Integrative metabolomics for the prediction and causal understanding of Alzheimer’s disease risk

Student thesis: Doctoral ThesisDoctor of Philosophy


Whilst the pathological hallmarks of Alzheimer’s Disease (AD) are well established, there continues to exist a critical lack in understanding of the mechanisms which give rise to the disease. Existing biomarkers from both brain and cerebral spinal fluid (CSF) demonstrate clinical utility, but these are limited by cost and invasiveness, and as such, offer limited accessibility both for treating and tracking AD at scale. Exposures like low educational attainment are also consistently associated with AD, but without understanding the biological pathways through which such relationships arise, the extent to which this knowledge can inform treatment progress is negligible. Blood metabolites are small molecules which are easily accessible and reflective of the interplay between biology and the wider environment. Consisting of molecules such as lipids and amino acids, they also hold biological relevance to AD. Identifying metabolites with aetiological relevance to AD and disentangling how these associate with exposures along the AD causal pathway could, therefore, offer unique insights into underlying mechanisms of the disease. With this in mind, this thesis aimed to elucidate blood metabolites with mechanistic relevance to AD and integrate knowledge from both genetic and cognition-based exposures, to further understand interlinking pathways into the disease.

Study one began by utilising cross-trait linkage disequilibrium score regression (LDSC) to compare phenotyping approaches across both AD and blood metabolite genomic data to establish appropriate measures for use in downstream analyses. Here, the trade-off between specificity and sample size was explored. For AD, strict phenotyping based on clinical AD status, and minimal phenotyping based on parental AD were compared. For metabolites, quantification via small sampled high precision methodology (mass-spectrometry) versus a larger sampled, lower precision method (nuclear magnetic resonance spectroscopy) were also compared. Strict phenotyping was favoured for AD, whilst larger sample sizes were favoured for metabolites.

Study two then explored causal relationships between blood metabolites and clinical AD through use of Mendelian randomisation (MR). Using knowledge from existing associations with midlife cognition, 19 candidate blood metabolites were selected to investigate whether mid-life associations translate through to later AD risk. Univariable MR was used to assess the bi-directional causal association between each metabolite and AD in-turn, and Bayesian methodology interrogated metabolite combinations, which may together be on the causal pathway to AD. Glycoprotein Acetyls demonstrated a risk-increasing causal association with AD, whilst a number of high density lipoproteins (HDLs), particularly Free Cholesterol in Extra Large HDLs (XL.HDL.FC), showed protective causal associations.

Study three extended MR causal analyses, but made use of cross-trait polygenic risk scoring (PRS) to prioritise AD-specific candidate metabolites, of which 34 were selected. Cognition and educational attainment were also introduced to disentangle independent and mediated relationships. Univariable MR was first used to interrogate bidirectional causal relationships between metabolites, education, cognition, and AD in-turn, and multivariable MR was then used to disentangle independent versus mediating mechanisms. Glutamine and XL.HDL.FC showed evidence of protective causal effects on AD, as did educational attainment and cognition. No evidence of metabolites mediating the effect of either education or cognition on AD was found, though cognition fully mediated the effect of educational attainment.

Finally, study four harnessed the use of longitudinal data to further understand possible mediating roles of blood metabolites in the relationship between educational attainment and AD, using AD endophenotypes in-place of clinical diagnosis. 118 longitudinal metabolites and four longitudinal AD endophenotypes (hippocampal volume, FDG-PET, MMSE scores, and plasma P-tau181) were utilised, and both data reduction and latent growth curve modelling analysed mediation at the single analyte and group-metabolite level. No robust evidence of metabolite mediators were found.

Taken together, results from this thesis offer novel insights into blood metabolites with causal relevance to AD and highlight the importance of measurement consideration in optimising power to detect relationships. No robust evidence was found for a mediating role of blood metabolites in the context of cognitive factors and AD. However, a number of metabolites are offered as direct causal candidates for future study, and methodological pipelines pave the way for wider mediating relationships to be investigated.

Date of Award1 Jun 2022
Original languageEnglish
Awarding Institution
  • King's College London
SupervisorPetra Proitsi (Supervisor) & Richard Dobson (Supervisor)

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