Differential associations between systemic markers of disease and cortical thickness in healthy middle-aged and older adults.

Neuroimage

MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA; Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Neuroimaging Research for Veterans Center, VA Boston Healthcare System, Boston, MA, USA.

Published: February 2017

Aside from cortical damage associated with age, cerebrovascular and neurodegenerative diseases, it's an outstanding question if factors of global health, including normal variation in blood markers of metabolic and systemic function, may also be associated with individual variation in brain structure. This cross-sectional study included 138 individuals between 40 to 86 years old who were physically healthy and cognitively intact. Eleven markers (total cholesterol, HDL, LDL, triglycerides, insulin, fasting glucose, glycated hemoglobin, creatinine, blood urea nitrogen, albumin, total protein) and five derived indicators (estimated glomerular filtration rate, creatinine clearance rate, insulin-resistance, average glucose, and cholesterol/HDL ratio) were obtained from blood sampling of all participants. T1-weighted 3T MRI scans were used to evaluate gray matter cortical thickness. The markers were clustered into five factors, and factor scores were related to cortical thickness by general linear model. Two factors, one linked to insulin/metabolic health and the other to kidney function (KFF) showed regionally selective associations with cortical thickness including lateral and medial temporal, temporoparietal, and superior parietal regions for both factors and frontoparietal regions for KFF. An association between the increasing cholesterol and greater thickness in frontoparietal and occipital areas was also noted. Associations persisted independently of age, presence of cardiovascular risk factors and ApoE gene status. These findings may provide information on distinct mechanisms of inter-individual cortical variation as well as factors contributing to trajectories of cortical thinning with advancing age.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5321862PMC
http://dx.doi.org/10.1016/j.neuroimage.2016.11.021DOI Listing

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