Approaches for Treatment of Diabetes

Approaches for Treatment of Diabetes

In the contemporary medical world, diabetes is among the many chronic conditions that affect a large proportion of persons. For instance, in the USA, approximately, 26 million persons of the total population have diabetes (Whalen, 2014). Worsening the situation further are the rising numbers of young adults presenting with obesity making the diagnosis of either diabetes type 1 or 2 difficult (Hamman et al., 2014).  Such a situation is worrying and necessitates a quick fix. Consistent with this need are the increased efforts in the development of new diagnostic strategies for the differentiation of the two types of diabetes. That is the case given the high number of studies focusing on addressing this subject. In essence, this paper aims at analyzing one of such studies and summarizing its findings that relate to diabetes and nursing practice. With such information, it is beyond doubt that a new understanding of the screening tests for diabetes will become inevitable.

Primarily, the chosen research article focused on determining if the scores generated from common genetic variants were reliable for the differentiation of Type 1 diabetes (T1D) and Type 2 Diabetes (T2D), as well as useful in prediction of severe insulin deficiency in diabetic young adults. Central to this aim was the high proportion of obesity, which made the classification of the two types of diabetes complicated (Oram et al., 2015). Such an inability presents a significant challenge given that without a proper distinction between T1D and T2D, T2D patients will receive unnecessarily insulin therapy whose effects are harsh on the individuals. Befitting examples of such effects include the high cost of the drugs as well as monitoring and many side effects (Farmer & Fox, 2011). On the contrary, misdiagnosis of T2D as T1D because of lack of this tool will also subject individuals to poor glycemic control, increased health contact, unsuitable insulin regimens and high-risk for life-threatening complications such as ketoacidosis. Also, the lack of specificity of tests used to diagnose the subtypes of diabetes was another driving force for this study. For instance, the autoantibodies commonly utilized for the screening of T1D can also occur in individuals without T1D making this test unreliable (Bingley, 2010). Based on such likely events, the researchers formulated their study objective.

Of the utmost importance to the achievement of this study’s objectives was the use of both case-controls and cohort study designs. The case-control was significant in the determination of the genetic differences between the T1D and T2D groups. As for the cohort study, the focus was on the progression of study participants (T1D and T2D patients) to insulin deficiency. The Wellcome Trust Case Control Consortium helped in the identification of the clinically defined T1D and T2D patients (Oram et al., 2015).

Last but not the least is the findings of this study, which are also worth noting. To begin with, the researchers arrived at the conclusion that the genetic risk scores (GRS) constituting of common single nucleotide polymorphisms (SNPs) has the capacity can help distinguish between T1D and T2D. Of the essence to this deduction was the derivation that T1D GRS was highly discriminative of clinically defined T1D and T2D. Such is the case given that the area under the curve (AUC) for T1D was 0.88 (95% CI 0.87–0.89) compared to T2D’s AUC of 0.64 (95% CI 0.63–0.66). Notwithstanding, the research further established that the T1D GRS were more likely than T2D GRS to depict individuals that would progress to insulin deficiency. That is for sure because the AUC for this cohort (T1D GRS) was 0.87 (95% CI 0.82–0.92). Additionally, the study determined that the T1D GRS’s discriminative ability is not only independent of but also an additive to that of BMI, age at diagnosis and islet autoantibodies (Oram et al., 2015). Based on these attributes, the researchers implicated that the classification of diabetes in young adults would become easy if healthcare professionals prioritize the utilization of the T1D GRS in all care settings.

Concisely, this paper aimed at analyzing an evidence-based article focusing on a new diagnostic tool for diabetes and summarizing its findings that relate to diabetes and nursing practice. Indeed, it has accomplished this objective successfully since the discussion has centered its focus on giving a detailed account of the findings of the selected article. Central to this new diagnostic test for diabetes is the use of genetic risk scores for the distinction of T1D and T2D. That said, an implication drawn from this analysis for nursing practice is the need for further studies that focuses on this matter to its practical details. That is for sure given the potential benefits that this diagnostic tool holds and urgency of such a tool. As such, going into the future, research in diabetes management must take this direction if the diabetic young adults are to benefit more from this intervention.


Bingley, P. J. (2010). Clinical applications of diabetes antibody testing. The Journal of Clinical Endocrinology & Metabolism95(1), 25-33.

Farmer, A., & Fox, R. (2011). Diagnosis, classification, and treatment of diabetes. BMJ342(jun09 4), d3319-d3319.

Hamman, R. F., Bell, R. A., Dabelea, D., D’Agostino, R. B., Dolan, L., Imperatore, G.,Lawrence, J.M., Linder, B., Marcovina, S.M., Mayer-Davis, E.J., & Pihoker, C. (2014). The SEARCH for Diabetes in Youth study: rationale, findings, and future directions. Diabetes care37(12), 3336-3344.

Oram, R., Patel, K., Hill, A., Shields, B., McDonald, T., & Jones, A. et al. (2015). A Type 1 Diabetes Genetic Risk Score Can Aid Discrimination Between Type 1 and Type 2 Diabetes in Young Adults. Diabetes Care39(3), 337-344.

Whalen, K. (2014). Pharmacology (6th ed.). Philadelphia: Lippincott Williams & Wilkins