Diagnostic Tool for Diabetes: Article Summary
Instructions:-
Identify a research or evidence-based article that focuses comprehensively on a specific intervention or new diagnostic tool for the treatment of diabetes in adults or children.
In a paper of 750-1,000 words, summarize the main idea of the research findings for a specific patient population. Research must include clinical findings that are current, thorough, and relevant to diabetes and the nursing practice.
Prepare this assignment according to the APA guidelines found in the APA Style Guide, located in the Student Success Center. An abstract is not required.
Solution
Diagnostic Tool for Diabetes: Article Summary
With the rising levels of obesity, it has become cumbersome for physicians to distinguish between type 1 that entails insulin injection treatment and type 2 diabetes which requires diet and weight loss management (Ozougwu, Obimba, Belonwu, & Unakalamba, 2013). As such, Oram et al. (2016) carried out research to act as a new diagnostic tool in determining whether a score engendered from common genetic variants could be utilized in discriminating between type 1 and type 2 diabetes. Also, the researchers focused on predicting severe insulin deficiency in younger adults diagnosed with diabetes.
The researchers identified some limitations encompassed in the current diagnostic tests for subtypes of diabetes. As such, it was noted that the presence of a single or one multiple islet autoantibodies such as islet antigen, ZnT8, and insulin autoantibody defines the features of type 1 diabetes with approximately over 90% of newly diagnosed patients depicting positivity of at least one of four antibodies during diagnosis. As such, it was stated that auto-antibodies are not valid discriminators since they can be present in individuals with type 2 diabetes and their extensive tests are not typically achieved clinically. Furthermore, individuals with type 1 diabetes show positive nature of islet autoantibody in their adulthood rather than in their childhood. Also, the positivity of islet autoantibody is lower in individuals with type 1 diabetes if they are evaluated after diagnosis. The researchers utilized current literature to depict the difficulty that physicians are facing in diagnosing the subtypes of diabetes
The researchers conducted a cross-sectional cohort study of individuals diagnosed with diabetes between the ages of 20-40 years and had diabetes for more than three years from South West England. The researchers developed genetic scores from a published type 1- and type 2- associated variations as they tested whether these scores could be able to clinically distinguish type 1 and type 2 diabetes derived from the Wellcome Trust Case Control Consortium (WTCCC) having approximately 3887 participants of age 20-40 years. Analysis entailed age groups that depicted diagnostic difficulty in clinical practice (223 participants) who had developed severe insulin deficiency of more than three years from diagnosis.
The findings in this research suggested that type 1 genetic risk score on 30 cases of type 1 diabetes- associated risk variants were vastly discriminative of type 1 diabetes and type 2 diabetes. Additionally, type 2 diabetes genetic risk score had little discrimination. As such, a high type 1 diabetes genetic scores were indicative of type 1 diabetes with most discriminative ability obtained from 9 single nucleotide polymorphisms. Furthermore, it was found out that for young adults diagnosed with diabetes, type 1 genetic risk scores projected a progression to insulin deficiency. Comparatively, the researchers related Winkler et al. (2014) research on 1722 children in ranking features of type 1 diabetes susceptibility genes to enhance the diagnoses of type 1 diabetes. They exclusively focused on individuals who had developed diabetes for more than three years rather than Winkler et al. (2014) which focused on less developed diabetic patients.
The researchers indicated that the study had some limitations that needed to be addressed. First, the cross-sectional study tested the ability to determine insulin deficiency in which the BMI and auto-antibodies during recruitment of participants and not at diagnosis, without changing the genotype. As such, they claim that this could have abridged the discriminatory ability of BMI and autoantibody in the sample used in the study. Also, it is stated that all type 1 diabetes risk alleles were not perfectly captured as they focused on alleles with the highest risk. Also, the study only measured the GAD and IA2 auto-antibody. Arguably, in most clinical settings both ZnT8 and insulin are not available.
The researchers concluded that type 1 genetic risk scores could precisely determine young adults with diabetes that needs insulin therapy. As such, this research has added a new analogy in the classification of diabetic patients when clinical features and auto-immune markers are ambiguous. Also, the research is in line with previous research in distinguishing the two types of diabetes (Klompas et al., 2013; Odegaard & Chawla, 2012).
Over the past six years, genome-wide association
studies(GWAS) have provided tremendous advances in the medical understanding of
the role of common genetic variation in complex diseases in humans such as
cancer and diabetes (Atanasovska, Kumar, Fu, Wijmenga, & Hofker, 2015;
Power, Parkhill, & de Oliveira, 2016; van der Sijde, Ng, & Fu, 2014;
Visscher, Brown, McCarthy, & Yang, 2012). As such, they have provided more insight to
understand the mechanism of several diseases. As such, the study provides a typical
example of the role of GWAS in contemporary diseases through utilization of the
type 1 diabetes genetic risk scores. Through such understanding, it is now
imperative for medical practitioners to use GWAS in diagnosing complex diseases
and offering the appropriate interventions to eradicate misdiagnosis and
medication errors as they administer health care services especially in cases
of diabetes.
References
Atanasovska, B., Kumar, V., Fu, J., Wijmenga, C., & Hofker, M. H. (2015). GWAS as a Driver of Gene Discovery in Cardiometabolic Diseases. Trends in Endocrinology and Metabolism. https://doi.org/10.1016/j.tem.2015.10.004
Klompas, M., Eggleston, E., McVetta, J., Lazarus, R., Li, L., & Platt, R. (2013). Automated detection and classification of type 1 versus type 2 diabetes using electronic health record data. Diabetes Care, 36(4), 914–921. https://doi.org/10.2337/dc12-0964
Odegaard, J. I., & Chawla, A. (2012). Connecting type 1 and type 2 diabetes through innate immunity. Cold Spring Harbor Perspectives in Medicine. https://doi.org/10.1101/cshperspect.a007724
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. https://doi.org/10.2337/dc15-1111
Ozougwu, J., Obimba, K., Belonwu, C., & Unakalamba, C. (2013). The pathogenesis and pathophysiology of type 1 and type 2 diabetes mellitus. Journal of Physiology and Pathophysiology, 4(4), 46–57. https://doi.org/10.5897/JPAP2013.0001
Power, R. A., Parkhill, J., & de Oliveira, T. (2016). Microbial genome-wide association studies: lessons from human GWAS. Nature Reviews Genetics. https://doi.org/10.1038/nrg.2016.132
van der Sijde, M. R., Ng, A., & Fu, J. (2014). Systems genetics: From GWAS to disease pathways. Biochimica et Biophysica Acta – Molecular Basis of Disease. https://doi.org/10.1016/j.bbadis.2014.04.025
Visscher, P. M., Brown, M. A., McCarthy, M. I., & Yang, J. (2012). Five years of GWAS discovery. American Journal of Human Genetics. https://doi.org/10.1016/j.ajhg.2011.11.029
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. https://doi.org/10.1007/s00125-014-3362-1