Brain structure and connectivity mediate the association between lifestyle and cognition: The Maastricht Study.

Life-course exposure to risk and protective factors impacts brain macro- and micro-structure, which in turn affects cognition. The concept of brain-age gap assesses brain health by comparing an individual's neuroimaging-based predicted age with their calendar age. A higher BAG implies accelerated brain ageing and is expected to be associated with worse cognition. In this study, we comprehensively modelled mutual associations between brain health and lifestyle factors, brain age and cognition in a large, middle-aged population. For this study, cognitive test scores, lifestyle and 3T MRI data for n = 4881 participants [mean age (± SD) = 59.2 (±8.6), 50.1% male] were available from The Maastricht Study, a population-based cohort study with extensive phenotyping. Whole-brain volumes (grey matter, cerebrospinal fluid and white matter hyperintensity), cerebral microbleeds and structural white matter connectivity were calculated. Lifestyle factors were combined into an adapted LIfestyle for BRAin health weighted sum score, with higher score indicating greater dementia risk. Cognition was calculated by averaging z-scores across three cognitive domains (memory, information processing speed and executive function and attention). Brain-age gap was calculated by comparing calendar age to predictions from a neuroimaging-based multivariable regression model. Paths between LIfestyle for BRAin health tertiles, brain-age gap and cognitive function were tested using linear regression and structural equation modelling, adjusting for sociodemographic and clinical confounders. The results show that cerebrospinal fluid, grey matter, white matter hyperintensity and cerebral microbleeds best predicted brain-age gap (R 2 = 0.455, root mean squared error = 6.44). In regression analysis, higher LIfestyle for BRAin health scores (greater dementia risk) were associated with higher brain-age gap (standardized regression coefficient ß = 0.126, P < 0.001) and worse cognition (ß = -0.046, P = 0.013), while higher brain-age gap was associated with worse cognition (ß=-0.163, P < 0.001). In mediation analysis, 24.7% of the total difference in cognition between the highest and lowest LIfestyle for BRAin health tertile was mediated by brain-age gap (ß indirect = -0.049, P < 0.001; ß total = -0.198, P < 0.001) and an additional 3.8% was mediated via connectivity (ß indirect = -0.006, P < 0.001; ß total = -0.150, P < 0.001). Findings suggest that associations between health- and lifestyle-based risk/protective factors (LIfestyle for BRAin health) and cognition can be partially explained by structural brain health markers (brain-age gap) and white matter connectivity markers. Lifestyle interventions targeted at high-risk individuals in mid-to-late life may be effective in promoting and preserving cognitive function in the general public.

Associations between social health factors, cognitive activity and neurostructural markers for brain health - A systematic literature review and meta-analysis.

Social health factors (e.g., social activities or social support) and cognitive activity engagement have been associated with dementia risk, but their neural substrates have not been well established. This systematic review and meta-analysis summarizes the available evidence regarding the association between these factors and cerebral macro- and micro-structure. A comprehensive literature search was conducted in various databases, following predefined criteria. Heterogeneity, risk of publication bias and overall certainty of evidence were assessed using standardized scales and, whenever appropriate, random effects meta-analysis was conducted. Of 6715 identified articles, 43 were included. Overall, consistency of findings was low and methodological heterogeneity high for all outcomes. However, in some studies cognitive and social activities were positively associated with total brain, global and cortical grey matter and hippocampal volume as well as white matter microstructural integrity. Furthermore, structural social network characteristics (e.g., social network size) were associated with regional grey matter volumes, while functional social network characteristics (e.g., social support) were additionally associated with total brain volume. Meta-analyses revealed small but significant partial correlations between cognitive and social activities and hippocampal (three studies; n = 892; rz =0.07) and white matter hyperintensity volume (three studies; n = 2934; rz =-0.04). More prospective studies are needed to assess temporal associations.

Cognitive resilience depends on white matter connectivity: The Maastricht Study.

INTRODUCTION: Differences in brain network connectivity may reflect the capability of the neurological substrate to compensate for brain damage and preserve cognitive function (cognitive reserve). We examined the associations between white matter connectivity, brain damage markers, and cognition in a population sample of middle-aged individuals. METHODS: A total of 4759 participants from The Maastricht Study (mean age = 59.2, SD = 8.7, 50.2% male) underwent cognitive testing and diffusion magnetic resonance imaging (dMRI), from which brain volume, structural connectivity, and vascular damage were quantified. Multivariable linear regression was used to investigate whether connectivity modified the association between brain damage and cognition, adjusted for demographic and cardiometabolic risk factors. RESULTS: More atrophic and vascular brain damage was associated with worse cognition scores. Increasing connectivity moderated the negative association between damage and cognition (χ2 = 8.64, df = 3, p ≤ 0.001); individuals with high damage but strong connectivity showed normal cognition. DISCUSSION: Findings support the reserve hypothesis by showing that brain connectivity is associated with cognitive resilience.

Sedentary behaviour is a key determinant of metabolic inflexibility.

Metabolic flexibility is defined as the ability to adapt substrate oxidation rates in response to changes in fuel availability. The inability to switch between the oxidation of lipid and carbohydrate appears to be an important feature of chronic disorders such as obesity and type 2 diabetes. Laboratory assessment of metabolic flexibility has traditionally involved measurement of the respiratory quotient (RQ) by indirect calorimetry during the fasted to fed transition (e.g. mixed meal challenge) or during a hyperinsulinaemic-euglycaemic clamp. Under these controlled experimental conditions, 'metabolic inflexibility' is characterized by lower fasting fat oxidation (higher fasting RQ) and/or an impaired ability to oxidize carbohydrate during feeding or insulin-stimulated conditions (lower postprandial or clamp RQ). This experimental paradigm has provided fundamental information regarding the role of substrate oxidation in the development of obesity and insulin resistance. However, the key determinants of metabolic flexibility among relevant clinical populations remain unclear. Herein, we propose that habitual physical activity levels are a primary determinant of metabolic flexibility. We present evidence demonstrating that high levels of physical activity predict metabolic flexibility, while physical inactivity and sedentary behaviours trigger a state of metabolic 'inflexibility', even among individuals who meet physical activity recommendations. Furthermore, we describe alternative experimental approaches to studying the concept of metabolic flexibility across a range of activity and inactivity. Finally, we address the promising use of strategies that aim to reduce sedentary behaviours as therapy to improve metabolic flexibility and reduce weight gain risk.

Detailed spectroscopic, thermodynamic, and kinetic characterization of nickel(II) complexes with 2,2'-bipyridine and 1,10-phenanthroline attained via equilibrium-restricted factor analysis.

The molar absorptivity curves of the mono and bis nickel(II) complexes of 2,2'-bipyridine and 1,10-phenanthroline have been deduced from the composite absorbance data of a series of equilibrium solutions in methanol using equilibrium-restricted factor analysis, a technique for extracting spectral and thermodynamic information for component species involved in solution equilibria. The solutions achieve equilibrium slowly (t(1/2) approximately 100 min) as the amounts of the tris and methanolato complexes are initially in excess. The relative formation constants at 293 K have been determined with a high degree of accuracy, especially for the bipyridine complexes: log(K(1)/K(2)) = 0.340(4) and log(K(2)/K(3)) = 1.091(6). Solubility limitations compromised the accuracy of the thermodynamic results for the phenanthroline complexes.