Breathtaking research

# # # # Breathing is one of those many aspects of life that we all take completely for granted for the vast majority of our time on planet Earth. It represents not only a magnificent means of providing our bodies with oxygen, but also disposing of waste. Recently researchers have attempted to see if there are any components in the waste part of our exhaled breath that could be useful in terms of diagnosing, stratifying and monitoring Parkinson’s. In today’s post, we will discuss what breath is made up of, what this new research found, and explore what the potential implications of the findings are. # # # #

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Source: Wired

“Breath is the finest gift of nature. Be grateful for this wonderful gift.”
― Amit Ray

On any given day, the average person takes 17,000 breaths (the normal rate for an adult at rest is 12 to 20 breaths per minute).

When we breath in, the inhaled air – made up of approximately 16% oxygen, 4% carbon dioxide, and 79% nitrogen – is taken down to a pair of organs we know of as the lungs. Most of us have two lungs, but they are not exactly alike. The lung on the left side of your body is divided into two lobes, while the lung on your right side is divided into three. And the left lung is also slightly smaller, making room for your heart.

Combined, your lungs contain approximately 2,400 kilometres (1,500 miles) of airways and 300 to 500 million air sacs (called alveoli – Source). Through the thin walls of the alveoli, oxygen from the inhaled air passes into your blood in the surrounding capillaries. At the same time that this is occurring, carbon dioxide moves from your blood and out into the air sacs.

When you breathe out (exhale), your diaphragm and rib muscles relax, reducing the space in your chest. As the chest cavity gets smaller, your deflating lungs push the carbon dioxide-rich air up your windpipe and then out of your nose or mouth.

Exhaled air consists of 78% nitrogen, 16% oxygen, and 4% carbon dioxide. In addition to this, there are also trace amounts of “other stuff”.

And it’s that “other stuff”, where our post starts today.

Ok, I’ll bite: What do you mean by “other stuff”?

Right. Before I answer that question or we go any further, I need to make a full disclosure:

I am one of the scientists involved in the research that will be discussed in today’s post.

I am certainly not the inventor of the technology involved (some very smart Israeli engineers get all the credit there), but I was involved in the study that will be reviewed today so I may be just a wee bit biased in terms of any opinions or interpretations of the data expressed in this post. Please feel free to chastise me in the comments section below if you feel like I have crossed any moral/ethical lines with this shameless and blatant self-promotion. It will never happen again – I promise – I just thought it might be of interest to you.

That said, let’s continue:

Back in 2013, I was working as a research scientist at the University of Cambridge with  Professor Roger Barker (Department of Clinical Neuroscience).

Prof Roger Barker. Source: Ukrmp

We had been banging around ideas on alternative means of stratifying and tracking Parkinson’s overtime. We had considered the gut microbiome, but others were already addressing this area and it didn’t feel very… um,… ‘user friendly’ in terms of the methods used to approach it (read: fecal matter collection and research freezers full of… well, you get the idea).

Then a colleague returned from a research conference and mentioned a very impressive presentation given an Israeli academic named Professor Hossam Haick.

Prof Hossam Haick. Source: Talknsave

Prof Haick is a Professor in the Department of Chemical Engineering and the Russell Berrie Nanotechnology Institute at the Technion and he is an expert in the field of nanotechnology and non-invasive disease diagnosis.

The Technion campus in Haifa, Israel. Source: JNS

Importantly, Prof Haick was interested in the potential applications of detecting the trace amounts of “other stuff” I mentioned above.

And the “other stuff” he is focused is called “Volatile Organic Compounds” (or VOCs).

What are Volatile Organic Compounds?

These are organic (meaning derived from living matter) chemicals that have a high vapour pressure at ordinary room temperature. They are numerous (there are 10,000+ of them), varied, and ubiquitous (they are all around you).

Source: Rabbitair

Most scents and odours you smell are VOCs.

Researchers and engineers (like Prof Haick and his team) have been exploring technologies that detect VOCs in all kinds of settings, from detectors of dangerous or explosive chemicals through to air quality control applications.

But of particular interest to Prof Haick is the potential for medical applications.

Medical applications? 

Yes.

Our bodies produce large amounts of VOCs through all the various cellular activities that are undertaken to keep us upright, and these VOCs make up a large portion of the “other stuff” that I mentioned above. Our bodies excrete these VOCs in different ways: through our skin, in our urine, and via our breathe.

Source: MDD

Animals – whose sense of smell is superior to our own – can smell a wide variety of VOCs, and some combinations of these VOCs have been associated with medical conditions. For example, dog can smell different types of cancers (Source).

We have previously discussed how Medical Detection Dogs can be trained to detect different types of medical conditions (Click here to read a previous SoPD post on this topic).

Source: MDD

Wait. Diseases have smells? 

Yes.

Bad breath (called halitosis) can help doctors to identify individuals with diabetes. Diabetes can also make ones urine smell like rotten apples.

Source: Express

And diabetes is not alone in having a distinctive odour. Typhoid is known to turn one’s body odour into the smell of baked bread (Source).

And long time readers of this website will be familiar with the story of Joy Milne – the lady that can smell Parkinson’s (Click here to read a SoPD post on this topic – it’s an amazing story!).

Joy Milne. Source: NewScientist

Prof Haick and his team have been developing technology that can not only detect VOCs, but they have also been conducting a large number of studies trying to find associations between some of those VOCs and various medical conditions, including Parkinson’s.

# RECAP #1: Volatile Organic Compounds (VOCs) are organic (meaning derived from living matter) chemicals that are gases at ordinary room temperature. They are numerous and everywhere. Many VOCs have been associated with specific medical conditions, which has led to the development of new technologies that may aid doctors in the diagnosis and tracking of diseases over time. #

So what research has Prof Haick conducted in the area of Parkinson’s?

So before Prof Barker and I spoke to Prof Haick and his team, they had already made significant progress in identifying VOCs associated with neurodegenerative conditions.

In 2013, they published this report:

Title: Detection of Alzheimer’s and Parkinson’s disease from exhaled breath using nanomaterial-based sensors.
Authors: Tisch U, Schlesinger I, Ionescu R, Nassar M, Axelrod N, Robertman D, Tessler Y, Azar F, Marmur A, Aharon-Peretz J, Haick H.
Journal: Nanomedicine (Lond). 2013 Jan;8(1):43-56.
PMID: 23067372

In this report, the researchers collected breath samples from 57 individuals (Individuals with Alzheimer’s, Parkinson’s, and unaffected controls). By analysing the VOCs in these breathe samples, the investigators found that combinations of VOCs could clearly distinguish the individuals with Alzheimer’s from those with Parkinson’s and the control participants. Likewise, VOCs in the breathe samples from the people with Parkinson’s were different to those in the Alzheimer’s group and the control group. The researchers could determine which group a participant came from by analysing their breath sample – and the classification accuracy rate was approximately 80%.

Just as each of us has different finger prints, Prof Haick and his team discovered that each of the conditions have specific “breath prints”.

And they took this concept to another level in their next study, where they compared breathe samples from people who had been diagnosed with different types of Parkinson’s-related conditions:

Title: Distinguishing idiopathic Parkinson’s disease from other parkinsonian syndromes by breath test.
Authors: Nakhleh MK, Badarny S, Winer R, Jeries R, Finberg J, Haick H.
Journal: Parkinsonism Relat Disord. 2015 Feb;21(2):150-3.
PMID: 25511331

In this study, the research team collected breath samples from 44 individuals with idiopathic Parkinson’s, 16 cases of non-idiopathic Parkinson’s, and 37 unaffected controls.

What does that mean: non-idiopathic Parkinson’s?

Idiopathic Parkinson’s refers to cases of PD that can not be explained be genetic or environmental influences. “Parkinson’s disease” belongs to a much larger group of conditions known as Parkinsonisms.

Parkinsonism is any condition that results in a combination of the movement abnormalities similar to those observed in ‘Parkinson’s disease’. These include:

• Multiple system atrophy (or MSA)
• Progressive supranuclear palsy (or PSP)
• Vascular Parkinsonism
• Corticobasal Syndrome (or CBS)
• Dementia with Lewy bodies (or DLB)
• Drug-induced Parkinsonism

The Parkinson’s Foundation has a good webpage on the different types of Parkinsonisms (Click here to read that page).

In this study, the phrase ‘non-idiopathic Parkinson’s’ referred to a 4 cases of drug induced Parkinsonism, 3 individuals with Progressive Supranuclear Palsy, 2 cases of Multiple System Atrophy, 1 individuals with Cortical Basal Ganglionic Degeneration, 2 cases of Diffuse Lewy Body Disease and 4 additional patients with combined subtypes of these.

Remarkably, the ‘breath prints’ collected from the idiopathic Parkinson’s and non-idiopathic Parkinson’s cases could be distinguished from each other by an accuracy rate of almost 90% – that is to say, 9 times out of 10 the researchers could accurately state if a breath sample came from a person with idiopathic PD rather than non-idiopathic PD.

And the results were not affected by any Parkinson’s-associated medication (such as L-Dopa or MAO-B inhibitors).

That’s amazing! What else have they done?

Well, equally remarkable, Prof Haick and colleagues applied this technology to preclinical models of Parkinson’s and observed similar results.

Firstly they published this report in 2014:

Title: Analysis of volatile organic compounds in rats with dopaminergic lesion: Possible application for early detection of Parkinson’s disease.
Authors: Khatib S, Finberg JP, Artoul F, Lavner Y, Mahmood S, Tisch U, Haick H, Aluf Y, Vaya J.
Journal: Neurochem Int. 2014 Oct;76:82-90.
PMID: 25034982

In this study, the research team induced a classical neurotoxic model of Parkinson’s – using a chemical called 6-hydroxydopamine – in 1/3 of a group of rodents. In another 1/3, they injected just saline. While saline has no effect, 6-hydroxydopamine is a powerful toxin for dopamine neurons. In the final 1/3, the investigators injected DSP-4 (which is a specific noradrenergic neuron toxin). Noradrenergic neurons are similar to dopamine neurons, and also badly affected in Parkinson’s.

After several weeks, the investigators collected blood samples from all of the rats, and they discovered combinations of VOCs that could allow them to accurately distinguish between the three groups.

And then they followed up that study, by demonstrating the ability to profile a genetic model of Parkinson’s. In 2018, they published this report:

Title: Altered Volatile Organic Compound Profile in Transgenic Rats Bearing A53T Mutation of Human α-Synuclein: Comparison with Dopaminergic and Serotonergic Denervation.
Authors: Finberg JPM, Aluf Y, Loboda Y, Nakhleh MK, Jeries R, Abud-Hawa M, Zubedat S, Avital A, Khatib S, Vaya J, Haick H.
Journal: ACS Chem Neurosci. 2018 Feb 21;9(2):291-297.
PMID: 29017011

In this study, the scientists used genetically engineered rats that carry the Parkinson’s-associated A53T genetic mutation in the alpha-synuclein gene. They collected breath and tissue samples from these animals, and compared the ‘breath-print’ and VOCs with rats that had been given neurotoxic lesions of dopamine neurons (a traditional preclinical model of PD). They also compared these samples with collections from rats that were given serotonergic neuronal lesions, and unaffected control rodents. Again, they found clear signals that could discriminate between the various groups.

These studies (in addition to experiments carried out in other medical conditions) demonstrate the potential of breath analysis as a useful possible clinical aid.

# # RECAP #2:  Researchers in Israel have developed technology that allows then to identify combinations of volatile organic compounds in breath samples that can not only distinguish people with Alzheimer’s or Parkinson’s from unaffected people, but also differentiate between different types of Parkinson’s. The researchers have also applied this technology to animal models of Parkinson’s and found similar results, indicating that ‘breath prints’ could be a powerful clinical aid. # #

So what project did you and Prof Barker conduct with Prof Haick and his team?

After meeting with Prof Haick and some members of his team, we agreed to apply for funding to conduct a large study (300 people in total: 200 people with Parkinson’s and 100 unaffected controls), with the goal of collecting multiple breath samples from all of the participants over a 3 year period. The reason for collecting from such a large group was to determine if subtypes of Parkinson’s could be determined via breath analysis, and the rationale of collecting multiple samples over time was to see how the breath print might be changing over time.

The project was funded by the British Research Council and Parkinson’s UK.

And it was called the “BIRAX/Parkinson’s UK Breath analysis study”, and it was a collaboration between Technion (Israel Institute of Technology) and Cambridge University (UK).

As mentioned above, it was a three year study – which seems like a long time, but it actually didn’t leave much time to collect multiple breath samples during that period from 200 people with Parkinson’s and 100 healthy controls.

Here is a video of Prof Haick describing the BIRAX project:

The aims of the study were simple:

1. To validate the next generation breath test technology that Prof Haick and his team had developed in determining people with Parkinson’s from people without the condition.

2. To determine the potential of the breath test technology in stratifying people with Parkinson’s into possible subtypes, and then track disease progression within those groups overtime.

My job in the project was relatively easy – I was responsible for the collection of breath samples from the individuals who were attending Prof Barker’s research clinic at the Brain Repair Centre in Cambridge. And we used two methods of breath collection:

• the off-line approach
• the on-line approach

The off-line approach involved asking participants in the study to breath through a device and their breath was collected in specially lined bags (illustrated in the left panel in the image below):

Source: Epos

These breath samples were then extracted from the bags using a pump system and the VOCs were collected in filaments within glass tubes that could be capped (see the glass tube with red caps in the image on the right hand side above). These capped tube were then sent to Prof Haick’s team in Haifa (Israel) for analysis. It was a slow, laborious process – what we really needed was a simpler, faster method.

The on-line approach (the second breath collection process we used) was a simpler, faster method.

It involved the participants simply breathing into a small box (see the image below), and their breath sample would be analysed within that box, and the results of that analysis would be sent (via the internet) to Israel.

Source: Medicaldaily

I can’t tell you how many times I marveled at the technology involved in this little box.

Why did you use two methods of breath sample collection?

The off-line approach was the method that Prof Haick’s team had used for all of their studies and the on-line version was the next generation development of breath sampling, so they wanted to do a comparative analysis. They wanted to be sure that they were seeing similar results across the two methods.

Over the three year period of the study (except for a 2 month “hiatus” where a screw up on new BREXIT rules meant I could not work – leave without pay – yeah, thanks for that UK), I collected over 1,100 off-line and 900 on-line breath samples from 177 people with Parkinson’s and 37 unaffected, matched control participants.

Why were less on-line samples collected?

The on-line approach involved collecting multiple samples during each visit and the device required time to process and analyse each breath. Due to time constraints in the clinic, it was not always possible to collect all of the on-line samples and this resulted in a slight discrepancy.

These samples were all sent to Israel where they were analysed and that work resulted in this publication:

Title: The Utility of Breath Analysis in the Diagnosis and Staging of Parkinson’s Disease.
Authors: Stott S, Broza YY, Gharra A, Wang Z, Barker RA, Haick H.
Journal: J Parkinsons Dis. 2022 Feb 8. Online ahead of print.
PMID: 35147553

By analysing the breath prints collected in Cambridge, Prof Haick and his team were able to clearly distinguish individuals who had Parkinson’s from unaffected control participants. In addition, they were able to correlate signals with the stage of Parkinson’s.

We conducted clinical evaluations for each participant (including UPDRS, NART, ACE-R, MMSE, BDI, etc). The off-line system had an accuracy rate across a range of clinical measures of between 73.6% to 95.6%. The on-line approach showed similar, but slightly lower levels of accuracy (between 33.5% to 82.4%).

More than two samples were collected from 1/3 of the participants, and 8% of the participants provided more than 3 samples over the course of the study. When the analysis looked at disease duration, we found that we could easily distinguish between individuals who had been diagnosed more than six years ago, and those who have only recently been diagnosed (with more than 80% accuracy). And importantly, none of these results were influenced by Parkinson’s medication.

When the Israeli researchers looked at the chemicals in the breath samples, they found 29 potential molecules that were significantly different between the Parkinson’s group and the control group. Some of these may relate to the pathogenic pathways associated with Parkinson’s.

What sort of chemicals?

Well, for example, they found that propanal, acetone, and pentane were all elevated in individuals with later stage Parkinson’s (H&Y > 3). All three of these are recognised byproduct of lipid peroxidation (the chain of reactions of oxidative degradation of lipids) and they have been shown to be elevated in Parkinson’s (Click here, here and here to read more about this).

Curiously, Acetoin – which has previously been reported in Parkinson’s saliva samples and linked to disease duration (Click here to read more about that) – was found in the breath samples, but its levels were not significant or associate with disease duration.

We concluded the study by stating that the results “confirms that individuals with PD have unique volatile signatures in their breath (which does not relate to medication) and that this could be used at their point-of-care as a fast and noninvasive diagnostic aid and monitoring approach“.

What is the future of this technology?

During a visit to the Barker lab in Cambridge by one of the Israeli engineers, I ask them this question. Well, more specifically, I was saying how amazing it was that they could fit all this incredible breath analysis technology into a small shoe-box sized device that could then transmit the collected data via the internet to Israel.

I remember her smiling at me, and then she explained that this is just the first step. She said that they were working on shrinking all of that technology down further to something that would fit into the bottom of a standard cell phone.

Below is a picture of Prof Haick with an early prototype of that project:

My phone can smell me. Source: Israel21c

The goal would be constant monitoring of your health (via your breath print) when using your phone. Each time you call someone, your phone would be assessing your breath and by association your health. And the collected information could be set to your doctor if any red flag signals get detected.

I was really stunned by the potential applications, and joked that ordering out for pizza might bring both a pizza delivery van and an ambulance. The Israeli engineered laughed and said “at least you’ll have something nice to eat on the way to the hospital“.

As I understand things, Prof Haick and his team are still working on this potential application of the technology. There is a project called Sniff Phone that is working on this.

I for one am certainly looking forward to seeing future developments in this area.

So what does it all mean?

I’m a bit of a wall flower. I do not like – nor seek – attention. And if anything, COVID has made me even more of a hermit. But I thought that the last project I worked on while in academic research was of an innovative nature and would be of interest to the wider Parkinson’s community. So (again) I hope you won’t mind this piece of shameless self promo.

I am really impressed by the technology that Prof Haick and his team have developed. The fact that we (as a species) have gone from finger painting on cave walls to analysing tiny fractions of breath samples and using that information to better understand disease really blows my mind, and it gives me hope for our future. The potential for this breath print analysis technology for Parkinson’s is really exciting – the chance to better stratify the condition and track progression over time would be an amazing development.

It is incredible to think that this technology is still young and has a long way to go, and as I said above I will be watching to see where it all leads.

 

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