ABSTRACT
Partitioned polygenic scores (pPS) have been developed to capture pathophysiologic processes underlying type 2 diabetes (T2D). We investigated the association of T2D pPS with diabetes-related traits and T2D incidence in the Diabetes Prevention Program. We generated five T2D pPS (ß-cell, proinsulin, liver/lipid, obesity, lipodystrophy) in 2,647 participants randomized to intensive lifestyle, metformin, or placebo arms. Associations were tested with general linear models and Cox regression with adjustment for age, sex, and principal components. Sensitivity analyses included adjustment for BMI. Higher ß-cell pPS was associated with lower insulinogenic index and corrected insulin response at 1-year follow-up with adjustment for baseline measures (effect per pPS SD -0.04, P = 9.6 × 10-7, and -8.45 µU/mg, P = 5.6 × 10-6, respectively) and with increased diabetes incidence with adjustment for BMI at nominal significance (hazard ratio 1.10 per SD, P = 0.035). The liver/lipid pPS was associated with reduced 1-year baseline-adjusted triglyceride levels (effect per SD -4.37, P = 0.001). There was no significant interaction between T2D pPS and randomized groups. The remaining pPS were associated with baseline measures only. We conclude that despite interventions for diabetes prevention, participants with a high genetic burden of the ß-cell cluster pPS had worsening in measures of ß-cell function.
Subject(s)
Diabetes Mellitus, Type 2 , Insulin-Secreting Cells , Prediabetic State , Humans , Insulin-Secreting Cells/metabolism , Prediabetic State/genetics , Male , Diabetes Mellitus, Type 2/genetics , Diabetes Mellitus, Type 2/metabolism , Female , Middle Aged , Genetic Predisposition to Disease , Multifactorial Inheritance , Adult , IncidenceABSTRACT
OBJECTIVE: To characterize high type 1 diabetes (T1D) genetic risk in a population where type 2 diabetes (T2D) predominates. RESEARCH DESIGN AND METHODS: Characteristics typically associated with T1D were assessed in 109,594 Million Veteran Program participants with adult-onset diabetes, 2011-2021, who had T1D genetic risk scores (GRS) defined as low (0 to <45%), medium (45 to <90%), high (90 to <95%), or highest (≥95%). RESULTS: T1D characteristics increased progressively with higher genetic risk (P < 0.001 for trend). A GRS ≥90% was more common with diabetes diagnoses before age 40 years, but 95% of those participants were diagnosed at age ≥40 years, and their characteristics resembled those of individuals with T2D in mean age (64.3 years) and BMI (32.3 kg/m2). Compared with the low-risk group, the highest-risk group was more likely to have diabetic ketoacidosis (low GRS 0.9% vs. highest GRS 3.7%), hypoglycemia prompting emergency visits (3.7% vs. 5.8%), outpatient plasma glucose <50 mg/dL (7.5% vs. 13.4%), a shorter median time to start insulin (3.5 vs. 1.4 years), use of a T1D diagnostic code (16.3% vs. 28.1%), low C-peptide levels if tested (1.8% vs. 32.4%), and glutamic acid decarboxylase antibodies (6.9% vs. 45.2%), all P < 0.001. CONCLUSIONS: Characteristics associated with T1D were increased with higher genetic risk, and especially with the top 10% of risk. However, the age and BMI of those participants resemble those of people with T2D, and a substantial proportion did not have diagnostic testing or use of T1D diagnostic codes. T1D genetic screening could be used to aid identification of adult-onset T1D in settings in which T2D predominates.
Subject(s)
Diabetes Mellitus, Type 1 , Veterans , Humans , Diabetes Mellitus, Type 1/genetics , Diabetes Mellitus, Type 1/epidemiology , Male , Middle Aged , Veterans/statistics & numerical data , Female , Adult , Aged , Genetic Predisposition to Disease , Diabetes Mellitus, Type 2/genetics , Diabetes Mellitus, Type 2/epidemiology , Risk FactorsABSTRACT
Purpose: Little is known about non-genetics health care specialists' attitudes toward the return and utilization of actionable genomic results from a research biobank. We surveyed primary care providers (PCPs) to explore their perspectives on these results and their preferences for return. Methods: We administered a paper and web-based 27-question survey to PCPs residing locally and caring for adult patients. Recruitment was conducted in person and by email, focusing on PCPs likely to interact with results generated by our institution's biobank. Results: Of the ~482 PCPs contacted, 77 (16%) returned surveys. Although most respondents (90%) prefer that a genetics specialist be involved in communicating biobank-generated genomic results to patients, about 40% of respondents reported that a PCP shares the responsibility to discuss these results along with other specialists. A majority of respondents (74%) felt uncomfortable communicating these results to patients. However, respondents reported significantly greater comfort with this process when offered targeted educational resources (62% with vs 10% without resources; P < 10-5). Conclusion: PCPs recognize the need to engage with their patients' biobank-generated genomic results but feel uncomfortable in doing so. Relevant resources are needed to improve PCPs' confidence in the use of these types of results to affect patient care.