Research Initiated September 2005
Behavioral-Genetic Prediction of Risk for Schizophrenia in Children
J. F. Cubells, M.D., Ph.D., Genetics and Psychiatry, School of Medicine
Purpose: This project seeks to develop both molecular genetic and cognitive/behavioral predictors of psychotic illness in children with the 22q11 deletion syndrome (22q11DS). Such children are at statistically very high risk for schizophrenia and other psychotic disorders. A major goal of the work is to generate preliminary data to support competitive applications for federal funding of a large, long-term, longitudinal follow-up of children with 22q11DS.
Biomarker Assessment of Metabolic and Vascular Risk
Larry Phillips, M.D., Department of Medicine, Endocrinology, School of Medicine
Purpose: Advances in medical science have led to therapies for common disorders such as diabetes, hypertension, and dyslipidemia, which confer major morbidity, mortality, and cost, but current treatment can be provided only once the disorder has been recognized—often after complications have already begun—and cannot restore normal tissue and organ function. In order to improve health, the disorders must be recognized when treatment is more efficacious and more costeffective; it would be ideal to identify patients at risk earlier in their natural histories, when mechanism-based therapies might be able to prevent loss of function. This predictive health project will test the hypothesis that profiling oxidative stress and inflammatory biomarkers will permit prediction of deterioration of metabolic and vascular function prior to the development of clinical disease.
Economic, Epidemiologic, and Behavioral Research
Kimberly Rask, M.D., Ph.D., Health Policy and Management, Rollins School of Public Health
Purpose: The purpose of this study is to explore the feasibility of including economic, epidemiologic, and behavioral risk factors in predictive health models, using the specific example of patients with type II diabetes, a common chronic disease. This project will use a national population-based survey and a pilot study at an Emory-affiliated clinical setting to (1) collect a comprehensive set of biologic, behavioral, and environmental risk factors likely to affect an individual’s health status and (2) explore the effectiveness of modifying selected risk factors, including health behaviors in persons at high risk of developing a clinical diagnosis of type II diabetes.
The clinical focus of this project has been broadened beyond hypertension to include a constellation of diseases that affect patients with diabetes. These diseases share both behavioral and biologic risk factors and pathways, providing a useful model for evaluating the potential impact of personalized health interventions for chronic health conditions.
Inflammation and Predictive Medicine
David Stephens, M.D., Department of Medicine, Infectious Diseases, School of Medicine
Cornelia Weyand, M.D., Ph.D., and J. Goronzy, M.D., Ph.D., Department of Medicine, Lowance Center, School of Medicine
Rafi Ahmed, Ph.D., Microbiology and Immunology, School of Medicine
Purpose: The goal of this project is to identify novel and feasible approaches that integrate exciting new fundamental discoveries at Emory and elsewhere in inflammation and immunity with predictive health. Specifically, the goal is to integrate new quantitative immune methodologies and discoveries into predictive health, to engage multidisciplinary science (genetics, biochemistry, bioinformatics, engineering, microbiology and human immunology, biostatistics and analytical epidemiology, behavioral research, economics, population biology, and clinical medicine) in addressing significant and complex problems in immune dysfunction and to develop strategies for acceptance and use of immunology and immune activation markers in predictive health.
Predictive Algorithms of Parkinson’s Disease
Gary Miller, Ph.D., and Scott Bartell, Ph.D., Environmental and Occupational Health, Rollins School of Public Health
Purpose: The goal of this project is to develop models to predict Parkinson’s disease. The strategy includes taking advantage of a wide collection of data, including epidemiologic, basic science, and clinical, to generate algorithms to identify individuals at greater risk of developing Parkinson’s disease. Identification of high-risk individuals will allow for early intervention designed to prevent or slow the progression of the disease.
Predictive Treatment for ALS (Lou Gehrig’s disease)
Jonathan Glass, M.D., Neurology, School of Medicine
Purpose: Approximately 10% of people with ALS inherit the disease directly from their parents. The most common known cause for this familial form of ALS (fALS) is a mutation in the gene superoxide dismutase 1 (SOD1), which accounts for about 20% of familial cases. Animals engineered to carry human SOD1 mutations develop ALS. Therapeutic interventions with a variety of drugs in these animals have shown positive effects on disease onset or progression, but none of these agents has shown efficacy in humans with non-familial forms of ALS. This project proposes to identify the population “at risk” for fALS in order to design a clinical trial to delay the onset or prevent ALS. Specifically, people harboring a mutation in SOD1 have a high likelihood of dying of ALS, and this target population, though small, will be ideal for testing some of the same agents that have been effective in SOD1 mutant animals.
Profiling Protein Expression in Gynecologic Tumors by Protein Arrays
R. P. Huang, M.D., Ph.D., Gynecology and Obstetrics, School of Medicine
Purpose: Protein arrays have emerged as a technology to study protein expression and protein function in a highthroughput manner. One of the obvious applications of protein arrays is to profile protein expression in a patients’ specimen. Through identification of unique biomarkers or biosignatures, antibody arrays may have great impact on predictive and personalized medicine. The purpose of this work is to establish a program for the application of antibody array technology in predictive and personalized medicine using cancer as example.
