Using the GTT to Estimate the Glucose Concentration

Using the glucose tolerance test and a spectrometer at absorbance level 500nm to estimate the blood glucose concentration of four patients at 0 minutes and 120 minutes and interpreting the results to determine if the patients are diabetic. The oral glucose tolerance test (OGTT) is a method of determining if a patient can maintain glucose homeostasis by metabolising an intake of glucose in a healthy manner. A blood sample is taken before and two hours after orally taking a glucose load. Patient results may come back normal, or it can indicate pre-diabetes, or show that a person already has diabetes, which is irreversible. Identifying diabetes is crucial for the patient; a lack of management will become increasingly harmful and may also translate into other serious health conditions like heart, eye, and kidney diseases etc. Glucose enters the body through eating and drinking in the form of carbohydrates. During digestion, the glucose is sent into the bloodstream, causing the pancreatic beta cells to produce insulin, a hormone which reduces blood glucose concentration by signalling cells to absorb glucose out of the blood. Glucose may be used for aerobic activity or stored in hepatic liver cells and muscle cells called myocytes for later use. However, in diabetic patients, blood glucose levels stay raised for a longer period of time (hyperglycaemia) as their body does not generate enough insulin or respond to it appropriately. Diagnosis may be of Type I or Type II diabetes. In Type I, antibodies attack the pancreatic beta cells in an auto-immune response, which consequently fail to produce adequate insulin (Lamichhane et al., 2018). The other possibility is Type II diabetes, where liver cells become insulin-resistant, reducing the amount of glucose that is absorbed from the bloodstream, leaving a high concentration of glucose in blood plasma. Type I is inherited as an autosomal recessive trait, whereas Type II is the consequence of an unhealthy diet and lifestyle. Aims and Hypothesis: The aim was to measure absorbance values at 500nm of mixtures of known glucose concentrations and a glucose reagent, which causes the colour to become red, with a starker colour indicating a higher [glucose] in comparison to a lighter colour. This would be used to create a standard curve, which was predicted to show a linear relationship between the variables, until a certain point at which the curve plateaus. The curve would be validated with the use of a control sample of 10mmol/L, and the margin for validation was between 9.2-10.8mmol/L. Patient results would then be interpreted to determine if they fall within diabetic ranges. Methods: Dilutions of glucose were prepared at intervals between 6mmol/L and 12mmol/L. 1ml of glucose reagent was added into 12.5l of the dilutions, which were sealed in cuvettes with parafilm and incubated at room temperature for 20 minutes, during which the red colour developed. The spectrometer gave a reading for the absorbance level at max of 500nm. Using the values given, a standard curve was formed using Excel of absorbance values against [glucose]. The quality control sample was used at this point to validate the curve: the absorbance reading given was 0.755, and using the trendline formula, the [glucose] is predicted to be 10.6mmol/L. This validates the accuracy of the graph. The 8 patient samples were prepared in a similar manner to the dilutions, with 12.5l being mixed with 1ml reagent, and being sealed and incubated for 20 minutes. Absorbance readings were taken, and then the estimated [glucose] was obtained using the formula of the trendline from the graph. The reproducibility was tested by obtaining 10 absorption readings of patient 4’s sample at 0 minutes and 3 readings of the sample at 120 minutes. Using SPSS, the mean absorbance at 0 minutes was 0.812, and there was a 1.6% standard deviation of 0.013. At 120 minutes, the mean absorbance was 0.461 and there was a 0.7% standard deviation of 0.003. Results: Figure 1 shows a scatter graph of the linear relationship between glucose concentrations in mmol/L and the absorbance readings at 500nm. As the [glucose] increases, the absorbance also increases, as was witnessed in the cuvettes as there was a gradient of a light to dark red colour as the glucose content rose. SPSS calculated the standard deviation from the trendline as 0.231, which none of the plots exceed. Patient Absorbance reading at 500nm 0 minutes 120 minutes [Glucose] in mmol/L 0 minutes 120 minutes 1 0.526 0.894 8.0 12.2 2 0.454 0.516 7.1 7.9 3 0.450 0.727 7.1 10.3 4 0.453 0.822 7.1 11.4 Figure 2 is a table showing the absorbance readings of 4 patients at 0 minutes and 120 minutes after a glucose intake, and their interpreted plasma glucose levels using the trendline formula of the standard curve in Figure 1. Using the interpretation values of the OGTT, patients 2 and 3 express healthy glucose management while patients 1 and 4 do not. Discussion The results support the hypothesis of there being a positive, linear correlation between the absorbance values and the glucose concentrations, as more dye complexes form when more glucose is available, and there are no inhibitors present to compete with the glucose molecules. The curve (Figure 1) does not plateau, possibly due to there not being a wide enough range of [glucose]. No anomalies were found within the measured data, as none of the plots vary from the trendline further than the standard deviation value. Patients 1 and 4 have been interpreted as having Diabetes Mellitus, due to their [glucose] at 0 minutes being > 7mM, and >11.1mM 2 hours post-load. It has been expressed that diagnosis of diabetes based solely on the OGTT may be inaccurate as results could have been influenced by non-diabetic factors (Sherwin, 1977), therefore further testing with stronger control measures would be required to confidently diagnose the patients, as well as considering relevant symptoms such as excessive urination and unexplained weight loss. Experimental improvements could involve using a blood sample 1-hour post-load instead of 2 hours, which may make the results more reliable in identifying individuals with reduced pancreatic beta-cell function (Kuo et al., 2021). Conversely, the significance of the OGTT results is highlighted by Coelho et al. (2018), who believe this method may indicate diabetes at an earlier occasion in comparison to other methods, and this will aid in avoiding future complications as risk management starts earlier. Another OGTT performed involved a larger test group of 1241 subjects, and they fit into the Caucasian population. This is dissimilar to the experiment outlined in this report as the patients did not specify their ethnic background, which is an influencing factor in the development of diabetes as some ethnicities have a genetic predisposition to diabetes ( Meijnikman et al., 2017). Conclusion: Overall, the experiment found a positive, linear correlation between glucose concentrations and absorbance of the sample and a reagent at 500nm, as expected. The results of two of the patients involved indicate Diabetes Mellitus, however further testing would be required for concrete diagnosis, and external factors affecting the results, such as ethnic background, family medical history and lifestyle, should be considered in the decision. References: Sherwin R.S. (1977). Limitations of the oral glucose tolerance test in diagnosis of early diabetes. Primary Care, 4(2):255-266. PMID: 586717. Coelho, A. R., Moreira, F. A., Santos, A. C., Silva-Pinto, A., Sarmento, A., Carvalho, D., & Freitas, P. (2018). Diabetes mellitus in HIV-infected patients: Fasting glucose, A1c, or oral glucose tolerance test - which method to choose for the diagnosis? BMC Infectious Diseases, 18(1), 309–309. https://doi.org/10.1186/s12879-018-3221-7 Kuo, F. Y., Cheng, K. C., Li, Y., & Cheng, J. T. (2021). Oral glucose tolerance test in diabetes, the old method revisited. World journal of diabetes, 12(6), 786–793. https://doi.org/10.4239/wjd.v12.i6.786 Lamichhane, S., Ahonen, L., Dyrlund, T. S., Kemppainen, E., Siljander, H., Hyöty, H., Ilonen, J., Toppari, J., Veijola, R., Hyötyläinen, T., Knip, M., & Oresic, M. (2018). Dynamics of Plasma Lipidome in Progression to Islet Autoimmunity and Type 1 Diabetes-Type 1 Diabetes Prediction and Prevention Study (DIPP). Scientific Reports, 8(1), 10635–12. https://doi.org/10.1038/s41598-018-28907-8 Meijnikman, A. S., De Block, C. E. M., Dirinck, E., Verrijken, A., Mertens, I., Corthouts, B., & Van Gaal, L. F. (2017). Not performing an OGTT results in significant underdiagnosis of (pre)diabetes in a high risk adult Caucasian population. INTERNATIONAL JOURNAL OF OBESITY, 41(11), 1615–1620. https://doi.org/10.1038/ijo.2017.165