Should You Use a Continuous Glucose Monitor (CGM) If You Don't Have Diabetes?
- The Facility Denver
- Sep 2
- 24 min read
Let's talk about continuous glucose monitors (CGMs) in people without diabetes. What’s helpful, what’s overhyped, and why the flatline might not be the gold standard we think it is.
We see this come up with patients all the time—whether it’s curiosity, social media influence, or a desire to optimize health. CGMs can be powerful tools, but they’re also misunderstood. In this post, we’ll share the science, our patient experience, and some practical ways you can use CGMs wisely. Spoiler: it’s not about chasing a flat line, and it’s definitely not about fearing your food.
👉 Want to cut through the hype and use a CGM with confidence? Download our Guide to Using a CGM (includes access to the Stelo Glucose Biosensor).
The Origins of Glucose Monitoring
CGMs were originally built for people with type 1 diabetes (and later type 2), to monitor blood glucose continuously and adjust insulin or medications accordingly. The technology uses a small filament inserted under the skin to measure glucose in interstitial fluid, which is similar to blood glucose (with a slight lag).
What started as a life-saving medical tool has since been adopted in wellness and functional medicine. Athletes, biohackers, and patients curious about prevention are wearing CGMs to learn more about how food, stress, and sleep affect their metabolism.
It’s a fascinating shift: from disease management → health optimization. But here’s the catch... what we know from diabetes care doesn’t fully translate to healthy individuals.
What We Do Know (and What We Don't)
Most of our standardized “glucose targets” come from people with diabetes. Time in range (TIR), upper and lower limits, and definitions of “control” were designed for those who rely on insulin or who have insulin resistance.
For healthy individuals? The data is newer, fuzzier, and nuanced.
Thanks to massive datasets—like those collected by ZOE, Levels, NutriSense, and more—we’re beginning to define what a “healthy” glucose response looks like. Sometimes you knowingly “opt in” to share your data; sometimes it’s buried in the terms and conditions. Either way, your data may be helping build the next nutrition algorithm.
So far, here’s what researchers and clinicians generally agree on:
Healthy range (non-diabetic): 70–140 mg/dL
Time in Range (70–140 mg/dL): ~96% of the day
Mean 24-hour Glucose: ~99 mg/dL (± 7)
References: (Shah et al., 2019) (Battelino et al, 2023) (Klonoff, 2022)
Clinicians in practice often use even tighter ranges (70–120 mg/dL), but here’s the problem: there’s no standardized “optimal” for healthy people. That hasn’t stopped influencers from posting graphics like “70–110 is the only true healthy range.” That kind of oversimplification creates fear, not clarity.
The reality? We don’t yet have clear cutoffs for “bad” or “good” patterns. What matters is context—spotting patterns over time, paired with lifestyle factors like sleep, stress, and meals.
👉 Curious how your glucose stacks up? Get the Guide to Using a CGM and learn how to interpret your data safely.
Personalized Glucose Responses
One of the most exciting findings from the ZOE PREDICT trials (run with King’s College London, Stanford, Harvard, and others) is that metabolic responses to the same meal vary significantly between individuals. A 2025 study out of Stanford further defined this interindividual variability:
Some people are “grape spikers” (sharp rises after fruit sugar).
Others are “rice spikers” (big jumps with starch, flat with fruit).
Some respond consistently no matter what’s on their plate.
These are called "glucose phenotypes"—basically, your body’s personal fingerprint for how it handles different carbs.
The key? While your response to a specific food is personal, it tends to be consistent. If rice spikes you today, it’ll probably spike you tomorrow. But your friend might stay totally flat.
This is why your “healthy” breakfast might keep you full, while the same meal leaves your spouse starving by 10 a.m. CGMs help you spot those unique patterns.
Misconceptions and Dogma to Challenge for "Healthy" Glucose
Here’s what we hear all the time—and what we push back on:
“All spikes are bad.” Not true. Post-meal spikes are normal. What matters is recovery (how quickly you come back down) and what your overall patterns look like. A single sushi night spike isn’t a diagnosis.
“Flat = optimal.” A flat line may actually mean restriction, delayed digestion, or cortisol changes...not necessarily health.
“More data = more clarity.” Without context, data is just noise. Or, as we like to say: a glucose curve without context is just a squiggle. It tells a story, but it’s not the whole plot.
Recent reviews even suggest nuance for healthy people:
A 2023 review notes that glycemic variability—those recurring rises and dips—has been linked to adverse cardiovascular outcomes and higher mortality, even in non-diabetics.
Other NIH studies confirm that mild, infrequent spikes are normal and not harmful.
So it’s complicated: variability may be a concern if it’s persistent and extreme, but occasional blips? Not pathology.
Why People Love CGMs Anyway
Despite the nuance, patients love CGMs; and we understand why.
Real-time feedback drives behavior change. Just like keeping a food log makes you eat healthier, wearing a CGM makes you more aware.
They reveal hidden patterns. Reactive hypoglycemia, poor sleep effects, caffeine tolerance, stress spikes (yes, toddler bath time shows up in the data).
They connect the dots. Patients often say, “Oh, now I see why I crash at 3 p.m.”
That said, CGMs aren’t perfect. Over-reliance can create food anxiety. Misinterpreting spikes can lead to unnecessary restriction. And cost/access to smart data interpretation remain barriers.
How We Use CGMs with Patients in our Functional Medicine Clinic
We don’t send every new patient home with a CGM. It’s a tool, not a starting point.
First comes blood work: insulin, A1C, fasting glucose, thyroid, lipids. We need to start with the fundamentals (explore direct order labs here).
When we do recommend CGM, it’s usually for:
PCOS or hormone dysregulation
Persistent fatigue or brain fog
Stubborn weight resistance
Athletes training or fueling
Curiosity + prevention (no gatekeeping!)
We care less about every tiny spike and more about patterns: how meals, sleep, and stress interact with glucose over weeks.
Used wisely, a CGM is a co-pilot. It won’t replace the basics (nutrition, sleep, stress management), but it can fine-tune them.
Our advice? Approach CGM data with curiosity, not control. Glucose biosensing is a tool.. NOT a moral compass. As we like to say: chasing a flat line might flatten your joy too.
👉 Want to learn how to use a CGM wisely? Download our Guide to Using a CGM and get access to the Stelo Glucose Biosensor.
If you prefer content in audio format, check out Facilitated Episode 25 | CGM: WHAT WE KNOW + WHAT WE DON'T for a candid discussion between Functional Medicine Nutritionist Kate Daugherty and Functional Medicine Doctor Mitchell Rasmussen | Listen here
Further Reading:
Healthy Glucose Ranges in Non-Diabetics – Journal of Clinical Endocrinology & Metabolism Read More »
Why Your Glucose Spikes Differ from Your Friend’s – ZOE PREDICT Trial Insights Read More »
When Rice, Grapes & Potatoes Tell Your Metabolic Story – Nature Medicine (2025) Read More »
Glycemic Variability & Heart Health – Acta Diabetologica Review (2023) Read More »
The Ambulatory Glucose Profile (AGP) – How CGM Data Gets Standardized Read More »
Episode Transcript | FACILITATED Episode 25| Continuous Glucose Monitors (CGMs) in Non-Diabetics: What We Know, What We Don't, and What to Watch
Kate:
No one just goes into a podcast blindly. They see the title. So: continuous glucose monitor CGMs in people without diabetes.
Mitchell:
One thing I’ll say why I like when people bring this up is during the visits I do most of the gabbing with the person and this is one aspect that I really sit back and let you teach me, and I learned so much from you about this. I think, as a nutritionist, you’ve gone above and beyond to really understand the importance of monitoring and management in the context of, like you said, non-diabetic individuals.
Kate:
Right. There’s so much nuance to it and I think social media influencers, health influencers have really taken it and simplified it too much and I don’t agree that a flat line is really the gold standard that we’re going for. So it’s exciting for me to help a patient understand the nuance in their individual glucose response.
Mitchell:
So are you saying spikes can be pretty normal?
Kate:
Of course. Yeah.
Mitchell:
And I think at some point today I’ll speak to my personal anecdote with a device, but that’s even something that I had to get over, was it’s not normal to be flatline? We exercise. We have someone cut in front of us in traffic. If we live in Denver, you know we eat a big meal. We might go too long without eating. I mean, it’s not as you say, it’s not normal, just to be flat.
Kate:
Yeah, yeah, yeah, okay. So let’s back up Brief history of CGMs. How they came about. They were originally designed for use as a medical device in type 1 and type 2 diabetes, starting with type 1 and then bridged into type 2 diabetes management. It is an implantable device, uses a little filament to measure the interstitial glucose and then they use an algorithm to extrapolate that into what the blood glucose would be. So it’s not actually measuring your blood, it’s measuring the interstitial fluid.
Mitchell:
Okay, so a lot of terms. There are filament, interstitial fluid, so how does that translate to when someone puts it in and what the information they get right away?
Kate:
The information you get. You see it as a blood glucose reading on an app. You may have a little transmitter, but most people know it’s an app. So on the app you’re seeing blood glucose. It’s already gone through the algorithm. So it is the same as if you were to prick your finger and measure blood glucose. You should get the same number. There’s a little bit of margin of error. I don’t want to get too much into the specifics of device to device and which ones are more accurate than others on this podcast, but essentially that’s how it works. It’s an algorithm too.
Mitchell:
And it takes a little bit to calibrate it does yeah, yeah. That’s so interesting to me because I know you always tell people like give it, you know, 12 hours, kind of let it get used to you and you’ll start to see accuracy, like if you were to prick your finger at the same time. But it’s interesting that it takes a little bit to kind of get to know you.
Kate:
Technically it shouldn’t, but just over an abundance of caution, I always throw out the first 12 hours. You can make some assumptions around it that are inaccurate and can be harmful narratives for the patients.
Mitchell:
Yeah, to put it simply, so you said it was originally developed for diabetes.
Kate:
Yes. So we have very tight ranges, very good standardization on what is optimal, what is ideal for that population. We do not have that for healthy individuals. So in the absence of diabetes, even in healthy individuals who have prediabetes, those same ranges can’t really be used. And that’s where it gets really tricky.
Kate:
We’re making big strides in defining what a healthy glucose response even looks like, mostly thanks to massive data sets from people who have chosen to wear a CGM and share their data, whether they realize it or not. So companies like Zoe Levels, Nutrisense they have these big database where, when you wear this device, you’re agreeing to share your data. It might be an opt-in and it might be somewhere buried in the terms and conditions. So, yes, your glucose might be helping build the next nutrition algorithm without you even realizing it. There are some third-party collection projects, like open apps, where it’s a little bit more transparent. You actually choose to upload your data so that they can build these algorithms and data sets and analysis of it. But anyway, combining the demographics, the food logs, the CGM output, researchers build these models to see how do real people respond to different meals and conditions, to test assumptions, refine our understanding and start to develop more of the standardized range or optimal range for healthy individuals.
Kate:
So far, most clinicians and researchers are setting targets. I think it was the Journal of Clinical Endocrinology who first published this and I think it was 2023. The healthy range for non-diabetic, the healthy working range for non-diabetics right now, 70 to 140. All of these ranges are in milligrams per deciliter. I’m not going to continue to say that unit, but that’s the unit for blood glucose. So, healthy range for non-diabetic 70 to 140. And the optimal time in range is 98% of the day. So, 98% of the day, are you within 70 to 140? You have what a 4% excursion. So that could be lows or highs, but what we’re shooting for is 96% of the day, 70 to one, 40, with a mean glucose over 24 hours of approximately 99, plus or minus seven. Okay, candidly Okay. Again, this is based on both clinicians, researchers and analysis and analyzers.
Mitchell:
Analysis, yeah, those words.
Kate:
Yeah, so using data sets and putting them into algorithms.
Mitchell:
And this will change as we get more data.
Kate:
Yeah, as it grows, that does seem a little tight to me.
Kate:
Well, what’s interesting is, candidly from most clinicians so people who are actually treating patients most of them set a tighter optimal range. Something more like 70 to 120 is really what I’m seeing. It will vary depending on the population they are treating. So if it’s a let’s say, it’s a clinician who’s mostly treating postmenopausal women, their range is much different than someone who is treating pro-athletes. It makes sense. I actually just saw on Instagram I call her a health fluencer, not a clinician, just a person interested in health and, deciding to post her thoughts, wrote I have no excursions outside of the optimal range of 70 to one 10. I looked at that and I thought where is she even getting this? How can she just decide okay, here’s the optimal range 70 to one 10. There is no standardized optimal range and so people can just say whatever they want it to be Just talking shit.
Mitchell:
Yeah, well, if you don’t have a license, to lose you can just say whatever, and also this screams to me someone who’s under eating.
Kate:
Yeah, I was going to get into that.
Mitchell:
Like I’m lean, I am very insulin sensitive. My fasting insulin is four and I will frequently, when I wear one, have excursions like post-workout when I purposely try to drive insulin. Keep in mind, insulin is a very anabolic hormone and since I don’t take performance-enhancing drugs, I need to utilize what my biology has and gain a glucose spike. Oh, a spike post-workout actually helps me create anabolism, but then again, coming back down within an hour is my key. But so that I, that screams to me under eating and also it makes people feel like, well, I can’t keep that. So it almost like pathologizes, normal spikes. If someone reads that and they’re like well, I spike to 130 after a meal. I must not be as healthy as them. That bothers me.
Kate:
I know it’s dangerous, yeah, but again, if you don’t have a license to lose, you can just say whatever, I guess.
Kate:
And I mean the reality is we don’t have these clear cutoffs for bad or good patterns. So if you’re using it for self insights, I think it’s less about chasing perfect flatness and more about paying attention to the patterns over time, and that’s why I love most of the monitors are 10 to 14 days, so you get a much wider window where you can actually start to see patterns over time and then you can layer in sleep, stress, specific carbohydrate responses.
Mitchell:
Alcohol yeah, yeah, things like that.
Kate:
One of the largest projects ongoing right now is Zoe’s Predict program. Are you familiar with zoe? Zoe’s awesome it it’s a direct to consumer. A lot of lab testing, things like cgm, other health devices. They’ll use uh wearables as well, but so they set a lower price point for access to these labs, but again as if you choose to purchase them and participate you’re sharing, yeah, yeah, yeah, but it’s given them this really cool insight so that they can start to make connections with.
Kate:
okay, this person’s wearing a cgm and we have their gut test, so that that’s where some of the microbiome CGM stuff comes in. Anyway, zoe has PREDICT programs. Predict stands for Personalized Responses to Dietary Consumption Trial. So this series of clinical studies exploring how different bodies respond to the same meals. It’s not only Zoe, it’s also led by MassGen’s college in london, stanford harvard and a couple others, so it’s like they’re sharing the wealth of their data I think they should call it predict what’s wait?
Mitchell:
I didn’t even notice that personalized response I need to get an e in there oh sorry, predict is what it is predict trial.
Kate:
Uh, I think they’re on round three right now, but they have up to 20 000 participants based on this opt-in using their technology.
Kate:
Metabolic responses to the same meal vary significantly between individuals is the biggest takeaway from that. We call it the personalized glucose response. So I, the way that I respond to rice, is different than how you respond to rice, right, yeah? Or for you it’s like oats or oats, yes, but each individual’s response tends to be consistent across identical meals. So if I’m a respond, okay, oats is mine. But if I’m an oats responder, I’m going to respond to oats the same way every time, assuming the meal is exactly the same. So lots of variables in that, okay. So again, the glucose tolerance and insulin sensitivity do vary by person. This is why I can eat a banana and be perfectly steady. But my friend, Unnamed friend.
Kate:
My unnamed friend can eat a banana and skyrocket his blood sugar. We are starting to define we’re calling them glucose phenotypes, so grape spikers, rice spikers, potato spikers and so on. I think there’s about nine of them right now. But these glucose phenotypes are basically patterns of how an individual’s body responds to different carbohydrate challenges. Some people see the sharpest rise after fruit sugar, so fructose-glucose mix, like grapes, and can stay pretty flat after starchy foods like rice, whereas others show the opposite. So what’s really cool is you can wear a CGM and start to understand your own glucose phenotype. I’m really interested to see where they go with this research. Big thing, big takeaway, which we’ll get into some of this the challenges with CGMs later. But just because you find out, okay, my phenotype is a rice spiker, I. That doesn’t mean you can never eat rice. It means you need to be very careful about how you’re eating rice to make it more friendly to your blood sugar, I guess.
Mitchell:
Pairing it.
Kate:
Pairing it, and maybe it’s not a staple in your routine, it’s more of a occasional thing. So starting to learn some of those insights for yourself, just thinking about health optimization and wellness over the longterm.
Mitchell:
And because the last thing I want to do is create more food fear. Right, but you even you had mentioned, uh, dr Peter Atiyah talked about grapes for him and he, you said he started like he would stare at it. He talked about on a podcast. I think he would stare at a bowl of grapes and have this like internal dialogue. I should avoid those. I felt so understood because that’s how I became with bananas, or I mean this random guy we’re talking about became with bananas you know, I remember when it happened.
Mitchell:
I texted you immediately a screenshot and it’s like well, maybe you could utilize this post-workout then. Right, don’t run away from it. But no, you know, maybe not the best thing to have, right when you wake up in the morning on its own, but you could utilize that spike to create anabolism potentially.
Kate:
Anabolism. Anabolism is muscle growth.
Mitchell:
Tissue growth Tissue growth.
Kate:
Okay, thanks, okay, some other things that we agree on. We collectively clinicians, researchers, glucose scientists I’m going to put myself in Well, fluencers. Health fluencers. Oh.
Kate:
Yeah, I guess there’s well fluencers too, anyway, health fluencers. Some variability in glucose is normal, especially postprandial, and a spike in that context doesn’t mean pathology, does not equal pathology. Just because you spike doesn’t mean you have diabetes. A spike is completely normal. It is how quickly you come down from that. It is what subsequent meal responses look like after that. There’s so much nuance to it. So a single spike after sushi night isn’t a sign you’re doomed, it’s more what’s the recovery, what’s the overall patterns? There are some NIH studies on normal glucose tolerance. Again, this is in healthy individuals outside of diabetes, and they’re showing that healthy individuals experience these mild and infrequent spikes without adverse effects. So there’s some peace in that right.
Kate:
I think that might be one of the biggest messages we can get across today is try to not over-pathologize and analyze your spikes to complicate that, there was a study in frontiers in 2023 again, healthy individuals that linked more glucose variability, so more incidence of highs and lows, to adverse cardiovascular outcomes and increased mortality risk. It so it just it builds on itself. It gets confusing, all these real-time rises and dips increasingly linked to less metabolic health. I think there is an opportunity to take some power back in that, in that we can use a CGM as a tool to detect these early risk signals, correct the patterns, correct the habits around whether it’s meal, stress, sleep to decrease the variability.
Mitchell:
But but again, that doesn’t necessarily mean a completely flat glucose curve see, but the problem is, uh, nuance isn’t sexy for people, right, they want the. Oh, this is the, this is what it is every time. This is what matters, because that’s easy to sell with these like well, fluencers, you know, but as we’re talking about, it’s like there’s so much nuance and it’s all. It’s an ever-evolving data set and it’s building too.
Kate:
So now we’re seeing there is glucose tolerance variability within a person based on time of day, menstrual cycle, sleep patterns, stress, and it’s not always consistent Meaning for me, my menstrual cycle might not change my glucose tolerance, but for another woman they might see significant changes in their glucose tolerance from one phase to another.
Mitchell:
That’s interesting.
Kate:
Again, we just need a much larger data set to continue.
Mitchell:
So keep not reading the terms and conditions. People Keep opting in Accept, accept Scroll and accept. Don’t read Accept, accept, scroll and accept.
Kate:
Don’t read Okay. So a lot of the common misconceptions. We’ve already hinted at some of them. And then I would say the dogma to challenge: All spikes are bad. I don’t really agree with that. I think context matters. We don’t even know if mild and moderate post meal spikes are harmful in metabolically healthy people. I will segue here in that we do not use CGM outside of blood work. Right, we are always looking at okay, I want to actually see the insulin number, the A1C, the fasting glucose the insulin number, the a1c, the fasting glucose.
Mitchell:
Well, and we’re using a lot of times when we suggest to somebody is we’re using it just to get strategy for that person. It’s the most fun thing to watch you do that with people and pull out their, their app and they’re you know 10 days at a time. Be like what was your 5 pm meal on the third. That was great for you. You know 10 days at a time and be like what was your 5 pm meal on the third.
Kate:
That was great for you, you know, you see the rest of the night.
Mitchell:
You see, your evening, your 3 am, your overnight Glucose. You know, and I love how you utilize it in that way where it’s like let’s play in the sand for the first week, don’t change anything, just do what you do. We’ll correlate that to, like you said, fasting insulin A1C, fasting glucose, thyroid function, lipids, all of that stuff, and then, if we are to do it again in a few months, let’s make experiments.
Kate:
So here’s a hot take. I do not think a CGM is a useful tool on its own. Right.
Kate:
I’m a huge fan of CGM. I really enjoy it. But it’s just not the first step. It is always the blood work first. We see most a lot of the times we’re using a CGM when someone’s A1C is elevated and their fasting glucose and insulin are low or the opposite. Just like you said, to kind of start to illuminate those patterns. But to me it is not helpful to look at someone’s CGM report if I don’t know their blood work, if I don’t know their history, if I don’t know their eating patterns, symptomatology. Yeah, yeah, so I caution people away from—well, this is a fancy tool. I have access. I can just go order it for myself. Great. But I don’t know that you’re going to get a lot out of it that way.
Mitchell:
Yeah, so we’re not those people that send every new client home with a CGM. No. And a lot of times when I think it’s fun to use, you’re like well, what are we going to do with that right now? Yeah, could we do this in 60 days? Yep, fine.
Kate:
Okay, let’s talk about flat equals optimal. We hinted at this. Is a flat line actually ideal, or is it just a marker of not eating carbs, of restriction, of delayed gastric emptying, of a cortisol influence? Like there’s so many variables that it’s unfair to say flat equals optimal.
Mitchell:
Someone who sits on the couch all day, I suppose. Yeah, someone who walks all day.
Kate:
Yeah, when we are analyzing glucose data, I’m having a patient record every single thing they eat, their activity, their stressful inputs when they notice them, their sleep patterns. So the more data I have, the more data touch points I have, the better analysis we can get from that report. Outside of those inputs, it’s again it’s really kind of useless information to me because we don’t learn anything. I mean, it’s not useless information but it’s not applicable information. There’s no context, right?
Kate:
And we again, we don’t know if lowering every post-prandial peak is even worth the effort, necessarily. I have another hot take. In certain populations the data doesn’t matter, meaning just wearing the CGM can be enough to drive behavior change. We see this with food logs as well. When people are asked to record a food log, they naturally eat healthier. Same thing with a CGM. I can put a CGM on someone and they naturally have the feedback right there, the tactile feedback of oh, I’m gonna do a better job because I’m wearing this and I know someone’s looking over my shoulder—whether it’s me looking over or just like Mark Zuckerberg. Yeah, that actually drives behavior change.
Kate:
I don’t actually do that to patients but I could. Maybe if they have like a faulty CGM, I just don’t tell them, just let them wear it. But that real-time feedback is powerful and it’s meaningful for a lot of people. I think the real-time feedback is really helpful to highlight reactive hypoglycemia. This is something that we don’t always pick up on standard blood work. So reactive hypoglycemia—this is when you’re going low, so it’s not going to flag as diabetes. Diabetes has an upper limit. Low blood sugar isn’t as recognized because your average is going to look pretty, if that makes sense.
Kate:
Having a CGM can really help to highlight hidden dysregulation or certain dietary habits that aren’t working for someone. I talked a little bit about the glucose phenotype. So when I was wearing a CGM, I multiple times have proven to myself that oat milk just does not do well for me. If I were reading this for a patient and they were consuming an oat milk latte every single morning, that would be a serious conversation to have to change. Is there another type of milk? Is there a different time that you can do this? Is there a different pairing that you can have if you want to have an oat milk latte? And really work within personal context to both get what they want but also improve their metabolic health, because it’s the daily, repetitive high spikes that are a problem, not the occasional diversion outside of an optimal range. Whatever we decide that is.
Kate:
It can be very helpful in understanding non-food related patterns. So thinking about sleep-related dysregulation, how you can get the real-time feedback when a poor night of sleep elevates your glucose baseline. So your average glucose can be higher simply because you had a poor night of sleep the night before. Again, it’s not every single person that sees this effect, but it’s really cool to see it personally in real time. We can see the impacts of—you said alcohol, but also caffeine—how caffeine interplays with stress hormones to affect people’s glucose individually. And that can help with optimal timing for yourself of when to consume caffeine, what’s your upper limit. It can help connect the dots between meal pairing and meal timing. So whether that means ratios of carbohydrates to fats, to proteins on your plate—that’s meal pairing—or meal timing.
Kate:
We have seen some really interesting cases where patients respond really poorly to fasting. I mostly see this in women, where they might have a great glucose response while fasting but if they break their fast at 36 hours, their glucose response to that meal is very high compared to if they break their fast at 24 hours and have the same meal. Their glucose response is different, which is fascinating to me, and you would only know that if you’re personally tracking your glucose, because we can’t extrapolate that to every single person. Not every single person is going to respond that way. It helps give us some tools for working around what we’re seeing.
Kate:
The best example I have for this is I was wearing a CGM and I actually got an alert on my Stelo monitor and it said your glucose is rising. What’s going on? Which is a really cool feature that they added. And I looked at it. I had not eaten. It was toddler bath time, which is a very stressful time in my life, and it was spiking my glucose. And guess what? Toddler bath time is not going away. It’s not a habit I can just cut out of my life. Unfortunately, it’s going to happen every night. So it allowed me to change the meal timing of my dinner. I moved it actually a little bit closer to toddler bath time and I make sure that I have a higher amount of protein in that meal to help attenuate that response, so that I know bath time is going to happen—how can I make sure that this stress of this event is not impacting my glucose response as maximally?
Mitchell:
So you’re not just like overdosing with alpha lipoic acid before bath time. That is an option.
Kate:
I mean we can play with like adaptogens or chromium, potentially or bitter melon.
Mitchell:
I would say the hard part is it’s at night, so we wouldn’t recommend berberine, probably not—it could stimulate you a little bit.
Kate:
Yeah, but there’s a lot of cool tools you can use and see the feedback on yourself to see which one you respond best to. So that’s fun.
Mitchell:
I think we just got to get Lucy to bathe herself. One day you’re going to have a baby and you’re gonna learn.
Kate:
I know. So often I think you can reason, but apparently you can’t. I mean maybe when she’s five. Maybe when she can confidently swim.
Mitchell:
That’s true. What is it about bath time that gets you so ramped up?
Kate:
Sometimes she likes it, a lot of times she hates it. She struggles with transitions, so getting her into the bath is really challenging. Once she gets in the bath, she’s great. Getting her out of the bath is really challenging, so it’s like I’m bookended no matter what we’re doing. The transitions are just tough.
Mitchell:
Okay, interesting.
Kate:
Yeah, and you see that on your CGM data.
Mitchell:
Yeah, that’s funny.
Kate:
Okay, big important thing, I don’t think everyone needs a CGM all the time. Certainly not all the time, but I don’t even know if everyone needs a CGM at all. Certain people—you hinted at this—the CGM creates so much food anxiety that lasts, it persists beyond wearing it.
Mitchell:
I still talk about it.
Kate:
Yeah, every time I eat a banana, you make a comment about it. And you’re a well-educated, reasonable person, you know rationally, but it still creates that same loop emotionally.
Mitchell:
Yeah, the same thing can happen with just misreading spikes and dips, without the context to make these bad decisions, and they can persist. So I think that’s where, if you choose to do it, I think having someone else interpret to help you make sense of it, I mean, you can just be too close to the situation sometimes.
Kate:
And to analyze the rest of the details around it.
Mitchell:
I mean, I think best use of the CGM is in the short-term approach. So do it for a couple weeks, get some data, make some tweaks. Maybe do it again a couple weeks later, same thing. You wear it for about 14 days, see how those changes impacted your new patterns and then take a break. I don’t think in a non-diabetic it doesn’t need to be a continuous year-long project. It’s seasons of: let’s check in, let’s see where we’re at, let’s do some diligent tracking, get some information from it, make those changes, come back to it later.
Kate:
I do like it a lot in someone who’s training regularly, especially not even professional athletes. But if people are training for an event and really increasing volume, to dial in fueling for exercise and recovery. I would say more often we use it in patients with, like we said, the lab result anomalies, PCOS or hormone dysregulation, perimenopause, stubborn weight issues and sometimes just curiosity.
Mitchell:
And brain fog and all sorts of other reasons. But yeah, like you said, it’s not the only tool and on its own it doesn’t mean a whole lot for us.
Kate:
Right, and we’re really focusing on global patterns, the lifestyle impact, the factors that you can actually manipulate, and not just micromanaging every single little number. So don’t throw the glucose baby out with the bathwater. That’s a great ending right there. And approach it with curiosity and not control.
References:
Shah VN, et al. Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants. J Clin Endocrinol Metab. 2019. PubMed
Pappe A, et al. Free Sugar Intake Does Not Alter Glucose Variability in Healthy Individuals. Frontiers in Nutrition. 2023. Full Text
Mishra A, et al. Glycemic Variability and Depression in Non-Diabetics. Frontiers in Psychiatry. 2023. Full Text
Yu H, et al. Long-Term Glucose Variability and Risk of Cardiovascular Disease and Mortality in Non-Diabetics.Medicine (Baltimore). 2019. PMC
Klonoff DC. Continuous Glucose Monitoring Profiles in Healthy Non-Diabetics: Early Insights. Diabetes Care / J Clin Endocrinol Metab. 2022. PMC
Marling C, et al. Glucodensity Metrics for CGM Analysis. arXiv. 2024. arXiv
Zhao Y, et al. GluFormer: Foundation Model for Predicting Metabolic Risk from CGM. arXiv. 2024. arXiv
Moscoso-Solorzano GT, et al. cgmquantify: Tools to Analyze Continuous Glucose Monitoring Data. arXiv. 2021. arXiv
Battelino T, et al. Clinical Trial Metrics for CGM Use. The Lancet Diabetes & Endocrinology. 2023. Abstract
Ambulatory Glucose Profile (AGP). Wikipedia. Link
Work with us at The Facility Functional Medicine:
If you’re curious about functional medicine and how it could work for you, we’d love to help. Book an initial consultation with our Denver-based clinic (we see patients locally and via telehealth) and take the first step toward a health journey that doesn’t stop with you.
Meet The Functional Medicine Team behind Facilitated:
Mitchell Rasmussen, DC, CFMP: Mitchell is a certified functional medicine practitioner with a doctorate in chiropractic at The Facility Functional Medicine Clinic in Denver, Colorado.
With lots of letters behind his name, he entered chiropractic with a clear goal: to practice Functional Medicine. His biggest passion is the immune system. He has focused his post-doctoral education on immunology and clinical applications for chronic diseases like Lyme, tick-borne pathogens, viral burden, and mold exposure.
About Kate Daugherty, MS, CNS: Kate is a certified nutrition specialist and functional nutritionist at The Facility Functional Medicine Clinic in Denver, Colorado.
She began her career journey in neuroscience, which seamlessly transitioned into human nutrition. Utilizing food as medicine to treat the mind-body connection is truly remarkable. Kate believes that our eating habits nourish our soul as profoundly as they do our body.
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