NURS 6501 Week 9: Diabetes Discussion

NURS 6501 Week 9: Diabetes Discussion

NURS 6501 Week 9: Diabetes Discussion

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Diabetes is one of the leading causes of mortality in the United States of America. A number of risk factors predispose people to developing diabetes (Johnson et al., 2018). With the rising prevalence rate of obesity in the United States of America, concerns have been raised about the difficulty to distinguish type 1 diabetes (T1D) from type 2 diabetes (T2D) among young adults. TID is an autoimmune disease that can occur at all ages and people with the condition are more likely to be obese. With the rising incidences if obesity, more young populations are developing T2D (Eakin et al., 2014). Due to inaccuracies in autoantibody measurements among young adults, it is difficult to diagnose a specific kind of diabetes for the population. As such, genetic risk score (GRS) has been developed to identify T1D and T2D among young adults diagnosed with obesity. However, the use of GRS to identify autoimmune diabetes is challenging due to a cluster of autoimmune diseases associated by polygenic risk and islet. Therefore, the subsequent illustration is based on the analysis of the research done on GRS, the associated clinical findings and their relevance in nursing practice for patients with diabetes.

In any clinical setting, it is important for nurses and other health workers to identify the type of a given diabetes to facilitate planning of treatment accordingly. The GRS is one of the novel tools that is readily used to discriminate monogenic from type 1 diabetes to facilitate in the development of a diagnosis for diabetes. According to Bonifacio et al. (2018), the tool measures 30 genetic variants of DNA in a person and combines all the potential risks with a single score to predict diabetes. If the score of genetic risk is high, then a person is likely to have a T1D. On the other hand, in the event that the score is low, a person will be predisposed to T2D.

The GRS works on a principle of identifying the common genetic variants for type 1 diabetes and type 2 diabetes. According to findings by Layton et al. (2018), a monogenic autoimmune condition occur during the very-early-onset of diabetes. The study indicates that individuals are likely to develop infantile-onset multisystem autoimmune diabetes due to mutations of the dominant gain-of-function STAT3 or as are result of recessively inherited LRBA. However, Thomas et al. (2018) postulates that some people host a causative mutation for a single gene for the very-early-onset diabetes which indicates that they have a strong polygenic risk of developing an autoimmune disease. Moreover, the study illustrates that autoimmune disease results from a shared predisposing genetic loci. Accordingly, Oram et al. (2016) affirm that HLA-DR3 haplotype is related to the development of type 1 diabetes due to its strong correlation with HLA-DQ2 haplotype. Besides, outside the region of HLA genes, the IL2RA polymorphism rs706778 are strongly linked to increased risk of type 1 diabetes and the onset of autoimmune disorders.

Johnson et al. (2018) illustrate that GRS is used for calculating factors predisposing one to diabetes by genotyping the top risk alleles followed by a summation of their effective weight so as to assign a numeric score. Furthermore, the researcher argues that despite being present in people with monogenic autoimmunity, islet autoantibodies are considered to be highly discriminatory for type 1 diabetes as compared to type 2 diabetes. In essence, GRS can be used to distinguish between monogenic autoimmunity and polygenic clustering which are vital in the development of an autoimmune disease.

According to Oram et al. (2016), current diagnostic tests used to categorize subtypes of obesity have limitations. For example the use of autoantibodies to diagnose diabetes cannot be used as perfect discriminators to classify different types of diabetes among young adults. Besides, the study indicates that islet autoantibody positivity is lower for persons diagnosed with type 1 diabetes especially if such individuals are tested after diagnosis. However, studies by Layton et al. (2018) affirm that the measurement of endogenous insulin, particularly using serum of C-peptide assessments can be accurately used to distinguish TID from T2D. Guided by this premise, Johnson et al. (2018) suggest that genetic components that makes one susceptible either to T1D or T2D can be measured through the assessment of single nucleotide polymorphism (SNP). According to the author, HLA regions are considered to have strong risks as well as protective alleles for developing type 1 diabetes. Moreover, the researcher posits that SNP genotyping approach can be accurately used to capture the high risk DR3 and DR4-DQ8 alleles potential in causing T1D.  The GRS is used to record the genetic scores of SNPs to classify patients into the sub-groups of diabetes. Findings by Layton et al. (2018) indicates that 40 SNPs found outside the HLA regions are associated with the risk of TID. However, high-risk HLA genotypes of over 69 SNPs have been linked to the development of T2D (Thomas et al., 2018).

According to Bonifacio et al. (2018), use of genetic risk score provide a simple but yet a very inexpensive test to distinguish type 1 diabetes from type 2 diabetes. Specifically, the GRS helps to classify individuals who require insulin treatment by assessing the possibility of a deficiency. Besides, GRS help to identify patients considered to have monogenic etiology for them to be classified as having type 1 diabetes or type 2 diabetes to help in prompt intervention.

References

Bonifacio, E., Beyerlein, A., Hippich, M., Winkler, C., Vehik, K., Weedon, M. N., … & Steck, A. K. (2018). Genetic scores to stratify risk of developing multiple islet autoantibodies and type 1 diabetes: a prospective study in children. PLoS medicine, 15(4), e1002548. DOI: 10.1371/journal.pmed.1002548

Eakin, E. G., Winkler, E. A., Dunstan, D. W., Healy, G. N., Owen, N., Marshall, A. M., … & Reeves, M. M. (2014). Living well with diabetes: 24-month outcomes from a randomized trial of telephone-delivered weight loss and physical activity intervention to improve glycemic control. Diabetes Care, 37(8), 2177-2185. DOI: 10.2337/dc13-2427

Johnson, M. B., Patel, K. A., De Franco, E., Houghton, J. A., McDonald, T. J., Ellard, S., … & Hattersley, A. T. (2018). A type 1 diabetes genetic risk score can discriminate monogenic autoimmunity with diabetes from early-onset clustering of polygenic autoimmunity with diabetes. Diabetologia, 61(4), 862-869. DOI: 10.1007/s00125-018-4551-0.

Layton, J., Li, X., Shen, C., de Groot, M., Lange, L., Correa, A., & Wessel, J. (2018). Type 2 diabetes genetic risk scores are associated with increased type 2 diabetes risk among African Americans by cardiometabolic status. Clinical Medicine Insights: Endocrinology and Diabetes, 11, 1179551417748942. DOI: 10.1177/1179551417748942

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. DOI: 10.2337/dc15-1111.

Thomas, N. J., Jones, S. E., Weedon, M. N., Shields, B. M., Oram, R. A., & Hattersley, A. T. (2018). Frequency and phenotype of type 1 diabetes in the first six decades of life: a cross-sectional, genetically stratified survival analysis from UK Biobank. The lancet Diabetes & endocrinology, 6(2), 122-129. DOI: 10.1016/S2213-8587(17)30362-5

According to the American Diabetes Association (2011), 25.8 million children and adults have been diagnosed with diabetes in the United States. Approximately 2 million more are diagnosed every year, with another 79 million people considered to be in a pre-diabetes state. These millions of people are at risk of several alterations, including heart disease, stroke, kidney failure, neuropathy, and blindness. Since diabetes has a major impact on the health of millions of people around the world, it is essential for nurses to understand the pathophysiology and associated alterations of this disorder. In this Discussion, you compare two types of diabetes—diabetes mellitus and diabetes insipidus.

To prepare for this NURS 6501 Week 9: Diabetes Discussion:

Review Chapter 18 in the Huether and McCance text and Chapter 18 in the McPhee and Hammer text. Identify the pathophysiology of diabetes mellitus and diabetes insipidus. Consider the similarities and differences between resulting alterations of hormonal regulation.

Select two of the following patient factors: genetics, gender, ethnicity, age, or behavior. Think about how the factors you selected might impact the diagnosis and prescription of treatment for these two types of diabetes.

By Day 3- NURS 6501 Week 9: Diabetes Discussion

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Post an explanation of the pathophysiology of diabetes mellitus and diabetes insipidus. Describe the differences and similarities

between resulting alterations of hormonal regulation. Then explain how the factors you selected might impact the diagnosis and prescription of treatment for these two types of diabetes.

Read a selection of your colleagues’ responses.

By Day 6 OF NURS 6501 Week 9: Diabetes Discussion

Respond to at least two of your colleagues on two different days who selected different factors than you, in one or more of the following ways:

Share insights on how the factor you selected impacts the pathophysiology of diabetes mellitus and diabetes insipidus.

Offer alternative diagnoses and prescription of treatment options for diabetes mellitus and diabetes insipidus.

Validate an idea with your own experience and additional research.

NURS 6501 Week 9: Diabetes Discussion Reference:

American Diabetes Association. (2011). Diabetes statistics. Retrieved from http://www.diabetes.org/diabetes-basics/diabetes-statistics/

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