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IBD 09-039 – HSR&D Study

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IBD 09-039
The impact of genetic testing for type 2 diabetes on health behaviors
Corrine I. Voils PhD
Durham VA Medical Center, Durham, NC
Durham, NC
Funding Period: April 2010 - March 2013

BACKGROUND/RATIONALE:
Type 2 diabetes mellitus (DM) is debilitating, deadly, and costly whose prevalence is increasing. Although the development of DM can be delayed or prevented by lifestyle changes, changes initiated too late may not delay DM onset indefinitely. Therefore, it is imperative to intervene earlier and to find new ways to increase motivation to initiate and maintain lifestyle changes. Risk communication is a key part of effective lifestyle behavior change strategies. Risk for DM has traditionally been estimated using patient age, sex, race, body mass index (BMI), family history of DM, and fasting plasma glucose (FPG) level. Genetic polymorphisms associated with the incidence of DM have been discovered and may further personalize risk, particularly because lifestyle changes can prevent DM even in patients with the polymorphisms. The ability of genetic test results to demonstrate improvement in patients' health outcomes is unknown, posing a major obstacle to translation.

OBJECTIVE(S):
In this randomized trial, we examined whether supplementing conventional DM risk counseling with communication of DM-related genetic test results affected clinical and behavioral outcomes.

METHODS:
Participants were Veteran outpatients aged 21-65 with body mass index (BMI) 27 and without DM. At baseline, fasting plasma glucose (FPG), family history of DM, and lifetime DM risk (based on age, sex, race, and BMI) were assessed. Patients were randomized to the genetic test (CR+G) or attention control eye disease counseling (CR+EYE) arm; randomization was stratified by family history of DM (unknown/low vs. moderate/high) and BMI (<35 vs. 35). Blood samples for the CR+G arm were sent to Duke University for testing of three genetic markers that are associated with increased DM risk. At this point, only the project coordinator and Master's statistician were aware of randomization assignment. Two to four weeks following enrollment, participants attended a DM risk counseling session conducted by a genetic counselor.
The DM risk counseling session began with information on DM prevalence, signs and symptoms, diagnosis, negative outcomes, and risk factors. Participants received personalized DM risk estimates based on their baseline FPG, family history, and lifetime risk. For each risk factor, participants were classified as being at low, moderate, or high risk, with an accompanying vertical bar graph color-coded as green, yellow, and red with an arrow pointing to the appropriate color indicating their risk. The genetic counselor then opened an envelope to reveal the randomization assignment; participants received either DM genetic test results or attention control eye disease counseling immediately thereafter. The session concluded with brief goal setting related to weight loss, diet, and physical activity, followed by assessment of perceived risk and illness representations. Prior to study startup, the DM risk counseling protocol was pilot-tested in 10 CR+EYE and 25 CR+G participants to assess preferences for presentation of risk results and to insure the length of the genetic and control eye disease counseling sessions matched.
Three and 6 months post-baseline, participants returned for outcome assessments, which included weight, fasting serum glucose and insulin levels to calculate HOMA-IR, self-reported dietary intake and physical activity, perceived risk, illness representations, and interim use of weight loss resources. Our primary hypothesis was that participants in the CR+G arm would lose more weight by 3 months than those in the CR+EYE arm. Secondary outcomes included perceived risk immediately following counseling; HOMA-IR, self-reported dietary intake and physical activity, and perceived risk at 3 and 6 months; and weight at 6 months. Power analyses indicated that we would need to enroll 600 patients to appropriately evaluate our primary hypothesis.
Linear mixed models were fit for weight, perceived risk, HOMA-IR, and dietary variables; generalized linear mixed models using a negative binomial distribution with a log link were used for walking and moderate physical activity. Outcomes were transformed when necessary to meet normal distributional assumptions. Models included a common intercept, time effect, time*treatment interaction, and randomization stratification variables (family history and BMI).

FINDINGS/RESULTS:
A total of 601 participants were enrolled; 303 were randomized to CR+G and 298 to CR+G. Mean age was 54, 42% were White, 53% were Black, 80% were male, 30% had BMI 35, and 53% had moderate/high family-history-based DM risk. Mean estimated weight at baseline was 224.3 lb. At 3 months there was no difference in mean weights between arms (p=0.64; CR+G=223.8, CR+EYE=223.5). There were no significant differences between arms in 9 of the 10 dimensions of perceived risk and illness representations immediately following counseling; however, mean perceived personal control over DM was higher in the CR+G arm than the CR+EYE arm (p=0.005; CR+G=8.4, CR+EYE=7.9). There were no differences between arms in means for any 3 or 6-month secondary outcomes with one exception: caloric intake was lower at 3 months in the CR+G arm than the CR+EYE arm (p=0.04; CR+G=1487 kcal, CR+EYE=1573kcal). In post-hoc analyses, treatment effects did not differ by level of family history risk (both arms) or genetic risk (CR+G arm).
Halfway through the funding period, our genetic counselor left VA, so we hired a second counselor. The cost of the intervention was $72.03 and $43.78 per session for our first and second genetic counselors, respectively. This difference was due to average amount of time spent conducting the intervention (118 and 82 min, respectively) and salary differences between the two counselors. Because the intervention was not effective, we did not calculate cost effectiveness.

IMPACT:
Because genetic testing for DM did not make a clinically important impact on patients at risk for DM, it may not be appropriate for widespread implementation.

PUBLICATIONS:

Journal Articles

  1. Raghavan S, Xu K, Coffman CJ, Pabich S, Edelman D, Voils CI. Associations of Diabetes Genetic Risk Counseling with Incident Diabetes and Weight: 5-Year Follow-up of a Randomized Controlled Trial. Journal of general internal medicine. 2019 Jul 16.
  2. Voils CI, Coffman CJ, Grubber JM, Edelman D, Sadeghpour A, Maciejewski ML, Bolton J, Cho A, Ginsburg GS, Yancy WS. Does Type 2 Diabetes Genetic Testing and Counseling Reduce Modifiable Risk Factors? A Randomized Controlled Trial of Veterans. Journal of general internal medicine. 2015 Nov 1; 30(11):1591-8.
  3. McVay MA, Beadles C, Wu R, Grubber J, Coffman CJ, Yancy WS, Reiner IL, Voils CI. Effects of provision of type 2 diabetes genetic risk feedback on patient perceptions of diabetes control and diet and physical activity self-efficacy. Patient education and counseling. 2015 Jun 30.
  4. Vorderstrasse AA, Cho A, Voils CI, Orlando LA, Ginsburg GS. Clinical utility of genetic risk testing in primary care: the example of Type 2 diabetes. Personalized medicine. 2013 Aug 1; 10(6):539-548.
  5. Voils CI, Coffman CJ, Edelman D, Maciejewski ML, Grubber JM, Sadeghpour A, Cho A, McKenzie J, Blanpain F, Scheuner M, Sandelowski M, Gallagher MP, Ginsburg GS, Yancy WS. Examining the impact of genetic testing for type 2 diabetes on health behaviors: study protocol for a randomized controlled trial. Trials. 2012 Aug 1; 13(1):121.
Conference Presentations

  1. Yancy WS. Lifestyle Modification and Pharmacotherapy for Weight Loss in Adults with Type 2 Diabetes. For symposium entitled “Weight Loss as a Diabetes Therapy”. Paper presented at: Obesity Society Annual Scientific Meeting; 2015 Nov 7; Boston, MA.
  2. Knight SJ, Provenzale DT, Voils CI, Scheuner MT, Venne VL. Access to genomic innovations in clinical care for Veterans: Learning from the VA genomic medicine collaboration. Paper presented at: VA HSR&D / QUERI National Meeting; 2015 Jul 9; Philadelphia, PA.
  3. Yancy WS. Nutrition and Exercise Prescriptions for Patients with Diabetes. Precourse: Diabetes for the Internist. Paper presented at: American College of Physicians Annual Meeting; 2015 Apr 28; Boston, MA.
  4. Voils CI, Grubber J, Coffman CJ, Edelman D, McVay M, Maciejewski ML, Bolton J, Cho A, Ginsburg GS, Yancy WS. Does genetic testing increase perceived diabetes risk? Results from a RCT. Poster session presented at: Society of Behavioral Medicine Annual Meeting and Scientific Sessions; 2014 Apr 23; Philadelphia, PA.
  5. Voils CI, Grubber J, Coffman CJ, McVay M, Gierisch JM, Edelman D, Yancy WS. Motivational Effects of Genetic Testing for Type 2 Diabetes. Presented at: Society of Behavioral Medicine Annual Meeting and Scientific Sessions; 2014 Apr 23; Philadelphia, PA.
  6. Voils CI, Coffman CJ, Grubber J, Edelman D, Sadeghpour A, Bolton J, Maciejewski ML, Cho A, Ginsburg GS, Yancy WS. The clinical utility of genetic testing for type 2 diabetes: Results from a randomized trial. Presented at: Obesity Society Annual Scientific Meeting; 2013 Nov 15; Atlanta, GA.
  7. Voils CI, Coffman CJ, Edelman D, McVay M, Maciejewski ML, Bolton J, Cho A, Ginsburg GS, Yancy WS. Does genetic testing increase perceived diabetes risk? Results from a RCT. Paper presented at: Society of Behavioral Medicine Annual Meeting and Scientific Sessions; 2013 Mar 20; San Francisco, CA.
  8. Richardson CR, Yancy WS, Moin T, Steinle N, Damschroder LJ. Implementing Diabetes Prevention in VA – Expanding the Reach. Presented at: VA HSR&D / QUERI National Meeting; 2012 Jul 18; National Harbor, MD.
  9. Yancy WS. Dietary Strategies for Prevention of Type 2 Diabetes. Paper presented at: VA HSR&D / QUERI National Meeting; 2012 Jul 18; National Harbor, MD.
  10. Grubber J, Coffman CJ, Yancy WS, Voils CI. Personalized Risk Graphs for Intervention Trials – in Technicolor! Using PROC GKPI and CALL EXECUTE to Get the Job Done. Paper presented at: Pharmaceutical Industry SAS Users Group Annual Conference; 2012 May 1; San Francisco, CA.


DRA: Health Systems, Diabetes and Related Disorders
DRE: Diagnosis, Prevention, Genomics, Treatment - Efficacy/Effectiveness Clinical Trial
Keywords: Behavior (patient), Genomics, Screening
MeSH Terms: none

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