A Genetic Risk Score for Classifying Diabetes

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AGenetic Risk Score for Classifying Diabetes

Cliniciansuse various procedures to diagnose diabetes. The increased cases ofthe condition among the youth have triggered researchers to develop amyriad of approaches to check for early symptoms. There has been aproblem with the early distinction of type 1 and 2 diabetes among theadolescents and the youth. A research conducted by Oram et al. (2015)dubbed AType 1 diabetes genetic risk score can aid discrimination betweenType 1 and Type 2 diabetes in young adults aimedat determining whether score generated from the common geneticvariants can be helpful in discriminating between T1D and T2D(Inzucchi et al., 2012).The increased reporting of diabetes among the youth postulates theintroduction of an accurate diagnostic method because the standardprocedures lead to a significant number of the patients being wronglyclassified

Backgroundand Rationale of the Study

Accordingto the background study of the research, it is evident that the ageat which people are developing the symptoms of obesity is on thedecline. Two decades ago, the sugar disease among the youth did nothave any pronounced public health importance (Oram et al., 2015).However, with the intense obesity that is mauling the young people,there have been increased frequencies of blood sugar levels in theyoung generation. The increased prevalence has made it difficult tomake out TD1 and TD2 (Inzucchiet al., 2012). The rationale for conducting the research is that the two conditionshave different treatment approaches (Oram et al., 2015). To make themedication effective, it is instrumental for clinicians todistinguish the two at the earliest time possible.

Accordingto the study, individuals suffering from T1D require insulinadministration due to the incapacitation of the cell that producesthe hormone in the pancreas. T2D is less severe, and it is a resultof the gradual decline of the effectiveness of the beta-cells. Dietmanagement is considered as the primary and most effective managementstrategy for the illness(Pettitt et al., 2014).The authors also indicate that more than 15% of the young adultspresenting with diabetes symptoms are wrongly diagnosed andconsequently given the inappropriate treatment (Oram et al., 2015).The wrong classification of T2D results in preliminary insulinadministration and intensive monitoring. Patients procure costlydrugs, and they are on the verge of suffering from side effectsincluding weight gain and hypoglycemia.

Methodology

Theauthors exploited the Wellcome Trust Case Control Consortium (WTCC)whereby the participants were classified according to theirpresenting clinical symptoms. The study recruited 223 individualsbetween 20 and 40 years who had diabetes for at least three years(Oram et al., 2015). The accurate imputation of classical HumanLeukocyte Syndrome (HLS) was derived from Type 1 diabetes’s geneticconsortium. The Genetic Scores for both conditions were generatedthrough robust associated genetic variants drawn from alreadypublished studies (Oram et al., 2015).

Inaddition, the authors combined the Single Nucleotide Polymorphism(SNP) within and outside the Human Leukocyte Antigen (HLA) togenerate accurate scores for T1D (Oram et al., 2015). Conversely, theodd ratios for T2D were drawn from SNPs provided in the latestconsortium meta-analysis. The validity of the genetic risk scores forboth T1D and T2D was facilitated by the use of Receiver OperatorCharacteristics Curve. Also, the discriminative capacity ofantibodies was assessed through a logical regression analysis.

Discussion

Thedata collected from the participants indicate that T1D risk score isdiscriminative of the persons who are at risk of insulin deficiency.It is also independent of the discriminators for identifying patientssuffering from diabetes. The authors successfully demonstrate thatthe GRS consisting of SNPs can distinguish the two types of sugardisease (Oram et al., 2015).

Thegenetic risk scores for type one diabetes can help to identifyindividuals who are susceptible to severe insulin deficiency.Furthermore, the method proves to be valuable that the conventionaldiagnosis approaches since it does not rely on islet antibodies (Oramet al., 2015). The amount of islet elements reduces with ageadvancement. Therefore, when testing for diabetes among older youths,it is possible to place them in the wrong category.

Besidesbeing independent of antibodies, the genetic risk score is autonomousof time after diagnosis. The rationale for this is that it relies onthe human genome to give the correct investigation. Since theelements do not change in an individual’s lifetime, it turns out tobe the most accurate method to discriminate the two types ofdiabetes. In addition, the continued sophistication of technologymakes the scientific procedure cheap and reliable.

Conclusion

Inconclusion, the increased prevalence of diabetes among the youngpeople necessitates to the introduction of an accurate diagnosisprocedure because the conventional methods that rely on isletantibodies lead to a significant number of the patients being wronglyclassified. The genetic risk score is a viable technique since it hasa high level of accuracy. It is also independent of islet elementssince it employs the genome technology. The research indicates thatthe complex technology being introduced in the medical arena willrender the process fast and cheap. When well exploited in theclinical setting, it can reduce to a great extent the number ofpatients who nurses put in the inappropriate treatments. The studycontributes immensely to the nursing practice since it derivesinformation from evidence-based procedures. It is also instrumentalin fulfilling the objective of patient-centered care that is pursuedby institutions.

References

Inzucchi,S. E., Bergenstal, R. M., Buse, J. B., Diamant, M., Ferrannini, E.,Nauck, M., &amp Matthews, D. R. (2012). Management of hyperglycaemiain type 2 diabetes: a patient-centered approach. Position statementof the American Diabetes Association (ADA) and the EuropeanAssociation for the Study of Diabetes (EASD). Diabetologia,55(6),1577-1596.

Oram,R. A., Patel, K., Hill, A., Shields, B., McDonald, T. J., Jones, A.,&amp Weedon, M. N. (2016). A type 1 diabetes genetic risk score canaid discrimination between type 1 and type 2 diabetes in youngadults. Diabetescare,39(3),337-344.

Pettitt,D. J., Talton, J., Dabelea, D., Divers, J., Imperatore, G., Lawrence,J. M., &amp Saydah, S. H. (2014). Prevalence of diabetes in US youthin 2009: the SEARCH for diabetes in youth study. Diabetescare,37(2),402-408.

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