Timothy M Hughes1, Kaycee M Sink2, Jeff D Williamson3, Christina E Hugenschmidt4, Benjamin C Wagner5, Christopher T Whitlow6, Jianzhao Xu7, S Carrie Smith8, Lenore J Launer9, Joshua I Barzilay10, Faramarz Ismail-Beigi11, R Nick Bryan12, Fang-Chi Hsu13, Donald W Bowden14, Joseph A Maldjian15, Jasmin Divers16, Barry I Freedman17. 1. Department of Internal Medicine, Section on Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA. Electronic address: tmhughes@wakehealth.edu. 2. Department of Internal Medicine, Section on Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA. 3. Department of Internal Medicine, Section on Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA. Electronic address: jwilliam@wakehealth.edu. 4. Department of Internal Medicine, Section on Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA. Electronic address: chugensc@wakehealth.edu. 5. Department of Radiology, Advanced Neuroscience Imaging Research (ANSIR) Laboratory, University of Texas Southwestern Medical Center, Dallas, TX, USA. Electronic address: Ben.Wagner@UTSouthwestern.edu. 6. Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC, USA. Electronic address: cwhitlow@wakehealth.edu. 7. Departments of Biochemistry & Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, NC, USA. Electronic address: jixu@wakehealth.edu. 8. Departments of Biochemistry & Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, NC, USA. Electronic address: suscsmit@wakehealth.edu. 9. National Institutes of Health, National Institute on Aging, Laboratory of Epidemiology, Demography, and Biometry, Bethesda, MD, USA. Electronic address: launerl@nia.nih.gov. 10. Kaiser Permanente, Atlanta, GA, USA. Electronic address: Joshua.Barzilay@kp.org. 11. Department of Internal Medicine, Division of Endocrinology, University of Cincinnati, Veterans Administration Medical Center, Cincinnati, OH. Electronic address: fxi2@case.edu. 12. Department of Radiology, University of Pennsylvania School of Medicine, Philadelphia, PA, USA. Electronic address: nick.bryan@uphs.upenn.edu. 13. Division of Public Health Sciences, Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, NC, USA. Electronic address: fhsu@wakehealth.edu. 14. Departments of Biochemistry & Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, NC, USA. Electronic address: dbowden@wakehealth.edu. 15. Department of Radiology, Advanced Neuroscience Imaging Research (ANSIR) Laboratory, University of Texas Southwestern Medical Center, Dallas, TX, USA. Electronic address: Joseph.Maldjian@UTSouthwestern.edu. 16. Division of Public Health Sciences, Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, NC, USA. Electronic address: jdivers@wakehealth.edu. 17. Department of Internal Medicine, Section on Nephrology, Wake Forest School of Medicine, Winston-Salem, NC, USA. Electronic address: bfreedma@wakehealth.edu.
Abstract
BACKGROUND: Relationships between cognitive function and brain structure remain poorly defined in African Americans with type 2 diabetes. METHODS: Cognitive testing and cerebral magnetic resonance imaging in African Americans from the Diabetes Heart Study Memory IN Diabetes (n = 480) and Action to Control Cardiovascular Risk in Diabetes MIND (n = 104) studies were examined for associations. Cerebral gray matter volume (GMV), white matter volume (WMV) and white matter lesion volume (WMLV) and cognitive performance (Mini-mental State Exam [MMSE and 3MSE], Digit Symbol Coding (DSC), Stroop test, and Rey Auditory Verbal Learning Test) were recorded. Multivariable models adjusted for age, sex, BMI, scanner, intracranial volume, education, diabetes duration, HbA1c, LDL-cholesterol, smoking, hypertension and cardiovascular disease assessed associations between cognitive tests and brain volumes by study and meta-analysis. RESULTS: Mean(SD) participant age was 60.1(7.9) years, diabetes duration 12.1(7.7) years, and HbA1c 8.3(1.7)%. In the fully-adjusted meta-analysis, lower GMV associated with poorer global performance on MMSE/3MSE (β̂ = 7.1 × 10-3, SE 2.4 × 10-3, p = 3.6 × 10-3), higher WMLV associated with poorer performance on DSC (β̂ = -3 × 10-2, SE 6.4 × 10-3, p = 5.2 × 10-5) and higher WMV associated with poorer MMSE/3MSE performance (β̂ = -7.1 × 10-3, SE = 2.4 × 10-3, p = 3.6 × 10-3). CONCLUSIONS: In African Americans with diabetes, smaller GMV and increased WMLV associated with poorer performance on tests of global cognitive and executive function. These data suggest that WML burden and gray matter atrophy associate with cognitive performance independent of diabetes-related factors in this population.
BACKGROUND: Relationships between cognitive function and brain structure remain poorly defined in African Americans with type 2 diabetes. METHODS: Cognitive testing and cerebral magnetic resonance imaging in African Americans from the Diabetes Heart Study Memory IN Diabetes (n = 480) and Action to Control Cardiovascular Risk in Diabetes MIND (n = 104) studies were examined for associations. Cerebral gray matter volume (GMV), white matter volume (WMV) and white matter lesion volume (WMLV) and cognitive performance (Mini-mental State Exam [MMSE and 3MSE], Digit Symbol Coding (DSC), Stroop test, and Rey Auditory Verbal Learning Test) were recorded. Multivariable models adjusted for age, sex, BMI, scanner, intracranial volume, education, diabetes duration, HbA1c, LDL-cholesterol, smoking, hypertension and cardiovascular disease assessed associations between cognitive tests and brain volumes by study and meta-analysis. RESULTS: Mean(SD) participant age was 60.1(7.9) years, diabetes duration 12.1(7.7) years, and HbA1c 8.3(1.7)%. In the fully-adjusted meta-analysis, lower GMV associated with poorer global performance on MMSE/3MSE (β̂ = 7.1 × 10-3, SE 2.4 × 10-3, p = 3.6 × 10-3), higher WMLV associated with poorer performance on DSC (β̂ = -3 × 10-2, SE 6.4 × 10-3, p = 5.2 × 10-5) and higher WMV associated with poorer MMSE/3MSE performance (β̂ = -7.1 × 10-3, SE = 2.4 × 10-3, p = 3.6 × 10-3). CONCLUSIONS: In African Americans with diabetes, smaller GMV and increased WMLV associated with poorer performance on tests of global cognitive and executive function. These data suggest that WML burden and gray matter atrophy associate with cognitive performance independent of diabetes-related factors in this population.
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