Scientists use urine to analyse metabolites that could provide valuable health information, potentially leading to development of a ‘smart toilet’.
Wearables or mobile health technologies passively and continuously collect medically relevant information. These measurements can provide a baseline of our physiology, which is important because that information can be used to track changes that influence our health. For example, Apple recently received FDA approval to alert consumers when atrial fibrillation is detected, this is a rapid and irregular heartbeat that increases the risk of stroke and heart attack. The challenge with these technologies is that they can diagnose symptoms but they provide little to no information on what causes these problems.
How do we know what causes an illness?
To get answers to these questions, clinically based precision medicine tools are often used for diagnosis and develop treatment plans. We live in a world filled with -omics data. This is where large amounts of data on a particular aspect of a cell’s biology are collected and analysed. Examples include genomics (studying DNA), transcriptomics (investigating RNA), proteomics (understanding proteins and their interactions) and metabolomics (analysing metabolites produced by the cell). These techniques all provide useful information on what causes a disease and the mechanism of illness but to get this information invasive procedures such as drawing blood or taking biopsies from patients is often required. This makes it difficult to continuously track the molecular signatures associated with an individual and their health status.
What is metabolomics?
Metabolomics is the large-scale study of small molecules (metabolites); this is done by using analytical chemistry and computational techniques to interrogate complex biochemical mixtures found in biofluids such as blood, urine, and saliva. Metabolomics allows for the identification of what are called biological markers (biomarkers) that can be used for the early diagnosis of disease.
Researchers use urine to continuously track metabolomics changes
In a recent study published in Digital Medicine, researchers from the U.S. may have come up with a way to combine wearables with a metabolomics approach that continuously tracks a person’s health status. The team set out to combine information they got from a smartphone app (calorie intake and sleep) with that of continuous metabolomics data from urine samples. Instead of collecting blood or salvia they decided to collect urine as this was none invasive and can be collected over long periods of time. The group followed two patients over the course of 10 days and collected a total of 109 urine samples. The scientists used a technique called gas chromatography and mass spectrometry (GC-MS) to determine the metabolomics profile for each urine sample. The team was able to show that the baseline metabolomics profile for each person was different. This was proof of principle that collection of urine samples could be used to continuously monitor patient health and has the potential to be a new tool for personalised medicine.
The researchers were then able to identify different metabolites that were associated with diseases ranging from cancer to Alzheimer’s. It is important to note that these metabolites were not validated biomarkers of disease – this means they have not been proven to have any diagnostic value for a particular disease.
The scientists went on to combine their metabolomics data with the biometric data provided by nutritional and fitness smartphone applications. The group was able to successfully track caffeine and alcohol intakes by looking at the change in metabolomics profiles. They were also able to identify when acetaminophen (a pain killer – normally taken for headaches) was taken by one of the subjects. This has implications for being able to monitor treatments and tailoring dosages to each patient’s needs.
Although the work presented in this study establishes urine collection as a unique way to track metabolomics data, there are a few considerations that need further discussion. The disease markers that were isolated in the study were not clinically validated biomarkers meaning it may be challenging to diagnose medical conditions until a broader database of validated markers is available. The group did not compare their findings from the urine metabolomics analysis with standard clinical assays (blood tests etc.) to validate their findings. There were also compounding variables that were not taken into account when doing the metabolomics analysis such as age, lifestyle (e.g., smoking), and hydration, which may affect how diluted the samples were. There were only two participants in this study, which makes the findings difficult to apply to larger populations.
Designing a smart toilet
It is important to note the participants included in this study were the researchers themselves, Joshua Coon and Ian Miller. They had to collect urine samples every 4-8 hours during the study and both indicated the practical challenges associated with frequent collection and storage on dry ice. The solution to this problem as put forward by Coon’s research group is the development of a smart toilet that would collect the samples and would be attached to a miniature GC-MS system for analysis. This may be feasible but is unlikely to be cost-effective, as a single GC-MS machine costs approximately $300, 000.
Despite these limitations, Dr. Joshua Coon, lead author on this study, is optimistic about the idea of a smart toilet. He stated in a press release, “And we’re pretty sure we can design a toilet that could sample urine. I think the real challenge is we’re going to have to invest in the engineering to make this instrument simple enough and cheap enough. That’s where this will either go far or not happen at all.”
Written by Tarryn Bourhill MSc, PhD Candidate.
Keywords: Urine, Metabolomics, Smart Toilet, wearables, mobile health technology, personalised medicine.
References:
- Steinhubl, S. R., Muse, E. D. & Topol, E. J. The emerging field of mobile health. Science translational medicine 7, 283rv283-283rv283 (2015).
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Wishart, D. S. Emerging applications of metabolomics in drug discovery and precision medicine. Nature reviews Drug discovery 15, 473 (2016).
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Zhang, A., Sun, H., Yan, G., Wang, P. & Wang, X. Metabolomics for biomarker discovery: moving to the clinic. BioMed research international 2015 (2015).
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Wu, J. & Gao, Y. Physiological conditions can be reflected in human urine proteome and metabolome. Expert review of proteomics 12, 623-636 (2015).
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Miller, I. et al. Real time health monitoring through urine metabolomics. bioRxiv, 681742 (2019).
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Mattmiller, B. Can ‘smart toilets’ be the next health data wellspring? , <https://www.eurekalert.org/pub_releases/2019-11/mifr-ct111319.php> (2019).
Image by Stefano Ferrario from Pixabay
