Investigating a sustainable means of detecting and monitoring Alzheimer's disease risk via non-invasive biomarkers

Abstract

Compelling evidence has identified the kynurenine pathway (KP) as an avenue to predict, diagnose, and measure the progression of neurodegenerative diseases. This provides promising directions for the development of novel and precise treatment options for individuals experiencing metabolic dysregulation and cognitive challenges. However, the complex interrelationship between the KP and Alzheimer's disease (AD) risk has not been characterised, in part due to a lack of understanding of: i) How normal KP function is characterised across the lifespan; ii) A lack of evidence from 'at-risk' cohorts; and iii) The heterogeneous and unvalidated methods used and the incomplete datasets available. This thesis will address these research gaps by developing and applying validated targeted metabolomics approaches to assess the diagnostic accuracy of the KP as a non-invasive biomarker for AD risk across various sample types and cohorts. Outcomes from this research aims to show or predict the relationship between the KP and AD risk. There are three aims to this thesis that will be addressed across three studies: Aim 1: Appraise and characterise the existing literature on how KP metabolites are measured and establish a set of normative data; Aim 2: Identify and develop a validated analytical method to quantify KP metabolites in the liquid biopsies of healthy adults in a range of human biofluids going beyond blood samples; and Aim 3: Analyse KP metabolites in the serum and urine of older adults at risk and not at risk of AD and assess their responsivity to treatment from clinical trial data. The overarching aim of this thesis is to profile a variety of non-invasive and minimally invasive biofluids from healthy adults across the lifespan, as well as older adults at risk of AD, to characterise the relationship between the KP and AD risk. The purpose of this is to seed new directions for pragmatic biomarker discovery in the fields of AD risk prediction and the monitoring of responsivity to treatment. As an additional note, this thesis will benefit from using analytical methods which have been objectively validated for diagnostic accuracy, and controlling for sex differences, lifestyle factors, and inter-sample variation.

Date of Award	2023
Original language	English