Abstract 38: Glucose Levels and Brain Gray Matter Volume in Middle-aged Adults: Findings From the Coronary Artery Risk Development in Young Adults CARDIA Study
Background: Type II diabetes has been widely linked to a higher risk of dementia. Some studies have also shown relationships between diabetes and magnetic resonance imaging (MRI) markers of exacerbated brain aging and neurocognitive pathology, such as gray matter (GM) volume atrophy. However, data on the earlier impacts of glucose levels on GM volume in younger subjects are scarce. Objective: We assessed the cross-sectional relationship of fasting glucose levels and GM volume measured at middle-age. Methods: Data come from the brain MRI sample of the CARDIA study, a bi-racial community-dwelling cohort of middle-aged adults (n=709, mean age=50 (SD=3.5)). We used multivariable linear regression models and adjusted for potential confounders, including several cardiovascular and metabolic factors (hypertension, body mass index, smoking, history of vascular disease, and hypercholesterolemia). Results: Higher fasting glucose levels were associated with smaller total GM volume (-1 mL (95%CI= -0.16, -0.04) smaller GM volume per each 1 mg/dL increase in glucose levels). In analyses exploring the normal (<100 mg/dL), pre-diabetic (≥100, <126 mg/dL), and diabetic glucose ranges (≥126 mg/dL), we found that subjects with diabetic glucose levels had -14 mL smaller GM (95%CI= -21.50, -5.61; p=0.001) than subjects with normal glucose levels; subjects with pre-diabetic levels were not significantly different from those with normal levels (p for trend for the glucose-range categories =0.08). Conclusion: Results suggest that important relationships between glucose levels and smaller GM can already be detected at middle-age. These associations were particularly pronounced in the diabetic glucose ranges. Findings strengthen the links between vascular factors and brain health and emphasize the importance of studying the earlier stages of these links to improve our understanding of the course of brain diseases and to identify optimal time-windows for prevention and treatment strategies.