TIIDM is "predictable and preventable."3 Early detection can help a patient who is considered to be at risk for TIIDM to either prevent or control the onset of the disease by either lifestyle and/or pharmacologic intervention. One of the missing links is to more precisely predict a patient's risk of developing diabetes.2,3 Though there are some known risks factors such as obesity, genetics, and even just higher than normal blood glucose which seem to place a person at higher risk, these factors don't always correlate to the development of diabetes.
Because early identification of diabetes risk is so important there have been various methods developed to try and identify patients at risk. One of these models, considered the gold standard to some, is the oral glucose tolerance test.3 Even though this method of assessing a patient's risk is very effective it is not convenient to both the practitioners and the patient in the clinical setting. This means that other markers such as fasting blood glucose and even glycated hemoglobin (HbA1C) are used more often.4 Even though this test has been accepted it still has a level of variability in the ability to precisely predict the development of TIIDM.3,4
A new tool that over the past few years has started to position itself in this battle is the PreDx™ Diabetes Risk Score (PreDx DRS). This risk assessment uses a set of algorithms designed to "identify individuals at high risk for developing type II diabetes" over the next 5 years, as well as stratifies the patients into risk categories.1 The development of this tool really came from better understanding of the disease and its effect on the body as well as identifying biological markers shown to be independently associated with the formation of TIIDM. Specifically this risk assessment takes into consideration seven different serum biomarkers which include glucose, insulin, HbA1C, adiponectin, C-reactive protein (CRP), Interleukin-2 receptor-α (IL-2Rα), and ferritin. The benefit of using all seven in the risk assessments is that the results from all seven help to "better discriminate than each biomarker's ability to assess risk independently." 1,5
Glucose is a marker that has been well recognized by many associations and organizations as a predictor of diabetes. Glucose is a simple sugar that can either be ingested and absorbed as such or comes from the breakdown of carbohydrates. The reason why glucose is used is that as a patient starts to progress into diabetes they tend to have either deficiency in either insulin effect on cells (resistance) or impaired insulin production or secretion from the pancreas. Either way these patients will begin to develop an impaired ability to utilize the circulating glucose in the body, which means that their fasting glucose levels will start to be elevated in comparison to a patient that is not at risk. Similar to this is the use of plasma insulin levels, which is another marker that is used in the assessment. Insulin is produced in the pancreatic beta cells and plays a pivotal role in our body's ability to effectively utilize and manage glucose levels. When the levels of glucose in the blood rises insulin is release by the pancreas to both "suppress the hepatic glucose production," but also works to activate the cells in our peripheral tissues to take up glucose and store it for use at a later time. As previously mentioned as a patients cells become less sensitive to insulin the pancreas will begin to produce more insulin to try and compensate, resulting in an increase in levels of insulin in the blood. Due to these mechanisms the levels of both glucose and insulin in the blood can help predict the risk of developing TIIDM.1,3,4
HbA1C more recently has been a way to look at the average levels of glucose in the blood, and has really been considered the gold-standard to evaluate a patient's glycemic control. The formation of glycated hemoglobin occurs when the body's red blood cells react with the circulating glucose in the blood.1 Once the red blood cell becomes glycated it will remain that way for the life span of the red blood cell, which is approximately 120 days. The reason why this measurement is so important is that this reaction is not one that occurs fast, but rather it occurs slowly which allows the HbA1C to reflect an average glucose level. This is important because our body's glucose level is constantly fluctuating throughout the day, and taking a random blood glucose level will only give us a part of the story. The HbA1C allows us to assess on average glucose level over a 3-4 month at which the patient stays at. The higher the HbA1C the more at risk a patient is for developing diabetes because they are not utilizing the sugar effectively. 1,4
Adiponectin is a very unique biomarker that is actually a hormone that is derived from our body's adipocytes (fat cells). The reason why this level becomes important is that the hormone plays a crucial role in the sensitization of our cells to insulin and eventually the proper utilization of glucose in the body. Patients with higher levels tend to have better glycemic control and are less likely to develop diabetes. A deficiency in adiponectin is thought to contribute to the formation of insulin resistance as well as increased glucose production in the liver. This deficiency has been shown in various studies to "plays a role in the development of insulin resistance and subsequent type 2 diabetes."1,6
Inflammation and the pathogenesis of diabetes is still not totally understand. What has been recognized is that patients that suffer from chronic disease such as in diabetes tend to have the continual presence of inflammatory markers such as CRP and IL-2Rα. The continual presence of these inflammatory markers is due to the fact that diabetes tends to create a state of "low-grade inflammation."1 The missing piece to this puzzle is the exact reason and mechanism as to why these patients suffer from a constant state of inflammation. Even though there is still a lot unknown about this effect, it has been shown that the use of these markers is useful in predicting a patients risk for developing diabetes. CRP is a protein that has proinflammatory properties, and the presence of this protein is usually associated with some form of systemic inflammation.7 The issue with assessing the levels is that it is a very nonspecific marker and can be elevated for many different reasons included autoimmune reactions or even simple infections. The correlation with diabetes is not completely understood yet, but what is understood is that there seems to be a relationship between persistently elevated CRP levels and development of diabetes.1,7 Much of what is said about CRP can be said for IL-2Rα, the difference lies in what the marker's function is in the body. The IL-2Rα is a cell membrane receptor for the cytokine IL-2, which functions in the differentiation of cells in our immune system. Similar to CRP, the presence of elevated levels IL-2Rα are associated with inflammation and the formation of TIIDM.1
The final biomarker, ferritin, is actually a storage form of iron and is also utilized in the analysis. Similar to the two previous markers the definitive link with diabetes is not completely understood, but there are many proposed mechanism why elevated ferritin levels are associated with development of TIIDM. The developers of the PreDx DRS have postulated a few mechanisms but ultimately all are proposed mechanism at this point in time. One mechanism that is mentioned that has been demonstrated in animal models is that the presence of the higher levels of iron can be toxic causing the cells in the pancreas, specifically the beta cells, to die which would result in a decline in the production of insulin.1,8
In conclusion, even though this method is new it is a step in the right direction to help identify patients that are at a greater risk of developing diabetes. At the same time rather than just stratifying patients as at risk or not, this tool categories the patients based on various levels of risk. This will allow practitioner to better approach the patients in trying to prevent the onset of diabetes as well as give the patient a better understanding of their own risk for progressing to diabetes. To date there is a limited amount of data that compares this tool to other currently accepted methods of identifying patients at risk. What studies have come out though do show promising results as well as better and more precise detection of the patients at risk for developing TIIDM.
1. Tethys Bioscience. PreDX™ Diabetes Risk Score (PreDx DRS): A review of the underlying Biology of a Multi-maker Approach to Type 2 Diabetes Risk Assessment. Tethys Bioscience, Inc. Emeryville, California. 2010.
2. Urdea M, Kolberg J, Wilber J, Gerwien R, Moler E, et al. Validation of Multimarker Model for assessing Risk of Type 2 Diabetes from a Five Year Prospective Study of 6784 Danish People(Inter99). J Diabetes Sci Technol. 2009;3(4):748-55.
3. Multiple Biomarker Prediction of Type 2 Diabetes. Editorial. Diabetes Care. July 2009;32(7):1346-8.
4. Triplitt CL, Reasner CA, Isley W. Diabetes Mellitus. In: DiPiro JT, Talbert RL, Yee GC, eds. Pharmacotherapy: A Pathophysiologic Approach, 7th ed. New York, NY: McGraw-Hill;2008:1205-41.
5. Shafizadeh TB, Moler EJ, Kolberg JA, Nguyen UT, Hansen T, et al. Comparison of Accuracy of Diabetes Risk Score and Components of the Metabolic Syndrome in Assessing Risk of Incident Type 2 Diabetes in Inter99 Cohort. PloS One. 2011;6(7): e22863.
6. McCulloch DK, Robertson RP. Pathogenesis of Type 2 Diabetes Mellitus. In: UpToDate (electronic version). Hudson, Ohio, USA. Available at: http://www.uptodateonline.com (Cited: Sept 1, 2011).
7. Kushner I. Acute Phase Reactants. In: UpToDate (electronic version). Hudson, Ohio, USA. Available at: http://www.uptodateonline.com (Cited: Sept 1, 2011).
8. McCulloch DK, Robertson RP. Risk factors for Type 2 Diabetes Mellitus. In: UpToDate (electronic version). Hudson, Ohio, USA. Available at: http://www.uptodateonline.com (Cited: Sept 1, 2011).
9. Sullivan SD, Garrison LP, Rinde H, kolberg J, Moler E. Cost-Effectiveness of Risk Stratification for Preventing Type 2 Diabetes using a Multi-Marker Diabetes Risk Score. J Med Econ. 2011;14(5):609-16.
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