Hemoglobin A1C ( HbA1c) is a tool that can be used to diagnose type 2 diabetes. The tool is applied in the form of a test and focuses on the concentration of red blood cells with molecules of glucose attached to them. These attachments serve as an indication of the level of concentration of glucose in the blood. The red blood cells that are contained in a blood sample for A1C test contains mixtures of cells of various ages (Gosmanov & Wan, 2014, p. 192).
It means for the tool or test to be effective it must be given a weighted average of the levels of blood glucose controls for the individual in the last two to three months. If the A1c level that is below 7% is the main measure that reveals an individual has diabetes. An A1c level that is between 5.7% and 6.4% indicates that the individual is pre-diabetic while levels of 6.5% or more indicates that the individual has diabetes (Bagley & Malabu, 2014, p. 87).
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To utilize the tool correctly, it is essential to ensure that the result obtained is representation of the glycemia levels in the patient. The result should also be within an acceptable level of deviation and must be correctly interpreted based on the diagnostic criteria. Despite the prevalence of positive correlations between the plasma glucose concentration and the levels of HbA1C, there can be differences. Any stable and rapid changes in the levels of plasma glucose would require up to thirty days for a steady level of HbA1c to be construed. It means the diagnostic tool could be unsuitable in diagnosing type 1 diabetes that is progressing rapidly.
It is also vital to note that the hemoglobin glyceration rates are different amongst individuals. Also the lifespan of the erythrocytes also plays a crucial role in influencing the levels of HbA1c in every red blood cell. An increase in the age of the erythrocytes leads to higher levels of HbA1c on the basis of average concentrations of glucose.
The use of the diagnostic tool is advantageous over other tests as it does no require fasting or the use of timed samples. Also, the test is a better indicator of the levels of glycemia as it is less prone to pre-analytic instability. However, there are concerns due to the likelihood of under-diagnosing individuals with overt diabetes due to the setting of the risk levels at 6.5%.
There are various studies that have evaluated the effectiveness of the A1C cut-offs where one study revealed that a level of 5.8% demonstrated the highest levels of sensitivity ( Guo, Moellering & Garvey, 2014, p. 263) . The determination of the optimal levels of A1C cut-offs that should be used when screening individuals for diabetes is arbitrary to a certain level. The exposure of individuals to the risks of diabetes is continuous along a diverse range of measures of glycemia. To enhance the levels of efficiency in diagnosis, the optimal cut-off levels for A1C should be a balance between the levels of sensitivity and those of specificity.
The use of HbA1c as a diagnostic tool is effective as individuals do not have to fast to use the tool. The attribute encourages more individuals to be willing to undergo the tests and reduce the number of those that are undiagnosed. Such tests can reveal those suffering from the disease and facilitate the adoption of suitable measures to contain it and minimize its progress in such individuals. Such individuals diagnosed with the disease can take measures such as adopting lifestyle changes to minimize the progress of the disease.
The use of the diagnostic tool is efficient in revealing chronic hyperglycemia when compared to other tests such as fasting and the two-hour oral tolerance of glucose test. These two alternative tests rely on the glucose levels in the blood for only one day. Also, the use of the two alternative tests to reveal chronic and complex cases of diabetes can be onerous and fallacious. It implies that the use of the hemoglobin A1C test is dependable as it the test can reveal the trends in glycemic levels over a longer period of time in comparison to other tests. The test is also user-friendly and can be used to encourage a higher number of individuals to check their diabetes status to facilitate prompt interventions.
However, there are some drawbacks on the use of the diagnostic tool as its main focus is on the glycemic levels. However, diabetes is clinically mostly defined by the presence of high levels of glucose in the blood rather than the glycation of proteins (Unnikrishnan & Mohan, p. 898). Also, the tool does not reveal most of the patho-physiological abnormalities associated with diabetes that are crucial in its diagnosis and treatment. Also, the claim that fasting enhances the levels of appeal of the tool is not necessary as fasting is not an essential requirement in the identification of the perturbation during the metabolic processes of glucose.
The use of the diagnostic tool in the monitoring of patients with diabetes requires the setting of target levels of hemoglobin. These target levels act as guidelines when onducting the tests. The target levels should be set on the basis of the risks and benefits of glycemic control, particularly the cardiovascular risks. However, the ability of the diagnostic tool to offer standardized measurements remains one of its main attributes when used to determine whether an individual has diabetes.
References
Bagley, A. & Malabu, U. (2014). Diabetes epidemic in the Asia Pacific region: Has hemoglobin A1C finally earned its place as a diagnostic tool? Asian Pacific Journal of Tropical Biomedicine , 4 (2): 85-89.
Gosmanov, A. & Wan, J. (2014). Low positive predictive value of hemoglobin A1C for diagnosis of prediabetes in clinical practice. American Journal of the Medical Sciences, 348 : 191-194.
Guo, F., Moellering, D. & Garvey, W. (2014). Use of HbA1C for diagnoses of diabetes and prediabetes: Comparison with diagnoses based on fasting and 2-hr glucose values and effects of gender, race, and age. Metabolic Syndrome and Related Disorders, 12 : 258-268.
Unnikrishnan, R. & Mohan, V. (2013). Challenges in estimation of glycated hemoglobin in India. Diabetes Technology & Therapeutics, 10: 897-899.
