Diabetes is a risk factor for cerebrovascular disease, cognitive impairment, and related dementia. Data derived from populations suggest that several comorbidities of diabetes increase the risk for cognitive impairment, structural brain changes associated with dementia as measured with magnetic resonance imaging (MRI), and dementia. The relationship between cerebrovascular disease and cognition, especially in diabetes remains understudied and poorly understood. Dementia due to cerebrovascular disease is often referred to as vascular dementia" or vascular cognitive impairment (VCI). Despite the high prevalence of VCI, the biological basis of this disease and its relationship with structural brain changes measured with MRI has been far less studied than Alzheimer's disease. A striking feature of VCI is that risk to date has been difficult to predict based on medical diagnosis alone. We hypothesize genetic factors are significant contributors to cerebrovascular disease and associated cognitive impairment in families enriched for type 2 diabetes. Further, the magnitude of these genetic factors can be measured, their interaction with environmental influences can be quantitated, and the chromosomal location of genes contributing to these traits can be mapped. These hypotheses will be tested in the Diabetes Heart Study (DHS) sample, an extensively characterized collection of families, by recruiting 1200 subjects from 500 families that previously participated in the DHS who will undergo cognitive testing and MRI brain scans. The relationships between MRI measures (white matter lesion score, diffusion anisotropy index, mean white matter perfusion, total brain volume, total white matter volume, total gray matter volume), cognitive ability, and extensive clinical measures available from the DHS will be evaluated to identify correlates of cerebrovascular disease and cognitive ability. The heritable component of cognition and MRI measures will be estimated and a comprehensive genetic analysis will be performed using preexisting genome scan data from the DHS to map regions that contain genes contributing to cognition and cerebrovascular disease. These studies will create unique data collection for genetic and other studies of cerebrovascular disease and cognition. This is a study of the genetics of MRI-derived measures of cerebrovascular disease and cognitive impairment in diabetes families. Successful completion of the study will create a unique database of information and provide insights into the genetic and lifestyle contributors to these disorders.

National Institute of Health (NIH)
National Institute of Neurological Disorders and Stroke (NINDS)
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Special Emphasis Panel (ZRG1-HOP-S (02))
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Corriveau, Roderick A
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Wake Forest University Health Sciences
Schools of Medicine
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Cox, Amanda J; Azeem, Amir; Yeboah, Joseph et al. (2014) Heart rate-corrected QT interval is an independent predictor of all-cause and cardiovascular mortality in individuals with type 2 diabetes: the Diabetes Heart Study. Diabetes Care 37:1454-61
Cox, Amanda J; Hsu, Fang-Chi; Ng, Maggie C Y et al. (2014) Genetic risk score associations with cardiovascular disease and mortality in the Diabetes Heart Study. Diabetes Care 37:1157-64
Palmer, Nicholette D; Sink, Kaycee M; Smith, Susan Carrie et al. (2014) Kidney disease and cognitive function: African American-diabetes heart study MIND. Am J Nephrol 40:200-7
Cox, Amanda J; Hugenschmidt, Christina E; Raffield, Laura M et al. (2014) Heritability and genetic association analysis of cognition in the Diabetes Heart Study. Neurobiol Aging 35:1958.e3-1958.e12
Cox, Amanda J; Hsu, Fang-Chi; Freedman, Barry I et al. (2014) Contributors to mortality in high-risk diabetic patients in the Diabetes Heart Study. Diabetes Care 37:2798-803
Adams, Jeremy N; Raffield, Laura M; Freedman, Barry I et al. (2014) Analysis of common and coding variants with cardiovascular disease in the Diabetes Heart Study. Cardiovasc Diabetol 13:77
Maldjian, Joseph A; Davenport, Elizabeth M; Whitlow, Christopher T (2014) Graph theoretical analysis of resting-state MEG data: Identifying interhemispheric connectivity and the default mode. Neuroimage 96:88-94
Freedman, Barry I; Bowden, Donald W; Smith, Susan Carrie et al. (2014) Relationships between electrochemical skin conductance and kidney disease in Type 2 diabetes. J Diabetes Complications 28:56-60
Cox, Amanda J; Hsu, Fang-Chi; Carr, J Jeffrey et al. (2013) Glomerular filtration rate and albuminuria predict mortality independently from coronary artery calcified plaque in the Diabetes Heart Study. Cardiovasc Diabetol 12:68
Cox, Amanda J; Hugenschmidt, Christina E; Wang, Patty T et al. (2013) Usefulness of biventricular volume as a predictor of mortality in patients with diabetes mellitus (from the Diabetes Heart Study). Am J Cardiol 111:1152-8

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