Episode Transcript
[00:00:00] Speaker A: She thought that it was going to be the healthy choice. She thought she was making a good decision on what she was eating, she thought.
But then she found out the actual information. The data came in, and her expectations were shattered, and she threw it away.
[00:00:20] Speaker B: Welcome in Stetters to episode 36 of the Duster Mud podcast. Today we're. We're going to go through Hannah's CGM journey. We teased it on Monday. Here we go.
[00:00:29] Speaker A: Most of you know, Hannah is a United States Air Force fighter pilot and lives pretty much on the edge every single day.
[00:00:36] Speaker B: Yeah, she's stationed over at ref Lake and Heath in England. And she is a healthy mid 20s female with normal metabolic functioning. Just normal, normal, normal, normal exercises. Quite a bit, yeah.
[00:00:52] Speaker A: So she decided, in order to get even more in tune with her body and her fitness levels, she ordered herself a continuous glucose monitor and has been tracking what her blood sugar does during the course of a day based on what she's eating, what she's doing, how she's sleeping.
[00:01:15] Speaker B: We discussed last time that we found the information so interesting that we wanted to get one ourselves and learned that in the United States, you're not allowed to have it without a prescription.
[00:01:26] Speaker A: So her initial response to one of the first meals that she had was quite shocking. The first thing that she thought was, wow, this is super amazing. This information could literally be life changing in that you may start making different decisions. Her mind was kind of blown as the data was coming in from her first couple of meals.
And her first meal, if I remember, was some egg noodles.
[00:01:57] Speaker B: Yeah, a plate full of noodles. Let's start by having a look at the data that her app provides. If we start at the bottom, you'll see that there's a time frame, and each time frame is different on each slide that we'll look at. This one goes from 07:00 p.m. To just past 11:00 p.m. On the left side of the graph then is her blood sugar levels. 60 to 140 is what this one shows on other slides. It will be a little bit different. We won't point out that each slide is different, but just take note that each slide is a little bit different. And then the graph of her blood sugar levels then is the colors in the middle of the page. And green means that she's in the zone. You can see it's sort of grayed out between 70 and 110. That's the end zone for her blood sugar levels. And then the further she gets out of the zone, it transitions from a yellow to an orange. To even a red. And then at the very top is a number. That is her glucose level at the time that she was looking at her app.
[00:03:08] Speaker A: Right? Her first meal was a meal of egg noodles. You can see in the chart that it started off pretty level, and then after about a half an hour of her eating, it started to climb.
[00:03:21] Speaker B: Her meal was recorded there, just right at 08:00 p.m. You can see the little symbol above the 08:00 p.m. Is a fork and a knife. That is her recorded meal. And then, as Shelley mentioned, about 30 minutes later, her blood sugar levels started to rise. The little symbols with eyes on them. That is her app giving her information. So the first eye, at around 09:00 p.m. Is her app telling her, your blood sugar levels are going out of the normal range. They're going abnormally high. And then there's another little eye symbol at about 09:30 p.m. That is her app telling you there's something else happening. Your blood sugar levels now are crashing. They're starting to fall faster than what would be normal.
[00:04:15] Speaker A: So you can see in this particular slide with this meal, she went up really fast. Her blood sugar shot up 30 minutes. She was already to 140. But her body does work, and it produced the insulin required and said, whoa, way too much sugar running around in your bloodstream. We need to do something about it. It injects some insulin after about 15 minutes ish, and all of a sudden, it starts bringing it down. So it brought it down to within the zone, and then you see it goes back up again. And apparently it is fairly normal for in this type of glucose response, for you to get two injections ish of insulin, if you will, injections your body, injecting insulin into the system.
[00:05:09] Speaker B: So, one, this is a look at a metabolically healthy physiological response to a high carbohydrate meal.
Your blood sugars go up, your body releases insulin, it goes down, the blood sugars can go back up because you're still digesting the food. The body releases more insulin, it comes down, and then you can see towards the right of the chart, it starts just sort of a little bit of up and down, which is just normal body function with your blood sugar levels. So that's a pretty good look at the information her app is providing, as well as her first meal. And the. Oh, my goodness, I love this information. Right?
[00:06:00] Speaker A: So, after an evening meal of high carbohydrates, sometimes what you can find is, or maybe all the time, what you find is while you're sleeping, your blood glucose levels will kind of fluctuate and go up and down and up and down as you're sleeping. As your body continues to deal with the insurgence of high amount of glucose near bedtime. And if you look at her noodle night graph, she kind of went up and down all the way through the night. Starting at about midnight, she just sort of zigzagged. And the interesting thing that we noticed was she got up at around 06:00 a.m. And she really bottomed out.
[00:06:43] Speaker B: Yeah. Just have a look at the right side of the chart where there's a spike and a dip as she's waking up in the morning. And we'll definitely come back to that.
Additionally, wait till you see pizza night.
[00:06:57] Speaker A: Oh, boy. Yeah, pizza night was interesting.
[00:07:00] Speaker B: This is a look. We asked her, hey, what does just a resting look like? What happens? Is there anything that happens with your glucose? And so she sent us this picture that shows her just resting on the couch. She sat down at about 03:00 p.m. And if you see on the graph, at about 03:00 p.m. Her blood sugar levels start to fall. And so it goes from about 100. So in the normal zone, over the 30 minutes that she was sitting on the couch, it dropped from 100 down to about 75. And at 330, where you see it spike right back up. She got up off of the couch and started walking around.
[00:07:48] Speaker A: It's just great information to see that our bodies give us exactly what we need as we're active and inactive. It gives us exactly the amount of insulin or glucose, whatever it is. It's just taking care of business while we're just kind of going about our lives. Just nice to see that.
[00:08:12] Speaker B: Yeah.
[00:08:13] Speaker A: I requested this stevia. What is stevia going to do to your blood glucose? Please do this.
[00:08:23] Speaker B: We know that it's not supposed to do anything because we don't have one.
[00:08:30] Speaker A: I don't have one, so I'm just having to get information from her. And lord knows my trust on people is minimal at best at this point. So asked her, please do a stevia test. So she was fasted, and she put about two tablespoons of some sort of granulated stevia into some water, and she drank it.
[00:08:53] Speaker B: You could see that with the fork and knife at about just after 09:00 a.m. Is when she drank it.
[00:09:00] Speaker A: And after about an hour, it just had no response at all.
Absolutely wonderful to hear.
[00:09:07] Speaker B: Yeah, it was as if she drank water. Yeah, it did nothing wonderful. Yeah, we were happy.
[00:09:12] Speaker A: Yay.
We can keep doing the stevia, right?
[00:09:15] Speaker B: Good.
This chart shows a really interesting thing. This was after one of her high carb meals, and at 03:00 is when she woke up. So on this chart, you can see that her blood glucose levels, her blood sugar levels were low. As she was coming into her wake up time, it spiked up a little bit into the normal range at about 75. And then within about 15 minutes, it had bottomed out to below 50. It was red in the color. At about 335. 03:40 a.m. Is when it got really red and bottomed out. This was her normal.
[00:09:58] Speaker A: She was waking up to actually get ready to go to work. It wasn't like she woke up just spontaneously and then tried to go back to sleep. This was an actual wake up.
[00:10:06] Speaker B: Right. She had a 06:00 showtime for her brief that morning. So she was waking up at three. So what you're seeing here is potentially what, in the diabetic realm, they refer to as the dawn phenomenon. And what happens for diabetics is this exact, very low dip and then spike in glucose that they refer to as the dawn phenomenon. And what we saw with Hannah's data through the different charts that she showed us was that on the nights where she ate a really high carbohydrate meal, the morning following, she had this dawn phenomenon as if her body were acting as a diabetic. The nights where she didn't eat the high carb meals, there really wasn't anything. There may be a little bit of a dip in spike as she woke up. And her body starts, you get a little bit of glucose to help get you up in the morning, your liver gives you a little jolt. And so we could see that. But on the nights, or, sorry, the mornings following a high carbohydrate meal, it really went out of normal. Out of the normal range.
[00:11:22] Speaker A: Yeah, that's an interesting physiological thing.
[00:11:28] Speaker B: Yeah, I'd say interesting.
[00:11:31] Speaker A: Okay, the next thing that we want to discuss is sleep quality. She definitely has noticed that her sleep is.
Her sleep quality is determined by kind of what she eats, and it's really interesting. So this next slide was the night that she had some low carb beef and broccoli supper. She had no spikes, she had no big dips, no crashes. It stayed pretty level within the zone all night long. And she did not wake up feeling tired. She still felt kind of full from dinner the night before. She didn't feel hungry or thirsty. She just felt really normal.
[00:12:21] Speaker B: It may look like a few spikes and dips here, but that's really within the normal range in his normal body. When you're sleeping, the time frame on the slide is from 12:00 a.m. To about 05:00, 05:30 a.m. And you can see that she stayed well within the normal zone for the entire night.
[00:12:41] Speaker A: And it appears, too, on this one, that she didn't do the big dip right before she woke up, right?
[00:12:50] Speaker B: No, she didn't.
[00:12:53] Speaker A: Interesting.
[00:12:54] Speaker B: Yeah.
We asked her to show us what happens when you exercise, because we know that your body starts to release sugar stores during exercise. And so we wanted to see exactly what that looks like on this slide. You can see that the 03:00 a.m. Is about the time that she was waking up. She has the fairly extreme blood sugar dips and spikes that were happening after a high carbohydrate meal the night before.
And you can see that it starts climbing back up. And then the little flame that's happening at about 06:00 a.m. That is a workout. That's her apps symbol for a workout. So her body was releasing some glucose to get her up and going in the morning. She was fasting, so she had not eaten since the night before. And then following the workout there, you can see that her blood sugar levels went from somewhere around 95 100 when she started the workout and peaked at about 121 25. The interesting thing with this one is it stayed fairly high following the workout. It has the normal ups and downs that happen, but it stayed pretty high for the next few hours. So what you see in the chart is that her workout started at 06:00 a.m. It took till about 09:00 a.m. For her body to come back down to that, around 100 for her blood sugar levels.
The interesting thing with that, though, is she was saying that that slow decrease in her blood sugar levels, she didn't get any cravings, right? She didn't get any shakiness. She didn't get anything. Like, she didn't feel like she needed to eat.
[00:14:57] Speaker A: No snacking afterwards. I remember so many times I would go to the gym, especially when I was living on a pretty high carbohydrate diet, and I would come back from the gym and I would just want to just eat the pantry, right? And she has experienced some of the same things over time. And this time, completely fasted workout. You get none of that. You just don't crash. Your body is dealing with it as bringing it down, bringing it down, dealing with your glucose. In a more moderate way. And it just wow the difference.
[00:15:36] Speaker B: Yeah, for sure.
[00:15:36] Speaker A: Wow.
[00:15:38] Speaker B: On the next slide, we'll be able to see a non normal, like a non exercise. This one will be a high carb spike followed by the reaction to the dip as it coming down from the high carbs.
[00:15:56] Speaker A: She thought she was making a really healthy lunch for herself. She had tested some foods. Cucumbers, carrots, avocados, salmon. All of these foods are whole food, amazing food for our bodies. She tested it and she got no spikes. And she thought, well, I'm going to have a really nice lunch for myself. And she made herself some spring rolls.
[00:16:18] Speaker B: Yeah. So in each of these meals, she had in her mind going into it, this is going to be a high carb, bad, quote, bad for me meal.
[00:16:28] Speaker A: Right.
[00:16:29] Speaker B: And then here's the reaction. This is going to be a good for me meal so far. We talked about the noodle meal. She knew that one was going to be a bad for her meal. We talked about the beef and broccoli meal. She knew that one was going to be a low carb meal. This meal was one of her, in her mind, good for me meals.
[00:16:47] Speaker A: Right.
[00:16:48] Speaker B: Because these spring rolls had a rice wrapper around them, and the rice wrappers were a vegan 40 calorie, good for you type of food. And she knew already that the stuff that she was putting in the rice wrapper did not cause a response.
[00:17:06] Speaker A: Right.
[00:17:07] Speaker B: So because the rice wrapper itself was a low calorie, good for you type, in her mind food, she was expecting this to be one of the good for you responses to eating this meal.
[00:17:21] Speaker A: Okay, so check out this slide.
[00:17:24] Speaker B: On the slide, what you see is at about 01:00 is the fork and knife symbol. That's when she ate her meal.
It had an initial spike. Within about 15 minutes, she was up to about 130. From there, in the next 15 minutes, it spiked even higher. Above 140, she got to 140. So the first I symbol is her app telling her, there is something going on. Your blood sugars are hyperglycemic. Yeah. The next I symbol you see is at about 03:15 p.m. That is your blood sugar is crashing. And it just went off a cliff. So at about 03:00 it was above 140. At about 315, it was below 100.
And it just immediately crashed.
[00:18:23] Speaker A: It was so high. Her body said, we got to get this down and we got to get it down now. So in comes the insulin, I mean, a bunch of it. And it brought it down in a short order.
[00:18:37] Speaker B: This one was one where we were real time interacting with Hannah as she was doing this, and she was saying, oh, my gosh, I can't believe this is what happened.
[00:18:48] Speaker A: She was freaking out.
[00:18:49] Speaker B: Yeah, she was freaking out as it was crashing. I said, hey, go do a workout and see if that brings it back up, because during this crash, I'm like, well, maybe your body will release some glucose and sort of try to level it back out so it's hard to see. But at about that 330 mark, there's the flame. And then almost on top of the flame is another eye with her body saying something's happening. So she got a little bit of a recovery there as she started the workout. And then that eye is her app telling her, again, you are crashing.
[00:19:29] Speaker A: You're crashing.
[00:19:30] Speaker B: And so she crashed all the way to below 60.
[00:19:36] Speaker A: Yeah. So at below 60, she was hungry, she was shaking, she was craving and she wanted something sweet. Why? Why did she want something sweet when she was down there in the bottom? Because she's now hypoglycemic. So within an hour, she's gone from hyperglycemic to hypoglycemic. So went from, I'm full, this is amazing. To crash, and now I need something because I'm bottoming out. Now. She didn't eat anything. She sat in it because she wanted to feel what's going on with my body, miserable as it may be.
[00:20:22] Speaker B: Yeah. So the shocking thing to her was that it was just as bad as the plate full of noodles.
The spring roll she thought was going to be good for her, that she was eating a meal that was great. And she found that just those four spring roll wrappers caused the same type of response as the plate full of noodles. And it was so shocking to her that at about that 06:00 period where you see she's finally starting to level out, she got up and threw them in the trash.
[00:20:59] Speaker A: Threw them in the trash. She couldn't believe it. Not going to eat those again, I guess.
Not worth it. Maybe that one wasn't worth.
[00:21:07] Speaker B: She didn't throw the noodles away because she was expecting it to be a bad for her.
[00:21:11] Speaker A: I hear tell that on another one, she wasn't quite convinced that she was never going to do it again, though.
Okay, so if anybody knows this person, they know she loves pizza. I mean, loves some pizza. Awesome. Love pizza. So she makes a lot of her italian foods, homemade. She did not go get Dejioro. She did not order Domino's. She made some homemade pizza with some real italian flour with some amazing ingredients, homemade, lots of cheese, fats, meats, thinking, I'm going to see if I can make a pizza that doesn't cause me to spike and crash.
[00:22:03] Speaker B: Yeah. She thought that maybe since she had enough protein with all of the meats that she put on it and enough fats with all of the cheeses that she put on it that maybe it would level out. She knew that the flour was going to have carbs, so the pizza crust was going to be higher carb, but she thought that maybe with the protein and fats associated, it would level it out. And what you can see on the chart is she ate the pizza at about 630. You see the fork and knife symbol there? And it actually dipped down. And her blood sugars dipped down to about 60. It went out of the normal range and then went up.
Not spiked up, went up fairly slowly. And at that first eye, just to the left of that first eye that you see, at about 09:00, she had texted us and said, hey, I think pizza may not be too bad.
[00:23:01] Speaker A: It wasn't that bad.
[00:23:02] Speaker B: Look at this.
I thought that it would be okay. And this one is actually following my expectations.
I think pizza night is going to be okay. And just after she texted us at about 930, it crashed.
It fell way down into a valley. And then at about 930 is when it started.
[00:23:27] Speaker A: And then it just started. She texted the next morning and said, I don't think the pizza was as magical as I thought it would be.
And very evident by the significant crashes.
Spike, crash, spike, crash, spike, crash. All night long. Now, this was a morning when she had to get up at 03:00 a.m. So she bottomed out at three and did that thing.
[00:23:54] Speaker B: Yeah, but look at. But she ate at about 630. She went to bed at about nine. She spiked at 140 at about 10:00 p.m.
[00:24:05] Speaker A: While she was trying to sleep.
[00:24:06] Speaker B: Yeah. And then went to 140 again at about 1130.
[00:24:11] Speaker A: She said she woke up in the night and the window was open and she woke up hot.
[00:24:15] Speaker B: Yeah.
[00:24:16] Speaker A: Like just burning. Burning, I guess. Just burning. All of the glucose. Her temperature just went up.
[00:24:22] Speaker B: Yeah.
And the 03:00 it's when she had to wake up again for her showtime. So you see this huge crashes first thing in the morning after she woke up.
[00:24:39] Speaker A: That's got to feel terrible.
[00:24:40] Speaker B: That's down to below 50.
[00:24:42] Speaker A: Right.
That'll make you hungry, thirsty. I need something now and not be able to kind of get going.
So what is all of this data telling us to me?
[00:25:00] Speaker B: I think we've had a discussion ongoing for the past couple of days about trust and about the lack of trust that we and other people feel right now.
Really, in general, you've got artificial intelligence and deep fakes and shallow fakes and even profile pictures.
It just feels like there's an overall lack of trust right now. Just everywhere, everywhere, everywhere. We've been talking about that. And for me, I'm not saying I still want to do this for ourselves, but what this is saying to me is there's a real person behind this data that we know, and we're watching this happen real time. And for me, the things that she is seeing with her data are the things that we expected to happen, which tells me that a lot of the, or all I don't know of the information that we've been learning over the past five years.
[00:26:24] Speaker A: Let's just go with most.
[00:26:25] Speaker B: Most.
[00:26:26] Speaker A: Yeah.
[00:26:26] Speaker B: Okay.
[00:26:27] Speaker A: Most of the information, most of the.
[00:26:29] Speaker B: Information that we've been learning over the past five years is validated in what she's seeing and the information that she's showing that's happening to her with this monitor.
It confirms what we've been learning.
[00:26:49] Speaker A: And I like that, being able to see something, and I'm not actually from Missouri, the show me state, but I'm kind of sort of, it kind of fits pretty good. The seeing is believing. They say Stevia doesn't spike your blood sugar. Seeing the fact now I can really kind of go confidently and say, hey, I know Stevia is not going to spike my blood sugar knowing that. Just not really confirmation bias. It's actually data confirmation. It's confirmation in what we're doing and in what we're eating. And I do still want one as well, so that we can get our own data. Because our bodies are all different, they respond differently, they've been injured differently. I've eaten maybe way more carbs over the years than maybe you have. And so my body response, if I look at a doughnut, I'm going to gain five pounds kind of thing, is because of the repetitive ask and request that I've put on my body over the years, and maybe you haven't done that. So I need to know.
[00:28:09] Speaker B: Everybody'S body, everybody's metabolism is different, right? Their metabolic functions are different. Mine will be different than yours. Ours are different than Hannah's. So seeing Hannah's, just because we want to believe the things that we've been learning doesn't necessarily mean that we can trust that either. We've seen the responses in our body, but we still haven't seen necessarily what the numbers are saying, right? We can get blood work that shows like an a one c that has the past three months, but we haven't had real time data to say, here's actually exactly what happens with each meal or each specific thing that we do.
[00:28:58] Speaker A: And information being so powerful and causing a person to make an immediate decision on what they are going to choose to eat can drive our health. And I go back to last podcast when we said we believe that everyone needs access to the continuous glucose monitors. And this is why. Because when you have real data in front of you about your body, you can help your body get well. And we don't have to be overweight, obese type two diabetics walking around and not knowing what to do. We can actually take action for our own selves and put in our mouth the thing that's going to drive our health forward rather than continuously making us sick.
[00:29:48] Speaker B: Seeing is believing it is.
[00:29:50] Speaker A: Absolutely.
[00:29:51] Speaker B: And seeing the data will help all of us make better decisions.
[00:29:56] Speaker A: Fact if you guys are enjoying these videos, please make sure that you've subscribed and like and share with your friends and family if this is something that you found interesting. And until next time. Bye y'all.
[00:30:10] Speaker B: Bye y'all.
