Salivary glucose as a non-invasive biomarker of type 2 diabetes mellitus

Salivary diagnostics: opportunities and challenges

Saliva contains a diverse array of biomarkers indicative of various diseases. Saliva testing has been a major advancement towards non-invasive point-of-care diagnosis with clinical significance. However, there are challenges associated with salivary diagnosis from sample treatment and standardization. This review highlights the biomarkers in saliva and their role in identifying relevant diseases. It provides an overview and discussion about the current practice of saliva collection and processing, and advancements in saliva detection systems from in vitro methods to wearable oral devices. The review also addresses challenges in saliva diagnostics and proposes solutions, aiming to offer a comprehensive understanding and practical guidance for improving saliva-based detection in clinical diagnosis. Saliva diagnosis provides a rapid, effective, and safe alternative to traditional blood and urine tests for screening large populations and enhancing infectious disease diagnosis and surveillance. It meets the needs of various fields such as disease management, drug screening, and personalized healthcare with advances in saliva detection systems offering high sensitivity, fast response times, portability, and automation. Standardization of saliva collection, treatment, biomarker discovery, and detection between different laboratories needs to be implemented to obtain reliable salivary diagnosis in clinical practice.

Correlational analysis between salivary and blood glucose levels in individuals with and without diabetes mellitus: a cross-sectional study

OBJECTIVE

To estimate the association of patient-related demographic, socioeconomic status, physical activity, stress, and dietary factors influencing the relationship between salivary and blood glucose levels in individuals with and without diabetes mellitus (DM).

METHOD

This cross-sectional study was conducted on 166 participants with and without DM. Saliva and blood were collected to estimate the glucose levels. Age, gender, occupation, socioeconomic and education level, BMI, hip to waist circumference, stress, dietary pattern, lifestyle, physical activity, family history of diabetes, and type of diabetes were recorded. The association of saliva to predict blood glucose levels was analysed using Spearman Rank Correlation and how these patient-related factors influence the correlation was estimated for future machine learning models. The difference in medians for various groups was calculated using the Mann-Whitney U Test or Kruskal Wallis Test.

RESULTS

Blood glucose level is not significantly correlated to salivary glucose level. However, a statistically significant difference in the median blood glucose levels for diabetic participants (median = 137) compared to healthy controls (p-value < .05) was noted. The correlation between blood and salivary glucose was more positive for higher levels of glucose (Spearman 0.4). Age, alcohol consumption, monthly wages, intake of vegetables, and socioeconomic status affect blood glucose levels.

CONCLUSION

A correlation between saliva and blood glucose levels in healthy individuals was weak. Saliva should only be used as a monitoring tool rather than a diagnostic tool and is more reliable for patients with poorly controlled diabetes mellitus.

Evaluation of Salivary Glucose and Salivary Cortisol in Association with Periodontitis in People with Type II Diabetes: A Case-control Study

BACKGROUND

The relationship between type II diabetes mellitus (DM) and periodontal disease (PD) has been discussed in recent years and may vary according to the patient's location, sex, age and family history.

OBJECTIVE

In view of this possible association between periodontitis and DM, this study aimed to evaluate salivary glucose and salivary cortisol levels with periodontitis (staging and degree) in people with type II diabetes compared to non-diabetic individuals.

METHODS

This was a case-control study, with 60 participants divided into two groups: DM2- people with type II diabetes (n=30) and NDM- non-diabetic individuals (n=30). For both, a clinical form and periogram were filled out, and blood samples (glycemia, glycated hemoglobin, and cortisol) and saliva (glucose and cortisol) were collected.

RESULTS

The diagnosis of periodontitis was 100% in the DM2 group and 80% in the NDM group. As for staging and degree, in the DM2 group, 70% of the participants were in stage IV and 86.67% were in grade C, while in the NDM group, 58.3% of the participants were in stage I and 70.8% were in grade A. Significant differences were observed for the following variables: bleeding to probing and clinical attachment level. The laboratory data presented significant results regarding serum glucose, salivary glucose, and glycated hemoglobin.

CONCLUSION

Regarding the influence of salivary glucose and cortisol on periodontal status (staging and degree), these had higher means in the DM2 group when compared to the NDM group. An association of salivary glucose and cortisol levels with the periodontal condition presented by type II diabetic individuals compared to non-diabetic individuals is suggested.

Non-invasive blood glucose monitoring technology in diabetes management: review

Diabetes is one of the leading non-communicable diseases globally, adversely impacting an individual's quality of life and adding a considerable burden to the healthcare systems. The necessity for frequent blood glucose (BG) monitoring and the inconveniences associated with self-monitoring of BG, such as pain and discomfort, has motivated the development of non-invasive BG approaches. However, the current research progress is slow, and only a few BG self-monitoring devices have made considerable progress. Hence, we evaluate the available non-invasive glucose monitoring technologies validated against BG recordings to provide future research direction to design, develop, and deploy self-monitoring of BG with integrated emerging technologies. We searched five databases, Embase, MEDLINE, Proquest, Scopus, and Web of Science, to assess the non-invasive technology's scope in the diabetes management paradigm published from 2000 to 2020. A total of three approaches to non-invasive screening, including saliva, skin, and breath, were identified and discussed. We observed a statistical relationship between BG measurements obtained from non-invasive methods and standard clinical measures. Opportunities exist for future research to advance research progress and facilitate early technology adoption for healthcare practice. The results promise clinical validity; however, formulating regulatory guidelines could foresee the deployment of approved non-invasive BG monitoring technologies in healthcare practice. Further, research prospects are there to design, develop, and deploy integrated diabetes management systems with mobile technologies, data analytics, and the internet of things (IoT) to deliver a personalised monitoring system.

Validation of salivary glucose as a screening tool of diabetes mellitus

Background: Improved prognosis and delay of clinical complications in diabetes mellitus can be ensured by early screening and regular monitoring after diagnosis. This requires venipuncture at regular intervals of time causing anxiety and discomfort to the patient. Hence, development of a painless, non-invasive procedure is desirable for which saliva is a potential tool. Also, this would provide easy and cost-effective means for large scale screening and epidemiological intervention.

Aim: To measure fasting plasma glucose (FPG) and compare and correlate with salivary glucose levels in normal and diabetic individuals. Also, the correlation between glycated hemoglobin (HbA1c) and salivary glucose is studied in the diabetics and controls.

Methods: Blood and salivary glucose was estimated by GOD-POD method and glycated hemoglobin by HPLC. Statistical analysis was done on SPSS 16. Mean, Standard deviation, independent t test, ANOVA (f test), Pearson’s correlation coefficient along with regression analysis was carried out and comparison was done between the control and diabetic groups and the different subgroups within the diabetic group.

Results: A significant difference between the salivary glucose levels in subjects indicating that a deranged glycemic status is reflected in saliva. Also, salivary glucose increases in proportion to an increase in the FPG and HbA1C of the diabetics. The regression coefficient was calculated and a formula was derived for prediction of FPG and HbA1c using salivary glucose.

Conclusion: Saliva can be used as a screening tool for diabetes. Standardization of the technique and setting up a reference range will also make it useful in diagnosing diabetes mellitus.

Studies about the Dietary Impact on "Free" Glycation Compounds in Human Saliva

Glycation reactions play a key role in post-translational modifications of amino acids in food proteins. Questions have arisen about a possible pathophysiological role of dietary glycation compounds. Several studies assessed the metabolic fate of dietary glycation compounds into blood and urine, but studies about saliva are rare. We investigated here the dietary impact on salivary concentrations of the individual Maillard reaction products (MRPs) N-ε-fructosyllysine, N-ε-carboxymethyllysine (CML), N-ε-carboxyethyllysine (CEL), pyrraline (Pyr), and methylglyoxal-derived hydroimidazolone 1 (MG-H1). Quantitation was performed using stable isotope dilution analysis (LC-MS/MS). We describe here, that a low MRP diet causes a significant lowering of salivary levels of Pyr from 1.9 ± 0.4 ng/mL to below the LOD and MG-H1 from 2.5 ± 1.5 ng/mL to 0.7 ± 1.8 ng/mL. An impact on the salivary protein fraction was not observed. Furthermore, salivary Pyr and MG-H1 levels are modified in a time-dependent manner after a dietary intervention containing 1.2 mg Pyr and 4.7 mg MG-H1. An increase in mean salivary concentrations to 1.4 ng/mL Pyr and 4.2 ng/mL MG-H1 was observed within 30–210 min. In conclusion, saliva may be a useful tool for monitoring glycation compound levels by using Pyr and MG-H1 as biomarkers for intake of heated food.

Colorimetric and Electrochemical Screening for Early Detection of Diabetes Mellitus and Diabetic Retinopathy-Application of Sensor Arrays and Machine Learning

In this review, a selection of works on the sensing of biomarkers related to diabetes mellitus (DM) and diabetic retinopathy (DR) are presented, with the scope of helping and encouraging researchers to design sensor-array machine-learning (ML)-supported devices for robust, fast, and cost-effective early detection of these devastating diseases. First, we highlight the social relevance of developing systematic screening programs for such diseases and how sensor-arrays and ML approaches could ease their early diagnosis. Then, we present diverse works related to the colorimetric and electrochemical sensing of biomarkers related to DM and DR with non-invasive sampling (e.g., urine, saliva, breath, tears, and sweat samples), with a special mention to some already-existing sensor arrays and ML approaches. We finally highlight the great potential of the latter approaches for the fast and reliable early diagnosis of DM and DR.

Effectiveness of Salivary Glucose in Diagnosing Gestational Diabetes Mellitus

Context:

Frequent monitoring of glucose is important in the management of diabetes. A noninvasive painless technique was used to detect glucose levels with the use of saliva as a diagnostic fluid.

Aims:

The aim of our study was to correlate the blood glucose levels with stimulated and unstimulated salivary samples and also to assess the reliability of using salivary glucose in diagnosing and monitoring the blood glucose levels in gestational diabetic patients.

Settings and Design:

The study was conducted among 100 clinically healthy nondiabetic individuals and 99 individuals suffering from gestational diabetes mellitus (GDM).

Subjects and Methods:

Fasting blood glucose estimation and postprandial salivary glucose estimation were done in stimulated and unstimulated salivary samples using glucose oxidase/peroxidase method.

Statistical Analysis Used:

Data obtained were subjected to normality test, and P ≤ 0.05 was considered to be statistically significant. The correlation between blood and salivary glucose levels was evaluated using Pearson's correlation test.

Results:

A positive correlation was obtained for stimulated and unstimulated salivary samples in fasting and postprandial conditions. Linear regression analysis and receiver operating characteristic curve were plotted, and the optimal cutoff value for unstimulated and stimulated salivary glucose under fasting conditions was 5.1 mg/dl and 5.4 mg/dl, respectively. The optimal cutoff value for unstimulated and stimulated salivary glucose was 8.8 mg/dl and 9.3 mg/dl, respectively, in postprandial conditions.

Conclusions:

Saliva appears to be a reliable biofluid to assess the blood glucose levels and can definitely be a reliable alternative to blood glucose in GDM patients.

Tandem Quantification of Multiple Carbohydrates in Saliva Using Surface-Enhanced Raman Spectroscopy

The detection of carbohydrates in human body fluids is critical for disease diagnosis and healthy monitoring. Despite recent advances in glucose sensing, multiplex detection of different carbohydrates within a single assay that is capable of efficiently providing richer health information remains challenging. Herein, we report a versatile surface-enhanced Raman spectroscopy-based platform for the quantitative detection of monosaccharides (glucose, fructose, and galactose) in one test using a displace-and-trap mechanism. Moreover, due to the use of multiple optical interference-free (1800-2200 cm^-1) signal-independent Raman probes, the detection range of this platform (0.125-7 mg/dL) perfectly covers physiological concentrations, enabling the quantitative detection of glucose and galactose in clinical human saliva samples. This work provides a noninvasive and high-efficiency potential tool for the screening of clinical diabetes and other carbohydrate-related diseases.

Changes in Salivary Amylase and Glucose in Diabetes: A Scoping Review

BACKGROUND AND OBJECTIVE

Diabetes mellitus (DM) is a common long-term disease which can be related with salivary amylase levels. DM has recently been associated with salivary amylase diagnostics that could further impair diagnoses in the diabetic population, as well as being an interesting alternative to traditional methods of determine glucose levels. The main advantage of this method is related to the fact that it is a fast diagnostic method. The DM population experiences changes to their metabolism which affects their salivary parameters, making this an alternative procedure for diagnosis and follow-up of the illness due to the non-invasive nature of salivary analyzes. The objective of this review is to summarize the evidence regarding the changes in salivary amylase and glucose levels, and their relationship with blood markers of glycemic control used in clinical settings such as blood glucose and glycated hemoglobin. The differences in salivary amylase levels depending on the method of saliva collection under fasting or non-fasting conditions. The changes in salivary amylase depends on the type of diabetes, the type of insulin treatment or the quality of glycemic control.

CONCLUSIONS

Salivary amylase concentration is increased in diabetic patients in most of the studies and salivary glucose concentration in all studies in both fasting and non-fasting (post-prandial) conditions. Salivary amylase and glucose concentration represent potential non-invasive biomarkers to evaluate glycemic control and clinical management of diabetic patients, although it is necessary to evaluate the influence of potential modulating factors such as age, duration diseases, sex and the effects of pharmacological treatments in these outcomes which remained to be elucidated.

MicroRNA-1323 serves as a biomarker in gestational diabetes mellitus and aggravates high glucose-induced inhibition of trophoblast cell viability by suppressing TP53INP1

Gestational diabetes mellitus (GDM) leads to poor pregnancy outcomes, and microRNAs (miRNAs/miRs) have been suggested to be associated with GDM, but the pathological mechanisms remain unclear. The present study aimed to investigate the diagnostic value of miR-1323 in GDM patients and its effects on trophoblast cell viability. Additionally, the present study investigated the correlation between miR-1323 and TP53INP1 to understand the pathological mechanism of GDM progression. Reverse transcription-quantitative polymerase chain reaction was used to detect the miR-1323 expression and TP53INP1 mRNA expression. The diagnostic value of serum miR-1323 was evaluated by receiver operating characteristic analysis. HTR-8/SVneo and BeWo cells were treated with high glucose (HG) to construct cell models of GDM, and trophoblast cell viability was assessed using an MTT assay. The protein expression of TP53INP1 was detected by western blot analysis. The correlation between miR-1323 and TP53INP1 was investigated by luciferase reporter assay. The miR-1323 expression was increased in patients with GDM, which had relatively high diagnostic accuracy for GDM screening and was positively correlated with fasting blood glucose in patients GDM. HG upregulated the miR-1323 expression and inhibited trophoblast cell viability. Overexpression of miR-1323 significantly inhibited the viability of HG-induced trophoblast cells. TP53INP1, a target gene of miR-1323, was negatively correlated with miR-1323. TP53INP1 overexpression reversed the inhibitory effect of miR-1323 overexpression on the viability of HG-treated trophoblast cells. Increased levels of serum miR-1323 may be a diagnostic biomarker for GDM. Additionally, miR-1323 may inhibit trophoblast cell viability by inhibiting TP53INP1, suggesting that it may be a potential therapeutic target for GDM.

Layered Double Hydroxide-Modified Organic Electrochemical Transistor for Glucose and Lactate Biosensing

Biosensors based on Organic Electrochemical Transistors (OECTs) are developed for the selective detection of glucose and lactate. The transistor architecture provides signal amplification (gain) with respect to the simple amperometric response. The biosensors are based on a poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) channel and the gate electrode is functionalised with glucose oxidase (GOx) or lactate oxidase (LOx) enzymes, which are immobilised within a Ni/Al Layered Double Hydroxide (LDH) through a one-step electrodeposition procedure. The here-designed OECT architecture allows minimising the required amount of enzyme during electrodeposition. The output signal of the biosensor is the drain current (I_d), which decreases as the analyte concentration increases. In the optimised conditions, the biosensor responds to glucose in the range of 0.1–8.0 mM with a limit of detection (LOD) of 0.02 mM. Two regimes of proportionality are observed. For concentrations lower than 1.0 mM, a linear response is obtained with a mean gain of 360, whereas for concentrations higher than 1.0 mM, I_d is proportional to the logarithm of glucose concentration, with a gain of 220. For lactate detection, the biosensor response is linear in the whole concentration range (0.05–8.0 mM). A LOD of 0.04 mM is reached, with a net gain equal to 400.

Homocysteine and the Risk of Cardiovascular Events and All-Cause Death in Elderly Population: A Community-Based Prospective Cohort Study

Background

The association between homocysteine and cardiovascular diseases (CVD) and all-cause death was inconclusive. A community-based prospective cohort study was carried out in Beijing to evaluate this association in elderly population for more effective clinical prediction and primary prevention of CVD.

Patients and Methods

Participants were randomly selected from Beijing, China. Questionnaire survey, physical examinations, and laboratory tests were carried out to collect baseline information and investigate clinical characteristics. Each participant was predetermined to be followed by 5 years. CVD events and death were collected as primary variables. A Cox regression analysis was performed to assess the risk of CVD events, CVD death, and all-cause death contributed by homocysteine as well as some other risk factors.

Results

A total of 1257 participants with an average age of 69.16 years were enrolled in this study. After adjusting for confounders, the hazard ratios (HRs) and 95% confidence intervals of CVD event, CVD death, and all-cause death caused by intermediate-to-severe hyperhomocysteinemia as compared with normal homocysteine levels were 1.68 (95% CI 1.06–2.67), 1.97 (95% CI 0.95–4.29) and 2.02 (95% CI 1.26–3.24), respectively. Intermediate-to-severe hyperhomocysteinemia increased the risks of CVD event (HR 2.07, 95% CI 1.01–4.26) and all-cause death (HR 3.08, 95% CI 1.56–6.07) among male participants. However, the positive association was not statistically significant among female participants (HR 1.59, 95% CI 0.83–3.04 for CVD event and HR 0.90, 95% CI 0.52–6.07 for all-cause death). Every 5μmol/L increment in homocysteine concentration was shown to be associated with a 4% (HR 1.04, 95% CI 1.01–1.07) and 5% (HR 1.05, 95% CI 1.01–1.07) higher risk of CVD events and all-cause death in all participants. There was no significant association between moderate hyperhomocysteinemia and the risk of the CVD events and all-cause death.

Conclusion

Intermediate-to-severe hyperhomocysteinemia was significantly associated with CVD events and all-cause death in elderly population without a history of ischemia or congestive heart failure (CHF). The positive association was pronounced among males.