Diabetes mellitus has been one of the major public health concerns affecting the global population. The recent studies have shown a significant increase in prevalence and incidence of the disease in the global population. The World Health Organization predicts that by 2025 there will be approximately 285 million people affected. This steady rise is expected in type 2 diabetes though a parallel in childhood diabetes in both type 1 and type 2 was reported. The increase in the incidence of this disease has prompted researchers and experts in the field to identify effective diagnostic tools for identifying the disease. This practice is an important measure for the clinicians who ensure that patients are provided with the most appropriate treatment that will improve their health condition. Recent studies have come up with new and more effective solutions for diagnosing diabetes in young adults whereby genetics are used as a primary factor of identifying the risk a patient faces. This will be the primary focus of the following report.
Specific Patient Population
The recent research has shown that there has been a significant rise in the prevalence and incidence of obesity in the US population. The growing numbers have affected children and adults, a clear indicator that the future of the country could be in great jeopardy. The primary concern has been on the difficulty of distinguishing between type 1 (T1D) and type 2 (T2D) diabetes mellitus. The research by Oram and colleagues (2016) shows that autoimmune T1D is more likely to affect individuals in older ages. However, with a steady increase in obesity in the society, more people from younger generations are susceptible to T2D and those who have T1D are more likely to become obese (Oram et al., 2016). It is important for medical practitioners to diagnose the disease correctly as the most appropriate treatments are different in each case. Patients suffering from T1D will require insulin treatment because the pancreatic beta-cells that produce insulin are rapidly destroyed (Oram et al., 2016). On the other hand, T2D is caused by a gradual decline in the functioning of the beta-cells in regards to insulin resistance. The most appropriate management practice is through diet or oral hypoglycemic agents (Oram et al., 2016).
Delegate your assignment to our experts and they will do the rest.
Clinical Findings
The population mentioned above is at risk of poor health and worsening conditions if the disease is not treated appropriately. In this case, there are approximately 15% of young adults with diabetes who receive the wrong diagnosis and incorrect treatment. The initial clinical diagnosis is not systematic hence when it is made it is rarely reversed (Keating, 2015). The research shows that T2D is usually misdiagnosed as T1D resulting in unnecessary insulin treatment, high costs incurred, and more negative side effects like weight gain. Such cases have been the primary cause for a need to establish effective diagnostic measures. Oram et al. (2016) proposed the use of genetic risk scores (GRS) as an effective discriminator of T1D and T2D associated variants. It was evident that a T1D-GRS was highly discriminative of clinically defined T1D and T2D while the T2D-GRS added little discrimination (Keating, 2015). The T1D-GRS was effective in identifying T1D with 50% sensitivity and 95% specificity. Walford and colleagues (2014) support this view while adding that metabolite traits could offer complementary results to the issue.
Impact on Nursing Field
It is evident that the nursing field is strongly affected by the recent discovery in diagnostic practices. The health workers in this field have a primary role to play in ensuring that the particular patient suffering from diabetes mellitus can receive the most accurate diagnosis so as to prevent cases of worsening the condition (Walford et al., 2015). The advancement in technology and quality of research has made it possible for members of the medical field to share information and improve care practices. In this regard, the T1D-GRS will help the nursing practitioners to educate each other on the appropriate measures and practices that will lead to improved care in addressing the public health issue (Walford et al., 2015). The research has come at a time when the public have increased their demands from the clinicians identifying that the cases of misdiagnosis and inaccurate treatment should be a thing of the past. In this regard, medical workers will be able to classify individuals especially when the clinical features are discordant with autoantibody testing results.
Recommendations
The above study by Oram and colleagues emphasizes the importance of providing patients with accurate diagnosis results as per the diabetes that is affecting them. It was found that in 83% of the discordant patients a high or low T1D-GRS would effectively provide an accurate classification which would determine whether the patient becomes insulin deficient or not. The research indicates that it is important that future studies avoid some of the limitations occurring in the study to improve the accuracy of results found (Winkler et al., 2014). For instance, auto-antibodies and BMI of the smaple population used for the study was gathered during recruitment and not during diagnosis. Though the genotype may remain unchanged, the practice reduces the discriminatory ability of auto-antibodies and BMI.
Genetic risk scores has been a revolutionary measure in diagnosis of diabetes among young adults. It is evident that individuals between 20 and 40 years are the most difficult to identify the particular ailment. In this regard, the T1D-GRS has helped improve results for this group with an effort to eliminate the incidence side effects occurring after misdiagnosis and incorrect treatment.
References
Keating, B. J. (2015). Advances in risk prediction of type 2 diabetes: integrating genetic scores with Framingham risk models. Diabetes, 64 (5), 1495-1497.
Oram, R. A., Patel, K., Hill, A., Shields, B., McDonald, T. J., Jones, A., ... & Weedon, M. N. (2016). A type 1 diabetes genetic risk score can aid discrimination between type 1 and type 2 diabetes in young adults. Diabetes Care, 39 (3), 337-344.
Walford, G. A., Colomo, N., Todd, J. N., Billings, L. K., Fernandez, M., Chamarthi, B., ... & Fanelli, R. R. (2015). The study to understand the genetics of the acute response to metformin and glipizide in humans (SUGAR-MGH): design of a pharmacogenetic resource for type 2 diabetes. PloS one, 10 (3), e0121553.
Walford, G. A., Porneala, B. C., Dauriz, M., Vassy, J. L., Cheng, S., Rhee, E. P., ... & Florez, J. C. (2014). Metabolite traits and genetic risk provide complementary information for the prediction of future type 2 diabetes. Diabetes Care, 37(9), 2508-2514.
Winkler, C., Krumsiek, J., Buettner, F., Angermüller, C., Giannopoulou, E. Z., Theis, F. J., ... & Bonifacio, E. (2014). Feature ranking of type 1 diabetes susceptibility genes improves prediction of type 1 diabetes. Diabetologia, 57 (12), 2521-2529.
