Continuous Glucose Monitoring: What It Means for the Definition of Diabetes

I recently published a Commentary on CNBC about the future of glucose tracking using continuous glucose monitors.  Here is the link –  – and here is the Twitter thread that I wrote with further thoughts.

In my editorial on use, I cite this fantastic article on ‘glucotypes’ from geneticists & endocrinologists (). One of my favorite papers from 2018. I want to explain a bit more why I think this paper is so important.

First, here is the editorial in – on where I see use going in next few years in management of diabetes and increasingly in use for people not diagnosed with diabetes.

How we define ‘diabetes’ and make a diagnosis has changed dramatically over the decades. See a short presentation I gave on this in 2012 here – . We’ve progressed from urine testing to OGTT to fasting glucose to A1c.

The paper from Hall et al demonstrates that our current diagnostic tests are probably insufficient. They’re missing lots of people, now labeled as ‘normal,’ who shows actually have dysregulated insulin responses to glucose consumption.

Do these people have diabetes? Prediabetes? These categories were historically defined based on what we know about A1c correlating to risk of microvascular complications (ie retinopathy). That is, it is ‘worth’ diagnosing someone with diabetes if A1c correlates w increased risk.

Really, what we mean is, would the benefits of treatment for diabetes outweigh the harms of treatment for a person with a certain degree of risk based on their A1c?

But… A1c is just an average, fraught with issues. What really matters is, is a person metabolically healthy and are they at increased risk for heart disease or microvascular complications down the road? So, there is a long road ahead for future research here.

How do we categorize ppl based on insulin-glucose response seen with ? Are people with abnormal ‘glucotypes’ at higher risk for heart disease & microvascular complications? What are long-term outcomes? Will they change behavior & improve outcomes when faced w CGM data?

So, to summarize: Not only is a necessary tool for all with , & massively valuable for most with type 2 diabetes, but I believe its use will help us redefine what we think of as , how we define a continuum of risk and categorize individual physiologic responses.

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Feedback Loops and Teachable Moments: The Future Diabetes Care Paradigm

The current paradigm of office visits every three months for PWDs (people with diabetes) is not the right model (nor is it for other similar chronic conditions).  The management of diabetes requires a patient to make dozens of daily self-management decisions.  “How much insulin should I give for this slice of pizza?  Do I need to eat a snack to prevent my blood sugar from going low before I go for a jog?”  Diabetes related questions and issues do not occur on an every-three month basis in synch with this current model for office visits.  They are predictably unpredictable.  Accordingly, to best serve our patients, our system must be flexible and nimble.

In the current model, I see a PWD in my office and let’s say, for example, that we decide together to make a change to his insulin to carbohydrate dosing ratio.  He then leaves my office and we wait three months to reconvene and see if that dosing plan change is working or not.  It’s not that it takes three months to decide.  We could probably know within a week or two if the change is working.  It’s just that healthcare isn’t set up that way.  Our entire world now, in every industry and facet of life, is about data, analytics, and metrics.  Other industries have learned that rapid feedback loops are effective.  Adjusting a PWD’s insulin to carbohydrate dosing ratio should be no different.  By the time he comes back to my office three months later, the opportunity for learning may already have been lost.  Neither one of us has gotten timely and relevant feedback about our decisions.  We may have lost the opportunity for a teachable moment.  Healthcare needs to develop a new model where these feedback loops are much tighter and much faster, actually capitalizing on opportunities for teachable moments.  (Sidebar: One doctor who realized this years ago was Dr. Jordan Shlain, who founded HealthLoop)  Research studies show that PWDs are more successful and confident with managing their diabetes when they feel like they have the backup and support of their clinical providers looking over their shoulders to make sure things are going ok.  If we were to design the system from scratch to accomplish these goals, we probably would not have built it to rest on the concept of office visits every three months.

So, what should be the future model of a Diabetes and Endocrinology clinical practice?  Here’s what I imagine my practice looking like in the (hopefully near) future.  Instead of having 16 office visit slots per day of 30 minutes each, I imagine myself seeing 5 patients a day for 45-60 minutes each, allowing us to take our time working together in person and truly addressing the needs and goals of the patient.  These longer visits are essential for a patient new to my practice, a patient with a complicated or unknown diagnosis, a patient with complications or a major change in their disease state, or for discussing major changes in therapeutic course or strategy.  The rest of my day will be spent using a dashboard to do remote population management, looking for trouble spots among my patient population and focusing in on those, and doing telemedicine, connecting with patients through video-chats to make more minor adjustments and to do brief “check ins.”  Ten minutes spent with a patient at the point where there is a teachable moment like a low blood sugar from walking the dog might be more effective than a standard 30 minute office visit every three months.  We’ll have to test this hypothesis, of course, but we must try it.

This is why I’m brimming with so much enthusiasm and excitement about working with the non-profit, Tidepool, who is building an open data platform and a new generation of software applications for the management of type 1 diabetes.  Tidepool will provide us with the technology infrastructure to reach this vision of more frequent feedback loops and teachable moments.  I’m also very excited about the work that my UCSF colleagues, Drs. Ralph Gonzales and Nat Gleason, are doing to pilot the use of telephone visits and e-visits with patients in place of office visits.  Their work is paving the way toward demonstrating efficacy of e-visits, helping to achieve payer reimbursement so that such a model can take root.

Asthmapolis: Why Can’t Inhaler Sensor Be Adapted For Diabetes and Insulin Pens?

Asthmapolis, which launched in 2010, has been in the news the last few days after announcing a $5 million series A venture capital round of funding.  They are an innovative mHealth company that is focused on improving care of asthma through a combination of hardware and software.  They developed a small sensor that attaches to the top of an asthma inhaler and wirelessly synchs with your smartphone.  The data can then be tracked, viewed, analyzed, sent to a physician, used for clinical research, etc.  Anything you can imagine.  The frequency with which someone uses their inhaler is often directly tied to how severe their asthma is, and can predict which people are headed for trouble.  So, rather than each squirt of an inhaler being an invisible act lost to history, it can now be tracked and used to generate meaningful data to help patients (and for research).  This is exactly what mHealth is all about.  The innovators at Asthmapolis have developed a relatively simple and straightforward intervention that should add no additional hassle to a patient’s life but might be life-saving if it can serve as an early-warning system for worsening asthma.

Asthmapolis TechCrunch Headline Apr 2013

Taking this one step further, we need to have such an add-on piece of hardware for insulin pens for use by people with diabetes. It is obviously not exactly the same: with an asthma inhaler, one press is one dose, whereas with an insulin pen, it would have to be able to capture the exact amount given; with asthma, an increasing use of an inhaler could be a sign of impending trouble, whereas with diabetes, daily fluctuations in insulin dose can often be a normal pattern.  However, there are enough important parallels that make this an invention that we need in the diabetes world.  We are always asking our patients to keep track of how much insulin they use, but it is an extra task for them in their already busy lives, one which could relatively easily be automated.  I’ve still yet to see a prototype of such a device for an insulin pen outside of the GluBalloon project from MIT about a year ago.  I hope that there is more to come in the near future for us in the world of diabetes.

GluBalloon Insulin Dose Tracker

Best of luck to Asthmapolis… they look to be poised to make a major difference in the lives of people with asthma.

 

The Future of Diabetes Management: Social Networking and New Technologies

I gave a talk yesterday to a great crowd at the annual UCSF CME conference, Diabetes Update.  The slides from my presentation, “The Future of Diabetes Management: Social Networking and New Technologies,” can be viewed on Slideshare.

From “Pull” to “Push”: A Transformation in Medicine

Weitzman et al just published a very interesting article in JAMA Internal Medicine called “Participatory Surveillance of Hypoglycemia and Harms in an Online Social Network.”  They looked at using the online social network, TuDiabetes, as a method for surveillance for hypoglycemia.  You can read their very novel article here.

I was flattered to be asked to write a commentary on this article.  Below are the first few paragraphs of my commentary and a link to the full text PDF for download here: JAMA Internal Medicine, Feb 2013, Aaron Neinstein, From “Pull” to “Push”: A Transformation in Medicine: Comment on “Participatory Surveillance of Hypoglycemia and Harms in an Online Social Network.

 

Consider the words we use to describe what a physician does when she or he sits across from a patient to perform a history. Take. Obtain. Elicit.

These words all conjure images of physicians extracting information from patients. We pull information not just from our patients but also from our information sys- tems, calling up vital signs and laboratory results when we want them, on our time and our terms. However, this is rapidly changing, as information will be coming to us from the patients themselves to create “push” medicine. Are we ready? Not yet, but with some pivoting and some preparation, we can be.

CONVERGING TRENDS LEADING TO “PUSH” MEDICINE

Several synergistic technological and cultural trends are leading us toward “push” medicine. Increasingly ubiquitous technologies such as broadband Internet, smartphones, and cloud computing have created fertile ground. There is increased focus on patient-centered decision making. Patients are increasingly well-informed; nearly 60% of adults have looked online for information about health topics.1

Data are coming from many new sources. Mobile applications enable patients to actively create data, such as by answering symptom questionnaires, or allow wireless sensing devices to semipassively generate data like heart rate or physical activity. Other mobile applications use your calendar, text messages, and e-mails to passively generate meaningful health information, such as mood or quality of life.2 The realm of data collected in the home is expanding beyond blood pressure and glucose log books to tracking daily pain and functioning scores for rheumatoid arthritis. Patients are also contributing data through social networks and personal health records and by direct entry into the electronic health record. Patients are increasingly requesting their personal genomes—and to do so they need only curiosity, an Internet connection, and a credit card. These activities are increasingly common, and 27% of Internet users, or 20% of all adults, have tracked their health online.1