Saliva Analysis for Diabetes: Pros and Cons
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Published: Mon, 21 May 2018
Diabetes mellitus is a common and widespread disease, characterised by poor regulation of glucose due to pancreatic insufficiency. Traditional methods of diagnosis and monitoring utilise blood glucose analysis and highly accurate mechanisms exist for this. However, as many insulin dependent diabetics have to monitor their own blood glucose, the constant obtaining and testing of blood can be very inconvenient and reduce patient compliance. Therefore the development of alternative methods that do not require blood samples are being investigated, one of which is the use of saliva. This account begins with a brief explanation of the disease of diabetes, before considering existing blood glucose monitoring options. The current state of knowledge about the use of saliva as a means of analysing glucose, and its benefits and drawbacks is then discussed.
Diabetes – the disease
Diabetes mellitus is a disease characterised by raised glucose levels due to aberrant processing of dietary intake as a result of pancreatic insufficiency. In particular it is the pancreatic insulin producing ï¢ cells that are destroyed, possibly due to the generation of toxic free radicals such as reactive oxygen species and nitric oxide. Diabetes mellitus affects 170 million people worldwide and has complications including cardiovascular disease and kidney failure as well as reducing both life quality and expectancy (Aydin 2007).
Blood glucose levels are controlled by insulin and glucagon, with a deficit in insulin production or activity leading to impaired glucose tolerance. In healthy individuals blood glucose levels vary from 4-8mM, whereas those with diabetes mellitus exhibit a much wider range, in the order of 2-30mM (Macaya et al. 2007). Therefore accurate and easy monitoring of blood glucose levels is essential to avoid health problems and complications that arise as a result of raised glucose levels, and also those from hypoglycaemia, including fainting and coma.
Diabetes can be diagnosed with the use of a glucose tolerance test, whereby individuals ingest a proscribed amount of glucose and then blood levels are measured over time. As can be seen from figure 1 below, the pattern of blood glucose is markedly different in those suffering from diabetes, compared to normal individuals with healthy functioning glucose clearance.
However, the method of detecting the glucose levels in the body is what is relevant in this account, particularly whether there are alternatives to the traditional method that involves sampling the blood.
Monitoring blood glucose levels
It has been found that control of blood glucose levels minimises health complications, such that every percentage point drop in A1C blood test results leads to a reduction in the risk of microvascular complications by 40% (Centers for Disease Control and Prevention 2005). Guides provided with portable blood glucose monitoring devices state that careful control of glucose levels can reduce complications by up to 60% (LifeScan 2006).
When measuring blood glucose levels glycosylated haemoglobin (A1C or HbA1c) shows the average blood glucose levels in the preceding 3 months. This is because glucose molecules attach to haemoglobin and leave a trail that lasts for up to 4 months. By using A1C measures it is possible to see the pattern in blood glucose levels over time. Haemoglobin A1C has a sugar moiety of glucose bound to its terminal amino acid residue, leading to what is known as glycated haemoglobin (Tolonen et al. 2002).
Enzymatic glucose tests utilise enzymatic reagents being incubated with the salivary sample and the subsequent optical density measured using a spectrophotometer and compared to known calibration curves (Yamaguchi, Mitsumori & Kano 1998b). Biosensors are also a possible alternative, showing promise in non-invasive analysis of blood or a saline solution (Gourzi et al. 2003). Early use of photometric analysis suffered from the difficulty that optical pathways in the human interfered with the measurements, leading to human error when assessing results (Yamaguchi, Mitsumori & Kano 1998b). However, modern machines are much more accurate, and recalibration reduces error.
The hexokinase method
The hexokinase method involves the catalysis of the phosphorylation of hexose sugars including glucose, mannose and galactose (Maeda et al. 2007). Specifically, and as can be seen by figure 2 below, hexokinase is responsible for the formation of glucose-6-phosphate and ATP. This is then further altered, using glucose-6-phosphate-dehydrogenase, to form NADH (Duxbury 2004). It is the NADH that can be analysed using a spectrophotometer in order to provide a colorimetric result.
In the early days of its use the hexokinase method did not initially become as widespread as it could have done due to the relative expense of using it (Garber et al. 1978). However the accuracy of the method soon seemed to outweigh these concerns.
The glucose oxidase method
The glucose oxidase method differs from the hexokinase method, in that different enzymes are used, and there are different start and end products to the reaction, as can be seen in figure 3 below.
As can be seen glucose is oxidised in the presence of atmospheric oxygen to form hydrogen peroxide and gluconolactone. It is then the hydrogen peroxide that is oxidised in the presence of 4-aminophenazone and phenol to form the red dye that can be analysed using a spectrophotometer (Duxbury 2004).
Enzyme electrodes are platinum electrodes that have been coated in a glucose oxidase gel that act by measuring the amount of hydrogen peroxide that is produced when the glucose oxidase reacts with the glucose (Macaya et al. 2007). However alternative methods involve measuring the amount of oxygen consumed by the enzymatic process, or the change in pH that occurs when gluconolactone is converted to gluconic acid. Figure 4 below shows the reactions that occur during glucose sensing.
Using hand held blood glucose measures
Many methods of analysing blood glucose levels rely on colorimetry and enzymatic breakdown of glucose, as explained above. This is particularly costly as the meters have to be regularly recalibrated in order for the user / mechanisms to be able to assess the current colour against a benchmark figure (Maeda et al. 2007).
The OneTouch Ultra Smart device requires a drop of blood thus requires patients to obtain this, usually via a fingertip drop, or from the forearm or elbow. The device contains a glucose oxidase biosensor that can detect results in the range of 20-600mg/dl (LifeScan 2006). Contraindications of the OneTouch Ultra system include the fact that measurements cannot be taken within 2 hours of a meal, or within 2 hours of an insulin injection or exercise, both of which lead to variability in results.
The Accu-Chek Active meter used reflectance photometry to determine the level of glucose in fresh blood, necessitating a new sample being obtained, usually via a finger prick, for each test. It measures in the range 0.6-33.3mmol.L(Roche Diagnostics 2007a). The Accu-Chek Active meter is no longer available in the UK, perhaps due to inaccuracies with the testing strips necessitating alternative testing procedures (Roche Diagnostics 2007e). However the published accuracy levels for the meter indicate that 95% of the individual glucose results fall within 0.08 mmol/L of the manufacturers measurement procedure. Further, 70% of results fell within 5% (Roche Diagnostics 2007c).
The Accu-Chek Compact Plus combines the testing machine and test strips all in one. It also uses reflectance photometry and requires fresh blood, although it can also analyse plasma (Roche Diagnostics 2007b). Its measuring range is 0.6-33.3 mmol/L. Published accuracy levels indicate that 95% of individual glucose results fall within 0.83 mmol/L of the manufacturers measurement procedure. Further, 59% of results fell within 5% (Roche Diagnostics 2007d). Thus it can be seen that the Accu-Check Compact Plus is actually slightly less accurate, but assumedly this accuracy is sacrificed due to the benefit of convenience of not having to use separate testing strips.
The relative accuracy of different hand held devices has been compared. For instance when comparing the OneTouch Ultra with the Freestyle Flash meter it was found that, whilst both were accurate, the Freestyle Flash meter was found to have significantly greater accuracy when forearm sampling was used (Rivers et al. 2006). This was mainly due to difficulties in obtaining a viable sample when using the OneTouch Ultra meter, rather than because the sample itself was unsuccessful when obtained. However this has useful applications in that salivary samples would not suffer from these difficulties, as discussed below.
Using saliva to measure glucose
The traditional method of diagnosis and monitoring of diabetes involves blood collection, which is not only invasive but brings with it the risk of disease and infection (Zloczower et al. 2007). Given that, even with the modern handheld devices, patients are supposed to obtain a blood glucose sample at least 3 times per day (Rivers et al. 2006), it can easily be seen that any methods that don’t involve blood sampling would be beneficial and increase patient compliance. The fact that diabetes mellitus is relatively free of subjective symptoms means that incurable damage can be occurring in the patient’s body if they are unaware of the fact that they blood glucose levels are not as required. It has been indicated that using a urinary test is fraught with difficulty, both due to the less than pleasant requirements of the collection, and also due to large numbers of false results (Yamaguchi, Mitsumori & Kano 1998a). The collection of saliva is an almost completely non-invasive mechanism for obtaining biological samples. Saliva is an encompassing term referring to all of the fluid located within the oral cavity, but specifically that originating in the salivary glands (Nieuw Amerongen, Ligtenberg & Veerman 2007). Of interest in pathological and diagnostic situations is the fact that fluid within the oral cavity may arise in places other than the salivary glands, e.g. from mucosal leakage or epithelial inflammation. The significance of this is that the analyst may assume that they are obtaining information solely from the oral cavity, but in fact there may be markers and substances present from other origins. One such example is where there is increased gingival inflammation, thus leading to increased secretions to add to saliva (Yoon et al. 2004).
It has been suggested that glucose is only found in the saliva of diabetic patients and is not present in healthy individuals (Amer et al. 2001). However no indication is given of this is other research, which simply indicates that salivary glucose levels are higher in diabetic patients compared to healthy controls (Darwazeh et al. 1991). Further the salivary glucose levels were well correlated with plasma glucose levels in that particular study. Whilst not overly detailed, research from 20 years ago indicated that salivary glucose levels correlated to the extent to which diabetes was controlled by patients (Reuterving et al. 1987).
It should be pointed out that a very raised blood glucose level does not necessarily mean diabetes, rather that that particular sample of blood contains a lot of glucose (Pesce, Spitalnik 2007). Thus it is necessary to be careful when obtaining the saliva sample, in order to be sure that it is an accurate reflection of the overall bodily situation. However, of course, this could easily apply to any method of obtaining glucose measurements.
Early analysis of salivary glucose utilised the glucose oxidation method, as described above (Amer et al. 2001). Typical salivary glucose levels have been shown to be around 0.008 and 0.21mM (Macaya et al. 2007) meaning that there is potentially a very large variance to deal with when analysing salivary glucose. Furthermore with such a wide range possible it is essential to know the baseline upon which each individual is being measured in order to avoid missing large variations, or similarly attributing significance to individual variation. Any analysis of glucose levels in excretory liquids, including urine, will be affected by differing patterns of excretion between individuals (Yamaguchi, Mitsumori & Kano 1998b).
Comparing blood and salivary glucose levels
A comparison of blood and salivary glucose levels found that blood glucose levels were two orders of magnitude higher than salivary levels (50-100 times more), with peak levels following oral administration being reached within 5 to 11minutes (Yamaguchi, Mitsumori & Kano 1998b). Further, this method (using an enzymatic sensor) was shown to have sensitivity for levels of glucose in the range of 0.1-10mg/dl. It has been found that the method of analysing salivary glucose has a large impact on the likelihood of there being a correlation between blood and salivary glucose levels. It was suggested that using an oxido-reductive method was much less likely to reveal a correlation, when compared to the use of an enzymatic method (Yamaguchi, Mitsumori & Kano 1998b). This is supported in theory by the fact that the use of a Beckman glucose analyser, which utilises the glucose oxidase method, found no association between blood and salivary glucose (Forbat et al. 1981). Even at this relatively early stage (1981) the authors admitted that it could well be the sampling technique, the method used to analyse glucose and in fact the sample population. Further it has been shown that the actual origin of the saliva, whether submandibular or parotid can also markedly influence the correlation observed between blood and salivary glucose levels (Yamaguchi, Mitsumori & Kano 1998a). However the same study showed that there was much greater correlation for diabetes mellitus patients when compared to healthy controls.
An attempt has been made to manufacture tape that would change colour in response to differing salivary glucose levels. This utilised glucose oxidase and peroxidase which change colour in response to the oxidation brought about by glucose. This trial showed a significant change in colour in response to salivary glucose when comparing a fasting and post-prandial situation (Yamaguchi, M. Kataoka, K. Kano, Y. Takai, N. Yoshida, Y. Egusa, G. 1999). The use of something as simple as a tape to measure salivary glucose levels would be very useful.
Other altered salivary constituents in diabetes
Saliva is known to contain a variety of biological substances, including proteins such as amylase, lysozyme and cystatins as well as glucose and electrolytes (Yoon et al. 2004). It has been suggested that, when using nuclear magnetic resonance imaging, there is a clear characteristic signal in diabetic patients that is not shared by healthy controls, possibly for a glycation end product (Yoon et al. 2004). However other researchers strongly refute this information, claiming that the methodology was flawed, as was the authors’ interpretation of their results. Indeed the primary critic (Grootveld, Silwood 2005) indicates that the significance placed upon the characteristic resonance observed by Yoon was false and it was merely an artefact of the processing method and of little overall value to the research area. However alternative research has found that the advanced glycation end products such as Nε-(carboxymethyl)lysine-protein adducts (CML), are found in higher levels in diabetics (Basta et al. 2006). Soluble forms of the receptor for advanced glycation end products (sRAGE) have been found to be found in lower levels in diabetic patients (Basta et al. 2006). This means that the glycation end products are more able to float freely in the plasma and cause further damage if there are fewer receptors to effectively remove them.
Ghrelin (Ghrelin Appetite Hormone) is mainly produced in the stomach but also expressed in other parts of the body, including the pancreas. As the name suggests, ghrelin promotes appetite. It acts to modulate insulin secretion in order to influence glucose metabolism(Aydin 2007). The salivary level of ghrelin is reduced in obese type 2 diabetic patients, along with raised salivary glucose levels, indicating that it might be aberrant processing of this hormone that could lead to the increased glucose levels.
Adinopectin is an amino acid protein synthesised by adipose tissue and known to have reduced levels in non-insulin dependent diabetes mellitus. In a recently developed method of measuring salivary adinopectin levels it was found that plasma and adinopectin levels were correlated in older individuals, but not younger ones (Toda, Tsukinoki & Morimoto 2007). This could introduce doubt into the use of salivary adinopectin as a measure for diabetes in younger people, as the actual levels of adinopectin are less correlated with salivary levels in younger people, which could lead to diagnostic errors. The corollary of this, of course, is that salivary adinopectin levels are actually useful biomarkers of diabetes in older patients.
Given the widespread nature of diabetes mellitus, and the known benefits of close monitoring of blood glucose on current and future health issues, it can be seen that anything that makes this monitoring easier for patients can only be beneficial. Whilst current research doesn’t always agree on the significance of salivary glucose measures, it does seem as if it is a useful measure for glucose handling in diabetes, with a linear relationship between salivary and blood glucose levels existing. Further the research into the reasons for altered salivary glucose levels does seem to point towards glycation end products and a role for free radicals and oxidation. The exact method of analysing the glucose also seems to make a difference, but again opinion does seem rather divided about which is the best method, glucose oxidase, hexokinase or hydrogen peroxide analysis.
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