Cardiovascular risk factors in ageing brains: Functional and structural correlates of modifiable risk factors of brain ageing and Alzheimer’s disease among older individuals

Abstract

3. Summary Dissertation zur Erlangung des akademischen Grades Dr. rer. med. Cardiovascular risk factors in ageing brains: Functional and structural correlates of modifiable risk factors of brain ageing and Alzheimer’s disease among older individuals Eingereicht von: Shahrzad Kharabian Masouleh Angefertigt am: Max-Planck-Institut für Kognitions- und Neurowissenschaften, Abteilung für Neurologie, Leipzig Betreut von: Prof. Dr. med. Arno Villringer Dr. A. Veronica Witte March 2018 Due to a world-wide demographic change ageing-associated complications including cognitive impairments and neurodegenerative diseases such as dementia are becoming increasingly prevalent. In 2015, almost 47 million people worldwide were estimated to be affected by dementia, and the numbers are expected to reach 75 million by 2030, and 131 million by 2050, with the greatest increase expected in low-income and middle-income countries (Prince, M.; Wimo, A.; Guerchet, M.; Ali, G.; Wu, Y.; Prina, 2015). As no cure or substantial symptom-relieving treatment is yet available for these ever growing pathologic conditions, identifying modifiable factors that causally impact the risk of these diseases has become an important mission (Barnes and Yaffe, 2011). Although age is known to be the most important risk factor for these conditions, not all older individuals develop these pathologic states and pathologic neurodegenerative changes are not considered as part of a normal aging process. However, observations show that almost all aged brains show characteristic changes that are linked to neurodegeneration (Wyss-Coray, 2016). These observations raise the possibility that fundamental mechanisms of ageing may display early disease changes or contribute to the pathogenesis of neurodegenerative disorders (Bartzokis, 2011; Bishop et al., 2010; Raz, 2005). A better understanding of possible modulators of function and structure of brain in regions that are known to be vulnerable in aging would thus open a novel window towards targets for intervention of disease progression. Epidemiological studies have begun to identify many environmental and genetic risk factors that influence prevalence of neurodegenerative diseases in older ages. Importantly, with respect to Alzheimer’s disease (AD), conditions such as depression, obesity and hypertension, specifically in midlife and diabetes are shown to independently affect increased prevalence of AD worldwide. In 2010, fifteen thousand AD-cases world-wide were attributed to cigarette smoking and low physical or mental activity (Norton et al., 2014). Moreover, disadvantageous metabolic profiles such as higher blood glucose levels or lower high-density lipoprotein (HDL) levels have also been associated with worse cognition, brain alterations in AD-vulnerable regions and ultimately increased likelihood of developing AD in older ages (Crane et al., 2013; Villeneuve et al., 2014). In the first study of this thesis, we reviewed the epidemiological evidence regarding the impact of a “Mediterranean style diet” (MeDi) on brain health in aging (Huhn et al., 2015). MeDi, which is based on high consumption of fruits, vegetables, grains as well as sea-fish and low intake of sweets, convenient food, meat and dairy products, is shown to reduce cardio-vascular risk factors and benefit lipid and glucose metabolism while reducing risk of AD and cognitive dysfunction in aging. Despite extensive epidemiological evidence, little is known about neurobiological mechanisms, linking these life-style and health related factors to alterations in cognitive performance and incidence of AD. In the recent years whole brain magnetic resonance (MR) measurements have immensely increased our knowledge about the brain in health and disease. Novel MR protocols and analysis routines have been invented to assess different aspects of structure of the brain regions and their function within the living individuals. Studies in elderly AD patients have linked deposition of amyloid plaques, assessed using positron emission tomography (PET), in vulnerable structures such as frontal lobe, medial temporal structures and posterior cingulate area to atrophy and lower metabolic rate of glucose within these brain regions in association with accelerated cognitive decline (Buckner et al., 2005). Also, within healthy ageing population it has been shown that these AD-prone structures create a network, in which grey matter (GM) volume follow a different ageing trajectory compared to the rest of the brain, with a late development during adolescence and accelerated decline in older ages (Douaud et al., 2014; Fjell et al., 2014). Such coordinated change, specifically in older ages, might be a result of shared susceptibility of regions within this network to selective pathologies or a network-based spread of toxic agents (Zhou et al., 2012). Consequently, the above-mentioned AD-risk factors could through similar mechanisms impact brain structures within vulnerable regions, resulting in accelerated ageing, possibly reducing resilience of these regions towards AD-related pathology and thus increasing risk of developing AD in older ages. Based on this working hypothesis, in the rest of this doctoral research we investigate cerebral correlates of these risk factors and their impact on cognitive performance in healthy older adults. We initially focused on obesity as a major epidemic of the twentieth century, a major component of metabolic syndrome and an important AD-risk factor. Here we used conventional techniques to identify effects of Body-mass index (BMI) on regional GM volume (n = 617) as well as resting-state network connectivity (n = 712) and relations to cognitive performance in well-characterized samples of community-dwelled older adults (60-80 years) from Leipzig Research Centre for Civilization Diseases (LIFE) adult-study. The LIFE-Adult-Study is a population-based cohort study, which has already completed the baseline examination of 10,000 randomly selected participants from Leipzig, out of which ~2600 underwent a 3Tesla MRI brain scan, structured interviews, neuropsychological tests, and an extensive set of medical assessments (Loeffler et al., 2015). Our results showed that independent of age and a wide range of other confounding factors such as diabetes, hypertension, smoking status and APOE-genotype, there is a robust linear association between a higher BMI and lower GM volume in multiple brain regions, including (pre)frontal, temporal, insular and occipital cortex, thalamus, putamen, amygdala and cerebellum, which partially mediated negative effects of higher BMI on memory performance in our sample of older adults (Kharabian Masouleh et al., 2016). Furthermore, in the follow-up study, we found reproducible association between higher BMI and lower functional connectivity of the posterior cingulate cortex with other nodes of the default mode network (Beyer et al., 2017). This network that consists of AD-prone regions within frontal, temporal and parietal lobes, exhibits similar alterations in normal ageing and among patients with AD (Damoiseaux et al., 2012; Tomasi and Volkow, 2012). Inspired by our results on network-based functional connectivity alterations and in-line with the hypothesis of network-based spread of toxic agents in neurodegenerative diseases, in our third MRI-study, we extended the number of risk factors to cover major “modifiable” risk factors of AD and identified the potential impact of these factors on morphological properties of large-scale structural covariance networks (Kharabian Masouleh et al., 2017). We therefore systematically assessed independent effects of obesity, smoking, blood pressure, as well as markers of glucose and lipid metabolism and physical activity on major GM networks in the same cohort as our first MR study. Furthermore, we detailed our analysis by adding both BMI as well as waist-to-hip ratio as measures of obesity and identified the structural networks based on information on area, thickness and volume of cortical structures. The spatial extent and composition of the co-varying GM measures within the different networks indicated that smoking and, to a lesser degree, higher blood pressure affected GM throughout the brain, which might be attributed to direct and indirect damage of neuronal tissue. Higher glycosylated hemoglobin, as a long-term marker of glucose metabolism, was found to predominantly affect areas that are known to have high glucose metabolism and early A-beta deposition. In addition, we detected negative effects of visceral obesity on a structural network consisting of multimodal regions, covering areas rich in intracortical myelinated fibres. This network spatially recapitulated the pattern of brain atrophy observed in Alzheimer’s disease and has been previously shown to develop relatively slowly during adolescence but present “accelerated” age-related degeneration at an old age. Accordingly, our findings possibly point towards detrimental effects of visceral fat-induced low-grade inflammation on myelin. This is a hypothesis that we are going to test in our future studies in LIFE (by direct assessment of visceral fat (VAT) on abdominal MRI and inflammatory markers). Future longitudinal studies that incorporate more detailed microstructural assessments are now needed to prove our proposed neurobiological hypotheses on the underlying mechanisms of the observed effects and to test if improving cardiovascular risk, specifically visceral obesity, would help to maintain the integrity of GM networks throughout old age and reduce the risk of AD.:List of Abbreviations 3 List of Figures 4 List of Tables 5 1. Introduction: 6 1.1: “Normal” cognitive ageing: 9 1.1.1. Ageing-associated changes in brain structure and function: 9 1.2. Modifiers of brain ageing and AD: 11 1.3. Methods: 18 1.3.1. Imaging protocols: 18 1.3.2. Network Identification: 19 1.3.2.1. Resting-state fMRI network extraction 19 1.3.2.2. Grey matter structural network extraction 20 1.4. Rationale of the work: 23 2. Publications: 25 2.1. Publication1: Review: Huhn et al, 2015 25 2.2. Publication2: Original article: Kharabian et al, 2016 36 2.3. Publication3: Original article: Beyer et al, 2017 47 2.4. Publication4: Original article: Kharabian et al, 2017 62 3. Summary: 76 References: 83 A. Supplemental Materials 93 Publication2- Kharabian Masouleh et. al., 2016 93 Supplementary Tables for Publication2 97 Supplementary Figures for Publication 2 101 Supplementary Figures for Publication4 105 B. Declaration of Authenticity 106 C. Author contributions to the publications 107 D. Curriculum Vitae 114 E. List of Publications: 117 F. Acknowledgements 11