The protective effect of mindfulness and compassion meditation practices on ageing: Hypotheses, models and experimental implementation

Alzheimer's disease (AD) represents a major health and societal issue; there is no treatment to date and the pathophysiological mechanisms underlying this disease are not well understood. Yet, there is hope that AD risk factors and thus the number of AD cases can be significantly reduced by prevention measures based on lifestyle modifications as targeted by non-pharmacological preventive interventions. So far, these interventions have rarely targeted the psycho-affective risk factors related to depression, stress, anxiety, and feeling of loneliness, which are all prevalent in ageing. This paper presents the hypothesis that the regular practice of mindfulness meditation (MM) and loving-kindness and compassion meditation (LKCM) in the ageing population constitutes a lifestyle that is protective against AD. In this model, these practices can promote cognition, mental health, and well-being by strengthening attention control, metacognitive monitoring, emotion regulation and pro-social capacities. Training these capacities could reduce the risk of AD by upregulating beneficial age-related factors such as cognitive reserve, and down-regulating detrimental age-related factors, such as stress, or depression. As an illustration, we present the Medit-Ageing study (public name Silver Santé Study), an on-going European project that assesses the impact and mechanisms of non-pharmacological interventions including meditation, in the ageing population

Alzheimer's disease

In this Seminar, we highlight the main developments in the field of Alzheimer's disease. The most recent data indicate that, by 2050, the prevalence of dementia will double in Europe and triple worldwide, and that estimate is 3 times higher when based on a biological (rather than clinical) definition of Alzheimer's disease. The earliest phase of Alzheimer's disease (cellular phase) happens in parallel with accumulating amyloid β, inducing the spread of tau pathology. The risk of Alzheimer's disease is 60-80% dependent on heritable factors, with more than 40 Alzheimer's disease-associated genetic risk loci already identified, of which the APOE alleles have the strongest association with the disease. Novel biomarkers include PET scans and plasma assays for amyloid β and phosphorylated tau, which show great promise for clinical and research use. Multidomain lifestyle-based prevention trials suggest cognitive benefits in participants with increased risk of dementia. Lifestyle factors do not directly affect Alzheimer's disease pathology, but can still contribute to a positive outcome in individuals with Alzheimer's disease. Promising pharmacological treatments are poised at advanced stages of clinical trials and include anti-amyloid β, anti-tau, and anti-inflammatory strategies

Persistent spatial clusters of high body mass index in a Swiss urban population as revealed by the 5-year GeoCoLaus longitudinal study.

OBJECTIVE: Body mass index (BMI) may cluster in space among adults and be spatially dependent. Whether and how BMI clusters evolve over time in a population is currently unknown. We aimed to determine the spatial dependence of BMI and its 5-year evolution in a Swiss general adult urban population, taking into account the neighbourhood-level and individual-level characteristics. DESIGN: Cohort study. SETTING: Swiss general urban population. PARTICIPANTS: 6481 georeferenced individuals from the CoLaus cohort at baseline (age range 35-74 years, period=2003-2006) and 4460 at follow-up (period=2009-2012). OUTCOME MEASURES: Body weight and height were measured by trained healthcare professionals with participants standing without shoes in light indoor clothing. BMI was calculated as weight (kg) divided by height squared (m(2)). Participants were geocoded using their postal address (geographic coordinates of the place of residence). Getis-Ord Gi statistic was used to measure the spatial dependence of BMI values at baseline and its evolution at follow-up. RESULTS: BMI was not randomly distributed across the city. At baseline and at follow-up, significant clusters of high versus low BMIs were identified and remained stable during the two periods. These clusters were meaningfully attenuated after adjustment for neighbourhood-level income but not individual-level characteristics. Similar results were observed among participants who showed a significant weight gain. CONCLUSIONS: To the best of our knowledge, this is the first study to report longitudinal changes in BMI clusters in adults from a general population. Spatial clusters of high BMI persisted over a 5-year period and were mainly influenced by neighbourhood-level income

Brain multiplexes reveal morphological connectional biomarkers fingerprinting late brain dementia states

Accurate diagnosis of mild cognitive impairment (MCI) before conversion to Alzheimer\u27s disease (AD) is invaluable for patient treatment. Many works showed that MCI and AD affect functional and structural connections between brain regions as well as the shape of cortical regions. However, \u27shape connections\u27 between brain regions are rarely investigated -e.g., how morphological attributes such as cortical thickness and sulcal depth of a specific brain region change in relation to morphological attributes in other regions. To fill this gap, we unprecedentedly design morphological brain multiplexes for late MCI/AD classification. Specifically, we use structural T1-w MRI to define morphological brain networks, each quantifying similarity in morphology between different cortical regions for a specific cortical attribute. Then, we define a brain multiplex where each intra-layer represents the morphological connectivity network of a specific cortical attribute, and each inter-layer encodes the similarity between two consecutive intra-layers. A significant performance gain is achieved when using the multiplex architecture in comparison to other conventional network analysis architectures. We also leverage this architecture to discover morphological connectional biomarkers fingerprinting the difference between late MCI and AD stages, which included the right entorhinal cortex and right caudal middle frontal gyrus

FREE ORAL COMMUNICATIONS 2: ALCOHOL AND LIVER—CLINICAL RESEARCHO2.1RAPID DECLINE OF LIVER STIFFNESS WITH ALCOHOL WITHDRAWAL IN HEAVY DRINKERS

Background and aims. Measurement of liver stiffness using real-time elastography appears as a promising tool to evaluate the severity of chronic liver diseases. Previous studies in patients with alcoholic liver disease have suggested that fibrosis was the only histological parameter to influence liver stiffness. To challenge this hypothesis, we have prospectively tested the short-term impact of alcohol withdrawal on liver stiffness value. Methods. All patients hospitalized for alcohol withdrawal in our Liver Unit between September 2008 and December 2010 had a liver stiffness determination (using a FibroScan® device) at entry (D0) and 7 days after alcohol withdrawal (D7). Stiffness values were compared using non-parametric test for paired-values. We compared (i) the 10 measures performed at D0 and at D7 for each patient; (ii) the variation of the median result of all patients (using Wilcoxon test in both cases). Results. A total of 138 patients were included in the study [median alcohol consumption: 150g/day (range: 40-400); hepatitis C: n=22 (15.9%); cirrhosis: n=29 (21.0%)]. From D0 to D7, the liver stiffness decreased significantly in 61 patients (44.2%) and increased significantly in 18 (13.0%). Considering all patients, median liver stiffness value decreased from 7.25 to kPa (P<0.001). The stage of fibrosis indicated by liver stiffness changed in 47 patients between D0 and D7 (decrease in 33 and increase in 14). Conclusion. Liver stiffness decreases significantly in nearly half of alcoholic patients after only 7 days of abstinence. This result strongly suggests that non-fibrotic lesions (such as inflammatory ones) may influence liver stiffness. From a practical point of view, it also shows that variation in alcohol consumption must be taken into account for the interpretation of liver stiffness valu

Harmonisation and Between-Country Differences of the Lifetime of Experiences Questionnaire in Older Adults

Background: The Lifetime of Experiences Questionnaire (LEQ) assesses complex mental activity across the life-course and has been associated with brain and cognitive health. The different education systems and occupation classifications across countries represent a challenge for international comparisons. The objectives of this study were four-fold: to adapt and harmonise the LEQ across four European countries, assess its validity across countries, explore its association with brain and cognition and begin to investigate between-country differences in life-course mental activities. Method: The LEQ was administered to 359 cognitively unimpaired older adults (mean age and education: 71.2, 13.2 years) from IMAP and EU-funded Medit-Ageing projects. Education systems, classification of occupations and scoring guidelines were adapted to allow comparisons between France, Germany, Spain and United Kingdom. We assessed the LEQ's (i) concurrent validity with a similar instrument (cognitive activities questionnaire - CAQ) and its structural validity by testing the factors' structure across countries, (ii) we investigated its association with cognition and neuroimaging, and (iii) compared its scores between countries. Results: The LEQ showed moderate to strong positive associations with the CAQ and revealed a stable multidimensional structure across countries that was similar to the original LEQ. The LEQ was positively associated with global cognition. Between-country differences were observed in leisure activities across the life-course. Conclusions: The LEQ is a promising tool for assessing the multidimensional construct of cognitive reserve and can be used to measure socio-behavioural determinants of cognitive reserve in older adults across countries. Longitudinal studies are warranted to test further its clinical utility

Morphometry Based on Effective and Accurate Correspondences of Localized Patterns (MEACOLP)

Local features in volumetric images have been used to identify correspondences of localized anatomical structures for brain morphometry. However, the correspondences are often sparse thus ineffective in reflecting the underlying structures, making it unreliable to evaluate specific morphological differences. This paper presents a morphometry method (MEACOLP) based on correspondences with improved effectiveness and accuracy. A novel two-level scale-invariant feature transform is used to enhance the detection repeatability of local features and to recall the correspondences that might be missed in previous studies. Template patterns whose correspondences could be commonly identified in each group are constructed to serve as the basis for morphometric analysis. A matching algorithm is developed to reduce the identification errors by comparing neighboring local features and rejecting unreliable matches. The two-sample t-test is finally adopted to analyze specific properties of the template patterns. Experiments are performed on the public OASIS database to clinically analyze brain images of Alzheimer's disease (AD) and normal controls (NC). MEACOLP automatically identifies known morphological differences between AD and NC brains, and characterizes the differences well as the scaling and translation of underlying structures. Most of the significant differences are identified in only a single hemisphere, indicating that AD-related structures are characterized by strong anatomical asymmetry. In addition, classification trials to differentiate AD subjects from NC confirm that the morphological differences are reliably related to the groups of interest

The Age-Well randomized controlled trial of the Medit-Ageing European project: Effect of meditation or foreign language training on brain and mental health in older adults

INTRODUCTION: The Age-Well clinical trial is an ongoing monocentric, randomized, controlled trial aiming to assess an 18-month preventive meditation-based intervention directly targeting the attentional and emotional dimensions of aging to promote mental health and well-being in elderly people. METHODS: One hundred thirty-seven cognitively unimpaired older adults are randomized to either an 18-month meditation-based intervention, a structurally matched foreign language training, or a passive control arm. The impact of the intervention and underlying mechanisms are assessed with detailed cognitive, behavioral, biological, neuroimaging and sleep examinations. RESULTS: Recruitment began in late 2016 and ended in May 2018. The interventions are ongoing and will be completed by early 2020. DISCUSSION: This is the first trial addressing the emotional and cognitive dimension of aging with a long-term nonpharmacological approach and using comprehensive assessments to investigate the mechanisms. Results are expected to foster the development of preventive strategies reducing the negative impact of mental conditions and disorders

Hierarchical Anatomical Brain Networks for MCI Prediction: Revisiting Volumetric Measures

Owning to its clinical accessibility, T1-weighted MRI (Magnetic Resonance Imaging) has been extensively studied in the past decades for prediction of Alzheimer's disease (AD) and mild cognitive impairment (MCI). The volumes of gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) are the most commonly used measurements, resulting in many successful applications. It has been widely observed that disease-induced structural changes may not occur at isolated spots, but in several inter-related regions. Therefore, for better characterization of brain pathology, we propose in this paper a means to extract inter-regional correlation based features from local volumetric measurements. Specifically, our approach involves constructing an anatomical brain network for each subject, with each node representing a Region of Interest (ROI) and each edge representing Pearson correlation of tissue volumetric measurements between ROI pairs. As second order volumetric measurements, network features are more descriptive but also more sensitive to noise. To overcome this limitation, a hierarchy of ROIs is used to suppress noise at different scales. Pairwise interactions are considered not only for ROIs with the same scale in the same layer of the hierarchy, but also for ROIs across different scales in different layers. To address the high dimensionality problem resulting from the large number of network features, a supervised dimensionality reduction method is further employed to embed a selected subset of features into a low dimensional feature space, while at the same time preserving discriminative information. We demonstrate with experimental results the efficacy of this embedding strategy in comparison with some other commonly used approaches. In addition, although the proposed method can be easily generalized to incorporate other metrics of regional similarities, the benefits of using Pearson correlation in our application are reinforced by the experimental results. Without requiring new sources of information, our proposed approach improves the accuracy of MCI prediction from (of conventional volumetric features) to (of hierarchical network features), evaluated using data sets randomly drawn from the ADNI (Alzheimer's Disease Neuroimaging Initiative) dataset