The scalable delivery of genomic medicine requires collaboration between genetics and non-genetics providers. Thus, it is essential to investigate and address the perceived value of and barriers to incorporating genetic testing into the clinical practice of primary care providers (PCPs). We used a mixed-methods approach of qualitative interviews and surveys to explore the experience of PCPs involved in the pilot DNA-10K population genetic testing program. Similar to previous research, PCPs reported low confidence with tasks related to ordering, interpreting and managing the results of genetic tests, and identified the need for additional education. PCPs endorsed high levels of utility for patients and their families but noted logistical challenges to incorporating genetic testing into their practice. Overall PCPs were not familiar with the United States’ Genetic Information Nondiscrimination Act and they expressed high levels of concern for patient data privacy and potential insurance discrimination. This PCP feedback led to the development and implementation of several processes to improve the PCP experience with the DNA-10K program. These results contribute to the knowledge base regarding genomic implementation using a mixed provider model and may be beneficial for institutions developing similar clinical programs.
Aims
To assess the clinical utility of two polygenic scores (PGSs) in differentiating between type 1/type 2 diabetes.
Methods
Patients diagnosed with diabetes in the UK Biobank were studied (N = 41,787), including 464 (1%) and 15,923 (38%) who met the criteria for classic type 1 and type 2 diabetes, respectively, and 25,400 (61%) atypical diabetes. The validity of two published PGSs for type 1 (PGST1D) and type 2 diabetes (PGST2D) in differentiating classic type 1/type 2 diabetes was assessed using C-statistic. The utility of genetic probability for type 1 diabetes based on PGSs (GenProb-T1D) was evaluated in atypical diabetes patients.
Results
The joint performance of PGST1D and PGST2D for differentiating classic type 1/type 2 diabetes was outstanding (C-statistic = 0.91), significantly higher than that of PGST1D alone (0.88) and PGST2D alone (0.70), both P < 0.001. Using an optimal cutoff of GenProb-T1D, 23% of patients with atypical diabetes had a higher probability of type 1 diabetes and its validity was independently supported by clinical presentations that are characteristics of type 1 diabetes.
Conclusion
PGST1D and PGST2D can be used to discriminate classic type 1/type 2 diabetes and have potential clinical utility for differentiating these two types of diseases among patients with atypical diabetes.
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