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Our current healthcare system causes more people to die… 🤨🏥

Ayaan Esmail
Feb 23, 2019 · 11 min read

The amount of people that have had died due to cancer in these past 2 years only increased. In 2017, 8 million people died due to cancer, and in 2018 9.6 million did. Up till 2018 The National Cancer Institute has spent 90 billion dollars trying to find a cure to this disease, and they still haven’t.

But in these past 10 years we’ve seen more innovation in the cancer curing industry than ever before. 10 minute cancer tests, CAR-T cell therapy, and immunotherapy are just a few innovations that we created to help cure this disease.

But why is it, that even though we have more advanced treating mechanisms, more technology, more smart people and more money. More people die due to cancer.

What could we possibly be doing wrong?

This is a question i’ve thought about for over a year now. And from all the knowledge I’ve gained researching and talking to professionals, the one thing I’ve realized we are doing wrong is our reactive approach to healthcare.

Now this doesn’t only apply to cancer, it applies to every single disease out there. See our framework right now for treating disease sucks, and it looks something like this: patient waits to get sick → patient goes into doctors → patient gets treatment, and they either get better or worse.

And how has this system been playing out for us so far? Obviously not very well. The only thing we’ve improved over time is better treatment (curing methods). But the problem is still there, more and more people are still getting sick and dying due to disease, we’re clearly not tackling this problem head on.

How do we solve it then? Well what if we just shifted our approach, from being reactive with our health - to being proactive. Right now, we wait for the sickness to come to us and then try to fix everything. But imagine a society in which we already know what sickness the patient will get 10 years in advance, so that we can prevent the disease from occurring in the first place. That’s the holy-grail of healthcare! Preventing disease. Yupee, we figured it out guys we’re going to prevent disease and people won’t get sick.

Well to prevent disease we’ll obviously need a lot more than a 14yr old saying Yupee! So to get things going, in this article i’ll be telling you about a technique that can be used to actually create a healthcare system, in which we no one gets sick. And it’s all with the help of metabolomics.

How to create a healthcare system in which no one dies due to disease.

To create a proactive healthcare system we need data. We need data on the most important, and reliant biomarkers of the patient because we can’t treat them if we know nothing about them. Now, although our genetic data is amazing, our DNA is pretty much stagnant. It changes a bit throughout our entire lives but isn’t an accurate representation of our current health state, whereas our metabolites are.

Metabolites are small molecules in our body that help with interactions and are continuously being absorbed, synthesized, degraded and are always in contact with other molecules both within and between biological systems, as well as with the environment. By understanding metabolites, we get a clear picture of what systems in the body are inactive or active and what disease(s) are being developed in certain areas. If we can compare metabolites of unhealthy patients to healthy patients, we can see the difference and based on the specific metabolites in certain areas, and what type, detect disease(s) before they start to fully develop.

This is basically the abstract/summary behind how were going to create a deathless healthcare system. Using different techniques to extract, analyze and detect metabolites, in short, we can proactively diagnose different diseases based on the composition of specific metabolites.

This is the simple framework that we can use to proactively diagnose patients:

Choose sample → Detect Metabolites → Analyze Metabolites → Formulate conclusions.

Let’s break it down.

Choose sample:

There are many samples one can choose from when detecting metabolites, samples are what we use to understand a disease and some of the main examples are urine, blood and tissue. However, the most accessible sample which also provides a sustainable amount of data is urine. But before we dive into how we can extract metabolites here are some rules we must use to get the most unbiased and accurate data possible. And for convenience sake, let’s just say were looking at urine samples:

  1. We must collect the total urine output over a 24 hour period, this variation can be averaged out.
  2. If a 24 hour collection sample is used then the next step is to store the urine at 4°C or lower and add sodium azide. Sodium azide is added to prevent the urine sample from growing any excess bacteria that could possibly make our data biased.
  3. Then we de-proteinate the sample since we do not want any proteins being attached to our molecules that could be mistaken as metabolites. We can do this using 50% acetonitrile containing 10µm chlorpropamide.

Detection of Metabolites:

Because of the extremely diverse chemical nature of the metabolome, there is no method that captures all of the metabolites. Another reason to choose urine, is because the metabolites are water-soluble, this means it can be dissolved in water, but also helps us to extract more metabolites compared to other samples. This is amazing since the more data the better conclusions we can make.

The metabolites in urine & other samples can be detected through two methods: mass spectrometry (MS) and nuclear magnetic resonance (NMR).

Mass Spectrometry:

MS is a simple 4 step process that works like this: ionization, acceleration, deflection & detection.

1.) During the ionization period, the atoms of the urine sample are first vaporized onto a plate. This vaporized sample then passes into the ionization chamber.

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2.) The electrically heated metal coil gives off electrons (negative charge) which are attracted to the electron trap, this is because it is a positively charged plate and just like you probably learned in school, opposites attract. In the ionization chamber the particles of the sample are bombarded with a stream of electrons, and some of the collisions are energetic enough to knock one or more electrons out of the sample particles, this creates a positive ion. Most of the positive ions formed will carry a charge of +1. These positive ions with a +1 charge, are then moved out into the rest of the machine by the ion repeller which is another metal plate carrying a slight positive charge.

3.) The positive ions (+1 charge) are repelled away from the ionization chamber, and now pass through three portions. These portions introduce voltage and accelerate the ions, which is needed for the next step - deflection.

4.) In deflection, the ions are deflected by a magnetic field. The amount of deflection depends on the mass of the ion, since lighter ions are deflected more than heavier ones, ions with 2 (or more) positive charges are deflected more than the ones with only 1 positive charge.

5.) For example, in the diagram below ion stream C is most deflected, this means it has the smallest mass, on the other side ion stream A is the least deflected meaning it contains ions with the greatest mass. Ion stream B is just in the middle of the two.

6.) Only ion stream B makes it right through the machine to the ion detector. The other ions collide with the walls where they will pick up electrons and become neutralized, meaning they will have no charge left.

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7.) When an ion hits the metal box, its charge is neutralized by an electron jumping from the metal on to the ion (right hand diagram). That leaves a space amongst the electrons in the metal, and the electrons in the wire shuffle along to fill it.

A flow of electrons in the wire is detected as an electric current which can be amplified and recorded. The more ions arriving, the greater the current. The above two factors are then combined into the mass/charge ratio, this helps us to easily detect things. For example, if an ion had a mass of 28 and a charge of +1, its mass/charge ratio would be 28, based on this we could easily identify the element since each one has a different mass/charge ratio.

The output from the chart recorder is usually simplified into a “stick diagram”. This shows the relative current produced by ions of varying mass/charge ratios. Below is an example:

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Nuclear Magnetic Resonance.

The principle behind NMR is that many nuclei have spin, and all nuclei are electrically charged. If an external magnetic field is applied, an energy transfer is possible between the base energy to a higher energy level (generally a single energy gap). The energy transfer takes place at a wavelength that corresponds to radio frequencies and when the spin returns to its base level, energy is emitted at the same frequency. The signal that matches this transfer is measured in many ways and processed in order to yield an NMR spectrum for the nucleus concerned.

NMR works by putting a sample in a very strong magnetic field, so that nuclear energy levels will split.

The first thing we need to do is spin all the molecules in the same direction, this can be done by obviously spinning the sample along a constant axis. Now that they are in motion, we need to be able to differentiate them from each other, and this can be done by placing the nuclei in a magnetic field. This will cause them to either align with the magnetic field or go against the magnetic field since each molecule has a different electric charge.

Then, blasts of radiation excite the molecules that have low magnetic resonance so they align with the highs, this brings everything back into equal resonance. Since different nuclei resonate at different frequencies we then measure the amount of energy needed to bring the lows and highs into alignment and boom: the amount of energy needed to bring the lows and highs to equal resonance, for each molecule, creates a peak on an NMR plot.

We can use either of these methods to detect metabolites in a sample, but wait. If we see all these peaks and random bars in our graph, what do they actually mean? Now that’s where analyzing metabolites comes in.

Analyzing Metabolites

These are the graphs we get after detecting metabolites using MS & NMR (I’ll explain what they mean below)

Mass Spectrometry (MS)

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Alright so first off, what does the header mean? Well, it’s saying that this is the data from a mass spectrometer for the molecule pentane, on the far right is just the chemical structure of the molecule written out. The x-axis title is m/z, this just means the mass to charge ratio. For example if the mass of a molecule is 56 and it has a charge of +2 then the m/z ratio would be 28. The y-axis is has the title, relative abundance. This is basically the amount of an ion produced in relation to the amount of the most abundant ion. It’s a ratio of intensity of a resolved peak to the intensity of the resolved peak that has the greatest intensity, this is usually called the base peak.

Nuclear Magnetic Resonance (NMR)

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Since NMR works by measuring the difference in the amount of energy needed to bring the molecules with low and high resonance to equal resonance, the peaks are just different energy readings for each atom in the solution being measured. Very simple. Since each molecule has a different electric charge each for each of the peaks it will be different for each metabolite.

Alright so we know how to prepare, detect, and analyze metabolites. The last thing we need to do is formulate conclusions.

Formulating Conclusions

Since we know the metabolites in the urine sample of our healthy patient we can now cross reference this to metabolites in a urine sample of unhealthy patients, and based on the differences, be able to tell what metabolites are in the healthy patient that currently shouldn’t be. Based on this we can also provide a percentage of their likelihood of having a certain disease as well. THAT’S SICKKKK … well not quite, since there are obviously other factors that allow one to have specific diseases. But overall, yes this does take us one step closer to creating a society in which we prevent disease, so again SICKKK!

Key Takeaways

Now this was probably a lot to take in, so to sum it up here are some key takeaways:

  1. Right now our healthcare system doesn’t work, its super reactive and if we look at the stats we can see it’s definitely not the optimal solution to eliminating disease. To allow less people to die due to disease we need to start to change our approach towards our health, from waiting to get sick, to being proactive with our health and preventing the sickness from occurring in the first place.
  2. To create a proactive healthcare system we need data on our patient. And in our case, we will use their metabolomic data since your genetic data is pretty stagnant and not an accurate representation of your current health. Metabolomic data, is the understanding of the patients metabolites (small molecules that are the basis of life). How does this help? Well, if we can compare metabolites of unhealthy patients to healthy patients, we can see the difference and based on the specific metabolites in certain areas, and what type, we can detect disease(s) before they start to fully develop.
  3. The process to create a proactive healthcare system with metabolomics looks like this: Choose a sample - it can be urine, blood or tissue → Detect the metabolites using different technologies such as mass spectrometry and nuclear magnetic resonance → Analyze the metabolites and make conclusions on what disease(s) will occur in the future by looking at the types of metabolites in the sample of healthy patients vs unhealthy patients!

Well, I guess that’s it for today! If you enjoyed this article:

  • Clap it.
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  • And follow my LinkedIn & Medium to keep updated with my venture in metabolomics :)

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