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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Diabetes Technology in 2018

Linking here to two resources I published recently:

First, my presentation at the April 2018 UCSF Diabetes CME course.  Those slides on a 2018 Update in Diabetes Technology are here:

Second, I wrote “A Clinician’s Guide to the Latest Diabetes Devices” for Medscape recently.  Here is the first section of that blog post:

This has been a huge year for technological advances in diabetes management. We are on a rapidly advancing path with continuous glucose monitoring (CGM) technology and finally approaching the holy grail of fully automated, closed-loop insulin delivery. Within a few years, patients with type 1 diabetes may never need to do another fingerstick or have another A1c test. For many clinicians, recent developments may seem to present an array of head-spinning options. Here, I’ll try to cut through the noise and focus on technologies that have the biggest implications for clinical practice and our patients.

CGM Data Directly to Your Smartphone

CGM technology has been advancing rapidly in accuracy, number of options, and ease of use, and the problem of inaccurate, painful, alarming, needy, and annoying CGMs feels long in the past. It is hard to believe that it was as recently as December 2016 that the US Food and Drug Administration (FDA) first decided that a CGM (the Dexcom G5®) was accurate enough to no longer require supplemental fingersticks for insulin dosing decisions.

In 2018, Dexcom released the G6 CGM, which is slimmer (and less likely to snag on clothing); requires no fingerstick calibration; and is the first to have the FDA indication of “interoperable,” meaning that it can “plug and play” in the future with other interoperable devices.

Medicare finally started covering CGMs in 2017, and in June 2018 agreed to stop blocking the ability of the Dexcom G5 (which is reimbursed by the Centers for Medicare & Medicaid Services) to transmit data directly to a smartphone, something most users of the G5 had already benefited from for a few years. This was a big deal, as I believe that the ability to view CGM data directly on a smartphone may be the technology advance that has most positively affected my patients with diabetes.

In the past year, billing codes for CGM improved to enable providers to be reimbursed for analysis and interpretation of CGM data. This brings diabetes management one step closer to population health, where a provider can review CGM data without an office visit; correspond with the patient over the telephone, by email, or by text; and be reimbursed for that work. I plan to try this out in my practice during the next few months, blocking off time every 1-2 weeks to review CGM data in web software and communicate recommendations to patients via our electronic health records portal or telephone, with no scheduled visit required.

Immediate Feedback, No Fingersticks Required

Of everything that has come out recently, Abbott’s FreeStyle® Libre Flash CGM has had the greatest impact on my practice. The Libre is a disk-shaped device worn on the back of the arm (see any recent photo of British prime minister Theresa May). My patients have been consistent in their appraisal that the Libre is relatively or even entirely painless to insert, nonintrusive on the arm, and stays on during activity or contact with water. Most important, it entirely changes their approach to diabetes management.

For more, please continue on to Medscape (free Medscape log-in required)


Do insulin pumps and continuous glucose monitors actually improve outcomes?

Nearly every day in my practice, a patient with diabetes asks me whether he or she should switch from multiple daily insulin injections to an insulin pump.  I often have a discussion with patients about whether or not they should be using a CGM (continuous glucose monitor) to help monitor blood glucose instead of just using SMBG (self-monitoring of blood glucose).  As an endocrinologist, it is very important to be able to advise patients about specifically what these new technologies have to offer them.  Do they decrease mortality?  Do they decrease long-term diabetes complications?  Do they improve glycemic control?  Do they improve quality of life for patients?  Do they lower costs?  All new medical technologies need to undergo a rigorous evaluation and testing with these types of questions in mind.  This is critical not just so that I can be honest and helpful to my patients, but also from the overall perspective of the healthcare system.

In that vein, Yeh et al recently published a meta-analysis in the Annals of Internal Medicine called “Comparative Effectiveness and Safety of Methods of Insulin Delivery and Glucose Monitoring for Diabetes Mellitus: A Systematic Review and Meta-analysis.”

This meta-analysis, funded by AHRQ, looked at the differences between:

  1. MDI vs CSII (multiple daily injections vs continuous subcutaneous insulin infusion)
  2. Type 1 vs type 2 diabetes
  3. SMBG (self-monitoring of blood glucose) vs rt-CGM (real-time continuous glucose monitoring)

What types of studies did they include in their meta-analysis?

  • Studies of adults, adolescents, or children with type 1 or type 2 diabetes mellitus
  • Studies from 1966-2012
  • 19 studies comparing CSII with MDI (>3 injections per day of either basal/bolus insulin or NPH/regular)
  • 10 studies comparing CGM with SMBG (>3 fingersticks per day)
  • 4 studies comparing SAP (Sensor-augmented pump) use with MDI + SMBG

* Studies were excluded if regular insulin was used in the CSII (pump) group (they felt this to be a weakness of prior analyses)

Here is the key data table:

A few things pop out from this table:

  1. Overall, they assessed the strength of evidence as relatively weak.
  2. In children and adolescents, CSII showed no difference in clinical outcomes from MDI.  CSII was better in terms of quality-of-life.
  3. In adults with type 1 diabetes, CSII led to more symptomatic hypoglycemia, but better hemoglobin A1c and quality-of-life.
  4. There were no differences between CSII and MDI in adults with type 2 diabetes.
  5. CGM, whether with an insulin pump or not, led to a benefit in glycemic control without any difference in hypoglycemia.

Some concerns and words of caution when interpreting these results:

  • Meta analyses can always suffer from publication bias.  That is, studies are much more likely to be published if they show positive results.  So it is possible that studies have been done that generated results that would have shown no difference between the two methods being studied, but these may never have been published and thus cannot be included in the meta-analysis.
  • These studies all had durations of 12-52 weeks.  There were no studies reporting on long-term outcomes like micro or macrovascular disease.
  • 24 of the articles (approximately 2/3) were supported by pharmaceutical companies

What does this mean?

According to this meta-analysis, CGMs did improve glycemic control.  Insulin pumps did not appear to have a significant effect on clinical outcomes, but did positively effect quality of life.  Remember that the studies included were all between 12 and 52 weeks, so one major limitation is that any longer-term effects would not be teased out.

While some may discount the quality of life improvements seen with the pump as being less important than clinical outcomes, I caution people from doing so.  In a condition as omnipresent as diabetes, maintaining good quality of life for the patient is critical and a very important goal.

In the end, the decision about whether or not to use one of these devices comes down to a conversation with the patient and their family, based on their personal preferences and what each device might offer them in terms of benefits and harms.  This meta-analysis adds some more information to that conversation.

Finally, this meta-analysis shows that we simply need more data to study so that more concrete conclusions can be drawn.

MedX 2012 Conference at Stanford

I spent a few hours this morning watching the livestream of the MedX conference at Stanford organized by Dr. Larry Chu.  From the portions I have been able to watch, this looks so far to be a really great conference.  The speakers this morning included a panel of self-trackers, Dr Paul Abramson (a physician who uses self-tracking in his office), Anne Wright of BodyTrack, and Sean Ahrens of Crohnology.  As I’ve always found with the mhealth and quantself communities, even from my office on the other end of the peninsula watching the video via livestream, the energy, excitement, and passion are phenomenal.  A few themes and ideas stand out from MedX itself and from the Twitter feed #medx, meriting some discussion.

1) Self-tracking is about the process, not the data.

This is absolutely true.  Using diabetes as a reference point, just looking at data for the sake of data is a waste of time.  People with diabetes make decisions all day, every day, most of which are minor and subtle decisions driven by habit.  Looking at data might help them make changes, but it’s not very likely.  More important is that they actively engage with the data and learn from it.  This process leads to crucial education about the self and personal habits that might help lead to different behavior the next time they are faced with a decision.  The act of gathering the data itself also enables personal exploration and revelations that would not be otherwise possible.  Self-care behaviors and self-awareness have no choice but to improve if someone is actively engaged in self-tracking.  Data should include not just numbers but life stories and context for what was going on at the time the numbers were collected.

2) Patients want doctors who believe them.

We heard this from many of the self-trackers and e-patients at MedX.  There are many patients who have found our current healthcare system too impersonal and uncaring.  I’ve heard this over and over, seen it happen in places where I work, and experienced it with family members.  There are a million reasons why this has happened and changes have to be made to medical education and healthcare economics in order to start making patients feel cared for again.  Regardless, we have no excuse.  No matter what pressures exist on doctors, we must retain compassion for the person sitting in front of us.  Everything else follows from this simple rule.

Sometimes, the problem can be that our current body of medical knowledge and experience is inadequate and only scratches the surface of the true pathophysiology of human disease and suffering.  All physicians have had experiences with patients where we listen to the patient describe their symptoms and we know something is wrong, but we have no diagnosis, no terminology with which to describe their particular situation.  This is always an incredibly frustrating experience for patient and physician alike.  As physicians, we are left not quite sure what to do, which frustrates us, because we are trained to do (though we should more often be like jazz musicians and place more focus on the empty space, ie what not to do).  Despite our frustration, physicians still have a choice in these situations.  The patient’s symptoms can be believed and empathy given, or the patient can be told, “I don’t know what you have, but at least you don’t have ______.”  I’d imagine patients prefer seeing physicians who do the former.

3) Patients want doctors who are willing to engage with their self-tracking data.

Even as a self-tracker and innovator in diabetes data and tracking, I find this leads me to some internal philosophical conflict.  Do I believe that self-tracking will help many patients improve their health?  Yes.  Do I believe that gathering self-tracked data will play a major role in the future of healthcare?  Yes.  Do I want my patients to self-track and to share their data and their experiences with me?  Absolutely!  Does the healthcare system have the resources and ability to handle this?  No.  Not right now.

First: We’re not ready yet for self-tracking data from a technical standpoint.

The simple fact is that most healthcare organizations are still trying to get an electronic health record implemented.  Getting self-tracked data into EHRs may be on the radar, but it is not imminent.  Once we have self-tracked data in the EHR, then what?  At least the patient’s medical record then becomes more whole, but EHRs have already left physicians in the position of having information overload, and this is in a situation where physicians already lack sufficient time to spend with each patient.  New piles of data will only exacerbate the situation.  Smarter ways of organizing and filtering the information are going to be absolutely necessary to make this manageable and to allow physicians to use the data with patients in the way it should be used.  (As a slight aside, having a single, unified patient-centered record is also a precondition for a functional system.  None of this works if each patient has their data fractured over ten different healthcare organizations, PHRs, and websites.)

Second: We need to find the proper place for self-tracking tools and data within healthcare, which requires a better understanding of their effect on healthcare quality and costs. 

There was a Twitter comment in the #medx stream expressing outrage, saying, “Cannot believe that some diabetes tracking tools are not covered by some insurers. Absolutely nonsense!! give them the tools!!!”

This was in reference to a panelist with type 2 diabetes who used a Dexcom CGM and had to pay for it out of pocket.  This Twitter comment shocked me.  It is not news to anyone that the resources of our healthcare system and entire economy are strained by rising costs.  There is just no way it is sustainable, acceptable, fair, ethical, or you-choose-the-word-to-put-here for everyone to expect that their healthcare insurance should cover every device they want.  Our country has to stop expecting this.  While there have been many new devices in healthcare that have achieved better health at a reasonable cost, history is littered with examples of new devices that have either been overwhelmingly expensive for minimal health benefit or have flat-out had negative impacts on health.  So, there simply HAS to be research done (hopefully efficiently and effectively) showing that a new device has health benefit, and at some reasonable cost, before we expect insurers to cover the device.  Using the specific example of a CGM for a type 2 diabetes patients, there are too many people in the country who don’t even get basic, proven care for type 2 diabetes, like eye and foot exams, blood pressure control, or metformin, for us to be claiming that all insurers should be covering this device for all type 2 patients.  If a patient wants to pay for an unproven device or treatment out of pocket, I have no qualms, but we cannot expect society to pay.

This serves as a clear reminder to those of us innovating in healthcare that successful innovation will be mindful of the value equation, ie either better healthcare quality or lower cost, or ideally both.  I’m incredibly optimistic and excited about what lies ahead in healthcare innovation, because I think we will create things that improve healthcare value.  I believe that self-tracking will be a major component of this, and will be especially important in empowering patients, bringing new and critical data into the doctor’s office, and creating a new paradigm for the doctor-patient relationship.

What a great conference!  Thank you to Dr. Chu and all of the speakers, panelists, attendees, and other remote attendees!

Quantified Self and Diabetes… the Perfect Match

Take a few minutes and watch this video of Jana Beck from a Quantified Self meeting as she explains how she took data from her Dexcom CGM (continuous glucose monitor) and created her own data visualizations (Thank you to Russ Cucina for sharing this video with me).  I think that the visualizations she created are very cool and obviously helped her in her journey to try out a new method of managing her diabetes with a low carbohydrate diet.

Beyond this particular video, I am also very excited about the Quantified Self movement and its overlap with diabetes.  Type 1 diabetes is a disease that requires patients to monitor their physiologic status on a frequent and routine basis, from food intake to activity levels to glucose levels.  Many people in the Quantified Self movement are voluntarily doing much of the same thing.  While these people never have to have the same concerns of a person with diabetes that, if they feel like “slacking off” for a day, something might go horribly wrong, they are at least starting to develop some empathy and interest.  There is a fantastic synergy here, introducing a new cadre of talented, engaged, and enthusiastic people to the field of diabetes technology.  This is happening whether or not they realize it!  This entire group of people are trying to monitor their every action and learn from the data visualizations… precisely the thing we try to help people with type 1 diabetes do!  I’ve not yet been to one of the QS MeetUp events in San Francisco, but plan to go sometime soon.  I am convinced that advancements in diabetes care will come from the QS movement, whether intentional or accidental.

Tip: If you already know about diabetes and CGM devices, you can skip to minute 6 and start there.  The visualizations start at about minute 8 of the video.

Diabetes technology is hitting the regular consumer media

Gizmodo, a well-known consumer electronics and technology blog, posted a story about progress towards the artificial pancreas.  Is diabetes technology moving more into the popular consciousness?