Alzheimer’s is a disease of data. The disease doesn’t have what we typically react to quickly: There is no pain, no blood, no pus, no rash, no dry mouth, nothing is visibly out of kilter.
Yet, Alzheimer’s disease generally starts its destruction 20 years before symptoms (most notably memory loss) are acknowledged. There can be more than 50 treatable causes to be dealt with simultaneously. More than half of the patients my company has worked with have more than a dozen of these treatable accelerants of brain degeneration — already active.
Remarkably, everything we need to know about dementia and its status is readily available through data: how to predict it, how to detect it, how to prevent it, how to treat it, and how to slow its progression.
We can make a checklist of what this data looks like; that is, what your doctor should be collecting. This isn’t particularly sophisticated, as much of it is already in their medical records. The data starts with the numbers from recent blood (and urine) tests, along with the list of medications you’re taking, the conditions and diseases that you might currently be addressing, allergies, immunizations, dentist records and so on. Another insight is what day-to-day life looks like: sleep, exercise, what you eat and how much stress you deal with. Add in the $99 raw data from collecting your genome (from a company like 23andMe or Ancestry.com), and you’ve got the data needed to pinpoint a path through the disease space.
Looking at these millions of data points yields good news, no matter your status. The first bit of good news is that we can see which of the treatable causes might be in good shape already. For example, you’re sleeping well, your insulin level looks good, your immune system isn’t combatting chronic inflammation, and you haven’t been taking a problematic drug for months on end (like a PPI, a benzodiazepine drug, or an oral corticosteroid).
The second piece of good news is that we can easily see the topics to work on — treatable causes that, if addressed, will slow the race to a diagnosis of Alzheimer’s disease or slow its progression if its symptoms have already appeared.
As one example, an individual may be encountering depression yet be taking drugs shown to make depression worse (depression is an accelerant of Alzheimer’s). Another: Someone’s blood test shows a high level of homocysteine (a potential brain toxin that can accelerate the path of Alzheimer’s disease), yet high homocysteine can be readily dealt with by adding a formulation of daily B vitamins.
The rub here is that there are indeed millions of data points to be looked at. The data analysis is complex, the data is interrelated, data affects other data in unexpected ways, and every person is profoundly different from one another. No one can do this kind of complex analysis in their heads, let alone calculations of trends, likelihoods and potentials. This includes your doctor and all the specialists with whom they might work. Sense needs to be made from the relationships among the diverse data from all of their fields, how they change over time, and the different ways that change can be influenced positively.
Your doctor and their team need a hand with this complexity. This is where computers come into play. They’ve been employed for decades on problems of similar complexity — like predicting the weather or the value of your portfolio. For example, my organization has implemented a software engine specifically for making sense of this massive amount of health data. It knows the rules of medicine, as well as every disease and condition, every lab test, every allergy, every medication. This engine has also incorporated the millions of interactions and relationships among medications, conditions and genes.
The software takes as input this myriad of data about a person’s health and medical status and uses a range of techniques to focus on the immense set of calculations needed to describe where someone might be on the spectrum of dementia — and what steps to take next. Maybe the newest Alzheimer’s drug offers a good path, or maybe a better approach might be to deal with raging gum disease and a deficiency of B vitamins. The software engine creates brief reports describing prioritized steps to be taken. The reports (known as care plans) provide doctors, a patient, their family and any caregivers with a recommended path forward, acting as a personal assistant to whomever is in charge of providing treatment.
Innovators are using a combination of AI, data and precision medicine to advance similarly forward-looking approaches to Alzheimer’s and dementia care. And more are likely on the horizon. The timing is urgent. Nearly 20 million Americans need help right now, and all of us are at risk. Doctors who embrace technologies that are driven on data and address the dozens of treatable causes can help close the vast gaps in care that patients and their caregivers face daily.
John Q. Walker, Ph.D, is a software engineer, entrepreneur, and Chief Technology Officer (CTO) and co-founder of uMETHOD Health. He leads a technology team on the development of a precision-medicine software engine for optimizing the treatment of chronic conditions such as Alzheimer’s disease and dementia. He holds the patents for creating systems to solve the treatment of these conditions.
The opinions expressed in McKnight’s Long-Term Care News guest submissions are the author’s and are not necessarily those of McKnight’s Long-Term Care News or its editors.
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