Physical Activity and Nutrition INfluences in Ageing: Current Findings from the PANINI Project

Background: The ageing of the population is a global challenge and the period of life spent in good health, although increasing, is not keeping pace with lifespan. Consequently, understanding the important factors that contribute to healthy ageing and validating interventions and influencing policy to promote healthy ageing are vital research priorities. Method: The PANINI project is a collaboration of 20 partners across Europe examining the influence of physical activity and nutrition in ageing. Methods utilised encompass the biological to the social, from genetics to the influence of social context. For example, epigenetic, immunological, and psychological assessments, and nutritional and sports science-based interventions have been used among older adults, as well as mathematical modelling and epidemiology. The projects are multi-disciplinary and examine health outcomes in ageing from a range of perspectives. Results: The results discussed here are those emerging thus far in PANINI from 11 distinct programmes of research within PANINI as well as projects cross-cutting the network. New approaches, and the latest results are discussed. Conclusions: The PANINI project has been addressing the impact of physical activity and nutrition on healthy ageing from diverse but interlinked perspectives. It emphasises the importance of using standardized measures and the advantages of combining data to compare biomarkers and interventions across different settings and typologies of older adults. As the projects conclude, the current results and final data will form part of a shared dataset, which will be made open access for other researchers into ageing processes.On behalf of the PANINI Consortiu

A Distance-Based Framework for the Characterization of Metabolic Heterogeneity in Large Sets of Genome-Scale Metabolic Models

Gene expression and protein abundance data of cells or tissues belonging to healthy and diseased individuals can be integrated and mapped onto genome-scale metabolic networks to produce patient-derived models. As the number of available and newly developed genome-scale metabolic models increases, new methods are needed to objectively analyze large sets of models and to identify the determinants of metabolic heterogeneity. We developed a distance-based workflow that combines consensus machine learning and metabolic modeling techniques and used it to apply pattern recognition algorithms to collections of genome-scale metabolic models, both microbial and human. Model composition, network topology and flux distribution provide complementary aspects of metabolic heterogeneity in patient-specific genome-scale models of skeletal muscle. Using consensus clustering analysis we identified the metabolic processes involved in the individual responses to resistance training in older adults. High-throughput techniques enable the analysis of complex biological systems at multiple levels, including genome, transcriptome, proteome, and metabolome. Integration of multi-omics data is often focused on dimensionality reduction and feature selection for classification tasks. Genome-scale metabolic models are extensive maps of the network of biochemical reactions taking place in a particular cell, tissue or organism. Each reaction is associated with the respective enzyme and gene, enabling the mapping of transcriptomics and proteomics data and providing a structure for the system-level interpretation of multi-omics datasets. The result of this process is a personalized model that gives a snapshot of the metabolic status of an individual. Analyzing these complex models, for example, to detect differences between individuals, is cumbersome. We applied consensus clustering to a set of data-driven models to monitor the progression of a lifestyle intervention in a cohort of older adults. Genome-scale metabolic models are maps of the metabolic network that function as structures for the integration of molecular data, such as transcriptomics and proteomics. We developed a method for the analysis of large sets of data-driven models, using different distance metrics to quantify model similarity. Consensus analysis is then used to reach a single metabolic distance. The method was applied to model the individual variability in the responses to resistance training in a cohort of older adults

Simulating metabolic flexibility in low energy expenditure conditions using genome-scale metabolic models

Metabolic flexibility is the ability of an organism to adapt its energy source based on nutrient availability and energy requirements. In humans, this ability has been linked to cardio-metabolic health and healthy aging. Genome-scale metabolic models have been employed to simulate metabolic flexibility by computing the Respiratory Quotient (RQ), which is defined as the ratio of carbon dioxide produced to oxygen consumed, and varies between values of 0.7 for pure fat metabolism and 1.0 for pure carbohydrate metabolism. While the nutritional determinants of metabolic flexibility are known, the role of low energy expenditure and sedentary behavior in the development of metabolic inflexibility is less studied. In this study, we present a new description of metabolic flexibility in genome-scale metabolic models which accounts for energy expenditure, and we study the interactions between physical activity and nutrition in a set of patient-derived models of skeletal muscle metabolism in older adults. The simulations show that fuel choice is sensitive to ATP consumption rate in all models tested. The ability to adapt fuel utilization to energy demands is an intrinsic property of the metabolic network

Knowledge of Nutrition and Physical Activity Guidelines is Not Associated with Physical Function in Dutch Older Adults Attending a Healthy Ageing Public Engagement Event

Purpose: Evidence-based guidelines on nutrition and physical activity are used to increase knowledge in order to promote a healthy lifestyle. However, actual knowledge of guidelines is limited and whether it is associated with health outcomes is unclear. Participants and Methods: This inception cohort study aimed to investigate the association of knowledge of nutrition and physical activity guidelines with objective measures of physical function and physical activity in community-dwelling older adults attending a public engagement event in Amsterdam, The Netherlands. Knowledge of nutrition and physical activity according to Dutch guidelines was assessed using customized questionnaires. Gait speed and handgrip strength were proxies of physical function and the Minnesota Leisure Time Physical Activity Questionnaire was used to assess physical activity in minutes/week. Linear regression analysis, stratified by gender and adjusted for age, was used to study the association between continuous and categorical knowledge scores with outcomes. Results: In 106 older adults (mean age=70.1 SD=6.6, years) who were highly educated, well-functioning, and generally healthy, there were distinct knowledge gaps in nutrition and physical activity which did not correlate with one another (R2=0.013, p=0.245). Knowledge of nutrition or physical activity guidelines was not associated with physical function or physical activity. However, before age-adjustment nutrition knowledge was positively associated with HGS in males (B= 0.64 (95% CI: 0.05, 1.22)) and having knowledge above the median was associated with faster gait speed in females (B=0.10 (95% CI: 0.01, 0.19)). Conclusion: Our findings may represent a ceiling effect of the impact knowledge has on physical function and activity in the this high performing and educated population and that there may be other determinants of behavior leading to health status such as attitude and perception to consider in future studies.Additional authors: Nadine Correia Santos, Sarianna Sipilä, Janice L Thompson, Carel GM Meskers, Marijke C Trappenburg, Andrea B Maie

Knowledge of Nutrition and Physical Activity Guidelines is Not Associated with Physical Function in Dutch Older Adults Attending a Healthy Ageing Public Engagement Event

Purpose: Evidence-based guidelines on nutrition and physical activity are used to increase knowledge in order to promote a healthy lifestyle. However, actual knowledge of guidelines is limited and whether it is associated with health outcomes is unclear. Participants and Methods: This inception cohort study aimed to investigate the association of knowledge of nutrition and physical activity guidelines with objective measures of physical function and physical activity in community-dwelling older adults attending a public engagement event in Amsterdam, The Netherlands. Knowledge of nutrition and physical activity according to Dutch guidelines was assessed using customized questionnaires. Gait speed and handgrip strength were proxies of physical function and the Minnesota Leisure Time Physical Activity Questionnaire was used to assess physical activity in minutes/week. Linear regression analysis, stratified by gender and adjusted for age, was used to study the association between continuous and categorical knowledge scores with outcomes. Results: In 106 older adults (mean age=70.1 SD=6.6, years) who were highly educated, well-functioning, and generally healthy, there were distinct knowledge gaps in nutrition and physical activity which did not correlate with one another (R2=0.013, p=0.245). Knowledge of nutrition or physical activity guidelines was not associated with physical function or physical activity. However, before age-adjustment nutrition knowledge was positively associated with HGS in males (B= 0.64 (95% CI: 0.05, 1.22)) and having knowledge above the median was associated with faster gait speed in females (B=0.10 (95% CI: 0.01, 0.19)). Conclusion: Our findings may represent a ceiling effect of the impact knowledge has on physical function and activity in the this high performing and educated population and that there may be other determinants of behavior leading to health status such as attitude and perception to consider in future studies.peerReviewe

A multi-element psychosocial intervention for early psychosis (GET UP PIANO TRIAL) conducted in a catchment area of 10 million inhabitants: study protocol for a pragmatic cluster randomized controlled trial

Multi-element interventions for first-episode psychosis (FEP) are promising, but have mostly been conducted in non-epidemiologically representative samples, thereby raising the risk of underestimating the complexities involved in treating FEP in 'real-world' services