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How to measure health improvement? : assessment of subtle shifts in metabolic phenotype

Authors
  • Fazelzadeh, Parastoo
Publication Date
Jan 01, 2017
Source
Wageningen University and Researchcenter Publications
Keywords
Language
English
License
Unknown
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Abstract

Human health is impacted by a complex network of interactions between biological pathways, mechanisms, processes, and organs, which need to be able to adapt to a continuously changing environment to maintain health. This adaptive ability is called ‘phenotypic flexibility’. It is thought that health is compromised and diseases develop when these adaptive processes fail. As the product of interactions between several factors such as genetic makeup, diet, lifestyle, environment and the gut microbiome, the ‘metabolic phenotype’ provides a readout of the metabolic state of an individual. Understanding these relationships will be one of a major challenges in nutrition and health research in the next decades. To address this challenge, the development of high-throughput omics tools combined with the application of elaborate statistical analyses will help characterize the complex relationship of (bio) chemicals in human systems and their interaction with other variables including environment and lifestyle to produce the measured phenotype. An important aim of this thesis was to identify phenotype shifts by looking at effect of prolonged resistance-type exercise training on skeletal muscle tissue in older subjects and the possible shift toward the features of younger subjects as a reference for a healthier phenotype. A second aim was to identify phenotype shifts by looking at the response to a challenge in obese subjects and the possible shift toward lean subjects as a reference for a healthier phenotype. Chapter 2 and 3 of this thesis show how the significant remaining plasticity of ageing skeletal muscle can adapt to resistance-type exercise training. The data indicate that frail and healthy older subjects have two distinct phenotypes according to the skeletal muscle tissue metabolite profiles and that exercise training shifts aged muscle towards a younger phenotype. We showed that the effect of exercise on amino acid derived acylcarnitines (AAAC’s) in older subjects points towards decreased branched chain amino acid catabolism, likely due to compromised activation of the branched chain α-keto acid hydrogenase (BCKDH) complex. Furthermore, we found that the protocadherin gamma gene cluster might be involved in aged-muscle denervation and re-innervation. Finally, plasma was found to be a poor indicator of muscle metabolism, emphasizing the need for direct assessment of metabolites in muscle tissue. Chapter 4 of this thesis examines whether a mixed meal challenge response provides a readout for a shift in phenotype upon weight loss in obese male subjects. We concluded that weight loss moderately affects the mixed meal challenge response of both plasma metabolome and transcriptome of peripheral blood mononuclear cells in obese subjects. Measurements at the fasted and postprandial state also provide us with a different type of information. In Chapter 5 it is demonstrated that the global testing of pathways could provide a concise summary of the multiple univariate testing approach used in Chapter 4. In Chapter 6 it is discussed how the findings of this thesis increase our understanding of how to measure phenotypic flexibility as a proxy of health. In this thesis it is shown that the correlations between tissue and plasma metabolites are rather weak, emphasising the need to perform organ-specific studies. Availability of less invasive/painful sampling techniques and the use of small amounts of tissue would enable larger scale human studies on adipose tissue and skeletal muscle to more accurately define phenotypical shifts due to diet or lifestyle interventions. With respect to the assessment of phenotypical flexibility by omics approaches, significant complications can be expected in trying to relate plasma metabolism to PBMC gene expression. Organ-focussed approaches that integrate multiple omics levels using system biology approaches are considered to be a lot more promising.

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