation. Interestingly, the IL-1β + IL-17 pair also discriminated CL from other conditions, including GI ( p = 0.0162). Tri-cytokine combinations further enhanced discriminative performance. Adding IL-1β to the IL-17 + TNF-α pair yielded the strongest significance for CL vs H ( p < 0.0001) and CL vs H + GI ( p = 0.0004). The IL-12p70 + IL-17 + TNF-α combination also remained significant for CL vs H ( p = 0.0012) and CL vs H + GI ( p = 0.007). Taken together, these findings support the involvement of both innate immune responses (TNF-α) and Th17-mediated mucosal immune pathways (IL-17) in the immunopathology of carious lesions. Diagnostic performance was further assessed using ROC analysis (Fig. 4 ). The best performances to discriminate between groups were as follows: the IL-17 + TNF-α combination yielded the highest accuracy for CL vs H (AUC = 0.901, p < 0.0001), while IL-17 + IL-1β showed good accuracy for CL vs H + GI (AUC = 0.795, p = 0.016). The tri-cytokine combination IL-17 + TNF-α + IL-1β also showed good accuracy in discriminating CL from combined controls (AUC = 0.798, p = 0.007). Overall, these results indicate that IL-17 combined with TNF-α and/or IL-1β may serve as valuable markers for discriminating CL from other oral conditions. Discussion The present study demonstrates the feasibility and robustness of the S-PLEX® electrochemiluminescence platform for ultrasensitive multiplex quantification of nine key cytokines involved in proinflammatory and regulatory immune responses in whole saliva. Analytical validation confirmed excellent assay performance, with femtogram-per-mL sensitivity, high reproducibility, and minimal matrix interference. The S-PLEX® technology was specifically developed to achieve ultrasensitive detection limits, up to 1000-fold lower than conventional immunoassays, making it particularly suitable for clinical studies investigating rare or low-abundance biomarkers [ 27 ]. Previous studies have validated the platform in plasma and serum [ 28 , 29 ], whereas its application in saliva has remained largely underexplored. In contrast, oral research still predominantly relies on conventional ELISA-based approaches [ 30 , 31 ]. However, the analytical performance of traditional bead-based multiplex ELISA systems remains limited, particularly due to their insufficient sensitivity for detecting low-abundance cytokines typically present in saliva [ 32 , 33 ]. In addition, bead-based multiplex assays may be affected by cross-reactivity and reduced accuracy at low concentration ranges, which can further compromise quantitative reliability [ 33 ]. By overcoming these limitations through enhanced sensitivity and signal amplification, the S-PLEX® platform represents a significant methodological advance for profiling mucosal immune responses in oral diseases [ 34 ]. Nevertheless, as with any emerging analytical technology, certain limitations should be considered. The requirement for specialized instrumentation and proprietary reagents may limit widespread accessibility, and validation across diverse sample types and clinical contexts is still ongoing. Moreover, although S-PLEX® offers clear methodological advantages, the S-PLEX® Proinflammatory Panel 1 targets a predefined set of nine cytokines and therefore lacks flexibility, excluding some clinically relevant markers such as IL-8 [ 35 ]. However, this limitation can be mitigated through the development of customized panels tailored to specific research questions. Beyond methodological validation, the present findings provide biological insight into the host inflammatory responses associated with dental caries and support the relevance of salivary immune profiling as a complementary approach for disease characterization. Among the cytokines assessed, TNF-α emerged as the most discriminative single marker of caries-associated inflammation compared with health. This result aligns with its established role as a central mediator of pulpal inflammation, orchestrating leukocyte recruitment, vascular and prostaglandin cascades, matrix degradation, and nociceptor sensitization [ 36 ]. Consistently, Alarcón-Sánchez et al. , 2024 also proposed TNF-α as a potential biomarker for assessing dental caries severity, with The combination of IL-6 and TNF-α effectively discriminating children with active dental caries from controls [ 35 ]. In our dataset, the combination of TNF-α + IL-1β, and to a lesser extent TNF-α + IL-17, was strongly associated with caries status, whereas the addition of a third cytokine did not significantly increase discriminatory power. Interestingly, IL-1β was not discriminatory on its own, but its inclusion in combinatorial models improved classification accuracy, as both the IL-1β + IL-17 bi-cytokine combination and the IL-1β + IL-17 + IFN-γ tri-cytokine combination successfully distinguished CL from other oral conditions, including GI (LSI > 0). This observation is particularly relevant, as IL-1β, a prototypical member of the IL-1 cytokine family, is widely implicated in pulpal and periapical inflammation [ 37 ]. Elevated IL-1β expression has been reported in carious dentin, pulp tissue, and saliva of patients with caries [ 38 , 39 ]. Mechanistically, it plays a pivotal role in amplifying local immune responses by stimulating inflammasome activation, promoting cytokine and chemokine release, osteoclastogenesis, and matrix metalloproteinase activation, thereby linking microbial invasion to tissue destruction [ 40 ]. The IL-17 cytokine, produced by T helper 17 lymphocytes cells (Th17), contributes to mucosal defense through neutrophil recruitment and epithelial activation [ 41 ]. Elevated IL-17 has been reported in carious dentin, inflamed pulp, and saliva of adolescents with active caries, reinforcing its biological relevance as a marker of cariogenic inflammation [ 39 , 42 ]. In our cohort, our results support the clinical potential of the IL-1β + IL-17 pair for distinguishing carious from non-carious inflammatory conditions. Notably, the ability of salivary IL-1β containing combinations to discriminate CL from GI suggests that these markers may reflect tissue-invasive inflammation rather than superficial gingival changes. From a translational perspective, exploratory testing of tri-cytokine panels indicated that the IL-1β + TNF-α + IL-17 combination retained statistical significance against both H and GI groups but did not provide a meaningful improvement over bi-cytokine combinations. Extending these observations to underlying mechanisms, recently, a new immune mechanism has been described in pulp tissue: the NLRP3 inflammasome. Activation of the NLRP3 inflammasome and the resulting pyroptosis promote the release of IL-1β, thereby amplifying local inflammatory signalling, including the induction of downstream mediators such as IL-6. In turn, IL-6 plays a central role in the differentiation of Th17 lymphocytes, leading to increased secretion of IL-17 [ 43 ]. Importantly, IL-17 can also induce the production of IL-1β, thereby establishing a positive feedback loop that may sustain a chronic inflammatory state. Within this framework, the IL-1β–IL-17 axis identified in our study may reflect the activation of interconnected inflammasome-driven and Th17-mediated pathways, providing a mechanistic basis for the observed discriminatory cytokine combinations. Although the IL-17–pyroptosis axis has been widely characterized in other tissues, its involvement in inflammatory mechanisms within oral tissues remains poorly understood [ 44 , 45 ]. Nevertheless, emerging evidence suggests that IL-17 may indirectly modulate pyroptotic pathways by intensifying local inflammatory responses [ 46 , 47 ], further supporting the biological plausibility of our findings. While these findings provide biologically plausible interpretations, several limitations should be considered when interpreting the results of this pilot study. The modest sample size, the narrow demographic of young adults, and the cross-sectional design inevitably restrict generalizability and preclude both causal inference and assessment of temporal dynamics. The primary objective of this study was to validate the applicability of the S-PLEX technology for salivary cytokine profiling using an existing cohort. As such, the study was designed as a methodological proof-of-concept intended to inform and support future large-scale investigations rather than a definitive biomarker discovery study. Consequently, the findings should be interpreted as indicative of cytokine profiles associated with dental caries rather than definitive biomarker signatures [ 48 ]. In addition, the absence of radiographic and periodontal probing data in the GI group limited the precision of characterizing non-carious inflammation; this subgroup was therefore retained primarily as a comparator to evaluate the specificity of caries-associated cytokine patterns. The decision to exclude mixed clinical cases should also be considered when interpreting the findings. This approach was adopted to maintain clearly defined oral health profiles and thereby facilitate the discrimination of salivary signatures within the cohort but may limit the representation of overlapping oral conditions encountered in clinical practice. Another important limitation of this study lies in the number of participants, and the choice of clinical parameters analyzed, which did not allow stratification of carious lesions according to their severity (initial, moderate, or severe). Such stratification is essential, as the progression of the lesion strongly influences the nature and intensity of immune responses [ 49 ]. More broadly, saliva represents a complex and variable biological fluid, in which factors such as flow rate, circadian rhythm, and systemic conditions may influence cytokine levels, introducing variability into biomarker studies [ 50 ]. Although strict eligibility criteria and standardized protocols were applied to minimize confounding, some residual confounding cannot be fully excluded, and the results should be interpreted with caution. Furthermore, the evaluation of cytokine combinations was performed as an exploratory, post hoc analysis. Although false discovery rate correction using the Benjamini–Krieger–Yekutieli method was applied to mitigate the risk of false-positive findings, these results should be considered hypothesis-generating and require validation in future studies with prespecified analytical frameworks. Finally, budgetary constraints restricted the number of assays that could be performed, limiting broader validation across additional markers. However, the selected panel still covered key pro-inflammatory and regulatory immune pathways, supporting the biological relevance of the observed patterns despite this constraint. Conclusions Taken together, this study provides both methodological and biological evidence supporting the use of ultrasensitive S-PLEX® multiplex platform for salivary immune profiling in oral diseases. The S-PLEX® technology demonstrated robust performance for the quantification of nine cytokines involved in both pro-inflammatory and regulatory immune responses in saliva. The choice of a sensitive analytical platform is crucial to ensure accurate assay performance assessment and reliable cytokines quantification for translational biomarker research and clinical use. Our findings further suggest that selected cytokine profiles can distinguish caries-associated inflammation from both health and non-carious inflammation, highlighting their potential relevance as immunological indicators in dental caries research. While preliminary, these results support the concept that coordinated cytokine signatures, rather than single markers, may better capture the complexity of host–microbial interactions in caries. Future large-scale, longitudinal studies integrating clinical, microbial, and immunological data will be necessary to validate these associations and to determine the potential role of cytokine-based profiling in advancing caries research and personalized caries management. Declarations Author contributions statement All authors contributed to the study’s conceptualization and design. Material preparation, data collection, and analysis were performed MD, MO, LT. Manuscript drafting, and critical revision were carried out by MD, MO, LT, MFB and AD. Figure preparation and statistical analysis were performed by MD and AD. and VM. Supervision and project administration were conducted by MFB and AD. All authors reviewed earlier versions of the manuscript, provided intellectual input, approved the final version of the manuscript and agree to be accountable for all aspects of the work. Competing interest The authors have no conflicts of interest to declare Ethics declarations The study protocol was approved by the Human Research Ethics Committee for the Protection of Persons, approval number ID-RCB 2022-A01682-41, February 2023. The study was conducted in accordance with the Declaration of Helsinki. All the patients were informed about the study protocol in written and oral form and signed a consent form to participate in the study. Patients signed informed consent regarding publishing their data. Funding This study was not supported by any sponsor or funder. Acknowledgement The authors thank Ms. Agnès Loubat, Ms. Faudrin Samantha, and Prof. Robert Marsault for their assistance with cytokine quantifications, and Prof. Laurence Lupi for her continuous and valuable support. 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J Dent. 2025;154:105611. https://doi.org/10.1016/j.jdent.2025.105611 Alam MI, Mae M, Farhana F, Oohira M, Yamashita Y, Ozaki Y, et al. NLRP3 Inflammasome Negatively Regulates RANKL-Induced Osteoclastogenesis of Mouse Bone Marrow Macrophages but Positively Regulates It in the Presence of Lipopolysaccharides. Int J Mol Sci. 2022;23:6096. https://doi.org/10.3390/ijms23116096 Iwakura Y, Ishigame H, Saijo S, Nakae S. Functional Specialization of Interleukin-17 Family Members. Immunity. Elsevier; 2011;34:149–62. https://doi.org/10.1016/j.immuni.2011.02.012 Kritikou K, Imre M, Tanase M, Vinereanu A, Totan AR, Spinu T-C, et al. Biochemical Mapping of the Inflamed Human Dental Pulp. Applied Sciences [Internet]. Multidisciplinary Digital Publishing Institute; 2021 [cité 15 mars 2026];11. https://doi.org/10.3390/app112110395 Schroder K, Tschopp J. The Inflammasomes. Cell. Elsevier; 2010;140:821–32. https://doi.org/10.1016/j.cell.2010.01.040 Al Natour B, Lundy FT, About I, Jeanneau C, Dombrowski Y, El Karim IA. Regulation of caries-induced pulp inflammation by NLRP3 inflammasome: A laboratory-based investigation. Int Endod J. 2023;56:193–202. https://doi.org/10.1111/iej.13855 Neha N, Abraham D, Goyal A, Gupta A, Sharma R, Malhotra RK. GCF NLRP3 as a Biomarker for Assessing Endodontic Treatment Outcomes in Symptomatic Irreversible Pulpitis: A Comparative Nonrandomized, Observational Cross-Sectional Study. J Int Soc Prev Community Dent. 2025;15:415–25. https://doi.org/10.4103/jispcd.jispcd_31_25 Lei L, Sun J, Han J, Jiang X, Wang Z, Chen L. Interleukin-17 induces pyroptosis in osteoblasts through the NLRP3 inflammasome pathway in vitro. Int Immunopharmacol. 2021;96:107781. https://doi.org/10.1016/j.intimp.2021.107781 Gu F, Huang D, Li R, Peng L, Huan T, Ye K, et al. Roles of Pyroptosis in the Progression of Pulpitis and Apical Periodontitis. J Inflamm Res. 2025;18:3361–75. https://doi.org/10.2147/JIR.S507198 Gornowicz A, Bielawska A, Bielawski K, Grabowska SZ, Wójcicka A, Zalewska M, et al. Pro-inflammatory cytokines in saliva of adolescents with dental caries disease. Ann Agric Environ Med. Institute of Rural Health; 2012;19:711–6. Twetman S. Prevention of dental caries as a non-communicable disease. European Journal of Oral Sciences. 2018;126:19–25. https://doi.org/10.1111/eos.12528 Navazesh M. Methods for Collecting Saliva. Annals of the New York Academy of Sciences. 1993;694:72–7. https://doi.org/10.1111/j.1749-6632.1993.tb18343.x Additional Declarations There is no conflict of interest Supplementary Files SupplementarytableS1.docx Supplementary table S1 STROBEchecklistcasecontrol.doc Supplementary table S2 STROBE checklist Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 30 Mar, 2026 Submission checks completed at journal 30 Mar, 2026 Editor assigned by journal 30 Mar, 2026 First submitted to journal 30 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9267095","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":614766936,"identity":"238ada06-2022-4c70-b9cf-a8dc4c95cc36","order_by":0,"name":"Margaux Dubois","email":"data:image/png;base64,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","orcid":"https://orcid.org/0009-0004-2612-4826","institution":"MICORALIS, Université Côte d'Azur","correspondingAuthor":true,"prefix":"","firstName":"Margaux","middleName":"","lastName":"Dubois","suffix":""},{"id":614766937,"identity":"eafa4d99-3d45-4c7a-932c-ab192a3d0cf0","order_by":1,"name":"Morgane Ortis","email":"","orcid":"","institution":"MICORALIS, Université Côte d'Azur","correspondingAuthor":false,"prefix":"","firstName":"Morgane","middleName":"","lastName":"Ortis","suffix":""},{"id":614766938,"identity":"0255ce9b-8b74-41c2-a8f3-70d0841fbce1","order_by":2,"name":"Lilit Tonoyan","email":"","orcid":"","institution":"MICORALIS, Université Côte d'Azur","correspondingAuthor":false,"prefix":"","firstName":"Lilit","middleName":"","lastName":"Tonoyan","suffix":""},{"id":614766939,"identity":"74737a83-f647-477a-9ddb-9f4bcb45f86e","order_by":3,"name":"Emma Dubois","email":"","orcid":"","institution":"Université Côte d'Azur","correspondingAuthor":false,"prefix":"","firstName":"Emma","middleName":"","lastName":"Dubois","suffix":""},{"id":614766940,"identity":"32a00b37-478b-4f35-9b69-12ddf7b2e8c4","order_by":4,"name":"Marie-France Bertrand","email":"","orcid":"","institution":"MICORALIS, Université Côte d'Azur","correspondingAuthor":false,"prefix":"","firstName":"Marie-France","middleName":"","lastName":"Bertrand","suffix":""},{"id":614766941,"identity":"b783c228-a50a-4514-a13e-89015348c1e5","order_by":5,"name":"Alain Doglio","email":"","orcid":"","institution":"MICORALIS, Université Côte d'Azur","correspondingAuthor":false,"prefix":"","firstName":"Alain","middleName":"","lastName":"Doglio","suffix":""}],"badges":[],"createdAt":"2026-03-30 12:51:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9267095/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9267095/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106288826,"identity":"3acaa7fd-3c21-43a7-bc11-6c090ff3f0a6","added_by":"auto","created_at":"2026-04-07 07:27:31","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":345208,"visible":true,"origin":"","legend":"\u003cp\u003eSalivary concentrations of nine cytokines measured with the S-PLEX® Proinflammatory Panel 1 (MSD\u003csup\u003e®\u003c/sup\u003e).\u003c/p\u003e\n\u003cp\u003eBoxplots display log₁₀-transformed concentrations (fg/mL) for each cytokine across all participants (n = 79), illustrating the broad dynamic range, inter-individual variability, and median concentrations generally between 100 and 10 000 fg/mL. Boxes represent the interquartile range (IQR), horizontal lines indicate the median, and whiskers extend to 1.5 × IQR. All cytokines were detectable in \u0026gt; 97% of samples, with eight cytokines consistently detected in 100% of participants.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-9267095/v1/35282452fd1d835c9e69c201.png"},{"id":106288851,"identity":"fa78402c-0e5f-4a4b-9349-5281befb96b2","added_by":"auto","created_at":"2026-04-07 07:27:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2731650,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInter-individual variability of salivary cytokine measurements compared across different normalization strategies.\u003c/strong\u003e Intra-assay coefficients of variation (CV, %) were calculated for each cytokine across 79 individuals using either volume-normalized concentrations (fg/mL of whole saliva, orange) or protein-normalized concentrations (fg/μg of total salivary protein, red, striped). Normalization by saliva volume yielded lower or comparable variability for all cytokines, supporting its use as a more robust and physiologically relevant approach in mucosal immunoassays.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-9267095/v1/d0509e1c1fa3f297358780ac.png"},{"id":106288836,"identity":"9b6042ec-cbd8-4e49-abc1-ec6b4f66bdff","added_by":"auto","created_at":"2026-04-07 07:27:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1728083,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eViolin plots showing the distribution of salivary cytokine z-scores across patient groups.\u003c/strong\u003e \u003cbr\u003e\nPanels show comparisons between: (A) healthy controls (H, blue) and carious lesions (CL, red), (B) gingival inflammation (GI, brown) and carious lesions (CL, red), (C) the combined H + GI group (green) and carious lesions (CL, red). Each violin represents the distribution of cytokine signature values after log₁₀(x+1) transformation and subsequent the z-score standardization. These composite values correspond to the cytokine signatures identified as significant (shown in Table 2). The CL group shows a consistent shift toward higher median values, most pronounced for the IL-17 + TNF-α signature, indicating its specificity for caries-associated inflammation. Violin width reflects data distribution; black bold dotted lines indicate medians; thin dotted lines represent interquartile ranges. \u003cem\u003ep\u003c/em\u003e-values were calculated using Mann-Whitney test, with p-values adjusted via the Benjamini–Krieger–Yekutieli test. Statistical significance: * p ≤ 0.05; ** p ≤ 0.01; *** p ≤ 0.001; **** p ≤ 0.0001.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-9267095/v1/39e024aabff8f7f154ed2b21.png"},{"id":106288835,"identity":"d9cf5283-b8d2-496f-8f8f-6e725dcce567","added_by":"auto","created_at":"2026-04-07 07:27:33","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":272098,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eReceiver Operating Characteristic (ROC) curves evaluating the performance of salivary cytokine signatures in discriminating oral health states.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) IL-17 + TNF-α distinguishing carious lesions (CL) from healthy controls (H) (AUC = 0.9013).\u003cbr\u003e\n(B) IL-17 + IL-1β distinguishing CL from gingival inflammation (GI) (AUC = 0.7336).\u003cbr\u003e\n(C) IL-17 + TNF-α + IL-1β distinguishing CL from the combined H + GI group (AUC = 0.7978).\u003cbr\u003e\nROC curves were generated and the area under the curve (AUC) was calculated together with 95% confidence intervals using log₁₀(x+1)-transformed and z-score-standardized values. The diagonal line represents the performance of a random classifier (AUC = 0.5).\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-9267095/v1/df45667a7ee09f188121c662.png"},{"id":106289198,"identity":"ec7584e2-280f-40f3-aa6b-1f26ba25076d","added_by":"auto","created_at":"2026-04-07 07:28:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7394605,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9267095/v1/41fa83f9-7054-4407-8b3b-b8cd6ec2fd8b.pdf"},{"id":106288837,"identity":"7b7740ef-d27b-4bd0-8131-2c0f2cfbc53a","added_by":"auto","created_at":"2026-04-07 07:27:33","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17658,"visible":true,"origin":"","legend":"Supplementary table S1","description":"","filename":"SupplementarytableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-9267095/v1/8db2077596d647d220cd5edb.docx"},{"id":106288957,"identity":"639f287d-6f09-46d0-a8eb-73367887682a","added_by":"auto","created_at":"2026-04-07 07:27:43","extension":"doc","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":82944,"visible":true,"origin":"","legend":"Supplementary table S2 STROBE checklist","description":"","filename":"STROBEchecklistcasecontrol.doc","url":"https://assets-eu.researchsquare.com/files/rs-9267095/v1/f524646a717a3c6340d89299.doc"}],"financialInterests":"There is no conflict of interest","formattedTitle":"Ultrasensitive Multiplex Salivary Cytokine Profiling Reveals Distinct Inflammatory Patterns in Dental Caries","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDental caries is one of the most prevalent chronic diseases worldwide, with significant impact on quality of life and healthcare costs [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. It is a multifactorial disease driven by dynamic interactions between the host, oral microbiota, and environmental factors [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], and is now recognized as a consequence of microbial dysbiosis, favoring acidogenic and aciduric bacteria that disrupt oral homeostasis and promote tooth demineralization [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The progression of carious lesions (CL) allows bacterial invasion of the dentin and pulp, triggering inflammation.\u003c/p\u003e \u003cp\u003eWithin this context, the dental pulp hosts various immune cells, including neutrophils, monocytes, dendritic cells, and lymphocytes, while odontoblasts and other resident pulp cells constitute a first line of defense by sensing microbial patterns and releasing pro-inflammatory cytokines and chemokines [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. These responses involve mediators such as IL-1β, IL-6, IL-8, and TNF-α, which drive pulpal disease progression [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], while IL-17 has emerged as a contributor to mucosal defense and local inflammation [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Accordingly, elevated cytokines have been detected in pulpal fluids, gingival crevicular fluid, and dentinal exudates, supporting their potential as biomarkers of pulpal pathology [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Clinical studies further link IL-1β, IL-8, and IL-6 with pulpitis severity and oral inflammation [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, despite this evidence at the tissue level, data on salivary cytokines in the context of caries remain limited, even though saliva represents a readily accessible, non-invasive diagnostic fluid. Importantly, biofilm dysbiosis and \u003cem\u003eStreptococcus mutans\u003c/em\u003e-derived products are known to induce proinflammatory cytokines such as TNF-α, IL-1β and IL-6 [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], suggesting that caries-associated microbial activity may also be reflected in salivary inflammatory profiles. In this context, recent studies have proposed combinatorial immune signatures as discriminators of oral conditions, including oral cancer and periodontitis [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], with pilot data suggesting their potential to distinguish deep carious lesions from gingivitis and health [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAs personalized medicine advances, identifying salivary immune signatures holds promise for improving the diagnosis and management of oral diseases [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. While clinical examination remains the standard for caries diagnosis, complemented by radiographic and adjunctive methods, salivary cytokine analysis offers complementary information on the underlying inflammatory response of the disease and may contribute to improving early diagnosis, risk assessment, and disease monitoring. Traditional approaches for cytokine quantification, such as enzyme-linked immunosorbent assays (ELISA) and bead-based multiplex assays, provide reliable measurements but often lack the sensitivity needed to reliably detect low-abundance cytokines in saliva [\u003cspan additionalcitationids=\"CR15 CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The S-PLEX\u0026reg; ultra-sensitive immunoassay technology, developed by Meso Scale Discovery (MSD), achieves femtogram-per-mL detection limits while maintaining a broad dynamic range and high specificity, which is particularly advantageous in oral and dental research where analyte concentrations are frequently low. Its multiplexing capability and high reproducibility enable the simultaneous quantification of multiple cytokines, supporting more comprehensive immunoprofiling and enhancing the translational potential of salivary biomarker studies. Despite these advantages, the application of S-PLEX\u0026reg; technology to unstimulated whole saliva has not yet been validated [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe hypothesized that salivary cytokine profiles reflect caries-associated inflammatory responses and that specific combinations of cytokines can discriminate caries from health and gingival inflammation. Accordingly, this proof of-concept study aimed to evaluate and validate S-PLEX\u0026reg; technology for salivary cytokine profiling among healthy individuals (H), patients with CL, and those with gingival inflammation (GI). Specifically, we tested the MSD S-PLEX\u0026reg; Proinflammatory panel 1 (human) kit, which enables detection of nine cytokines representing key immune pathways: innate immunity (IL-1β, TNF-α), Th1 (IL-12p70, IFN-γ), Th2 (IL-4), Th17 (IL-17), regulatory responses (IL-10), and general T-cell activation (IL-2).\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and participants\u003c/h2\u003e \u003cp\u003eThis case-control ancillary study was conducted within the ORASPORT cohort, a retrospectively registered clinical trial (NCT05765422), at Universit\u0026eacute; C\u0026ocirc;te D'Azur, Nice, France [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The present investigation used the ORASPORT cohort as a pre-existing, well-characterized population for saliva biobanking and clinical data collection. Data collected within the ORASPORT framework were subsequently reused for this ancillary research through secondary analysis, independent of the original study aim related to exercise and oral microbiota. Of the 200 participants planned for inclusion in the ORASPORT cohort, 79 were included in the present ancillary study, reflecting an interim analysis based on the data available at the time of analysis, as the ORASPORT cohort is still ongoing and has not yet reached its final recruitment target. Seventy-nine healthy young adults were recruited from university campuses, the Dental Department of CHU Nice, and local sports events. Inclusion criteria were age 18\u0026ndash;30 years, documented written informed consent, and completion of a standardized questionnaire covering medical, dental, and lifestyle history. Exclusion criteria were use of anti-inflammatory medication within 48 hours, or antibiotic/probiotic treatment within three weeks prior to sampling. Demographic characteristics (age, sex, body mass index [BMI]) were recorded, and psychological stress levels were assessed using the standardized Likert scale. The study was approved by the Committee for the Protection of Persons (CPP; approval ID-RCB 2022-A01682-41, March 2023) and registered under RCB: 2022-A01682-41-22-PP-11. All procedures complied with the Declaration of Helsinki.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eClinical examination\u003c/h3\u003e\n\u003cp\u003e Standardized clinical oral health assessments were conducted on the recruitment day, before saliva collection, by a single calibrated dental professional (MD), ensuring consistency across all evaluations. Given the design of the ORASPORT study, which involved recruitment in non-clinical settings (university campuses and community environments), comprehensive diagnostic procedures such as radiographic imaging and full periodontal probing were not feasible.\u003c/p\u003e \u003cp\u003eCaries status was recorded using the Decayed, Missing, and Filled Teeth (DMFT) index following the World Health Organization (WHO) 5th edition criteria and International Caries Detection and Assessment System (ICDAS) guidelines [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Gingival status was evaluated using the Loe and Silness Gingival Index (LSI) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Additional assessment included tooth wear and Plaque Index (PI) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eParticipants were classified into four groups: healthy controls without oral inflammation or pathology (H, n\u0026thinsp;=\u0026thinsp;31), carious lesions (CL, n\u0026thinsp;=\u0026thinsp;17), gingival inflammation (GI, n\u0026thinsp;=\u0026thinsp;17), and individuals presenting multiple conditions, mainly CL\u0026thinsp;+\u0026thinsp;GI (CL\u0026thinsp;+\u0026thinsp;GI, n\u0026thinsp;=\u0026thinsp;14). GI status was determined for patients with LSI\u0026thinsp;\u0026gt;\u0026thinsp;0 and without concomitant oral lesions. The 14 individuals presenting multiple conditions (CL\u0026thinsp;+\u0026thinsp;GI), were excluded from comparative cytokine analyses to ensure clear discrimination between groups and avoid potential confounding effects on biomarker profiles.\u003c/p\u003e\n\u003ch3\u003eSaliva sampling and salivary cytokine quantification\u003c/h3\u003e\n\u003cp\u003e Saliva samples were collected immediately after the oral health examination. Participants were instructed not to eat, drink, brush their teeth, or smoke for at least 30 minutes prior to sampling [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Unstimulated whole saliva sample (1 mL) was collected by passive drooling (spit method) into sterile tubes containing protease inhibitors (Pierce\u0026trade; Protease Inhibitor Tablets, Thermo Fisher Scientific) to prevent protein degradation. The biological samples were transported and processed within 2 hours of collection. All the samples were subsequently stored at -80\u0026deg;C until analyses [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCytokines were quantified using the ultrasensitive S-PLEX\u003csup\u003e\u0026reg;\u003c/sup\u003e Proinflammatory Panel 1 (MSD), which detects nine cytokines at femtogram-per-mL sensitivity (IL-1β, IL-2, IL-4, IL-6, IL-10, IL-12p70, IL-17, IFN-γ, and TNF-α). The quantification process is based on a multiplex electrochemiluminescence sandwich immunoassay (MULTI-ARRAY\u0026reg; technology), in which analytes are captured by immobilized antibodies and detected using SULFO-TAG\u0026ndash;labeled secondary antibodies coupled with signal amplification, enabling ultra-low detection limits. This method provides high sensitivity together with a broad dynamic detection range. Assays were performed in duplicate according to the manufacturer\u0026rsquo;s instructions and measured on a MESO\u0026reg; QuickPlex SQ 120 instrument. Briefly, saliva samples were incubated in precoated multi-spot plates, followed by sequential incubation with detection antibodies and electrochemiluminescent readout after application of an electrical stimulus. Analytical validation included assessments of sensitivity, linearity, reproducibility, and matrix effect. Calibration curves (8 points) were fitted using a 4-parameter logistic (4PL) model with Discovery Workbench 4.0 analysis software. All assays were performed using the same reagent lot to ensure consistency. Cytokine concentrations were expressed as fg/mL of whole saliva. An additional dataset normalized to total salivary protein (fg/\u0026micro;g) was also generated for comparison. Main analyses were conducted using the fg/mL dataset.\u003c/p\u003e\n\u003ch3\u003eData management and statistical analysis\u003c/h3\u003e\n\u003cp\u003eCytokine concentrations were log₁₀-transformed and standardized as z-scores prior to analysis. Z-scores were calculated by standardizing cytokine concentrations relative to the mean and standard deviation of the healthy reference group (H). Values near 0 indicate levels close to the healthy mean, while positive and negative values indicate higher or lower concentrations, respectively. Outlier filtering was applied only to the healthy group using the Tukey\u0026rsquo;s method (values\u0026thinsp;\u0026lt;\u0026thinsp;Q1\u0026ndash;1.5 \u0026times; IQR or \u0026gt;\u0026thinsp;Q3\u0026thinsp;+\u0026thinsp;1.5 \u0026times; IQR), as part of a predefined data-cleaning strategy aimed at minimizing the impact of technical variability in baseline measurements. This strategy was restricted to the H group based on the assumption that extreme values in H more likely reflected technical noise or random variation rather than pathology [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. A total of 23 data points were identified as outliers and set to missing (NaN), corresponding to 8.2% of the dataset (279 measurements derived from 31 participants across nine cytokines), while all participants were retained in the analysis. All subsequent analyses were performed using the remaining available data. Multiple group comparisons were performed using the Kruskal\u0026ndash;Wallis test for overall effects, followed by pairwise comparisons using the Mann\u0026ndash;Whitney U test based on data distribution assessed with Shapiro\u0026ndash;Wilk test. The significance of single-, double, and triple-cytokine combinations between groups was assessed with Mann\u0026ndash;Whitney U tests, and p-values were adjusted using the Benjamini\u0026ndash;Krieger\u0026ndash;Yekutieli procedure.\u003c/p\u003e \u003cp\u003eDiagnostic performance of single-, bi-, and tri-cytokine models was evaluated using receiver operating characteristic (ROC). For each biomarker (or predefined composite score), ROC curves were generated and the area under the curve (AUC) was calculated together with 95% confidence intervals. When relevant, AUC values were statistically compared between groups to assess differences in discriminatory performance. Statistical analyses were conducted using GraphPad Prism V10.5.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of the cohort\u003c/h2\u003e \u003cp\u003e A total of 79 participants were included and classified as H (n\u0026thinsp;=\u0026thinsp;31), CL (n\u0026thinsp;=\u0026thinsp;17), GI (n\u0026thinsp;=\u0026thinsp;17), or multiple oral pathologies (n\u0026thinsp;=\u0026thinsp;14). Demographic variables (age, sex, BMI, and stress level) were comparable across groups (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In contrast, clinical indices demonstrated clear differences between groups, supporting their classification. The DMFT index was significantly higher in the CL group (4.59\u0026thinsp;\u0026plusmn;\u0026thinsp;3.71) compared to both GI (0.76\u0026thinsp;\u0026plusmn;\u0026thinsp;1.64, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and H (0.84\u0026thinsp;\u0026plusmn;\u0026thinsp;1.53, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). PI was significantly higher in the GI group (0.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69) relative to H (0.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59, p\u0026thinsp;=\u0026thinsp;0.0002). Similarly, LSI was markedly higher in GI (1.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59) compared to CL (0\u0026thinsp;\u0026plusmn;\u0026thinsp;0, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and H (0.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Erosion scores did not differ significantly between groups.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographics and oral parameters of Carious lesion (CL), Gingival inflammation (GI), and Healthy (H) groups.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCL\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;17\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGI\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;17\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eH\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;31\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCL vs GI\u003c/p\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCL vs H\u003c/p\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eGI vs H\u003c/p\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eGeneral demographic characteristics\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (M/F)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 / 9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 / 7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 / 21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStress score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.41\u0026thinsp;\u0026plusmn;\u0026thinsp;1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.29\u0026thinsp;\u0026plusmn;\u0026thinsp;1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDental parameters\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDMFT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.59\u0026thinsp;\u0026plusmn;\u0026thinsp;3.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.76\u0026thinsp;\u0026plusmn;\u0026thinsp;1.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.84\u0026thinsp;\u0026plusmn;\u0026thinsp;1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.0002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026thinsp;\u0026plusmn;\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eErosion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003ea\u003c/sup\u003e Data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD; \u003csup\u003eb\u003c/sup\u003e Overall p-values were calculated using the Kruskal\u0026ndash;Wallis test; \u003csup\u003ec\u003c/sup\u003e Post-hoc p-values were calculated using the Mann\u0026ndash;Whitney test with Benjamini\u0026ndash;Krieger\u0026ndash;Yekutieli adjustment. \u003cem\u003en.s.\u003c/em\u003e = non-significant results. BMI, Body Mass Index; DMFT, Decayed, Missing and Filled Teeth; PI, Plaque Index; LSI, L\u0026ouml;e\u0026ndash;Silness Index.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eValidation of ultrasensitive multiplex cytokine detection in saliva\u003c/h3\u003e\n\u003cp\u003eTo ensure the reliability of cytokine quantification in unstimulated saliva, we validated the performance of the S-PLEX\u0026reg; Proinflammatory Panel 1. Standard curves for all nine cytokines, generated from serial dilutions of recombinant standards, showed excellent 4PL fits, with R\u0026sup2; values ranging from 0.998 to 0.999 (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The assay covered a broad dynamic range (\u0026gt;\u0026thinsp;4 log\u003csub\u003e10\u003c/sub\u003e) without signal saturation or hook effect at high concentrations. The limits of detection and quantification confirmed fg-level sensitivity.\u003c/p\u003e \u003cp\u003eAcross all saliva samples (n\u0026thinsp;=\u0026thinsp;79) cytokine distributions on a log₁₀ scale (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) demonstrated robust assay performance. Intra-assay coefficients of variation (CV, %) were calculated for each cytokine based on duplicate measurements, using either volume-normalized concentrations (fg/mL) or protein-normalized concentrations (fg/\u0026micro;g). Volume-based normalization consistently resulted in reduced variability, reflecting high intra-assay precision, with CV below 10% across all concentration ranges (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These findings support volume-based normalization as a more robust and relevant approach for mucosal immunoassays compared with protein-based normalization. Median concentrations typically ranged between 100 and 10000 fg/mL (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), indicating physiological relevance and technical robustness. Notably IL-1β reached very high levels, with two samples exceeding the upper detection limit (\u0026gt;\u0026thinsp;1.70 \u0026times;10⁵ fg/mL).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTogether, these results confirm the high analytical sensitivity, reproducibility, and suitability of the S-PLEX\u0026reg; Proinflammatory Panel 1 multiplex immunoassay for comprehensive non-invasive profiling of salivary cytokines.\u003c/p\u003e\n\u003ch3\u003eSalivary cytokine combinations show distinct patterns in health, caries, and gingival inflammation\u003c/h3\u003e\n\u003cp\u003e To identify immune biomarkers distinguishing caries from other oral conditions, we compared salivary cytokine distributions in patients with CL vs H, GI, and combined controls (H\u0026thinsp;+\u0026thinsp;GI) (n\u0026thinsp;=\u0026thinsp;65) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). From the initial cohort (n\u0026thinsp;=\u0026thinsp;79), individuals presenting multiple conditions (CL\u0026thinsp;+\u0026thinsp;GI, n\u0026thinsp;=\u0026thinsp;14), were excluded from cytokine analyses to maintain clear group distinctions and minimize potential confounding effects on biomarker profiles.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSignificance of cytokine signatures discriminating carious lesions (CL) from healthy controls (H), gingival inflammation (GI), and combined controls (H\u0026thinsp;+\u0026thinsp;GI).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCytokine signature\u003csup\u003ea,b\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSignature type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCL vs H\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCL vs GI\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCL vs H\u0026thinsp;+\u0026thinsp;GI\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNF-α\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003esingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL-6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003esingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL-17\u0026thinsp;+\u0026thinsp;TNF-α\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBi-cytokine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNF-α\u0026thinsp;+\u0026thinsp;IL-1β\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBi-cytokine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL-17\u0026thinsp;+\u0026thinsp;IL-1β\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBi-cytokine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.0117\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.0162\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0117\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL-1β\u0026thinsp;+\u0026thinsp;IL-17\u0026thinsp;+\u0026thinsp;IFN- γ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTri-cytokine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL-17\u0026nbsp;+ TNF-α+\u0026nbsp;IL-1β\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTri-cytokine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL-12p70 +\u0026nbsp;IL-17\u0026thinsp;+\u0026thinsp;TNF-α\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTri-cytokine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e Only cytokine combinations with at least one significant comparison (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) are shown. \u003csup\u003eb\u003c/sup\u003e Quantification of salivary cytokines was performed using the S-PLEX\u0026reg; Proinflammatory Panel 1 (MSD); \u003csup\u003ec\u003c/sup\u003e p-values were calculated using Mann-Whitney test, with p-values adjusted via the Benjamini\u0026ndash;Krieger\u0026ndash;Yekutieli test on log₁₀(x\u0026thinsp;+\u0026thinsp;1)-transformed, z-score-normalized cytokine concentrations. \u003cem\u003en.s.\u003c/em\u003e = non-significant results.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe discriminative performance of cytokine patterns was first assessed for individual cytokines, followed by pairwise combinations (bi-cytokine models) and tri-cytokine models obtained by adding a third marker to the best-performing pairs. Among single cytokines, TNF-α was the most robust biomarker, showing significantly higher levels in CL vs H (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and CL vs H\u0026thinsp;+\u0026thinsp;GI (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0003), but not discriminating CL from GI (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). IL-6 was also significantly higher in CL compared with H (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01), whereas IL-1β alone was not significant (data not shown).\u003c/p\u003e \u003cp\u003eBi-cytokine models improved discrimination. The IL-1β\u0026thinsp;+\u0026thinsp;TNF-α combination showed the strongest significance for CL vs H (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and remained highly significant for CL vs H\u0026thinsp;+\u0026thinsp;GI (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0005). Similarly, IL-17\u0026thinsp;+\u0026thinsp;TNF-α was significantly higher in CL compared with both H and H\u0026thinsp;+\u0026thinsp;GI (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0002, respectively), with a clear rightward shift in CL compared with H and minimal overlap between groups, supporting its specificity as a marker of caries-associated inflammation. Interestingly, the IL-1β\u0026thinsp;+\u0026thinsp;IL-17 pair also discriminated CL from other conditions, including GI (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0162).\u003c/p\u003e \u003cp\u003eTri-cytokine combinations further enhanced discriminative performance. Adding IL-1β to the IL-17\u0026thinsp;+\u0026thinsp;TNF-α pair yielded the strongest significance for CL vs H (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and CL vs H\u0026thinsp;+\u0026thinsp;GI (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0004). The IL-12p70\u0026thinsp;+\u0026thinsp;IL-17\u0026thinsp;+\u0026thinsp;TNF-α combination also remained significant for CL vs H (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0012) and CL vs H\u0026thinsp;+\u0026thinsp;GI (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007). Taken together, these findings support the involvement of both innate immune responses (TNF-α) and Th17-mediated mucosal immune pathways (IL-17) in the immunopathology of carious lesions.\u003c/p\u003e \u003cp\u003eDiagnostic performance was further assessed using ROC analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The best performances to discriminate between groups were as follows: the IL-17\u0026thinsp;+\u0026thinsp;TNF-α combination yielded the highest accuracy for CL vs H (AUC\u0026thinsp;=\u0026thinsp;0.901, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), while IL-17\u0026thinsp;+\u0026thinsp;IL-1β showed good accuracy for CL vs H\u0026thinsp;+\u0026thinsp;GI (AUC\u0026thinsp;=\u0026thinsp;0.795, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016). The tri-cytokine combination IL-17\u0026thinsp;+\u0026thinsp;TNF-α\u0026thinsp;+\u0026thinsp;IL-1β also showed good accuracy in discriminating CL from combined controls (AUC\u0026thinsp;=\u0026thinsp;0.798, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e Overall, these results indicate that IL-17 combined with TNF-α and/or IL-1β may serve as valuable markers for discriminating CL from other oral conditions.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study demonstrates the feasibility and robustness of the S-PLEX\u0026reg; electrochemiluminescence platform for ultrasensitive multiplex quantification of nine key cytokines involved in proinflammatory and regulatory immune responses in whole saliva. Analytical validation confirmed excellent assay performance, with femtogram-per-mL sensitivity, high reproducibility, and minimal matrix interference. The S-PLEX\u0026reg; technology was specifically developed to achieve ultrasensitive detection limits, up to 1000-fold lower than conventional immunoassays, making it particularly suitable for clinical studies investigating rare or low-abundance biomarkers [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Previous studies have validated the platform in plasma and serum [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], whereas its application in saliva has remained largely underexplored.\u003c/p\u003e \u003cp\u003eIn contrast, oral research still predominantly relies on conventional ELISA-based approaches [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. However, the analytical performance of traditional bead-based multiplex ELISA systems remains limited, particularly due to their insufficient sensitivity for detecting low-abundance cytokines typically present in saliva [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. In addition, bead-based multiplex assays may be affected by cross-reactivity and reduced accuracy at low concentration ranges, which can further compromise quantitative reliability [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. By overcoming these limitations through enhanced sensitivity and signal amplification, the S-PLEX\u0026reg; platform represents a significant methodological advance for profiling mucosal immune responses in oral diseases [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Nevertheless, as with any emerging analytical technology, certain limitations should be considered. The requirement for specialized instrumentation and proprietary reagents may limit widespread accessibility, and validation across diverse sample types and clinical contexts is still ongoing. Moreover, although S-PLEX\u0026reg; offers clear methodological advantages, the S-PLEX\u0026reg; Proinflammatory Panel 1 targets a predefined set of nine cytokines and therefore lacks flexibility, excluding some clinically relevant markers such as IL-8 [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. However, this limitation can be mitigated through the development of customized panels tailored to specific research questions.\u003c/p\u003e \u003cp\u003eBeyond methodological validation, the present findings provide biological insight into the host inflammatory responses associated with dental caries and support the relevance of salivary immune profiling as a complementary approach for disease characterization. Among the cytokines assessed, TNF-α emerged as the most discriminative single marker of caries-associated inflammation compared with health. This result aligns with its established role as a central mediator of pulpal inflammation, orchestrating leukocyte recruitment, vascular and prostaglandin cascades, matrix degradation, and nociceptor sensitization [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Consistently, Alarc\u0026oacute;n-S\u0026aacute;nchez \u003cem\u003eet al.\u003c/em\u003e, 2024 also proposed TNF-α as a potential biomarker for assessing dental caries severity, with The combination of IL-6 and TNF-α effectively discriminating children with active dental caries from controls [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn our dataset, the combination of TNF-α\u0026thinsp;+\u0026thinsp;IL-1β, and to a lesser extent TNF-α\u0026thinsp;+\u0026thinsp;IL-17, was strongly associated with caries status, whereas the addition of a third cytokine did not significantly increase discriminatory power. Interestingly, IL-1β was not discriminatory on its own, but its inclusion in combinatorial models improved classification accuracy, as both the IL-1β\u0026thinsp;+\u0026thinsp;IL-17 bi-cytokine combination and the IL-1β\u0026thinsp;+\u0026thinsp;IL-17\u0026thinsp;+\u0026thinsp;IFN-γ tri-cytokine combination successfully distinguished CL from other oral conditions, including GI (LSI\u0026thinsp;\u0026gt;\u0026thinsp;0). This observation is particularly relevant, as IL-1β, a prototypical member of the IL-1 cytokine family, is widely implicated in pulpal and periapical inflammation [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Elevated IL-1β expression has been reported in carious dentin, pulp tissue, and saliva of patients with caries [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Mechanistically, it plays a pivotal role in amplifying local immune responses by stimulating inflammasome activation, promoting cytokine and chemokine release, osteoclastogenesis, and matrix metalloproteinase activation, thereby linking microbial invasion to tissue destruction [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The IL-17 cytokine, produced by T helper 17 lymphocytes cells (Th17), contributes to mucosal defense through neutrophil recruitment and epithelial activation [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Elevated IL-17 has been reported in carious dentin, inflamed pulp, and saliva of adolescents with active caries, reinforcing its biological relevance as a marker of cariogenic inflammation [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. In our cohort, our results support the clinical potential of the IL-1β\u0026thinsp;+\u0026thinsp;IL-17 pair for distinguishing carious from non-carious inflammatory conditions. Notably, the ability of salivary IL-1β containing combinations to discriminate CL from GI suggests that these markers may reflect tissue-invasive inflammation rather than superficial gingival changes. From a translational perspective, exploratory testing of tri-cytokine panels indicated that the IL-1β\u0026thinsp;+\u0026thinsp;TNF-α\u0026thinsp;+\u0026thinsp;IL-17 combination retained statistical significance against both H and GI groups but did not provide a meaningful improvement over bi-cytokine combinations.\u003c/p\u003e \u003cp\u003eExtending these observations to underlying mechanisms, recently, a new immune mechanism has been described in pulp tissue: the NLRP3 inflammasome. Activation of the NLRP3 inflammasome and the resulting pyroptosis promote the release of IL-1β, thereby amplifying local inflammatory signalling, including the induction of downstream mediators such as IL-6. In turn, IL-6 plays a central role in the differentiation of Th17 lymphocytes, leading to increased secretion of IL-17 [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Importantly, IL-17 can also induce the production of IL-1β, thereby establishing a positive feedback loop that may sustain a chronic inflammatory state. Within this framework, the IL-1β\u0026ndash;IL-17 axis identified in our study may reflect the activation of interconnected inflammasome-driven and Th17-mediated pathways, providing a mechanistic basis for the observed discriminatory cytokine combinations. Although the IL-17\u0026ndash;pyroptosis axis has been widely characterized in other tissues, its involvement in inflammatory mechanisms within oral tissues remains poorly understood [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Nevertheless, emerging evidence suggests that IL-17 may indirectly modulate pyroptotic pathways by intensifying local inflammatory responses [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], further supporting the biological plausibility of our findings.\u003c/p\u003e \u003cp\u003eWhile these findings provide biologically plausible interpretations, several limitations should be considered when interpreting the results of this pilot study. The modest sample size, the narrow demographic of young adults, and the cross-sectional design inevitably restrict generalizability and preclude both causal inference and assessment of temporal dynamics. The primary objective of this study was to validate the applicability of the S-PLEX technology for salivary cytokine profiling using an existing cohort. As such, the study was designed as a methodological proof-of-concept intended to inform and support future large-scale investigations rather than a definitive biomarker discovery study. Consequently, the findings should be interpreted as indicative of cytokine profiles associated with dental caries rather than definitive biomarker signatures [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. In addition, the absence of radiographic and periodontal probing data in the GI group limited the precision of characterizing non-carious inflammation; this subgroup was therefore retained primarily as a comparator to evaluate the specificity of caries-associated cytokine patterns. The decision to exclude mixed clinical cases should also be considered when interpreting the findings. This approach was adopted to maintain clearly defined oral health profiles and thereby facilitate the discrimination of salivary signatures within the cohort but may limit the representation of overlapping oral conditions encountered in clinical practice. Another important limitation of this study lies in the number of participants, and the choice of clinical parameters analyzed, which did not allow stratification of carious lesions according to their severity (initial, moderate, or severe). Such stratification is essential, as the progression of the lesion strongly influences the nature and intensity of immune responses [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMore broadly, saliva represents a complex and variable biological fluid, in which factors such as flow rate, circadian rhythm, and systemic conditions may influence cytokine levels, introducing variability into biomarker studies [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Although strict eligibility criteria and standardized protocols were applied to minimize confounding, some residual confounding cannot be fully excluded, and the results should be interpreted with caution. Furthermore, the evaluation of cytokine combinations was performed as an exploratory, post hoc analysis. Although false discovery rate correction using the Benjamini\u0026ndash;Krieger\u0026ndash;Yekutieli method was applied to mitigate the risk of false-positive findings, these results should be considered hypothesis-generating and require validation in future studies with prespecified analytical frameworks. Finally, budgetary constraints restricted the number of assays that could be performed, limiting broader validation across additional markers. However, the selected panel still covered key pro-inflammatory and regulatory immune pathways, supporting the biological relevance of the observed patterns despite this constraint.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003e Taken together, this study provides both methodological and biological evidence supporting the use of ultrasensitive S-PLEX\u0026reg; multiplex platform for salivary immune profiling in oral diseases. The S-PLEX\u0026reg; technology demonstrated robust performance for the quantification of nine cytokines involved in both pro-inflammatory and regulatory immune responses in saliva. The choice of a sensitive analytical platform is crucial to ensure accurate assay performance assessment and reliable cytokines quantification for translational biomarker research and clinical use. Our findings further suggest that selected cytokine profiles can distinguish caries-associated inflammation from both health and non-carious inflammation, highlighting their potential relevance as immunological indicators in dental caries research. While preliminary, these results support the concept that coordinated cytokine signatures, rather than single markers, may better capture the complexity of host\u0026ndash;microbial interactions in caries. Future large-scale, longitudinal studies integrating clinical, microbial, and immunological data will be necessary to validate these associations and to determine the potential role of cytokine-based profiling in advancing caries research and personalized caries management.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eAuthor contributions statement\u003c/h2\u003e \u003cp\u003eAll authors contributed to the study\u0026rsquo;s conceptualization and design. Material preparation, data collection, and analysis were performed MD, MO, LT. Manuscript drafting, and critical revision were carried out by MD, MO, LT, MFB and AD. Figure preparation and statistical analysis were performed by MD and AD. and VM. Supervision and project administration were conducted by MFB and AD. All authors reviewed earlier versions of the manuscript, provided intellectual input, approved the final version of the manuscript and agree to be accountable for all aspects of the work.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interest\u003c/h2\u003e \u003cp\u003eThe authors have no conflicts of interest to declare\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eEthics declarations\u003c/h2\u003e \u003cp\u003eThe study protocol was approved by the Human Research Ethics Committee for the Protection of Persons, approval number ID-RCB 2022-A01682-41, February 2023. The study was conducted in accordance with the Declaration of Helsinki. All the patients were informed about the study protocol in written and oral form and signed a consent form to participate in the study. Patients signed informed consent regarding publishing their data.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study was not supported by any sponsor or funder.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e \u003cp\u003eThe authors thank Ms. Agn\u0026egrave;s Loubat, Ms. Faudrin Samantha, and Prof. Robert Marsault for their assistance with cytokine quantifications, and Prof. Laurence Lupi for her continuous and valuable support.\u003c/p\u003e\u003ch2\u003eData Availability Statement\u003c/h2\u003e \u003cp\u003eAll data generated or analyzed during this study are included in this article. Further enquiries can be directed to the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGiacaman RA, Fern\u0026aacute;ndez CE, Mu\u0026ntilde;oz-Sandoval C, Le\u0026oacute;n S, Garc\u0026iacute;a-Manr\u0026iacute;quez N, Echeverr\u0026iacute;a C, et al. 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Annals of the New York Academy of Sciences. 1993;694:72\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1749-6632.1993.tb18343.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1749-6632.1993.tb18343.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bdj-open","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"bdjopen","sideBox":"Learn more about [BDJ Open](http://www.nature.com/bdjopen/)","snPcode":"41405","submissionUrl":"https://mts-bdjopen.nature.com/cgi-bin/main.plex","title":"BDJ Open","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Oral Health, Dental Caries, Cytokines, Multiplex Analysis, Saliva","lastPublishedDoi":"10.21203/rs.3.rs-9267095/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9267095/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBACKGROUND\u003c/h2\u003e \u003cp\u003eDental caries is a prevalent chronic disease characterized by complex inflammatory responses in the oral environment. This case-control pilot study evaluated the analytical performance of an ultra-sensitive electrochemiluminescence multiplex assay (MSD S-PLEX\u0026reg;) for salivary cytokine profiling and explored whether combined cytokine signatures can discriminate dental caries lesions (CL) from healthy (H) and non-carious gingival inflammation (GI).\u003c/p\u003e\u003ch2\u003eMETHODS\u003c/h2\u003e \u003cp\u003eUnstimulated whole saliva from 79 individuals (H, CL, GI) was analyzed using the S-PLEX\u0026reg; assay to quantify nine salivary cytokines (IL-1β, TNF-α, IL-6, IL-4, IL-10, IL-12p70, IFN-γ, IL-17, and IL-2). Group comparisons were performed using Mann-Whitney tests, and diagnostic performance was assessed by logistic regression and the area under the receiver operating characteristic curve.\u003c/p\u003e\u003ch2\u003eRESULTS\u003c/h2\u003e \u003cp\u003eThe assay showed high sensitivity and reproducibility. Cytokine profiling revealed selective elevation of certain cytokines in saliva from CL. Among the models tested, TNF-α\u0026thinsp;+\u0026thinsp;IL-17 provided the best discrimination between CL and H (AUC\u0026thinsp;=\u0026thinsp;0.901, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and between CL and H\u0026thinsp;+\u0026thinsp;GI (AUC\u0026thinsp;=\u0026thinsp;0.795, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0003). Inclusion of additional cytokines did not improve performance. However, discrimination between CL and GI was limited with TNF-α\u0026thinsp;+\u0026thinsp;IL-17, while IL-1β\u0026thinsp;+\u0026thinsp;IL-17 provided a modest but significant discrimination (AUC\u0026thinsp;=\u0026thinsp;0.737, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004).\u003c/p\u003e\u003ch2\u003eCONCLUSION\u003c/h2\u003e \u003cp\u003eThis study provides evidence that ultrasensitive multiplex salivary cytokine profiling can reveal caries-associated inflammatory patterns. These findings highlight the translational potential of salivary cytokine panels as complementary tools for improved discrimination of caries and underline their potential for advancing precision approaches in caries research and management.\u003c/p\u003e","manuscriptTitle":"Ultrasensitive Multiplex Salivary Cytokine Profiling Reveals Distinct Inflammatory Patterns in Dental Caries","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-07 07:24:43","doi":"10.21203/rs.3.rs-9267095/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-03-30T17:10:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-30T16:55:35+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-30T12:48:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"BDJ Open","date":"2026-03-30T12:48:22+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bdj-open","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"bdjopen","sideBox":"Learn more about [BDJ Open](http://www.nature.com/bdjopen/)","snPcode":"41405","submissionUrl":"https://mts-bdjopen.nature.com/cgi-bin/main.plex","title":"BDJ Open","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6f061e16-0cf1-4ea3-abbb-b9612a7ce1a9","owner":[],"postedDate":"April 7th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":65416166,"name":"Health sciences/Health care/Dentistry/Dental conditions"},{"id":65416167,"name":"Health sciences/Diseases/Oral diseases"}],"tags":[],"updatedAt":"2026-04-07T07:24:43+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-07 07:24:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9267095","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9267095","identity":"rs-9267095","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}