Impact of Genetic Testing and Family Health History Based Risk Counseling on Behavior Change and Cognitive Precursors for Type 2 Diabetes.

Abstract

Family health history (FHH) in the context of risk assessment has been shown to positively impact risk perception and behavior change. The added value of genetic risk testing is less certain. The aim of this study was to determine the impact of Type 2 Diabetes (T2D) FHH and genetic risk counseling on behavior and its cognitive precursors. Subjects were non-diabetic patients randomized to counseling that included FHH +/- T2D genetic testing. Measurements included weight, BMI, fasting glucose at baseline and 12 months and behavioral and cognitive precursor (T2D risk perception and control over disease development) surveys at baseline, 3, and 12 months. 391 subjects enrolled of which 312 completed the study. Behavioral and clinical outcomes did not differ across FHH or genetic risk but cognitive precursors did. Higher FHH risk was associated with a stronger perceived T2D risk (pKendall < 0.001) and with a perception of "serious" risk (pKendall < 0.001). Genetic risk did not influence risk perception, but was correlated with an increase in perception of "serious" risk for moderate (pKendall = 0.04) and average FHH risk subjects (pKendall = 0.01), though not for the high FHH risk group. Perceived control over T2D risk was high and not affected by FHH or genetic risk. FHH appears to have a strong impact on cognitive precursors of behavior change, suggesting it could be leveraged to enhance risk counseling, particularly when lifestyle change is desirable. Genetic risk was able to alter perceptions about the seriousness of T2D risk in those with moderate and average FHH risk, suggesting that FHH could be used to selectively identify individuals who may benefit from genetic risk testing.

Department

Description

Provenance

Citation

Published Version (Please cite this version)

10.1007/s10897-016-9988-z

Publication Info

Wu, R Ryanne, Rachel A Myers, Elizabeth R Hauser, Allison Vorderstrasse, Alex Cho, Geoffrey S Ginsburg and Lori A Orlando (2017). Impact of Genetic Testing and Family Health History Based Risk Counseling on Behavior Change and Cognitive Precursors for Type 2 Diabetes. J Genet Couns, 26(1). pp. 133–140. 10.1007/s10897-016-9988-z Retrieved from https://hdl.handle.net/10161/12509.

This is constructed from limited available data and may be imprecise. To cite this article, please review & use the official citation provided by the journal.

Scholars@Duke

Wu

Rebekah Ryanne Wu

Adjunct Associate Professor in the Department of Medicine

Dr. Wu is an internal medicine physician and health services researcher. Her main research interest is studying the implementation of precision medicine applications to improve clinical care. She is involved in projects currently looking at a patient-facing family history risk assessment tool, MeTree, which provides individualized risk stratification and clinical decision support recommendations to clinicians and patients. In addition she is also involved in a large scale sequencing program in Singapore looking at the intersection of family health history and genomics to better understand how these data elements can complement one another and create more precise risk predictions.  She is a member of NHGRI's IGNITE network as a co-investigator on a multi-site pragmatic clinical trial of the impact of pharmacogenetic testing on management of depression and acute, and chronic pain.  She is the implementation science advisor for the VA's Pharmacogenomic Testing for Veterans (PHASER) program, which is working to complete preemptive PGx testing on up to 250,000 Veterans by 2024.

Myers

Rachel Myers

Research Scientist, Senior

I am a bioinformatician cross trained as biostatistician and research scientist with the Department of Medicine Clinical Research Unit. In this role, I manage the Bioinformatics and Clinical Analytics Team, a team of bioinformaticians, biostatisticians, and data scientists in supporting the data and quantitative research needs of the Department of Medicine. 

I am interested in genomic translational research and enjoy studying all aspects of genomic translation, from the discovery of new signatures and biomarkers of drug or infection exposure through the implementation of genomics interventions in the clinical setting. I enjoy the complex analysis of "Omic datasets and generating new knowledge. My favorite part of the analysis is when all the clinical and 'omic datasets come together and I can start exploring the data. On the other end of the spectrum, I enjoy watching data accumulate for a clinical trial and preparing for testing the primary and secondary endpoints as well as designing new ways to use the data. 


Hauser

Elizabeth Rebecca Hauser

Professor of Biostatistics & Bioinformatics

The incorporation of personalized medicine to all areas of human health represents a turning point for human genetics studies, a point at which the discoveries made have real implications for clinical medicine.  It is important for students to gain experience in how human genetics studies are conducted and how results of those studies may be used.  As a statistical geneticist and biostatistician my research interests are focused on developing and applying statistical methods to search for genes causing common human diseases.  My research programs combine development and application of statistical methods for genetic studies, with a particular emphasis on understanding the joint effects of genes and environment. 

These studies I work on cover diverse areas in biomedicine but are always collaborative, with the goal of bringing robust data science and statistical methods to the project.  Collaborative studies include genetic and ‘omics studies of cardiovascular disease, health effects of air pollution, genetic analysis of adherence to an exercise program, genetic analysis in evaluating colon cancer risk, genetic analysis of suicide, and systems biology analysis of Gulf War Illness.

Keywords: human genetics, genetic association, gene mapping, genetic epidemiology, statistical genetics, biostatistics, cardiovascular disease, computational biology, diabetes, aging, colon cancer, colon polyps, kidney disease, Gulf War Illness, exercise behavior, suicide




Orlando

Lori Ann Orlando

Professor of Medicine

Dr. Lori A. Orlando, MD MHS MMCI is a Professor of Medicine and Director of the Precision Medicine Program in the Center for Applied Genomics and Precision Medicine at Duke University. She attended Tulane Medical Center for both medical school (1994-1998) and Internal Medicine residency (1998-2000). There she finished AOA and received a number of awards for teaching and clinical care from the medical school and the residency programs, including the Musser-Burch-Puschett award in 2000 for academic excellence. After completing her residency, she served as Chief Medical Resident in Internal Medicine (2001) and then completed a Health Services Research Fellowship at Duke University Medical Center (2002-2004). In 2004 she also received her MHS from the Clinical Research Training Program at Duke University and joined the academic faculty at Duke. In 2005 she received the Milton W. Hamolsky Award for Outstanding Junior Faculty by the Society of General Internal Medicine. Her major research interests are decision making and patient preferences, implementation research, risk stratification for targeting preventive health services, and decision modeling. From 2004-2009 she worked with Dr. David Matchar in the Center for Clinical Heath Policy Research (CCHPR), where she specialized in decision modeling, decision making, and technology assessments. In 2009 she began working with Dr. Geoffrey Ginsburg in what is now the Center for Applied Genomics and Precision Medicine (CAGPM) and in 2014 she became the director of the Center’s Precision Medicine Program. Since joining the CAGPM she has been leading the development and implementation of MeTree, a patient-facing family health history based risk assessment and clinical decision support program designed to facilitate the uptake of risk stratified evidence-based guidelines. MeTree was designed to overcome the major barriers to collecting and using high quality family health histories to guide clinical care and has been shown to be highly effective when integrated into primary care practices. This effort started with the Genomic Medicine Model, a multi-institutional project, whose goal was to implement personalized medicine in primary care practices. The success of that project has led to funding as part of NHGRI’s IGNITE (Implementing Genomics in Clinical Practice) network. She is currently testing methods for integrating patient preferences and decision making processes into clinical decision support recommendations for patients and providers to facilitate management of patients’ risk for chronic disease using mHealth and other behavioral interventions.


Unless otherwise indicated, scholarly articles published by Duke faculty members are made available here with a CC-BY-NC (Creative Commons Attribution Non-Commercial) license, as enabled by the Duke Open Access Policy. If you wish to use the materials in ways not already permitted under CC-BY-NC, please consult the copyright owner. Other materials are made available here through the author’s grant of a non-exclusive license to make their work openly accessible.