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Gray matter structural networks are associated with cardiovascular risk factors in healthy older adults

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Kharabian,  Shahrzad
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Beyer,  Frauke
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Lampe,  Leonie
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Leipzig Research Center for Civilization Diseases (LIFE), University of Leipzig, Germany;

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Schroeter,  Matthias L.
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Leipzig Research Center for Civilization Diseases (LIFE), University of Leipzig, Germany;
Clinic for Cognitive Neurology, University of Leipzig, Germany;

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Villringer,  Arno
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Clinic for Cognitive Neurology, University of Leipzig, Germany;

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Witte,  Veronica
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Citation

Kharabian, S., Beyer, F., Lampe, L., Loeffler, M., Luck, T., Riedel-Heller, S. G., et al. (2018). Gray matter structural networks are associated with cardiovascular risk factors in healthy older adults. Journal of Cerebral Blood Flow and Metabolism, 38(2), 360-372. doi:10.1177/0271678X17729111.


Cite as: http://hdl.handle.net/21.11116/0000-0000-846A-4
Abstract
While recent ‘big data' analyses discovered structural brain networks that alter with age and relate to cognitive decline, identifying modifiable factors that prevent these changes remains a major challenge. We therefore aimed to determine the effects of common cardiovascular risk factors on vulnerable gray matter (GM) networks in a large and well-characterized population-based cohort. In 616 healthy elderly (258 women, 60–80 years) of the LIFE-Adult-Study, we assessed the effects of obesity, smoking, blood pressure, markers of glucose and lipid metabolism as well as physical activity on major GM-networks derived using linked independent component analysis. Age, sex, hypertension, diabetes, white matter hyperintensities, education and depression were considered as confounders. Results showed that smoking, higher blood pressure, and higher glycated hemoglobin (HbA1c) were independently associated with lower GM volume and thickness in GM-networks that covered most areas of the neocortex. Higher waist-to-hip ratio was independently associated with lower GM volume in a network of multimodal regions that correlated negatively with age and memory performance. In this large cross-sectional study, we found selective negative associations of smoking, higher blood pressure, higher glucose, and visceral obesity with structural covariance networks, suggesting that reducing these factors could help to delay late-life trajectories of GM aging.