Wastewater clues could revolutionize Alzheimer's detection

By Hugo Francisco de SouzaReviewed by Susha Cheriyedath, M.Sc.Feb 18 2024

In a recent review published in the journal Environment International, researchers discuss the benefits of wastewater-based epidemiology (WBE) over conventional diagnostic techniques, the latter of which are often expensive and invasive. They then explore studies in the relatively novel field of biomarker- and peripheral body fluid-based Alzheimer's disease diagnosis, the potential for integrating these detection approaches in existing WBE platforms, and the challenges currently faced in implementing these integrations.

Study: Urine biomarkers for Alzheimer's disease: A new opportunity for wastewater-based epidemiology? Image Credit: Avigator Fortuner / Shutterstock

The evolution of Alzheimer's disease research

Alzheimer's disease (AD) is a neurodegenerative condition characterized by the abnormal buildup of proteins (especially amyloid and tau) in and around brain cells, resulting in a progressive cognitive decline. It is a severely debilitating condition, the most common cause of dementia, and the fifth most lethal chronic disease in humans above the age of 65.

Alarmingly, the global incidence of AD is rising rapidly, with research revealing a more than two-fold increase in less than three decades between 1990 and 2019. Clinical and economic prediction models estimate that by the year 2050, between 114 and 152 million patients will suffer from the disease, with dementia-related financial losses projected to far exceed 2.8 trillion US dollars.

AD is multifactorial in nature, with a combination of genetic and environmental variables thought to trigger the condition. Unfortunately, this complicates AD diagnoses, with a large portion of cases remaining undetected or detected too late. Conventional diagnostic approaches involve the use of magnetic resonance imaging (MRI) and positron emission tomography (PET) scans. These techniques are expensive and require specialized equipment and expertise, reported to be upwards of 753 euros in Sweden, 649 euros in Germany, and more than 8,400 US dollars in America.

While less expensive methods of AD diagnoses do exist, they often involve highly invasive surgical procedures. These problems are compounded in low to middle-income countries (LMIC) that often lack the resources required for AD and dementia diagnoses of any kind. Recent AD diagnostic research has focused on using non- or minimally-invasive diagnostic techniques involving biofluids such as blood, urine, and saliva to overcome these challenges. Biomarker concentrations within these biofluids, especially when analyzed using machine learning and other artificial intelligence models, have been shown to be capable of detecting AD long before the emergence of externally observable symptoms, further promoting the adoption of these novel approaches.

But what if we could go further?

Following the World Health Organization's (WHO's) recommendation for the development of population-level diagnostic and risk-assessment approaches to facilitate better preventive efforts and resource management accompanying its AD response plans, recent studies have investigated the potential of peripheral biofluids, especially urine contents, in AD management.

Most recently, wastewater-based epidemiology (WBE) is being investigated as a community-scale health monitoring tool utilizing the characterization and quantification of chemicals and biomarkers in sewage water.

Where are we in WBE implementation?

Despite WBE first being reported in a more primitive form in 1954 and its current methodology established in 2001, WBE's implementation remains primarily restricted to the detection of monitoring of pathogenic diseases such as the coronavirus disease 2019 (COVID-19) causing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Unfortunately, the implementation of WBE in monitoring the bulk of global mortality – chronic, non-communicable diseases (41 million deaths per year representing 74% of annual mortality) – is exceedingly limited.

Encouragingly, that limited information is promising – thus far, sewage-derived proteomics analyses have identified biomarkers related to cardiovascular disease (CVD), cancers, the presenilin pathway, Huntington's disease, and potentially even AD. This suggests that the large-scale integration of chronic disease biomarkers into existing WBE platforms could result in substantial savings for human life and the global economy.

So, why isn't large-scale implementation happening?

The population-scale implementation of chronic disease biomarker analyses into WBE infrastructure cannot occur without first overcoming specific challenges. Firstly, we do not know the effects of endogenous biomarker concentrations and their correlations with disease risk. Previous studies have only identified risk-associated biomarkers, not quantify them.

Secondly, there is a lack of universally validated population parameter markers, let alone in wastewater flows, where (thirdly) the complex matrix of wastewater mixture is expected to render biomarker concentrations extremely low. Current technological limitations may prevent the detection, much less quantification, of a spectrum of biomarkers assessing the community-level risk of multiple diseases.

What must scientists do to overcome these challenges, at least for AD risk assessments?

Researchers have identified a number of protein biomarkers associated with AD. These included ones such as AB, Tau, and AD7C-NTP, known to be directly involved in the condition's pathologic mechanisms, and long noncoding RNAs (LncRNAs) such as BDNF-AS, BACE1-AS, MAGI2-AS3, RMRP, and EBF3-AS. LncRNAs are a fascinating class of large RNA molecules that serve epigenetic functions despite not coding for any functional proteins.

Implementing mechanisms to measure oxidative stress and neurotransmitter dysregulations could serve as validatory evidence in the risk assessment of AD and many other diseases.

"…advanced analytical techniques that have proven successful in clinical settings have been suggested as solutions to these challenges, including ultra-performance liquid chromatography coupled with a hybrid quadrupole orthogonal time-of-flight mass spectrometer (UPLC-Q-TOF/MS/MS), capillary electrophoresis time-of-flight mass spectrometry (CE-TOF-MS), microfluidics-based separation methods, SIMOA or antibody or aptamer-based biosensors."