Integrative analysis of the metabolic signatures of ageing and age-related diseases

Abstract

Ageing is a complex process and is the strongest risk factor for many diseases. To elucidate processes underlying biological ageing, many studies have investigated the associations of individual metabolites or genes with age and age-related phenotypes. While these studies improved our understanding of the pathogenic mechanisms for many diseases, they have their limitations. Analysing phenotypes separately in classical association studies typically neglects relationships and interactions between and across different phenotypes (e.g. comorbidities), genes (e.g. pleiotropy), and metabolites (e.g. through shared biochemical pathways). The aim of this thesis was to better understand age-related phenotypes and their interdependencies through exploring shared underlying processes. To this end, I first identified biomarkers of ageing and age-related diseases, focusing on chronic kidney disease, using metabolomics and glycomics technologies. I identified several metabolites associated with leukocyte telomere length, a common marker of biological ageing. Then I investigated the associations of molecular phenotypes with renal disease. Analysing metabolomic profiles associated with renal function in diabetic and non-diabetic cohorts illustrated similarities between the different aetiologies of kidney disease, such as the lack of renal conversion of amino acids, but also differences, particularly of lipid and energy metabolism. Subsequent analyses identified changes of Immun-oglobin G glycosylation as a novel inflammatory pathway involved in renal disease. Then, I assessed the potential of the faecal metabolome as a functional readout of the gut microbial community to investigate its association with biological ageing. While faecal metabolites were only moderately associated with age and renal function, they showed great potential as novel profiling method for studying the microbiome, particularly with respect to obesity. Next, I integrated metabolomics data from plasma, urine, and saliva to model cross-fluid metabolism individually for kidney disease patients and healthy controls. By comparing both models, I identified metabolic key processes impaired in kidney disease. Finally, I integrated metabolomic and glycomic biomarkers of ageing with other omics markers as well as extensive phenotypic data to investigate their multivariate interdependencies, underlying the comorbidities of age-related diseases. This comprehensive integration of age-related phenotypes highlighted several molecular mechanisms that potentially cause the joint occurrence of diseases with age. Con-sidering the complex aetiologies of different diseases and their dependencies will be needed to facilitate personalised healthcare. In conclusion, I have shown the future potential of sys-tems and network biology approaches for understanding disease mechanisms and precision medicine.

Date of Award	2017
Original language	English
Awarding Institution	King's College London
Supervisor	Tim Spector (Supervisor) & Cristina Menni (Supervisor)