Diabetes Technology Report
The world of diabetes research and innovation is moving forward at a lightning pace. At Diabetes Technology Society (DTS) we recognize the need for a free and easily accessible resource that provides clinicians, researchers, innovators and people with diabetes with up-to-date and authoritative information on the latest developments in diabetes technology research and innovation.
Diabetes Technology Report is a new podcast from DTS co-hosted by endocrinologists David Klonoff (UCSF), and David Kerr (Sutter Health). Here, you can learn about the latest advances in glucose monitoring, insulin delivery, digital health, cybersecurity, wearables, and artificial intelligence applied to diabetes. We will be interviewing opinion leaders, inventors, researchers, and clinicians, as well as authors of the latest scientific research.
Diabetes Technology Report
Nicole Spartano, PhD from Boston University on CGM Beyond Diabetes
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We talk with Boston University researcher Nicole Spartano, PhD about what continuous glucose monitoring can reveal in people without diabetes and why those patterns may matter for future diabetes and cardiovascular risk. We dig into Framingham Heart Study findings (https://diabetesjournals.org/care/article/doi/10.2337/dc26-1197/172437/Beyond-Traditional-Glycemic-Measures-CGM-Glycemic), why A1C may not match CGM in non-diabetes ranges, and how to avoid overreacting to glucose spikes.
Welcome And Guest Introduction
IntroWelcome to Diabetes Technology Report, co-hosted by endocrinologist David Klonoff from UCSS and David Kerr from Sutter Health.
David KlonoffHello, welcome to Diabetes Technology Report. I'm Dr. David Klonoff from Sutter Health, and uh I'm with my co-moderator, Dr. David Kerr. We have an outstanding person to interview today, and David will begin the interview.
David KerrExcellent. Hello, everyone. I'm David Kerr, speaking as usual from Santa Barbara, California, and I'm absolutely thrilled and delighted to have Nicole Spartano from Boston University, who is a guru when it comes to continuous glucose monitoring, particularly in people not on insulin. So I'm absolutely thrilled to have you on board. Welcome, Nicole.
Nicole SpartanoThanks. Thanks so much for having me.
David KerrI'm always intrigued as to how you ended up where you are. I mean, what is it about diabetes that gets you up in the morning? How did how did this come about?
From Lifestyle Science To CGM
Nicole SpartanoSure. I think, you know, I've been interested in chronic disease development and how different lifestyle habits might lead to prevention of chronic disease. And that really led me to sort of do some early training in nutrition and basic science. And then in a postdoc in exercise science, really trying to link different lifestyle behaviors together. But really in that exercise science program, I developed a love for digital health. And so, you know, it was in the early days of Fitbit and sort of using accelerometry as a way to assess uh physical activity behavior over the course of the day or multiple days. And, you know, it it drove this interest that that led me to sort of be aware of other other technology in this space and and how they might be used to help us understand about behavior and deeper understanding about physiology in sort of chronic disease prevention. And so you know, glucose monitoring, you know, continuous glucose monitoring was was one of those technologies that that I really fell in love with.
David KerrSo so have we all, I think. Well, so what at the moment, um I mean, we're everyone, I'm sure, is familiar with the recent work looking at CGM profiles in people without diabetes, pre-diabetes, and so on. So what how have you taken that and what are you up to at the moment?
Framingham CGM And Risk Patterns
Nicole SpartanoYeah, so um, so I work with a large cohort study, the Framingham Heart study, and we've used different types of digital technology within that um within that study. Um, and just actually last year completed our collection of um of uh continuous glucose monitor data within that cohort. And so that's mostly people without diabetes. And so, you know, I think the the eventual goal of the study is to follow these people over time and understand um patterns that might you know indicate higher risk for developing diabetes and potentially cardiovascular disease and other sort of um cardiometabolic diseases.
David KlonoffNicole, since you're working with CGM and people with prediabetes, what do you think is the value of using CGM to diagnose diabetes? Is that a viable diagnostic test?
Nicole SpartanoUm I you know, I've I I I'm hopeful that one day it it may be. Um I think we're we're understanding that um, you know, in some of this early, early work, understanding that it can be useful in identifying um, you know, uh people that that might have um you know impaired glucose tolerance and and things like that that might not be picked up by um you know standard uh clinical measures like uh fasting glucose or hemoglobin A1C, but might be picking up more nuanced patterns that might reflect pathophysiology that sort of isn't as apparent in those sort of um, you know, single time point sort of markers.
David KlonoffWell, a couple uh ways that CGM could be used. One would be to see how it correlates with a known marker like impaired glucose tolerance. Another might be for it to see how it links with some type of uh mechanism or aspect of pathophysiology of diabetes. Do you think uh either of those would happen?
Nicole SpartanoUm I think definitely. I I think there's there's a few groups looking at um sort of deep phenotyping of the um sort of insulin resistance, pre-diabetes um uh sort of pathway to diabetes and understanding that, you know, observing that that there are um you know CGM traits that that might um sort of link to those those different pathologies.
David KerrNicole, just to have you found a link or has anyone found a link between glucose parts and CGM and maybe lipid disturbance or hypertension or waist circumference? This I mean this that's closing the gap.
Nicole SpartanoYeah, sure. I can I can um be a proud uh mentor for a moment and and salute one of our trainees, um PhD student Bahart Bakhs. She in our group just um had a paper accepted in diabetes care where she um used our framing heart study um glucose monitoring data and showed that um, you know, we were able to identify sort of subtypes, sub-phenotypes within people who don't have diabetes. Um, and some of them were some of those phenotypes were tied to things like um higher time spent above 140. Um and she was able to show that you know, some of that um that phenotype of sort of higher glucose burden was related to um higher prevalence of hypertension and dyslipidemia. Um and the effect sizes are really strong. Um, so I so I think it's it's gonna be an important paper that people should go, people should go check out. Um and um I think it sort of uh builds some promise that that this tool might be something um that's used for people without diabetes to help to identify those that might be at high risk for, again, not only diabetes development, but also potentially sort of on a path toward um cardiovascular disease risk.
Over The Counter CGM And Pitfalls
David KerrCongratulations. Um I'm just putting my consumer hat on here. There's 101 metrics you can look at from CGM, and that's something we're interested in. What's what's the what's your guidance at the moment if you don't have diabetes and you decide to go and get your CGM from the local pharmacy? What is it you should be looking at?
Nicole SpartanoYeah, that's a that's a great question and one I think um we've we've thought a lot about. I I think um as of today, I I don't think I would make major difference, you know, major changes to um, you know, my um, you know, I don't know that clinicians anyway could could guide really differences in in terms of medication or things like that based on um CGM data. Um and I think, you know, part of the the um part of the concern about um having these um devices sort of available over the counter to individuals who aren't being um, you know, who may not be followed uh with a sort of healthcare provider or clinician, um that, you know, people might make decisions that that maybe doesn't make sense um holistically for their health, just because they might see sort of a spike in glucose. Um, but but I think that doesn't mean that in the in the future this this technology won't be um really important for helping, you know, precision interventions. And I I do think that that is sort of where the field is going and and sort of there is um sort of promise there. I I I think um I do want to mention one of the one of the challenges we run into um on the research end and in making sort of recommendations like this is um there are really few comparison studies of the different devices out there. And um I think you know it could be that that path to be able to do these comparison studies could be a little bit easier um by by the companies who create the devices because I think there are differences. And you know, I'm sure you two are well aware, and uh patients with individuals with diabetes out there listening are are well aware of of sort of these differences. Um, but for a lay consumer who you know doesn't have sort of um guidance on this, I think it's it's really important that we're able to put out there sort of understanding what the differences are and you know um when we should expect to see differences.
AI Insights And A1C Discordance
David KlonoffNicole, are you using AI in your research? And do you think that AI will be uh helpful for patients to interpret their CGM data?
Nicole SpartanoWell, um definitely our team is using AI um to help um you know make sense of this this large time series data, and um we're doing things like um you know the that people in the in the um diabetes space are doing, but you know, thinking about glucose forecasting and um you know uh identifying um using machine learning um tools to identify you know different subtypes of of um uh on the risk and pathway to diabetes. Um I think it's really important that we can translate what we learn from those those studies to you know consumers and and patients and individuals who are using the devices. So I think at this point um that that translation hasn't been quite as um as um you know uh done as well as as it could be.
David KlonoffNicole, last year you were senior author on an important article about uh correlating CGM metrics with hemoglobin A1C. Could you uh comment on what you learned from doing that study?
Nicole SpartanoYes, that was with um Jorge Rodriguez. Um uh and yeah, I think um we know that in people with diabetes, there is this strong relationship between um mean glucose, sort of the average of your CGM glucose over the time period you wear it, and someone's um hemoglobin A1C levels. And of course, there's some discordance, and and um those that discordance is is also important. Um but we were interested in exploring when you get down to the non-diabetic level, um, what the relationship would be like. And we sort of assumed we would see a similar relationship. Um, but we really saw very little relationship at all. They're almost um uh you know completely discordant from one another. And I think part of um what we see in with the CGM data is we see a much wider range of mean glucose um levels than than I think that um we would expect to see. Like if you know, if if anyone out there wears a glucose monitor, you know, depending on the device you use, you often people without diabetes will um will have a fasting glucose level above 100, right? So it there may be you know some overreading um depending on devices and um uh potentially also um CGM may be capturing um you know uh sort of shorter or more acute dynamics in in um glucose fluctuations throughout the day that hemoglobinal and see as sort of uh um sort of more holistic um measure might not be picking up. Um so but yeah, the important I think takeaway is that um we sh we shouldn't be translating amine glucose level from someone without diabetes um in the same way we should be tran we could translate it in someone with diabetes.
Behavior Change And Safety Concerns
David KerrNicole, um can I ask you, I'm just this question's always intrigue me. If you just give someone a CGM and you ask them to wear it, how much impact do you think that has on their day-to-day behavior immediately and subsequently? Um without adding on you know education programs and and all that good stuff, I just wonder if you've got a feel for the magnitude of the impact for someone, uh a human to have access to real-time physiological data.
Nicole SpartanoYeah, it's a great question. I think um as with other sort of digital health tools that tell us about our behavior or physiology, I think while we're wearing the devices, we, you know, may change our behaviors. If, you know, if we see something like a spike in glucose, you know, we we may try to avoid whatever behavior it was that that created that, um, such as a higher carb meal or um uh you know, I could, you know, for for myself, I think um, as I've looked at the data, a lot of um glucose spikes um or excursions that sort of last overnight right has been something I've I've observed that um, you know, might might be something for for individuals without diabetes to sort of um check and see whether that's that's happening for them and might be you know due to their meal timing and and things like that. Um but I think it's really uh personality dependent, right? And um I do think we need really good education programs that will help guide individuals in in how to interpret the data that they're seeing and um in in uh understanding how people even like to visualize data, right? Some people are numbers people and and some people may, you know, not really um key into that. Um, you know, and I I think another sort of concern is that there are people that um, you know, as I mentioned, might might interpret, might overinterpret the data they're seeing. You know, I think especially people um with eating disorders and things like that should be, I think, really, really cautious and careful. And um, you know, clinicians who work with this this patient population might really want to um you know counsel people closely if if they're going to be using the technology.
Conclusion
David KlonoffNicole, thank you for uh agreeing to be interviewed. You're helping to make Boston University a powerhouse in diabetes research. So uh as we conclude this uh session of Diabetes Technology Report, uh the interview will be available at the Apple Store at Spotify and at the Diabetes Technology Society website. So until our next uh interview on behalf of Dr. David Kerr and me, thank you very much. Uh bye bye. Thanks.
