Saliva diagnostic utility in patients with type 2 diabetes: Future standard method

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.

A Review on Saliva-Based Health Diagnostics: Biomarker Selection and Future Directions

The human body has a unique way of saying when something is wrong with it. The molecules in the body fluids can be helpful in the early detection of diseases by enabling health and preventing disease progression. These biomarkers enabling better healthcare are becoming an extensive area of research interest. Biosensors that detect these biomarkers are becoming the future, especially Point Of Care (POC) biosensors that remove the need to be physically present in the hospital. Detection of complex and systemic diseases using biosensors has a long way to go. Saliva-based biosensors are gaining attention among body fluids due to their non-invasive collection and ability to detect periodontal disease and identify systemic diseases. The possibility of saliva-based diagnostic biosensors has gained much publicity, with companies sending home kits for ancestry prediction. Saliva-based testing for covid 19 has revealed effective clinical use and relevance of the economic collection. Based on universal biomarkers, the detection of systemic diseases is a booming research arena. Lots of research on saliva-based biosensors is available, but it still poses challenges and limitations as POC devices. This review paper talks about the relevance of saliva and its usefulness as a biosensor. Also, it has recommendations that need to be considered to enable it as a possible diagnostic tool.

Graphical Abstract

[Latest Research Findings on Health Management Based on Saliva Testing]

Being one of the most important exocrine fluids of the human body, saliva can reflect the health status of the body. Soliva collection has various advantages--it is simple, painless, fast, and safe, and soliva can be collected multiple times a day. Therefore, it has been gradually applied in the exploration for biomarkers for disease detection, providing a basis for the monitoring of the course of diseases, medication monitoring, and efficacy evaluation. We should implement health management based on saliva testing, collect the medical data of the healthy and diseased individuals and monitor their whole life cycle health, perform clinical cohort study, aggregate the data on platforms for big data on health and medicine, manage and provide guidance for the health status of populations, pinpoint the high-risk factors for pathogenesis, and provide effective intervention, early warning, and assessment of the vital signs of individuals, thereby reinforcing health management of the whole life cycle and safeguarding people's health in an all-round way. In addition, it drives the development of the health industry and bears strategic significance for the promotion of national economic development. It is becoming a hot research topic promising great potential and impressive applicational prospects. Herein, we reviewed new techniques for clinical saliva testing and health management based on saliva testing.

Salivary Biomarkers Associated with Psychological Alterations in Patients with Diabetes: A Systematic Review

The care of individuals with diabetes needs a holistic perspective, taking into account both the physical disease and the mental health problems that may be associated. Different studies show a higher prevalence of depression or anxiety issues in diabetes patients than in the general population, which is why diabetes can be considered one of the chronic diseases in which psychological care is crucial to maintain quality of life. The objective of this review is to examine the published articles that relate the bidirectional associations between objective and subjective measures of anxiety, depressive symptomatology, stress, sleep quality, and salivary biomarkers in patients with diabetes. For this, a search was carried out in the electronic databases PubMed, Cochrane, and SCOPUS using the keywords "diabetes", "saliva", "sleep", "anxiety", "depression", and "stress" for works published up until May 2022 and limited to the English and Spanish languages. The sample comprised 14 articles, 5 of which analysed the associations between depressive symptomatology and salivary biomarkers in people with diabetes. Among the salivary biomarkers most frequently used to evaluate psychological alterations in persons with diabetes are cortisol and melatonin. Thus, significant changes in the levels of these biomarkers were observed in most studies. Four out of five studies reported a statistically significant relationship between increased salivary cortisol in the evening/midnight or the cortisol awakening response and depressive symptoms. In contrast, lower cortisol levels upon waking in the morning were observed when there was no depression or anxiety. Regarding the association between salivary cortisol values and sleep quality in patients with diabetes, lower morning cortisol values related to prolonged nighttime sleep were common in the analysed studies. Low melatonin concentrations showed a negative correlation with sleep quality. As it is an easy-to-apply and non-invasive method, the measurement of salivary biomarkers can be very useful for predicting psychological alterations in patients with diabetes. Further scientific studies are required to determine the sensitivity of these biological substances acting as biomarkers for detecting sleep disorders and psychological alterations.

Changes in salivary biomarkers associated with periodontitis and diabetic neuropathy in individuals with type 1 diabetes

The objective of this pilot clinical study was to identify salivary biomarkers that are associated with periodontal disease and measures of diabetic autonomic dysfunction. Saliva samples from 32 participants were obtained from 3 groups: healthy (H), type 1 diabetes mellitus (DM), and type 1 diabetes mellitus with neuropathy (DMN). Based on the periodontal examination, individuals’ mean Periodontal Screening and Recording scores were categorized into two groups (periodontally healthy and gingivitis), and correlated to specific salivary inflammatory biomarkers assessed by a customized protein array and enzyme assay. The mean salivary IgA level in DM was 9211.5 ± 4776.4 pg/ml, which was significantly lower than H (17,182.2 ± 8899.3 pg/ml). IgA in DMN with healthy periodontium was significantly lower (5905.5 ± 3124.8 pg/ml) compared to H, although IgA levels in DMN patients with gingivitis (16,894. 6 ± 7084.3) were not. According to the result of a logistic regression model, IgA and periodontal condition were the indicators of the binary response given by H versus DM, and H versus DMN, respectively. These data suggest that selected salivary biomarkers, such as IgA, combined with a periodontal examination prior to obtaining salivary samples can offer a non-invasive method to assess risk for developing diabetic neuropathy.

Salivary alpha-amylase-biomarker for monitoring type II diabetes

Background:

Diabetes is one of the most important causes of mortality worldwide. People having diabetes are vulnerable to infectious diseases and have been clinically recognized; which may be because of their deregulated immune system. Hyperglycemia in diabetes is consequence of chronic resistance to insulin and relative insulin deficiency on target cells. Alpha-amylase, a salivary enzyme is shown to increase in diabetic individuals compared to nondiabetics and can be used as a marker for the diagnosis of diabetes.

Aim and Objective:

The present study was undertaken to investigate the levels of salivary amylase in type II diabetic patients.

Materials and Methods:

A total of 80 participants in the age range of 30–60 years, were divided into three groups as Group I: Uncontrolled diabetics (30), Group II: Controlled diabetics (30) and Group III: Age- and sex-matched healthy controls (20) were taken for the study. Unstimulated whole saliva was collected for salivary amylase level estimation and blood samples were from the antecubital vein, after 12 h of overnight fasting of each individual for the estimation of blood glucose levels.

Results:

The mean random blood sugar and glycated hemoglobin A_1c levels were found to be statistically significant among diabetics and healthy individuals. Salivary amylase levels were much higher in uncontrolled and controlled diabetics compared to healthy individuals showing a significant correlation (P = 0.001) between all groups. However, the salivary amylase levels nonsignificant (P = 0.060) between the controlled diabetics (Group II) and healthy individuals (Group III).

Conclusion:

Our study confirms the considerable increase in salivary amylase levels in diabetes patients as compared to healthy individuals. Studies with a larger sample size comprising prediabetics, type I diabetics and type II diabetics in all age groups are required to validate these results. Further, if our results are established, salivary amylase can be used as biomarker for the diagnosis of diabetes and also monitoring it.