Assessment of Alzheimer's disease-related blood and urine biomarkers for wastewater-based epidemiological studies

Abstract

Incidence of Alzheimer's disease, the leading cause of dementia and the fifth cause of death among elderly patients, has been rapidly increasing in recent years due to continued demographic aging. However, access to diagnosis and adequate care remains limited, especially in low-to-middle income countries, leaving an approximate 41 million cases currently undiagnosed. Such limitations can crucially compromise the quality and availability of care that can be provided to those in need. Wastewater surveillance, which is based on the detection and quantification of biomarkers in wastewater samples, has emerged as a promising tool to assess public health in a time and resource-efficient manner, providing important information for public health authorities and healthcare providers when used in tandem with relevant socioeconomic data and clinical reports. While its potential for monitoring infectious diseases has been proven, efforts towards the integration of biomarkers of chronic and degenerative diseases into such surveillance platforms are still needed. This dissertation aims to evaluate the main biomarkers related to Alzheimer’s disease, including proteins, long non-coding RNAs, and oxidative stress biomarkers, for their integration into wastewater surveillance biomarkers. Moreover, machine learning-based algorithms to correlate the concentration of biomarkers in wastewater to the clinical reports of incidence of a disease were developed using SARS-CoV-2 surveillance in university campuses across Mexico as a relevant case study, to develop effective data analysis strategies to integrate wastewater surveillance data into epidemiological models that allow for public health risk assessment and forecasting. This dissertation contributes to the consolidation of wastewater surveillance as a tool for comprehensive public health risk assessment and data-driven decision-making by demonstrating a pipeline for the integration of new biomarkers into surveillance platforms and effective, easily-interpretable data integration.