Comparative Efficacy of Salivary, Buccal, and Hemoglobin Biomarkers in Blood Glucose Monitoring: Implications for Type 2 Diabetes Management

Comparative Efficacy of Salivary, Buccal, and Hemoglobin Biomarkers in Blood Glucose Monitoring: Implications for Type 2 Diabetes Management | 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 Research Article Comparative Efficacy of Salivary, Buccal, and Hemoglobin Biomarkers in Blood Glucose Monitoring: Implications for Type 2 Diabetes Management Mohammed Mohammed, May Salem, Mohammed Mohaibes This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7025858/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background and objectives: Blood-based monitoring remains the gold standard for diabetes management, yet non-invasive alternatives like salivary diagnostics offer significant potential. This study evaluated salivary glucose, buccal cell glycogen, oral glucose tolerance (OGTT), and glycated hemoglobin (HbA1c). Materials and Methods: 370 participants classified into three groups: uncontrolled diabetes (n=135), managed diabetes (n=135), and non-diabetic controls (n=100). Salivary and blood samples were collected under standardized protocols, with glucose levels quantified via enzymatic assays, HbA1c via fluorescence immunoassay, and buccal cell glycogen via periodic acid-Schiff (PAS) staining. Results: A significant differences in salivary glucose levels across groups (highest in uncontrolled diabetics: 204 mg/dl vs. controls: 111 mg/dl; p<0.05), correlating strongly with blood glucose (r=0.372, p=0.043). PAS-positive buccal cells were markedly elevated in diabetics (uncontrolled: 9.15 ± 2.37; controlled: 4.47 ± 1.83; controls: 0.93 ± 1.31; p=0.001), with staining intensity reflecting glycemic status. OGTT profiles in saliva mirrored serum trends, peaking at 1-hour post-glucose intake. ROC analysis revealed high diagnostic accuracy for salivary glucose (AUC=0.932) and PAS-positive cells (AUC=0.752) in distinguishing diabetics from controls. Conclusions: The integration of salivary glucose and buccal cell glycogen assessments offers a robust, non-invasive approach for diabetes screening. Standardization of collection protocols and interdisciplinary collaboration are critical to advancing saliva-based diagnostics, enabling early detection and holistic management of Type 2 Diabetes Mellitus. Molecular Biology Biomedical Engineering Laboratory Diagnostics Biochemical Research Methods Saliva diabetes management (DM) HbA1c oral glucose tolerance (OGT) 1. INTRODUCTION Diabetes mellitus represents a significant global health challenge, characterized by chronic hyperglycemia and metabolic disturbances arising from insulin deficiency, cellular resistance, or both [ 1 ]. This condition is associated with severe complications affecting cardiovascular, renal, ocular, and other organ systems, substantially diminishing quality of life and life expectancy [ 2 ]. The prevalence of diabetes has escalated alarmingly, rising from 200 million cases in 1990 to 830 million by 2022, with low- and middle-income countries experiencing the most rapid increase [ 3 ]. Notably, over 50% of individuals with diabetes in 2022 did not utilize medication, and treatment coverage remains disproportionately low in resource-limited settings [ 3 , 4 ]. Complications such as blindness, kidney failure, cardiovascular events, and lower-limb amputations further underscore the urgency of effective management strategies [ 5 ]. Current diagnostic and monitoring practices rely heavily on plasma glucose measurements, which necessitate invasive blood sampling. Repeated needle insertions cause patient discomfort, psychological stress, and compliance challenges, particularly among pediatric and elderly populations [ 6 , 7 ]. Consequently, there is a critical need for non-invasive, cost-effective alternatives. Saliva has emerged as a promising bio fluid due to its ease of collection, non-invasiveness, and suitability for large-scale screening [ 8 ]. Whole saliva, collected via draining or spitting methods without specialized equipment, is particularly advantageous for systemic disease assessment, whereas gland-specific secretions aid in detecting localized pathologies [ 9 , 10 ]. The correlation between salivary glucose levels (SGL) and blood glucose levels (BGL) remains contentious. While some studies report a positive association in diabetic patients, others find inconsistent or inconclusive results [ 11 – 13 ]. Further complexity arises from diagnostic tools such as glycated hemoglobin (HbA1c), which reflects average glycemic control over three months and offers advantages over fasting plasma glucose (FPG) or oral glucose tolerance tests (OGTT). HbA1c eliminates fasting requirements, reduces biological variability, and correlates strongly with long-term complications [ 14 , 15 ]. Nonetheless, OGTT remains valuable for clarifying ambiguous cases, requiring plasma glucose measurements at fasting and post-glucose intervals [ 16 ]. OGTT is typically performed to help diagnose diabetes and determine how serious the condition is. [ 17 , 18 ]. Therefore, the purpose of this study was to compare the levels of OGTT in the saliva and serum of patients with and without diabetes to those of those who appeared to be in good health. Nowadays, measuring blood glucose levels is the main method used to diagnose diabetes. Nonetheless, glycated hemoglobin (HbA1c) level monitoring has increased in popularity and frequency, offering a precise indicator of average glycemic management throughout the previous three months [ 19 ]. According to the New Zealand Society of Diabetes, the HbA1c test should typically be used as the initial screening and diagnostic method for type 2 diabetes. [ 20 ]. This proposal is generally consistent with many international standards and attempts to update and supplement the New Zealand standards Group's current guidance. Up until recently, the two-hour post-oral glucose tolerance test or fasting plasma glucose levels have been the suggested diagnostic and screening procedures for type 2 diabetes [ 21 ]. For most patients, HbA1c offers a number of benefits over these tests. First of all, fasting is not required. The accuracy of fasting plasma and oral glucose tolerance tests is decreased since many people do not adhere to the fasting requirement, according to research and anecdotal data. Daily fluctuations in plasma glucose caused by medications, nutrition, exercise, and smoking had less of an impact on HbA1c. Additionally, compared to fasting plasma glucose measurement, HbA1c has reduced biological variability. The association between future retinal risk and HbA1c is precise and well-established. Samples and analyses for HbA1c are less complicated [ 22 ]. Emerging research highlights additional biomarkers, such as glycogen accumulation in buccal mucosal cells linked to reduced Glycogen Synthase Kinase-3 (GSK-3) phosphorylation in diabetics [ 23 , 24 ]. These findings underscore the potential of oral biomarkers to complement existing diagnostic frameworks. The present study aims to (1) evaluate the association between plasma and salivary glucose levels in diabetic and non-diabetic participants, with emphasis on age-related variations, and (2) explore the diagnostic utility of salivary glucose and buccal mucosal cell glycogen content in Type II Diabetes Mellitus. By addressing gaps in non-invasive methodologies, this research seeks to contribute to accessible, patient-friendly diagnostic strategies, particularly in resource-constrained settings. 2. MATERIALS AND METHODS 2.1. ETHICS STATEMENT The study protocol was approved by the Ethics Committee at Gilgamesh University. Written informed consent was obtained from all participants prior to sample collection. Saliva and blood samples were collected in accordance with ethical guidelines for human research. 2.2. PARTICIPANTS In this study, a total of 370 participants were included, comprised three groups based on their blood glucose level (BGL). Group I : uncontrolled diabetics, n=135 (more than 300 mg/dl of BGL), Group II : controlled diabetics, n=135, (more than 130-200 mg/dl of BGL) and Group III : non-diabetic controls, n=100, (less than 130 mg/dl of BGL). Demographic characteristics are summarized in Table 1. Group I included 70 males (51.9%) and 65 females (48.1%), aged 29–88 years. Group II consisted of 72 males (53.3%) and 63 females (46.7%), aged 27–75 years, while Group III had 55 males (55%) and 45 females (45%), aged 30–66 years. Table 1: Gender and age distribution of study subjects Group Male (%) Female (%) Age Range I 70 65 29–88 I 72 63 27–75 III 55 45 30–66 Total 197 173 — Participants were required to be ≥18 years old, in good general health, and free of fever or acute oral conditions (e.g., mucosal lesions, active periodontal disease) on the day of sampling. Individuals exhibiting inadequate oral hygiene or reduced salivation were excluded. 2.3. INCLUSION CRITERIA Each study participant verbally consented and completed a data sheet containing their name, age, sex, and relevant medical history. They were asked to arrive at the clinic between 8:00 and 10:00 a.m., at which time two milliliters of venous blood were collected from them. Participants are required to be in good overall health. On the day of sample collection, all participants were free of fever and exhibited high standards of dental hygiene. If the oral inspection revealed any indicators of inadequate oral hygiene, reduced salivation, oral complaints, or other oral health problems (such as mucosal lesions or clinical signs of active periodontal disease), those participants were swiftly removed from the study. Participants were instructed to rinse their mouths with tap water and to expectorate two or three times. Subsequently, they were asked to continuously spit the saliva that accumulated in their mouths for a duration of 10 minutes into a sterile sample collection container. This procedure aimed to measure the salivary flow rate within the case study population. The quantitative analysis of fasting plasma glucose (FPG) and fasting saliva glucose (FSG) was performed using the glucose oxidase method with the enzymatic kit GOD-POD, which employs glucose oxidase and peroxidase [10]. 2.4. SAMPLE COLLECTION Pre-collection Protocol: Participants abstained from smoking, eating, drinking, or tooth brushing for 30 minutes prior to sampling. Oral cavities were rinsed with tap water to remove food debris. The first step in evaluating sampling options involves selecting the most suitable method for saliva collection. Following this, a more detailed investigation will utilize the most effective method to assess the correlation between individual relationships and their stability. Here, the term correlation specifically refers to the relationship between glucose concentrations in blood and saliva. 2.4.1. Saliva Collection First, saliva and blood samples were collected from a cohort of 370 participants, and a population correlation analysis was conducted to identify the optimal saliva collection technique for the morning period (7:30–8:00). Next, from the 40 individuals in each group, 20 DM patients and 20 healthy controls were randomly selected. These participants provided saliva and blood samples daily (before and after breakfast) for a week, using the established saliva collection method to perform an individual correlation analysis. 2.4.1.1. Unstimulated Saliva: Participants expectorated accumulated saliva into sterile containers over 10 minutes to measure salivary flow rate. 2.4.1.2. Stimulated Saliva: A Salivette® (Sarstedt 51.5134) with citric acid-treated swabs was used for alternative collection methods (six protocols). 2.4.2. Blood Collection: Venous blood (5 mL) was drawn via venipuncture between 8:00–10:00 a.m. into additive-free vacuum tubes. Plasma was separated by centrifugation (2000 × g, 10 minutes) and analyzed immediately. 2.5. ANALYTICAL METHODS 2.5.1. Salivary Parameters Approximately 3 ml of saliva was collected from each volunteer and immediately analyzed for conductivity (Cond.), redox potential (ORP), pH, and calcium, potassium and sodium ionic concentrations. The pH and ORP values were measured using the F-71 Laqua Lab (Japan) pH/ORP meter. The conductivity and concentration of Na+, K+, and Ca++ electrolytes were recorded using the Horiba L- aqua twin series ion selective models. The venous plasma FBGL values of these volunteers were measured using an automatic biochemical analyzer (COBAS INTEGRA 400 plus). 2.5.2. Glucose Assays Fasting plasma glucose (FPG) and fasting saliva glucose (FSG) were analyzed via the glucose oxidase-peroxidase (GOD-POD) enzymatic kit [10, 11 and 17]. 2.5.3. HbA1c Estimation Serum glycated hemoglobin (HbA1c) levels were determined using a Chroma™ fluorescence-based point-of-care immunoassay analyzer (EDTA-anticoagulated blood). 2.5.4. Oral Glucose Tolerance Test (OGTT) Participants ingested 75 g glucose in 300 mL water within 5 minutes. Blood and saliva samples were collected at baseline, 1-hour, and 2-hour intervals post-consumption. Samples were centrifuged (2000 × g, 10 minutes), and glucose levels were assayed immediately. 2.5.5. Histochemical Analysis of Buccal Mucosal Cells Exfoliated buccal cells were collected using a sterile wooden spatula, spread onto glass slides, fixed with Biofix™ spray, and stained with periodic acid-Schiff (PAS) to detect glycogen. Fifty cells per slide were evaluated microscopically for PAS-positive (magenta) staining. Staining intensity was coded as: Code 1: Mild Code 2: Moderate Code 3: Intense 2.6. STATISTICAL DESIGN Population Correlation Analysis: Initial saliva collection timing (7:30–8:00 a.m.) was optimized using data from all 370 participants. Individual Correlation Analysis: A subset (n = 40 per group) provided daily samples (pre- and post-breakfast) for one week. Longitudinal Monitoring: A final cohort (n = 10 per group) underwent daily fasting sampling for one month. Coefficients of variation (CV) were calculated to assess correlation stability between salivary and blood glucose levels. 2.6.1. Data Management All measurements were recorded electronically, with identifiers anonymized to ensure confidentiality. Statistical analyses were performed using SPSS v26.0 (α = 0.05). 3. RESULTS In this cohort study, there were no significant differences between groups in age distribution and gender composition (P ≤ 0.67 and P ≤ 0.57) in sequence. Table 1 . In Table 2 . A comparative results between salivary glucose (SGL) and blood glucose (BGL) levels by using Student t-test for statistical analysis. The highest mean of SGL was exhibited by Group I (204 mg/dl) and BGL (209 mg/dl), in compare to Group II (SGL: 166 mg/dl; BGL: 158 mg/dl) and Group III (SGL: 111 mg/dl; BGL: 118 mg/dl). No significant differences were found between groups for SGL or BGL (p ≤ 0.05). Table 2 Comparison of salivary glucose (SGL) and Blood glucose levels (BGL) between cases and control groups (Student t test): Study Population Mean SGL (mg/dl) Mean BGL (mg/dl) p-value Group I 204 209 NS Group II 166 158 NS Group III 111 118 NS NS = Non-significant (p > 0.05); SD = Standard Deviation Oral glucose tolerance test (GTT) results are detailed in Table 3 . In Group I , serum glucose peaked at 1-hour post-glucose intake (269 mg/dl), declining to 188 mg/dl at 2 hours. Salivary glucose followed a similar trend, peaking at 246 mg/dl (1 hour) and decreasing to 156 mg/dl (2 hours). Table 3 The mean values and SD of serum and unstimulated whole saliva glucose concentrations in oral GTT of diabetic and apparently healthy participants Groups Test Fasting 1 hr. 2 hr. Group I Serum 204 269 188 Saliva 209 246 156 Group II Serum 166 196 139 Saliva 158 188 142 Group III serum 111 122 95 Saliva 118 134 119 Group II showed lower peaks (serum: 196 mg/dl; saliva: 188 mg/dl at 1 hour), while controls displayed minimal fluctuations (serum: 122 mg/dl; saliva: 134 mg/dl at 1 hour). Two-hour glucose levels in all groups approximated 70% of 1-hour values. Pearson correlation analysis confirmed a significant association between fasting salivary and blood glucose levels (p ≤ 0.05). HbA1c distributions are presented in Table 4 . Among Group I , 70.3% had HbA1c 9%. Group III universally maintained HbA1c < 7%. A significant difference in HbA1c distribution was observed between uncontrolled and controlled diabetics (p ≤ 0.001). Furthermore, salivary glucose levels correlated positively with HbA1c in both diabetic groups (p ≤ 0.05). Table 4 Distribution of HbA1c (gram %) in three groups of patients studied HbA1c (gram %) Group I: (158 mg/dl) Group II: (209 mg/dl) Group III: 80–120 m/dl) 10 7 (5.1%) 14 (10.3%) 0 (0.0) Total 135 (100) 135 (100) 100 (100) Inference HbA1c Levels are significantly more in uncontrolled diabetes with P ≤ 0.001 The mean counts of PAS-positive buccal mucosal cells were significantly higher (p ≤ 0.001) in Group I (9.148 ± 2.369) compared to Group II (4.467 ± 1.833) and Group III (0.933 ± 1.311) (Table 5 . ) Table 5 Comparison of intensity of PAS staining in diabetics and controls Parameter Non-Diabetics Diabetics Mann-Whitney U p-value PAS intensity 0.93 ± 1.311 Group III Group I 9.467 ± 1.83 223.500 ≤ 0.001 Group II 4.968 ± 1.963 157. 300 ≤ 0.001 The number of PAS-positive cells showed a positive correlation with fasting serum glucose levels (r = 0.372, p ≤ 0.043), but no relationship existed between staining intensity and glucose levels (r = 0.359, p ≤ 0.198). Statistical comparisons confirmed a highly significant increase in PAS-positive cells in diabetics versus controls (p ≤ 0.001). Table 6 . Showed PAS-positive cells distribution among the study groups. The highest mean count of PAS-positive cells were recorded for Group I (9.148 ± 2.369), followed by Group II (4.467 ± 1.833). In contrast, Group III showed a significant lower mean count (0.933 ± 1.311). This observation indicates a progressive reduction in PAS-positive cells from Group I to Group III, with Group III values being nearly 5 times lower than those of Group II and 10 times lower than those of Group I. The standard deviations indicate moderate variability in Groups I and II, while Group III presents a relatively higher uniformity in its measurements. Table 6 Distribution of mean value of PAS positive cells in diabetic groups and healthy control group Parameter Group Mean SD Number of PAS positive cells Group I 9.148 2.369 Group II 4.467 1.8333 Group III 0.933 1.3113 Quantitative analysis revealed a statistically significant increase in PAS-positive cells in diabetic subjects compared to controls (Table 7 .). The mean difference between groups was 1.5333 (95% CI not reported), with a standard error of the difference of 0.4115. This difference was highly significant (t = 6.726, p ≤ 0.001), indicating robust evidence against the null hypothesis of no difference between groups. Table 7 Statistical comparison of PAS positive cells in diabetics and controls Parameter t-value Mean SE p- value Number of PAS positive cells 6.726 1.5333 0.4115 ≤ 0.001 The relationship between the total number of PAS-positive cells and fasting serum glucose levels indicated a statistically significant positive correlation (Pearson's r = 0.372, p ≤ 0.043) across the study groups, as detailed in Table 8. This indicates that higher fasting glucose levels are associated with increased PAS-positive cell counts Table 8 Correlation of total number of PAS positive cells with fasting serum glucose in study groups measured via Pearson correlation coefficient. Parameter Pearson correlation p-value PAS positive cell/Fasting Serum Glucose 0.372 ≤ 0.043 However, staining intensity did not correlate with fasting glucose (r = 0.359, p ≤ 0.198). ROC curve analysis (Table 9 .) demonstrated strong diagnostic utility for salivary glucose (AUC = 0.932, p ≤ 0.001) and PAS-positive cells (AUC = 0.752, p ≤ 0.001) in identifying diabetes mellitus. Table 9 Area under Curve calculation analysis Area Under Curve (AUC) Parameter Area Standard Error p-value Salivary Glucose 0.932 0.034 ≤ 0.001 PAS positive cell 0.752 0.063 ≤ 0.001 An analysis of the correlation between staining intensity and fasting serum glucose levels was conducted utilizing Pearson's correlation coefficient. Table 10 illustrates that a moderate positive correlation was found (r = 0.359), yet this correlation did not attain statistical significance (p = 0.198). Table 10 Correlation of staining intensity with Fasting Serum Glucose in study group Parameter Pearson correlation p-value Staining intensity/Fasting Serum Glucose 0.359 ≤ 0.198 4. DISCUSSION Blood remains the primary biological fluid for disease diagnosis and monitoring. However, whole saliva has emerged as a promising non-invasive alternative, offering unique advantages for diagnostic applications. Saliva contains plasma-derived components and locally synthesized biomarkers that reflect systemic health, enabling the detection of various conditions, including diabetes mellitus (DM). Its non-invasive collection, cost-effectiveness for population-wide screening, and ease of access underscore its diagnostic potential [ 26 ]. Saliva from patients with and without diabetes differs significantly, likely having higher levels of glucose, enzymes, and total protein [ 26 ]. The current investigation found a high positive correlation between the study population's salivary and plasma glucose levels. Salivary composition in diabetic individuals exhibits variability influenced by study design, sample selection criteria, and methodological approaches [ 27 ]. In the present study, salivary glucose levels were analyzed across three patient subgroups stratified by plasma glucose levels. Statistically significant differences were observed among these groups, suggesting a correlation between salivary glucose concentrations and blood glucose levels. These findings align with prior research [ 28 ], though discrepancies exist, as some studies reported no such association [ 29 ]. These inconsistencies may arise from differences in study populations, sampling protocols, or analytical techniques, emphasizing the need for standardized methodologies. The mechanisms underlying glucose secretion into saliva remain debated. While paracellular and intercellular pathways have been proposed [ 30 , 31 ], chronic hyperglycemia is hypothesized to induce structural alterations in salivary gland microvasculature and basement membranes [ 32 , 33 ]. Such changes may enhance glucose diffusion from blood into saliva via ductal cells, elevating salivary glucose levels in diabetics. This metabolic dysregulation not only complicates systemic health but also exacerbates oral pathologies, including opportunistic infections like candidiasis, due to glycosylation product accumulation in buccal tissues [ 34 , 35 ]. The diagnostic utility of saliva extends beyond glucose. Components such as sodium, potassium, proteins, amylase, albumin, and immunoglobulin A (IgA) exhibit alterations in diabetic individuals, potentially reflecting disease severity and oral complications like periodontal disease [ 36 , 37 ]. Furthermore, this study evaluated the oral glucose tolerance test (OGTT) in unstimulated saliva, revealing patterns akin to serum: salivary glucose peaked at 60 minutes and declined after two hours in healthy controls, while remaining elevated in diabetics. These results, consistent with prior work [ 38 ], validate saliva as a viable medium for OGTT, particularly in resource-limited settings. To enhance diagnostic robustness, this study integrated salivary glucose analysis with glycogen-positive (PAS-stained) buccal mucosal cells. Diabetic patients exhibited significantly higher PAS-positive cell counts compared to controls (p ≤ 0.001), correlating positively with fasting blood glucose (p ≤ 0.043). This simple, equipment-free method complements cytomorphometric analyses and aligns with existing literature [ 39 ]. ROC curve analysis further affirmed the diagnostic efficacy of combined salivary glucose and PAS-positive cell assessments, with near-perfect area-under-curve values, underscoring their clinical utility. Despite these advances, challenges persist. Glycated hemoglobin (HbA1c), while a gold standard for glycemic monitoring, has limitations: it reflects average glucose over months, is influenced by anemia, hemoglobinopathies, and renal dysfunction [ 40 ], and lacks sensitivity to acute fluctuations. Although salivary glucose correlated with HbA1c in controlled diabetics [ 41 ], this relationship remains contested [42], likely due to temporal disparities between short-term salivary glucose levels and long-term HbA1c values. 4.1. CONCLUSION Saliva based diagnostics offer a transformative approach for non-invasive diabetes management. However, methodological standardization encompassing sample collection, assay protocols, and demographic considerations is critical to reconcile conflicting findings and ensure reproducibility. Clinically, integrating salivary diagnostics with routine dental care could enhance early DM detection and oral health management. Interdisciplinary collaboration among healthcare providers is essential to optimize patient outcomes, leveraging saliva’s potential as a holistic diagnostic fluid. Since the amount of glucose in a patient's plasma may be mirrored in their saliva, salivary glucose may be helpful for routine diabetes screening. It may be possible to ascertain more definitively if salivary glucose estimation will eventually take the position of plasma glucose measurement with more study, including random and postprandial glucose correlation and thorough data on the possible impacts of salivary glucose levels. 5. References International Diabetes Federation. Chapter 3. The global picture. In: Diabetes Atlas. 8 th ed Brussels, Belgium: International Diabetes Federation; 2017. https://idf.org/e-library/epidemiology-research/diabetes-atlas/134-idf-diabetes-atlas-8th-edition.html. Accessed March, 2019. Mohammed Mohammed , Salem May and Latif, A. 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Doi: 10.1111/doi.12015 Yu Feng ShangYu Feng Shang,Yi Yang ShenYi ,Yang Shen1Meng Chen ZhangMeng Chen Zhang,Min Chao LvMin Chao Lv,Tong Ying Wang,Tong Ying Wang,4Xue Qun Chen,Xue Qun Chen,Jun Lin,Jun Lin. ( 2023). Progress in salivary glands: Endocrine glands with immune .Experimental Endocrinology ,Volume 14 – 2023 | https://doi.org/10.3389/fendo.2023.1061235. Hyung Rae, Woo Kwon, Junghyun Kim . (2023). Effect of glucagon-like peptide 1 on salivary gland hypofunction in diabetic db/db mice. J Biomed Transl Res 2023; 24(4):139-150 .pISSN: 2508-1357, eISSN: 2508-139X. DOI: https://doi.org/10.12729/jbtr.2023.24.4.139 Vineet Gupta , Amanpreet Kaur.(2020). Salivary glucose levels in diabetes mellitus patients: A case–control study. J Oral Maxillofac Pathol.,8;24(1):187. Doi: 10.4103/jomfp.JOMFP_15_20. Mohammed Jasim Mohammed, Abbas S. Al-mizraqchi,2and Salah M. Ibrahim. (2024). Oral Findings, Salivary Copper, Magnesium, and Leptin in Type II Diabetic Patients in Relation to Oral Candida Species. International Journal of Microbiology, Article ID 8177437, 13 pages https://doi.org/10.1155/2024/8177437 Marwan Showayter , Mohammad Aljariri , Ahmed Al Dalalah , Hossam Al-Fuqaha 3, Ahmad AlKhatib , Abeer Mohammad , Saif Aburumman . ( 2024). Prevalence and Severity of Periodontal Disease in Diabetic Patients in South Jordan: A Cross-Sectional Study. Cureus ,5;16(8):e66203. Doi: 10.7759/cureus.66203. Swati Kumari, Mesk Samara, Remya Ampadi Ramachandran, Sujoy Gosh et. al. A Review on Saliva-Based Health Diagnostics: Biomarker Selection and Future Directions. Biomedical Materials & Devices. 2, :121–138, (2024). Chen K, Wang S, Zhong P, Peng Y, Lu J, Liu L, He J, Liu W. Uniformity and stability of saliva composition based on glucose concentration analysis. Clinica Chimica Acta. 2025 Jun 1;573:120283. https://doi.org/10.1016/j.cca.2025.120283 Yangyang Cui Hankun Zhang,Jia Zhu ,Zhenhua Liao ,Song Wang andWeiqiang Liu . Correlations of Salivary and Blood Glucose Levels among Six Saliva Collection Methods. Int. J. Environ. Res. Public Health 2022, 19(7), 4122; https://doi.org/10.3390/ijerph19074122. Annika Borg, Andersson Dowen ,Birkhed Kerstin ,Berntorp Kerstin Berntorp. Glucose concentration in parotid saliva after glucose/food intake in individuals with glucose intolerance and diabetes mellitus. European Journal of Oral Sciences. 2023. 106(5):931 – 937. DOI: 10.1046/j.0909-8836.1998. eos106505.x. Jurysta C, Bulur N, Oguzhan B, Satman I, Yilmaz TM, Malaisse WJ, Sener A. Salivary glucose concentration and excretion in normal and diabetic subjects. BioMed Research International. 2009;2009(1):430426. https://doi.org/10.1155/2009/430426 Roth J, Müller N, Lehmann T, Heinemann L, Wolf G, Müller UA. HbA1c and age in non-diabetic subjects: an ignored association?. Experimental and Clinical Endocrinology & Diabetes. 2016 Nov;124(10):637-42. DOI: 10.1055/s-0042-105440 Qi J, Su Y, Song Q, Ding Z, Cao M, Cui B, Qi Y. Reconsidering the HbA1c cutoff for diabetes diagnosis based on a large Chinese cohort. Experimental and Clinical Endocrinology & Diabetes. 2021 Feb;129(02):86-92. DOI: 10.1055/a-0833-8119 Gülsen Ş, Deniz KE, Başak C, Alper G, Yeşil BS, Betül E. The effect of age and gender on HbA1c levels in adults without diabetes mellitus. Journal of Medical Biochemistry. 2023 Oct 27;42(4):714. doi: 10.5937/jomb0-44190 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted 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. 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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-7025858","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":479423690,"identity":"75513c47-c633-449a-85f5-07ffeb315b99","order_by":0,"name":"Mohammed Mohammed","email":"","orcid":"","institution":"Gilgamesh University","correspondingAuthor":false,"prefix":"","firstName":"Mohammed","middleName":"","lastName":"Mohammed","suffix":""},{"id":479423691,"identity":"151d3518-d886-44d0-ae27-28b6e7b8f28d","order_by":1,"name":"May Salem","email":"","orcid":"","institution":"Gilgamesh University","correspondingAuthor":false,"prefix":"","firstName":"May","middleName":"","lastName":"Salem","suffix":""},{"id":479423692,"identity":"df02dc5f-beab-4baf-a6af-0bec09ba3580","order_by":2,"name":"Mohammed Mohaibes","email":"data:image/png;base64,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","orcid":"https://orcid.org/0009-0009-7458-6626","institution":"Gilgamesh University","correspondingAuthor":true,"prefix":"","firstName":"Mohammed","middleName":"","lastName":"Mohaibes","suffix":""}],"badges":[],"createdAt":"2025-07-02 06:37:50","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":true,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7025858/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7025858/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85913391,"identity":"b630c77c-2241-41d6-b904-77629fc1a5b5","added_by":"auto","created_at":"2025-07-03 06:10:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1019069,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7025858/v1/a1b3d58e-5f68-40ac-a6bb-f67b905bf238.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eComparative Efficacy of Salivary, Buccal, and Hemoglobin Biomarkers in Blood Glucose Monitoring: Implications for Type 2 Diabetes Management\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eDiabetes mellitus represents a significant global health challenge, characterized by chronic hyperglycemia and metabolic disturbances arising from insulin deficiency, cellular resistance, or both [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. This condition is associated with severe complications affecting cardiovascular, renal, ocular, and other organ systems, substantially diminishing quality of life and life expectancy [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The prevalence of diabetes has escalated alarmingly, rising from 200\u0026nbsp;million cases in 1990 to 830\u0026nbsp;million by 2022, with low- and middle-income countries experiencing the most rapid increase [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Notably, over 50% of individuals with diabetes in 2022 did not utilize medication, and treatment coverage remains disproportionately low in resource-limited settings [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Complications such as blindness, kidney failure, cardiovascular events, and lower-limb amputations further underscore the urgency of effective management strategies [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCurrent diagnostic and monitoring practices rely heavily on plasma glucose measurements, which necessitate invasive blood sampling. Repeated needle insertions cause patient discomfort, psychological stress, and compliance challenges, particularly among pediatric and elderly populations [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Consequently, there is a critical need for non-invasive, cost-effective alternatives. Saliva has emerged as a promising bio fluid due to its ease of collection, non-invasiveness, and suitability for large-scale screening [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Whole saliva, collected via draining or spitting methods without specialized equipment, is particularly advantageous for systemic disease assessment, whereas gland-specific secretions aid in detecting localized pathologies [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe correlation between salivary glucose levels (SGL) and blood glucose levels (BGL) remains contentious. While some studies report a positive association in diabetic patients, others find inconsistent or inconclusive results [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Further complexity arises from diagnostic tools such as glycated hemoglobin (HbA1c), which reflects average glycemic control over three months and offers advantages over fasting plasma glucose (FPG) or oral glucose tolerance tests (OGTT). HbA1c eliminates fasting requirements, reduces biological variability, and correlates strongly with long-term complications [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Nonetheless, OGTT remains valuable for clarifying ambiguous cases, requiring plasma glucose measurements at fasting and post-glucose intervals [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOGTT is typically performed to help diagnose diabetes and determine how serious the condition is. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Therefore, the purpose of this study was to compare the levels of OGTT in the saliva and serum of patients with and without diabetes to those of those who appeared to be in good health.\u003c/p\u003e \u003cp\u003eNowadays, measuring blood glucose levels is the main method used to diagnose diabetes. Nonetheless, glycated hemoglobin (HbA1c) level monitoring has increased in popularity and frequency, offering a precise indicator of average glycemic management throughout the previous three months [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAccording to the New Zealand Society of Diabetes, the HbA1c test should typically be used as the initial screening and diagnostic method for type 2 diabetes. [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. This proposal is generally consistent with many international standards and attempts to update and supplement the New Zealand standards Group's current guidance. Up until recently, the two-hour post-oral glucose tolerance test or fasting plasma glucose levels have been the suggested diagnostic and screening procedures for type 2 diabetes [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. For most patients, HbA1c offers a number of benefits over these tests. First of all, fasting is not required. The accuracy of fasting plasma and oral glucose tolerance tests is decreased since many people do not adhere to the fasting requirement, according to research and anecdotal data. Daily fluctuations in plasma glucose caused by medications, nutrition, exercise, and smoking had less of an impact on HbA1c. Additionally, compared to fasting plasma glucose measurement, HbA1c has reduced biological variability. The association between future retinal risk and HbA1c is precise and well-established. Samples and analyses for HbA1c are less complicated [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEmerging research highlights additional biomarkers, such as glycogen accumulation in buccal mucosal cells linked to reduced Glycogen Synthase Kinase-3 (GSK-3) phosphorylation in diabetics [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. These findings underscore the potential of oral biomarkers to complement existing diagnostic frameworks.\u003c/p\u003e \u003cp\u003eThe present study aims to \u003cb\u003e(1)\u003c/b\u003e evaluate the association between plasma and salivary glucose levels in diabetic and non-diabetic participants, with emphasis on age-related variations, and \u003cb\u003e(2)\u003c/b\u003e explore the diagnostic utility of salivary glucose and buccal mucosal cell glycogen content in Type II Diabetes Mellitus. By addressing gaps in non-invasive methodologies, this research seeks to contribute to accessible, patient-friendly diagnostic strategies, particularly in resource-constrained settings.\u003c/p\u003e"},{"header":"2. MATERIALS AND METHODS","content":"\u003cp\u003e\u003cstrong\u003e2.1. ETHICS STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was approved by the Ethics Committee at Gilgamesh University. Written informed consent was obtained from all participants prior to sample collection. Saliva and blood samples were collected in accordance with ethical guidelines for human research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2. PARTICIPANTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, a total of 370 participants were included, comprised three groups based on their blood glucose level (BGL). \u003cstrong\u003eGroup I\u003c/strong\u003e: uncontrolled diabetics, n=135 (more than 300 mg/dl of BGL), \u003cstrong\u003eGroup II\u003c/strong\u003e: controlled diabetics, n=135, (more than 130-200 mg/dl of BGL) and \u003cstrong\u003eGroup III\u003c/strong\u003e: non-diabetic controls, n=100, (less than 130 mg/dl of BGL). Demographic characteristics are summarized in \u003cstrong\u003e\u003cem\u003eTable 1. \u003c/em\u003eGroup I\u003c/strong\u003e included 70 males (51.9%) and 65 females (48.1%), aged 29\u0026ndash;88 years. \u003cstrong\u003eGroup II\u003c/strong\u003e consisted of 72 males (53.3%) and 63 females (46.7%), aged 27\u0026ndash;75 years, while \u003cstrong\u003eGroup III\u003c/strong\u003e had 55 males (55%) and 45 females (45%), aged 30\u0026ndash;66 years.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1: Gender and age distribution of study subjects\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eGroup\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMale (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eFemale (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAge Range\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e29\u0026ndash;88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e27\u0026ndash;75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e30\u0026ndash;66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eParticipants were required to be \u0026ge;18 years old, in good general health, and free of fever or acute oral conditions (e.g., mucosal lesions, active periodontal disease) on the day of sampling. Individuals exhibiting inadequate oral hygiene or reduced salivation were excluded.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3. \u003c/strong\u003e\u003cstrong\u003eINCLUSION CRITERIA \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEach study participant verbally consented and completed a data sheet containing their name, age, sex, and relevant medical history. They were asked to arrive at the clinic between 8:00 and 10:00 a.m., at which time two milliliters of venous blood were collected from them. Participants are required to be in good overall health. On the day of sample collection, all participants were free of fever and exhibited high standards of dental hygiene. If the oral inspection revealed any indicators of inadequate oral hygiene, reduced salivation, oral complaints, or other oral health problems (such as mucosal lesions or clinical signs of active periodontal disease), those participants were swiftly removed from the study. \u003c/p\u003e\n\n\u003cp\u003eParticipants were instructed to rinse their mouths with tap water and to expectorate two or three times. Subsequently, they were asked to continuously spit the saliva that accumulated in their mouths for a duration of 10 minutes into a sterile sample collection container. This procedure aimed to measure the salivary flow rate within the case study population. The quantitative analysis of fasting plasma glucose (FPG) and fasting saliva glucose (FSG) was performed using the glucose oxidase method with the enzymatic kit GOD-POD, which employs glucose oxidase and peroxidase [10].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4. SAMPLE COLLECTION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePre-collection Protocol:\u003c/strong\u003e Participants abstained from smoking, eating, drinking, or tooth brushing for 30 minutes prior to sampling. Oral cavities were rinsed with tap water to remove food debris. The first step in evaluating sampling options involves selecting the most suitable method for saliva collection. Following this, a more detailed investigation will utilize the most effective method to assess the correlation between individual relationships and their stability. Here, the term correlation specifically refers to the relationship between glucose concentrations in blood and saliva.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4.1. Saliva Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFirst, saliva and blood samples were collected from a cohort of 370 participants, and a population correlation analysis was conducted to identify the optimal saliva collection technique for the morning period (7:30\u0026ndash;8:00). Next, from the 40 individuals in each group, 20 DM patients and 20 healthy controls were randomly selected. These participants provided saliva and blood samples daily (before and after breakfast) for a week, using the established saliva collection method to perform an individual correlation analysis. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4.1.1. \u003cem\u003eUnstimulated Saliva:\u003c/em\u003e\u003c/strong\u003e Participants expectorated accumulated saliva into sterile containers over 10 minutes to measure salivary flow rate.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4.1.2. \u003cem\u003eStimulated Saliva:\u003c/em\u003e\u003c/strong\u003e A Salivette\u0026reg; (Sarstedt 51.5134) with citric acid-treated swabs was used for alternative collection methods (six protocols).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4.2. Blood Collection:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVenous blood (5 mL) was drawn via venipuncture between 8:00\u0026ndash;10:00 a.m. into additive-free vacuum tubes. Plasma was separated by centrifugation (2000 \u0026times; g, 10 minutes) and analyzed immediately.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5. ANALYTICAL METHODS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5.1. Salivary Parameters\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eApproximately 3 ml of saliva was collected from each volunteer and immediately analyzed for conductivity (Cond.), redox potential (ORP), pH, and calcium, potassium and sodium ionic concentrations. The pH and ORP values were measured using the F-71 Laqua Lab (Japan) pH/ORP meter. The conductivity and concentration of Na+, K+, and Ca++ electrolytes were recorded using the Horiba L- aqua twin series ion selective models. The venous plasma FBGL values of these volunteers were measured using an automatic biochemical analyzer (COBAS INTEGRA 400 plus).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5.2. Glucose Assays\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFasting plasma glucose (FPG) and fasting saliva glucose (FSG) were analyzed via the glucose oxidase-peroxidase (GOD-POD) enzymatic kit [10, 11 and 17].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5.3. HbA1c Estimation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSerum glycated hemoglobin (HbA1c) levels were determined using a Chroma\u0026trade; fluorescence-based point-of-care immunoassay analyzer (EDTA-anticoagulated blood).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5.4. Oral Glucose Tolerance Test (OGTT)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants ingested 75 g glucose in 300 mL water within 5 minutes. Blood and saliva samples were collected at baseline, 1-hour, and 2-hour intervals post-consumption. Samples were centrifuged (2000 \u0026times; g, 10 minutes), and glucose levels were assayed immediately.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5.5. Histochemical Analysis of Buccal Mucosal Cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExfoliated buccal cells were collected using a sterile wooden spatula, spread onto glass slides, fixed with Biofix\u0026trade; spray, and stained with periodic acid-Schiff (PAS) to detect glycogen. Fifty cells per slide were evaluated microscopically for PAS-positive (magenta) staining. Staining intensity was coded as:\u003cstrong\u003e Code 1:\u003c/strong\u003e Mild\u003cstrong\u003e Code 2:\u003c/strong\u003e Moderate \u003cstrong\u003e Code 3:\u003c/strong\u003e Intense\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6. STATISTICAL DESIGN\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePopulation Correlation Analysis: Initial saliva collection timing (7:30\u0026ndash;8:00 a.m.) was optimized using data from all 370 participants.\u003c/p\u003e\n\u003cp\u003eIndividual Correlation Analysis: A subset (n = 40 per group) provided daily samples (pre- and post-breakfast) for one week.\u003c/p\u003e\n\u003cp\u003eLongitudinal Monitoring: A final cohort (n = 10 per group) underwent daily fasting sampling for one month. Coefficients of variation (CV) were calculated to assess correlation stability between salivary and blood glucose levels.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6.1. Data Management\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll measurements were recorded electronically, with identifiers anonymized to ensure confidentiality. Statistical analyses were performed using SPSS v26.0 (\u0026alpha; = 0.05).\u003c/p\u003e"},{"header":"3. RESULTS","content":"\u003cp\u003eIn this cohort study, there were no significant differences between groups in age distribution and gender composition (P\u0026thinsp;\u0026le;\u0026thinsp;0.67 and P\u0026thinsp;\u0026le;\u0026thinsp;0.57) in sequence. Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003eIn Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. A comparative results between salivary glucose (SGL) and blood glucose (BGL) levels by using Student t-test for statistical analysis. The highest mean of SGL was exhibited by \u003cstrong\u003eGroup I\u003c/strong\u003e (204 mg/dl) and BGL (209 mg/dl), in compare to \u003cstrong\u003eGroup II\u003c/strong\u003e (SGL: 166 mg/dl; BGL: 158 mg/dl) and \u003cstrong\u003eGroup III\u003c/strong\u003e (SGL: 111 mg/dl; BGL: 118 mg/dl). No significant differences were found between groups for SGL or BGL (p\u0026thinsp;\u0026le;\u0026thinsp;0.05).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComparison of salivary glucose (SGL) and Blood glucose levels (BGL) between cases and control groups (Student t test):\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStudy Population\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean SGL (mg/dl)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean BGL (mg/dl)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGroup I\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGroup II\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGroup III\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e\u003cstrong\u003eNS\u0026thinsp;=\u0026thinsp;Non-significant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05); SD\u0026thinsp;=\u0026thinsp;Standard Deviation\u003c/strong\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eOral glucose tolerance test (GTT) results are detailed in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. In\u0026nbsp;\u003cstrong\u003eGroup I\u003c/strong\u003e, serum glucose peaked at 1-hour post-glucose intake (269 mg/dl), declining to 188 mg/dl at 2 hours. Salivary glucose followed a similar trend, peaking at 246 mg/dl (1 hour) and decreasing to 156 mg/dl (2 hours).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe mean values and SD of serum and unstimulated whole saliva glucose concentrations in oral GTT of diabetic and apparently healthy participants\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGroups\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTest\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFasting\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1 hr.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2 hr.\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eGroup I\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSerum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e188\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSaliva\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e156\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eGroup II\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSerum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e139\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSaliva\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e142\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eGroup III\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eserum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSaliva\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e119\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eGroup II\u003c/strong\u003e showed lower peaks (serum: 196 mg/dl; saliva: 188 mg/dl at 1 hour), while controls displayed minimal fluctuations (serum: 122 mg/dl; saliva: 134 mg/dl at 1 hour). Two-hour glucose levels in all groups approximated 70% of 1-hour values. Pearson correlation analysis confirmed a significant association between fasting salivary and blood glucose levels (p\u0026thinsp;\u0026le;\u0026thinsp;0.05).\u003c/p\u003e\n\u003cp\u003eHbA1c distributions are presented in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. Among \u003cstrong\u003eGroup I\u003c/strong\u003e, 70.3% had HbA1c\u0026thinsp;\u0026lt;\u0026thinsp;7%, compared to 51.1% in \u003cstrong\u003eGroup II\u003c/strong\u003e. Only 5.1% of \u003cstrong\u003eGroup I\u003c/strong\u003e and 22.2% of \u003cstrong\u003eGroup II\u003c/strong\u003e exhibited HbA1c levels\u0026thinsp;\u0026gt;\u0026thinsp;9%. \u003cstrong\u003eGroup III\u003c/strong\u003e universally maintained HbA1c\u0026thinsp;\u0026lt;\u0026thinsp;7%. A significant difference in HbA1c distribution was observed between uncontrolled and controlled diabetics (p\u0026thinsp;\u0026le;\u0026thinsp;0.001). Furthermore, salivary glucose levels correlated positively with HbA1c in both diabetic groups (p\u0026thinsp;\u0026le;\u0026thinsp;0.05).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDistribution of HbA1c (gram %) in three groups of patients studied\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHbA1c (gram %)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGroup I: (158 mg/dl)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGroup II: (209 mg/dl)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGroup III: 80\u0026ndash;120 m/dl)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69 (51.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95 (70.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u0026ndash;8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30 (22.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (5.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u0026ndash;9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20 (14.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 (7.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u0026ndash;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (5.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 (6.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (5.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14 (10.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e135 (100)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e135 (100)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e100 (100)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eInference HbA1c Levels are significantly more in uncontrolled diabetes with\u003c/strong\u003e \u003cstrong\u003eP\u003c/strong\u003e\u0026thinsp;\u0026le;\u0026thinsp;\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe mean counts of PAS-positive buccal mucosal cells were significantly higher (p\u0026thinsp;\u0026le;\u0026thinsp;0.001) in \u003cstrong\u003eGroup I\u003c/strong\u003e (9.148\u0026thinsp;\u0026plusmn;\u0026thinsp;2.369) compared to \u003cstrong\u003eGroup II\u003c/strong\u003e (4.467\u0026thinsp;\u0026plusmn;\u0026thinsp;1.833) and \u003cstrong\u003eGroup III\u003c/strong\u003e (0.933\u0026thinsp;\u0026plusmn;\u0026thinsp;1.311) (Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003cem\u003e)\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComparison of intensity of PAS staining in diabetics and controls\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNon-Diabetics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDiabetics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMann-Whitney U\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003ePAS intensity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93\u0026thinsp;\u0026plusmn;\u0026thinsp;1.311 \u003cstrong\u003eGroup III\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGroup I\u003c/strong\u003e 9.467\u0026thinsp;\u0026plusmn;\u0026thinsp;1.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e223.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026le; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGroup II\u003c/strong\u003e 4.968\u0026thinsp;\u0026plusmn;\u0026thinsp;1.963\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e157. 300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe number of PAS-positive cells showed a positive correlation with fasting serum glucose levels (r\u0026thinsp;=\u0026thinsp;0.372, p\u0026thinsp;\u0026le;\u0026thinsp;0.043), but no relationship existed between staining intensity and glucose levels (r\u0026thinsp;=\u0026thinsp;0.359, p\u0026thinsp;\u0026le;\u0026thinsp;0.198). Statistical comparisons confirmed a highly significant increase in PAS-positive cells in diabetics versus controls (p\u0026thinsp;\u0026le;\u0026thinsp;0.001).\u003c/p\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e. Showed PAS-positive cells distribution among the study groups. The highest mean count of PAS-positive cells were recorded for Group I (9.148\u0026thinsp;\u0026plusmn;\u0026thinsp;2.369), followed by Group II (4.467\u0026thinsp;\u0026plusmn;\u0026thinsp;1.833). In contrast, Group III showed a significant lower mean count (0.933\u0026thinsp;\u0026plusmn;\u0026thinsp;1.311). This observation indicates a progressive reduction in PAS-positive cells from Group I to Group III, with Group III values being nearly 5 times lower than those of Group II and 10 times lower than those of Group I. The standard deviations indicate moderate variability in Groups I and II, while Group III presents a relatively higher uniformity in its measurements.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDistribution of mean value of PAS positive cells in diabetic groups and healthy control group\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of PAS positive cells\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGroup I\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.369\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGroup II\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.8333\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGroup III\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.933\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.3113\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eQuantitative analysis revealed a statistically significant increase in PAS-positive cells in diabetic subjects compared to controls (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e.). The mean difference between groups was 1.5333 (95% CI not reported), with a standard error of the difference of 0.4115. This difference was highly significant (t\u0026thinsp;=\u0026thinsp;6.726, p\u0026thinsp;\u0026le;\u0026thinsp;0.001), indicating robust evidence against the null hypothesis of no difference between groups.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab7\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eStatistical comparison of PAS positive cells in diabetics and controls\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003et-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep- value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of PAS positive cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.726\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe relationship between the total number of PAS-positive cells and fasting serum glucose levels indicated a statistically significant positive correlation (Pearson\u0026apos;s r\u0026thinsp;=\u0026thinsp;0.372, p\u0026thinsp;\u0026le;\u0026thinsp;0.043) across the study groups, as detailed in Table 8. This indicates that higher fasting glucose levels are associated with increased PAS-positive cell counts\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab8\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCorrelation of total number of PAS positive cells with fasting serum glucose in study groups measured via Pearson correlation coefficient.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePearson correlation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePAS positive cell/Fasting Serum Glucose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.372\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eHowever, staining intensity did not correlate with fasting glucose (r\u0026thinsp;=\u0026thinsp;0.359, p\u0026thinsp;\u0026le;\u0026thinsp;0.198). ROC curve analysis (Table \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e.) demonstrated strong diagnostic utility for salivary glucose (AUC\u0026thinsp;=\u0026thinsp;0.932, p\u0026thinsp;\u0026le;\u0026thinsp;0.001) and PAS-positive cells (AUC\u0026thinsp;=\u0026thinsp;0.752, p\u0026thinsp;\u0026le;\u0026thinsp;0.001) in identifying diabetes mellitus.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab9\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eArea under Curve calculation analysis\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eArea Under Curve (AUC)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eArea\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStandard Error\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSalivary Glucose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.932\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePAS positive cell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAn analysis of the correlation between staining intensity and fasting serum glucose levels was conducted utilizing Pearson\u0026apos;s correlation coefficient. Table \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e illustrates that a moderate positive correlation was found (r\u0026thinsp;=\u0026thinsp;0.359), yet this correlation did not attain statistical significance (p\u0026thinsp;=\u0026thinsp;0.198).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab10\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCorrelation of staining intensity with Fasting Serum Glucose in study group\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePearson correlation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStaining intensity/Fasting Serum Glucose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;0.198\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eBlood remains the primary biological fluid for disease diagnosis and monitoring. However, whole saliva has emerged as a promising non-invasive alternative, offering unique advantages for diagnostic applications. Saliva contains plasma-derived components and locally synthesized biomarkers that reflect systemic health, enabling the detection of various conditions, including diabetes mellitus (DM). Its non-invasive collection, cost-effectiveness for population-wide screening, and ease of access underscore its diagnostic potential [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSaliva from patients with and without diabetes differs significantly, likely having higher levels of glucose, enzymes, and total protein [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The current investigation found a high positive correlation between the study population's salivary and plasma glucose levels.\u003c/p\u003e \u003cp\u003eSalivary composition in diabetic individuals exhibits variability influenced by study design, sample selection criteria, and methodological approaches [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In the present study, salivary glucose levels were analyzed across three patient subgroups stratified by plasma glucose levels. Statistically significant differences were observed among these groups, suggesting a correlation between salivary glucose concentrations and blood glucose levels. These findings align with prior research [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], though discrepancies exist, as some studies reported no such association [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. These inconsistencies may arise from differences in study populations, sampling protocols, or analytical techniques, emphasizing the need for standardized methodologies.\u003c/p\u003e \u003cp\u003eThe mechanisms underlying glucose secretion into saliva remain debated. While paracellular and intercellular pathways have been proposed [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], chronic hyperglycemia is hypothesized to induce structural alterations in salivary gland microvasculature and basement membranes [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Such changes may enhance glucose diffusion from blood into saliva via ductal cells, elevating salivary glucose levels in diabetics. This metabolic dysregulation not only complicates systemic health but also exacerbates oral pathologies, including opportunistic infections like candidiasis, due to glycosylation product accumulation in buccal tissues [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe diagnostic utility of saliva extends beyond glucose. Components such as sodium, potassium, proteins, amylase, albumin, and immunoglobulin A (IgA) exhibit alterations in diabetic individuals, potentially reflecting disease severity and oral complications like periodontal disease [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Furthermore, this study evaluated the oral glucose tolerance test (OGTT) in unstimulated saliva, revealing patterns akin to serum: salivary glucose peaked at 60 minutes and declined after two hours in healthy controls, while remaining elevated in diabetics. These results, consistent with prior work [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], validate saliva as a viable medium for OGTT, particularly in resource-limited settings.\u003c/p\u003e \u003cp\u003eTo enhance diagnostic robustness, this study integrated salivary glucose analysis with glycogen-positive (PAS-stained) buccal mucosal cells. Diabetic patients exhibited significantly higher PAS-positive cell counts compared to controls (p\u0026thinsp;\u0026le;\u0026thinsp;0.001), correlating positively with fasting blood glucose (p\u0026thinsp;\u0026le;\u0026thinsp;0.043). This simple, equipment-free method complements cytomorphometric analyses and aligns with existing literature [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. ROC curve analysis further affirmed the diagnostic efficacy of combined salivary glucose and PAS-positive cell assessments, with near-perfect area-under-curve values, underscoring their clinical utility.\u003c/p\u003e \u003cp\u003eDespite these advances, challenges persist. Glycated hemoglobin (HbA1c), while a gold standard for glycemic monitoring, has limitations: it reflects average glucose over months, is influenced by anemia, hemoglobinopathies, and renal dysfunction [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], and lacks sensitivity to acute fluctuations. Although salivary glucose correlated with HbA1c in controlled diabetics [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], this relationship remains contested [42], likely due to temporal disparities between short-term salivary glucose levels and long-term HbA1c values.\u003c/p\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.1. CONCLUSION\u003c/h2\u003e \u003cp\u003eSaliva based diagnostics offer a transformative approach for non-invasive diabetes management. However, methodological standardization encompassing sample collection, assay protocols, and demographic considerations is critical to reconcile conflicting findings and ensure reproducibility. Clinically, integrating salivary diagnostics with routine dental care could enhance early DM detection and oral health management. Interdisciplinary collaboration among healthcare providers is essential to optimize patient outcomes, leveraging saliva\u0026rsquo;s potential as a holistic diagnostic fluid.\u003c/p\u003e \u003cp\u003eSince the amount of glucose in a patient's plasma may be mirrored in their saliva, salivary glucose may be helpful for routine diabetes screening. It may be possible to ascertain more definitively if salivary glucose estimation will eventually take the position of plasma glucose measurement with more study, including random and postprandial glucose correlation and thorough data on the possible impacts of salivary glucose levels.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. References","content":"\u003col\u003e\n \u003cli\u003eInternational Diabetes Federation. Chapter 3. The global picture. 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EBioMedicine. 2017 Feb 1;16:284-91.\u003c/li\u003e\n \u003cli\u003eAmerican Diabetes Association Professional Practice Committee. Diagnosis and Classification of Diabetes: Standards of Care in Diabetes\u0026mdash;2024. Diabetes Care 2024;47(Supplement_1):S20\u0026ndash;S42 .https://doi.org/10.2337/dc24-S002\u003c/li\u003e\n \u003cli\u003eDahiru Saleh Mshelia, Sani Adamu and Rebecca Mtaku Gali. Oral Glucose Tolerance Test (OGTT): Undeniably the First Choice Investigation of Dysglycaemia, Reproducibility can be Improved. Type 2 Diabetes \u0026ndash; From Pathophysiology to Cyber Systems. Published 2021.DOI: 10.5772/intechopen.96549\u003c/li\u003e\n \u003cli\u003eSilva GA, Souza CL, Oliveira MV. Oral glucose tolerance test: unnecessary requests and suitable conditions for the test. Jornal Brasileiro de Patologia e Medicina Laboratorial. 2020 Mar 16;56:e0932020. https://doi.org/10.5935/1676-2444.20200010\u003c/li\u003e\n \u003cli\u003eWang M, Hng TM. HbA1c: More than just a number. 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Med Res Rev. 2021 Nov 3;42(2):946\u0026ndash;982. Doi: 10.1002/med.21867\u003c/li\u003e\n \u003cli\u003eVisvanathan R, Jayathilake C, Liyanage R, Sivakanesan R. Applicability and reliability of the glucose oxidase method in assessing \u0026alpha;-amylase activity. Food chemistry. 2019 Mar 1;275:265-72. https://doi.org/10.1016/j.foodchem.2018.09.114\u003c/li\u003e\n \u003cli\u003eWHO. Expert committee on diabetes mellitus. Third report. World Health Organ Tech Rep 1985;727: 1 113).\u003c/li\u003e\n \u003cli\u003eChopra A, Rao RR, Kamath SU, Arun SA, Shettigar L. Predicting blood glucose level using salivary glucose and other associated factors: A machine learning model selection and evaluation study. Informatics in Medicine Unlocked. 2024 Jan 1;48:101523. https://doi.org/10.1016/j.imu.2024.101523\u003c/li\u003e\n \u003cli\u003eRagunathan H, Aswath N, Sarumathi T. Salivary glucose estimation: A noninvasive method. Indian Journal of Dental Sciences. 2019 Jan 1;11(1):25-7. DOI: 10.4103/IJDS.IJDS_78_18\u003c/li\u003e\n \u003cli\u003eShettigar L, Sivaraman S, Rao R, Akhila Arun S, Chopra A, U Kamath S, Rana R. Correlational analysis between salivary and blood glucose levels in individuals with and without diabetes mellitus: a cross-sectional study. Acta Odontologica Scandinavica. 2023 Oct 12:1-1. https://doi.org/10.1080/00016357.2023.2267678\u003c/li\u003e\n \u003cli\u003eShahin AM, Abdel Ati RI, Fayd SM, Mokhtar MA. Salivary Glucose level as Noninvasive Diagnostic Tool for Monitoring Glycemic Control of Type 1 Diabetic Children. Benha Journal of Applied Sciences. 2021 Jan 1;6(1):273-7. DOI: 10.21608/bjas.2021.169126\u003c/li\u003e\n \u003cli\u003eKavitha A Puttaswamy, Jaishankar H Puttabudhi , Shashidara Raju. (2017). Correlation between Salivary Glucose and Blood Glucose and the Implications of Salivary Factors on the Oral Health Status in Type 2 Diabetes Mellitus Patients.J Int Soc Prev Community Dent.;7(1):28\u0026ndash;33. Doi: 10.4103/2231-0762.200703\u003c/li\u003e\n \u003cli\u003eGolamari, U.M.R., Lakshmanan, A. and Balakrishnan, R.K., 2019. Correlation between salivary glucose and blood glucose levels in diabetic and non-diabetic individuals.\u003cbr\u003eAnjali Gupta , Siddharth Kumar Singh , BN Padmavathi , SY Rajan , GP Mamatha , Sandeep Kumar , Sayak Roy , Mohit Sareen . ( 2015). Evaluation of Correlation of Blood Glucose and Salivary Glucose Level in Known Diabetic Patients J Clin Diagn Res. 1;9(5): doi: 10.7860/JCDR/2015/12398.5994.\u003c/li\u003e\n \u003cli\u003eOjo OA, Ibrahim HS, Rotimi DE, Ogunlakin AD, Ojo AB. Diabetes mellitus: From molecular mechanism to pathophysiology and pharmacology. Medicine in Novel Technology and Devices 19, 100247 [Internet]. 2023. https://doi.org/10.1016/j.medntd.2023.100247\u003c/li\u003e\n \u003cli\u003eBhattarai KR, Junjappa R, Handigund M, Kim HR, Chae HJ. The imprint of salivary secretion in autoimmune disorders and related pathological conditions. Autoimmunity reviews. 2018 Apr 1;17(4):376-90. https://doi.org/10.1016/j.autrev.2017.11.031\u003c/li\u003e\n \u003cli\u003eS Zolotukhin. ( 2012). Metabolic hormones in saliva: origins and functions. Oral Dis. 2;19(3):219\u0026ndash;229. Doi: 10.1111/doi.12015\u003c/li\u003e\n \u003cli\u003eYu Feng ShangYu Feng Shang,Yi Yang ShenYi ,Yang Shen1Meng Chen ZhangMeng Chen Zhang,Min Chao LvMin Chao Lv,Tong Ying Wang,Tong Ying Wang,4Xue Qun Chen,Xue Qun Chen,Jun Lin,Jun Lin. ( 2023). Progress in salivary glands: Endocrine glands with immune .Experimental Endocrinology ,Volume 14 \u0026ndash; 2023 | https://doi.org/10.3389/fendo.2023.1061235.\u003c/li\u003e\n \u003cli\u003eHyung Rae, Woo Kwon, Junghyun Kim . (2023). Effect of glucagon-like peptide 1 on salivary gland hypofunction in diabetic db/db mice. J Biomed Transl Res 2023; 24(4):139-150 .pISSN: 2508-1357, eISSN: 2508-139X. DOI: https://doi.org/10.12729/jbtr.2023.24.4.139\u003c/li\u003e\n \u003cli\u003eVineet Gupta , Amanpreet Kaur.(2020). Salivary glucose levels in diabetes mellitus patients: A case\u0026ndash;control study. J Oral Maxillofac Pathol.,8;24(1):187. Doi: 10.4103/jomfp.JOMFP_15_20.\u003c/li\u003e\n \u003cli\u003eMohammed Jasim Mohammed, Abbas S. Al-mizraqchi,2and Salah M. Ibrahim. (2024). Oral Findings, Salivary Copper, Magnesium, and Leptin in Type II Diabetic Patients in Relation to Oral Candida Species. International Journal of Microbiology, Article ID 8177437, 13 pages https://doi.org/10.1155/2024/8177437\u003c/li\u003e\n \u003cli\u003eMarwan Showayter , Mohammad Aljariri , Ahmed Al Dalalah , Hossam Al-Fuqaha 3, Ahmad AlKhatib , Abeer Mohammad , Saif Aburumman . ( 2024). Prevalence and Severity of Periodontal Disease in Diabetic Patients in South Jordan: A Cross-Sectional Study. Cureus ,5;16(8):e66203. Doi: 10.7759/cureus.66203.\u003c/li\u003e\n \u003cli\u003eSwati Kumari, Mesk Samara, Remya Ampadi Ramachandran, Sujoy Gosh et. al. A Review on Saliva-Based Health Diagnostics: Biomarker Selection and Future Directions. Biomedical Materials \u0026amp; Devices. 2, :121\u0026ndash;138, (2024).\u003c/li\u003e\n \u003cli\u003eChen K, Wang S, Zhong P, Peng Y, Lu J, Liu L, He J, Liu W. Uniformity and stability of saliva composition based on glucose concentration analysis. Clinica Chimica Acta. 2025 Jun 1;573:120283. https://doi.org/10.1016/j.cca.2025.120283\u003c/li\u003e\n \u003cli\u003eYangyang Cui Hankun Zhang,Jia Zhu ,Zhenhua Liao ,Song Wang andWeiqiang Liu . Correlations of Salivary and Blood Glucose Levels among Six Saliva Collection Methods. Int. J. Environ. Res. Public Health 2022, 19(7), 4122; https://doi.org/10.3390/ijerph19074122.\u003c/li\u003e\n \u003cli\u003eAnnika Borg, Andersson Dowen ,Birkhed Kerstin ,Berntorp Kerstin Berntorp. Glucose concentration in parotid saliva after glucose/food intake in individuals with glucose intolerance and diabetes mellitus. European Journal of Oral Sciences. 2023. 106(5):931 \u0026ndash; 937. DOI: 10.1046/j.0909-8836.1998. eos106505.x.\u003c/li\u003e\n \u003cli\u003eJurysta C, Bulur N, Oguzhan B, Satman I, Yilmaz TM, Malaisse WJ, Sener A. Salivary glucose concentration and excretion in normal and diabetic subjects. BioMed Research International. 2009;2009(1):430426. https://doi.org/10.1155/2009/430426\u003c/li\u003e\n \u003cli\u003eRoth J, M\u0026uuml;ller N, Lehmann T, Heinemann L, Wolf G, M\u0026uuml;ller UA. HbA1c and age in non-diabetic subjects: an ignored association?. Experimental and Clinical Endocrinology \u0026amp; Diabetes. 2016 Nov;124(10):637-42. DOI: 10.1055/s-0042-105440\u003c/li\u003e\n \u003cli\u003eQi J, Su Y, Song Q, Ding Z, Cao M, Cui B, Qi Y. Reconsidering the HbA1c cutoff for diabetes diagnosis based on a large Chinese cohort. Experimental and Clinical Endocrinology \u0026amp; Diabetes. 2021 Feb;129(02):86-92. DOI: 10.1055/a-0833-8119\u003c/li\u003e\n \u003cli\u003eG\u0026uuml;lsen Ş, Deniz KE, Başak C, Alper G, Yeşil BS, Bet\u0026uuml;l E. The effect of age and gender on HbA1c levels in adults without diabetes mellitus. Journal of Medical Biochemistry. 2023 Oct 27;42(4):714. doi: 10.5937/jomb0-44190\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Gilgamesh University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Saliva, diabetes management (DM), HbA1c, oral glucose tolerance (OGT)","lastPublishedDoi":"10.21203/rs.3.rs-7025858/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7025858/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground and objectives: \u003c/strong\u003eBlood-based monitoring remains the gold standard for diabetes management, yet non-invasive alternatives like salivary diagnostics offer significant potential. This study evaluated salivary glucose, buccal cell glycogen, oral glucose tolerance (OGTT), and glycated hemoglobin (HbA1c).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterials and\u003c/strong\u003e \u003cstrong\u003eMethods:\u003c/strong\u003e 370 participants classified into three groups: uncontrolled diabetes (n=135), managed diabetes (n=135), and non-diabetic controls (n=100). Salivary and blood samples were collected under standardized protocols, with glucose levels quantified via enzymatic assays, HbA1c via fluorescence immunoassay, and buccal cell glycogen via periodic acid-Schiff (PAS) staining.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e A significant differences in salivary glucose levels across groups (highest in uncontrolled diabetics: 204 mg/dl vs. controls: 111 mg/dl; p\u0026lt;0.05), correlating strongly with blood glucose (r=0.372, p=0.043). PAS-positive buccal cells were markedly elevated in diabetics (uncontrolled: 9.15 ± 2.37; controlled: 4.47 ± 1.83; controls: 0.93 ± 1.31; p=0.001), with staining intensity reflecting glycemic status. OGTT profiles in saliva mirrored serum trends, peaking at 1-hour post-glucose intake. ROC analysis revealed high diagnostic accuracy for salivary glucose (AUC=0.932) and PAS-positive cells (AUC=0.752) in distinguishing diabetics from controls.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e The integration of salivary glucose and buccal cell glycogen assessments offers a robust, non-invasive approach for diabetes screening. Standardization of collection protocols and interdisciplinary collaboration are critical to advancing saliva-based diagnostics, enabling early detection and holistic management of Type 2 Diabetes Mellitus.\u003c/p\u003e","manuscriptTitle":"Comparative Efficacy of Salivary, Buccal, and Hemoglobin Biomarkers in Blood Glucose Monitoring: Implications for Type 2 Diabetes Management","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-03 05:54:07","doi":"10.21203/rs.3.rs-7025858/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4356ff38-e5cf-4b53-90cd-3879b42e1666","owner":[],"postedDate":"July 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":50905545,"name":"Molecular Biology"},{"id":50905546,"name":"Biomedical Engineering"},{"id":50905547,"name":"Laboratory Diagnostics"},{"id":50905548,"name":"Biochemical Research Methods"}],"tags":[],"updatedAt":"2025-07-03T05:54:07+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-03 05:54:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7025858","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7025858","identity":"rs-7025858","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}