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.

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.

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

[This corrects the article DOI: 10.1016/j.patter.2020.100080.].

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 endurance training in older adults.