Can Reddit Reveal GLP-1 Side Effects that Clinical Trials Miss? With Neil Sehgal

What can hundreds of thousands of Reddit posts tell us about the side effects of GLP-1 medications?

In this episode of Causes or Cures, I talk with researcher Neil K. R. Sehgal about his recent study examining self-reported side effects of semaglutide and tirzepatide, commonly known by brand names including Ozempic, Wegovy, Mounjaro, and Zepbound.

Neil and his colleagues used AI to analyze more than 410,000 Reddit posts mentioning the medications. Among users who reported taking them, researchers found plenty of familiar side effects, particularly gastrointestinal symptoms, but they also identified potential signals involving fatigue, menstrual changes, and temperature-related symptoms such as chills and hot flashes. 

We talk about why clinical trials can't capture every possible side effect before a drug reaches the broader population, how AI can sift through enormous amounts of online conversation, and why finding a signal on Reddit is not the same thing as proving that a medication caused it.

We also get into a bigger question: Could social media eventually become a useful early-warning system for drug safety?

In this episode, we discuss: why Neil turned to Reddit to study GLP-1 experiences; how AI analyzed more than 400,000 posts; which potential side-effect signals emerged; the difference between correlation, a safety signal, and causation; how researchers distinguish genuine signals from social-media influence; and how AI and online communities could complement traditional pharmacovigilance.

About Neil: Neil K. R. Sehgal is a researcher affiliated with the University of Pennsylvania's Department of Computer and Information Science and Leonard Davis Institute of Health Economics. His research includes using large-scale digital data to better understand patient experiences and healthcare. He led the analysis and was first author of the study discussed in this episode. 

Read the study:
Self-reported side effects of semaglutide and tirzepatide in online communities — Nature Health

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Transcript

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SPEAKER_01 0:00

Welcome to the Consistor Cures Podcast, your gateway to understanding health and groundbreaking medical research in a fun and easy to understand way. With Dr. Eats as your host, join us as we sit down with the world's leading doctors and scientists to unravel the mysteries of health. From practical tips on well-being to the latest breakthroughs in medical research, we cover it all. Don't forget to subscribe. Let's ignite our curiosity and together dive into today's episode.

SPEAKER_03 0:31

Hello, everybody, and welcome to this episode of Causes or Cures. I'm Dr. Eeks, your host. Hope you're all doing well. So you I we all hear a lot about GLP1 drugs. You know, maybe it's a new side effect or a benefit. They're always in the news. But what if some potential side effects are showing up somewhere researchers don't usually look? Like Reddit. Reddit. In this episode, I chat with Neil Sahal about a study that used AI to analyze more than 400,000 Reddit posts about semaglutide and terzepatide, better known by brand names like Ozempic, Wagovi, Mongero, and Zepfound, to look for potential side effect signals that may not have been captured in clinical trials. So we're going to talk about what they found, what Reddit can and can't tell us, and whether social media and AI could become an early warning system for drug safety. So let's dive in. All right, everyone, we're connecting with Neil Sagal, and this we're going to talk about really timely topic with the GLP1s and side effects. But first, Neil, can you tell our listeners a little bit about yourself and what you do? And of course, thank you for joining in uh from wherever you are. I'm not actually sure where you are at the moment.

SPEAKER_00 2:07

I'm a PhD student at the University of Pennsylvania in computer science. And uh broadly I'm interested in studying how we can use AI and recent advances in large language models to study public health and global health. So I'm interested in how people use AI to consume health information and how we can use AI to study large-scale online digital data. Um so you know, in the study that we're gonna talk about later, uh I was really just interested in how can we use these new advances in AI to do things that traditionally would have taken a lot longer.

SPEAKER_03 2:44

All right, very cool. So what led you to study GLP1 specifically using Reddit? How did you get there?

SPEAKER_00 2:54

Yeah, so I mean I think as everyone knows, um, GLP1 medications um like Ozempic um are just you know huge cultural and medical phenomena over the past few years. And clinical trials tell us a lot, but they happen under you know very controlled conditions with selected participants, um, and they don't possibly capture every possible real world experience. Um and so at the same time, if you look on Reddit, you'll see lots and lots of discussions about how people are taking these medications and their experiences and details. And so we just wanted to see what was happening in these conversations at scale. Were they mostly describing the side effects we already know about, like nausea, or were there other things that they were talking about that maybe the medical community or even the news media was missing out on? And so we're not the first people to study Reddit in the context of health information, but we are one of the first studies to look at um specifically GLP1 conversations on Reddit.

SPEAKER_03 3:51

All right. And you know, when people hear that a drug has gone through safety studies and went through large randomized placebo control trials like you just mentioned, a lot of people assume that all the side effects or many of the side effects have been, you know, studied or reported. Uh and you kind of started talking about that a little bit, but can you just emphasize for our listeners why that may not be the case?

SPEAKER_00 4:15

Yeah, so I mean, randomized control trials are very important. Clinical trials are very importantly very important. They're like the gold standard for establishing whether a drug works and for identifying common side effects before approval, but they're not designed to tell us everything. Um so, for example, they have a limited number of participants. So, you know, recruiting and doing a clinical trial is expensive. Um, even some of the large GLP1 trials, they might have maybe 15,000 people. That would be pretty large, but then you have to remember that half of the people in the clinical trial, or maybe a third of the people in the clinical trial, are the ones getting the treatment, and the other arms might be getting a placebo. So you really only have less than 10,000 people on the medication in the trial. Um they often also follow people for a limited period of time, and so we haven't had GLP1s for that long, and you can imagine that for some medications, some side effects might appear after five years or ten years, or even after two years, and a clinical trial can't go on forever. Um, and then they also only enroll people who meet specific inclusion criteria. So once a drug is actually approved, it may be used by millions of people who are older or younger or sicker or taking other medications or using different doses or staying on the medication longer than people in the original trials. So there's there's a lot of differences between what happens in a clinical trial and what happens in the real world when people are taking these medications. And so, you know, there's potentially some side effects that are rare or delayed or just not reported in a way that crosses a trial's reporting threshold. So, you know, a certain number of people have to have the side effect in the trial for it to actually be considered the side effect. Um so I think uh one other thing to remember is that a clinical trial is not like the end of the story. The the government also does post-market surveillance, but they currently don't use social media, they use a different type of method. And so we think that what we did here is just kind of complementing the current system, but we're not replacing it in any way, and and again, clinical trials are the gold standard for for for this type of thing.

SPEAKER_03 6:12

Yeah, no, I think that that was actually a great overview of all the reasons um a side effect could still pop up uh in, you know, it's somebody there could be an interaction. It just it wasn't captured in the clinical trials. So um that's why it's you still have to do the post-market surveillance, and it's still important to do that. It makes you think about safety maybe differently for some people. They might, you know, it make you think a little bit different like, oh, is it 100% safe? Well, I guess nothing, nothing is 100% safe. But so Reddit specifically, why did you think, oh, this will be a useful place to study real-world, you know, peace people's experiences?

SPEAKER_00 6:53

Sure, yeah. So I mean there's a there's a number of reasons, and again, we're not the first people to use Reddit to study real-world medical experiences. There's been a lot of research using it to study different types of drugs. Um, people have looked at you know, depression medications, people have looked at all sorts of things. And Reddit is great because the conversations on Reddit relative to some other social media platforms are really long and detailed, and they're organized around specific topics. So if you're not familiar with Reddit, um it's broken up into subreddits or forums, and so there's a specific forum for weight loss, there's a specific forum for Ozempic, there's a specific forum for ZipBound, there might be a specific forum just for um women in the UK on Zipbound. So there's very niche topics, and people write in a way that is very different from how they might answer a formal survey. So they they describe the symptoms or their experiences in their own words. They often explain the timing, they compare doses, they ask others to share their experiences, um, and sometimes they might mention things that they might not even bring up in a doctor's visit. So it's it's a really rich data source. Um, and also importantly, for for us, it's actually accessible to analyze. Unlike a lot of other social media data sets, um, you can imagine that in Facebook groups, there's probably lots of conversations in private Facebook groups about um Ozempic, but those are kind of closed off to researchers. Twitter is also kind of closed off to researchers, but but Reddit is kind of publicly accessible for researchers to study. But importantly, Reddit is not representative of all patients, both in the US or globally. People have done research and found that Reddit users skew younger, they're more likely to be male, they're more likely to be based in the US than the general population, and people who post about side effects on Reddit may be different from people who never post. And so Reddit gives us this like very large window into what people are discussing voluntarily, but it's not a representative sample of people in the real world taking these these drugs. It's a it's just a a biased window. It's a it's a convenience sample.

SPEAKER_03 8:49

Right. Uh yeah, it's a I like the convenience sample. And there's of also a lot of people who aren't on Reddit taking these drugs, and probably people who aren't online at all or don't use it, you know, they're not on social media posting all day long. Uh so okay, so let's my mind was going somewhere else in terms of for a second. Back to this. So can you walk us through how AI analyzed more than 400,000 Reddit posts and how a computer recognizes someone describing a possible side effect for like someone who is a computer moron, kind of like myself. Like even you said you were in computer science, I was like, oh, I would never have picked that.

SPEAKER_00 9:27

But yeah, so it's actually not that complicated. I think um when people hear AI or large language models, they think that something really fancy is happening, and sometimes that's the case. But here, what we did is actually very simple. And so at a high level, we kind of have a two-stage process. So, first, we collected Reddit posts and comments that mentioned semaglutide or terzepatide, so drugs like Ozempic or Wagovi or Zeppbound across several subreddits or forums that were about weight loss. So I think we had like about a dozen or maybe ten forums that were around weight loss. And so we just looked for posts from these subreddits, and we asked an AI model to identify posts where the author, um the author of the post or the comment was talking about their own use of the medication. So you can imagine that there's lots of posts in there where people are asking about asking people's experiences about the medications, or they're just talking about something else, or they're talking about a news article, but we just prompted the AI to look for posts or comments where someone self-disclosed that they were on this drug themselves. And so then once we had kind of a data set of posts and comments on Reddit from people who were sharing that they were on the drug, we used another AI model to extract side effects that the author attributed to their own GLP1 use. And so basically what the model had to do is it had to recognize everyday language about side effects and using the medication and map it to a standardized medical terminology. So if someone said, um, I'm taking five milligrams of Ozempic and I've been puking, that would, and they said specifically I'm I'm puking a lot, we would have the AI map that to the standardized medical terminology, and so that would map to vomiting, which is kind of the term that's that's used uh in the medical terminology. If someone said, Oh, I've been on GLP1s for three months and I'm exhausted all the time, that would get mapped to fatigue. A lot of people use these different colloquial terms to describe their side effects. So a lot of people on Reddit were talking about sulfur burps, and sulfur burps isn't really actually the term that people use. In this standardized medical terminology, the word is eructation. And so the AI just reads the posts and kind of maps it to that term. And so a key point is that the AI is not deciding whether the drug truly caused the symptom. It's just identifying what the user said and whether they attributed it to their own medication use or their own GLP1 use, and then we mapped that language into a standardized medical terminology so that we can kind of analyze this at scale. And then importantly, we don't just blindly trust that the AI works. We manually validated the AI's performance on a subset of the data to make sure that it could actually do this task accurately. So we don't just believe that the the AI can can do this without making sure it works first.

SPEAKER_03 12:14

Alright, alright. That was good. I think uh someone who's not into AI could understand that. So can you tell our listeners a little bit about what you found, like some of the side effects? Anything surprise you?

SPEAKER_00 12:27

Yeah, so I think um what we found broadly, it it tracked with what the clinical trialists have found. So things like nausea, which are what are most commonly reported in the clinical trials, were most commonly reported by the Redditors in our sample. There were some interesting signals that were kind of surprising. And so the one of the signals we found were menstrual changes. So nearly 4% of Reddit users in our sample who were reporting side effects mentioned some sort of reproductive changes. So things like irregular cycles or heavy bleeding or spotting or bleeding between periods. And so it was just 4%. So that number might sound small, but our denominator here is kind of a mixed gender Reddit sample. And so Reddit skews male, and we're looking across men and women, and so you know, perhaps it would be higher in just a female-only sample. Another thing that stood out was fatigue. So in our sample, fatigue was the second most commonly reported individual symptom after nausea, and that was kind of notable because if you look at the different clinical trials that have been done, fatigue has not always met the reporting thresholds in the clinical trials. And so in some it has, and some it hasn't. Um I think what's important to kind of understand is that in clinical trials you have a control group. And so what they do is they ask people on the placebo to report how often they have symptoms, and they ask people on the actual drug to report how often they have symptoms. And so in some of these clinical trials, they found that maybe 20% of people on the GLP1 were having fatigue, and actually 19% of people in the placebo group were also reporting fatigue because you know people get tired in everyday life. Um, and so even though 20% of people on the drug reported fatigue, when you can compare that to the control group, actually it's not really a side effect, maybe. So we don't have a we don't have a control group here. We're just kind of reporting what people are describing about their experience. And then one of the other unusual symptoms that we found was that a lot of people were talking of, not not a lot, but a significant number were talking about temperature-related symptoms. So chills, feeling cold, hot flashes, related things like that. And that's not something that's kind of been well documented in the clinical trials. So again, we're not establishing anything causally. Um these are just what people are saying. We're we're not closing the book on anything, but they stood out as surprising signals that we think are worth taking a look at more closely in in more rigorously designed studies.

SPEAKER_03 14:46

Was there anything you saw that was like, whoa, like totally out in left field?

SPEAKER_00 14:51

I mean, I think everything, like the the ones that that I mentioned, the the the period issues and the temperature-related issues, like these don't map what what we're seeing from the clinical trials and the adverse event reporting systems. Um so it's in some sense out of the field, but also it makes sense um physiologically, so so you know, rapid weight loss can cause these types of issues. Also, GLP1s interact with a part of the the brain that um controls temperature regulation. So I mean there's lots of hypotheses for why we could say what why why we think we found what we found. Um so it's not like anything is kind of impossible to kind of make sense of, but at the same time, um nothing here is causal, and it could just be that what we're seeing is kind of is not not real.

SPEAKER_03 15:38

And you've been very careful in your study and here on the podcast, you're not claiming these drugs are causing these side effects or these symptoms. So for our listeners, can you explain the difference between finding a signal that's worth investigating and proving cause and effect?

SPEAKER_00 15:56

Yeah, so I think um, as I mentioned in these clinical trials, they have a control group and they have a treatment group. And often in the clinical trial, the participants who enrolled, they don't know whether or not the medication they're getting is the placebo or the or the or the real drug. Um and so then you can kind of compare what's happening with the treated group who's getting the real medication and the placebo group who's not getting the real medication. And that's kind of a good way to identify what are the symptoms that are really or the side effects that are really coming from the medication and what's the true effect of the medication. But again, here we're just looking at people on Reddit who are describing the side effects they have. Um we don't have a control group. You know, you could imagine that if someone reports an issue with their period while taking a GLP1, the medication could be involved. But also if you've just started a new medication and randomly something in your life separately happens, unrelated to the medication, but but you might just tie these two together. The medication could also just make you have rapid weight loss or change your diet, or it could make you stressed or less stressed. And these could down the line impact your period, but they're not directly related, they're kind of indirectly related. And so our study can't really separate these possibilities. So I would describe our findings as hypothesis generating, there's their signals. They tell us where we should potentially look next, but they don't close the case and they're not they're not causal.

SPEAKER_03 17:15

You know, we're constantly warned not to treat not to treat social media, you know, as medical evidence. But you know, sometimes you'll see an anecdote, and anecdotes can be uh they can resonate because they're colorful, they're descriptive, a person is describing their experience, and at the end of the day, we love we love stories. But your study suggests that, hey, there may still be valuable signals hidden in these conversations. So my question to you, do you think patient discussions on platforms like Reddit could become an important tool for drug safety surveillance? And if so, what would that look like?

SPEAKER_00 17:48

Yeah, I mean, I definitely think they could complement traditional systems. They shouldn't replace it. You know, as we've discussed, social media is noisy. Um so people are going to misattribute misattribute symptoms, they're gonna repeat things they heard from others, um, they're gonna post when they're especially worried. So if you think about like, you know, maybe the last time you left a Google Maps review or a Yelp review at a restaurant, if you have, you probably only ever left a review if the restaurant was like the most amazing restaurant ever, or kind of a really bad experience. But when you have kind of a an average experience if the restaurant was just was good, or if it was just slightly bad, you probably wouldn't post. And so again, um we're getting this really biased sample, but still these social media sites are great because they're really fast to study and they're large scale. So people are discussing their symptoms in real time, and you know, the next day I can grab all this data and see what's happening before these concerns are showing up in the formal databases or or in the published research. Um so I think the most realistic model is kind of an early warning system. So the the AI systems could, you know, basically in real time monitor the public conversations and identify unusual clusters of symptoms, um, map these symptoms to standardized medical terms, and you know, this is just hypothesis generating. These are just signals, but they're not closing the book on anything. And you know, in the the private industry, um, you know, businesses like that that sell products to people, they're already looking at social media. They've been doing this for a decade, looking at what people are talking about when they launch a new ad, what products are people buying, uh, you know, there's there's there's whole industries around this, but it's just not happening here for for the the medical safety side of things.

SPEAKER_03 19:24

Yeah, yeah. I I know there's a bunch of social listening tools, you know, they're trying to gauge what people are talking about in different fields. They do, I mean, they use those in public health too. So if thousands of people start talking about the same symptom online, how do you know or how do you distinguish from AI that you're seeing a potential real safety signal and not simply people becoming more aware of something and just being like, oh yeah, me too, me too, right? Because you kind of you can see how that could happen online.

SPEAKER_00 19:53

Yeah, definitely. So I think one thing that's important to mention is like the where we're not just looking for mentions of uh of side effects, or we're not just looking at everything that's mentioned about GLP ones on social media. We're specifically having the AI look for people saying that they took the medication themselves and they're disclosing that they had the side effects. So, you know, maybe everyone starts talking about headaches, and so there's lots of discussions about headaches, but you know, you can imagine that some people are saying, Oh, I don't have headaches, some people are saying, Did you have headaches? But we're really just looking for the the self-disclosure of the symptom. And so, yes, you know, maybe if people start talking about it online, you're gonna see more people mentioning it, but I don't know that that's necessarily bad. If if a lot of people actually are having this symptom and we don't we don't know about it, it it's good that people are more aware of the symptom and that will mean that we can pick it up in the in the data. But again, I don't think you know social media is gonna solve everything. We we still need multiple data sources to identify whether the signal is likely to be real, but I I think you know there's there's there's still good information we can glean from from this type of phenomenon.

SPEAKER_03 20:59

Just kind of like no understanding its limitations and knowing what you can use it for and what you can't. Did you hear I'm I just thought of this. I'm curious, did you find people writing about like looking older or like you know, you hear like Ozempic face, right? That's been in the headlines and news. Like, did you see a lot of that? Like people concerned about their appearance after like the weight loss?

SPEAKER_00 21:21

Yeah, surprisingly, some of these, you know, there's a lot of um I Ozempic face, I think Osempic personality, there's like a lot of these things I had seen in the news media that I thought I would pick up on. Um, but actually we didn't find like a significant number of people describing these. And so I was surprised by that. But yeah, we actually did not see Ozempic face as one of the significant side effects.

SPEAKER_03 21:46

Okay. Okay. So uh what's the biggest mis misconception you hope people don't take away from your study?

SPEAKER_00 21:54

Yeah, so I mean, I think we kind of already hammered on this topic, but I think the biggest misconception would be You know, this study proves that GLP1 drugs cause period issues or temperature symptoms. It doesn't prove that. And like another misconception would be that patients should be alarmed or stop taking their medication because of the study. So I think the right takeaway is just patients are reporting certain symptoms online that are not especially prominent in current prescribing information or trial reporting. These symptoms deserve more systematic study. And if a patient experiences something unexpected while taking a GLP1 medication, and it's not something that is on the medication's list of side effects, they should still bring it up with their doctor.

SPEAKER_03 22:35

So do you think going forward that researchers should look into some of these signals that you found to see if there's anything to them?

SPEAKER_00 22:46

I think we're still learning so much about these drugs all the time. There's like every every week it seems like there's a new study talking about its amazing benefits for a different thing. So I think there's just so much we don't know. And I I mean I'm not saying this one study should lead like 10 researchers to pivot and spend their next study looking at this, but I think you know, as more evidence comes out about both the positive benefits and and the side effects, maybe maybe we'll we'll have a better picture. So I think you know, no study alone can set the agenda or settle the case. But I think you know, as we build up more research on this, we'll we'll just know more.

SPEAKER_03 23:20

Yeah. Okay, so since this is your field, uh more broadly, do you think AI will change how we detect medication side effects in the future? And could it become an early warning system for pharmacovigilance, or in plain language, I guess, determining can the continual safety profile of a drug once it's on the market?

SPEAKER_00 23:41

Yeah, I think I think uh it it can and it should. There is an adverse event reporting system that the US government currently has, and it takes in data from you know hospitals and insurance claims and a few other data sources to try to understand what's happening in the in the real world after a drug is approved. But social media is not currently one of the inputs to this, um, and they don't use like AI in this new AI sense on social media for this reporting system. So I think maybe if we put like 10% of the weight on social media or 5% or 2%, like not saying that the whole reporting system should be revamped to focus completely on social media, um, but I think supplementing it with with social media could be could be helpful.

SPEAKER_03 24:25

That's an interesting point. And you know, you hear the word misinformation a lot too, and it seems like anything that uh is posted on social media risks being categorized as that, you know, even if it's just someone sharing their experience. I think it would be great to find a way forward to utilize uh somebody's personal experience that they're sharing in a in a helpful way, you know, not like automatically running with it, but also not automatically labeling it as, oh, that's misinformation. Um that's hard to do, I think, though. On in social media land, it's hard to do. But well, you kind of already told us. I I was gonna ask you like a one practical takeaway. Yeah, I think you kind of already drove that point home. But I guess my last question would be, are you doing any more research in this area or what's next for you?

SPEAKER_00 25:08

Yeah, so I think uh we have uh I I generally I'm interested in AI and public health. And I think there's just so many different novel applications that the recent advances in in AI let you do. So I'm interested in a lot of different areas. Um definitely interested in how people are using chatbots to you know get health advice. Um so that that's that's one area that I'm really interested in. But in terms of this direct research topic, um something that we recently looked at and we recently put a preprint out, so it's it's not peer-reviewed yet, is looking into this new triple agonist new generation of GLP1s called Radotrutide. So rate atrutide is this new experimental drug from Eli Lilly. It's not currently approved anywhere in the world, um, but there's this huge gray market for it. And so a lot of people are ordering it from China and you know, taking it themselves in the US. It's again, it's not approved anywhere in the world, but you can buy it online. There's a lot of guides on Reddit on how to buy it online. Um, there was a recent article from Stat News last week that Eli Lilly for some reason gave one 79-year-old access to radotrutide for compassionate use, and Stat News was suggesting that it might be Donald Trump. So this drug is getting a lot of news attention. It's it's talked a lot on social media, and it seems like a lot of people are using it off-label. Um, so we we we kind of repeated what we did here with terzepatide and semaglutide, but with radotrutide to try to see what kind of side effects people are having from this drug that they're taking off label and ordering from China. Um and we found a lot of interesting uh side effects that kind of don't map with what the phase two uh rated clinical trial results from Eli Lilly kind of report. Um, but it's kind of hard to make sense of it because the people in the Eli Lilly trial are kind of taking the drug how it's meant to, and then people on Reddit are kind of stacking it with lots of different types of peptides. They're using it in different doses, they're ordering it from China, and so it's not clear how you like what the quality of it is. But this was kind of just saying like what what are what are the side effects from people taking this drug off label? Um, and so I think just kind of one like next step for this study is just kind of trying to apply the same framework to other medications that are exploding in popularity faster than our formal safety monitoring infrastructure can keep up with. Um, and so you could imagine that there's all these different types of peptides that RFK and other people are kind of promoting. Um, and so we could repeat this analysis there. You could take this approach and do it not just on Reddit, but other online platforms. Um again, Reddit kind of skews mail and is very US focused, it's very English focused. Um it doesn't really represent the whole world. And so you could do this on other platforms that represent other regions better. And so I think just kind of taking this framework and pushing it to other medications, other gray market areas, other languages and communities is kind of something that excites excites me.

SPEAKER_03 28:07

You say gray market?

SPEAKER_00 28:09

Uh gray market. I don't know if that's the right term. Maybe I don't know if black market is the right term either, but kind of they're no just so our listeners understand.

SPEAKER_03 28:17

I think I I mean I know what you mean. People are accessing these drugs not necessarily they're not the FDA-approved way, basically.

SPEAKER_00 28:24

Aaron Powell Yeah, so this drug is not approved by the FDA or any government body in the world. But you can just go online and you know, through PayPal, pay for this drug. You can get it shipped from China. Technically, it's illegal for it to come through the US postal service, but I don't think they're able to kind of check you know every package that's coming in. Um and it's it's very cheap to order. Um and I think people also are ordering like compounded versions of casepatite and semagutide from China, but that sort of is is different than ordering this you know compounded version of rate of truthide, which is not formally approved anywhere in the world.

SPEAKER_03 28:57

Yeah, I can't.

SPEAKER_00 28:58

And you can order it for very cheap, like like for a few dollars per dose as opposed to you know how much people are paying here.

SPEAKER_03 29:04

Yeah, what yeah. I mean, people in the US are always looking for ways to get medication cheaper. Although it sounds kind of risky like to just order this power.

SPEAKER_00 29:13

Yeah, I mean, and then the other thing that you learned from the Reddit experiences is that people order it from China, but then they send it to purity testing labs here in the US or in Canada. And so there is kind of a way to order it and then you know send it to another lab once you receive it to make sure that what you're taking is is is pure in some sense. But again, this even if it was pure, this this drug has not undergone a full clinical trial. We don't actually know that it's safe to use or you know, fully effective. But the phase three clinical trial is still ongoing. As I said, these these Reddit conversations are very rich. You learn a lot about how people are taking this drug.

SPEAKER_03 29:46

Sounds like it. Sounds like it. I was recently emailed, someone wanted me to like be an influencer on my Instagram page for a compounding peptide, and I just wrote back like, you know, I don't take them, I don't feel comfortable doing that. But like it's it's just kind of weird to me, you know, like to to pump something up. I mean, if a person should do what they want to do responsibly and that kind of thing, but it's a very strange environment out there right now.

SPEAKER_00 30:11

Yeah, and I mean uh I think one of the other, as I mentioned, the difficult things when we studied retail is that you find lots of people taking more than just one peptide. They're stacking it with all these different peptides that that do different things. Some are for skin, some are for collagen, some are for weight loss, multiple ones for weight loss. And so it's it's it's really hard to make sense of what's what's happening.

SPEAKER_03 30:31

And they're just doing it like just trying it out on themselves and seeing what happens, like hoping for the best, I guess.

SPEAKER_00 30:38

Um yeah, I mean, I think people think that rate of true tide is is is is better than than Ozempic or Govi or um the current formulations. And so um, you know, potentially it is, but but we don't we don't we don't know until it's okay.

SPEAKER_03 30:50

I'm such a minimalist. I'm like I don't I don't take any I'm like always scared to try anything. I'm like, nope, no, but try to get as natural as as I can. But thanks so much, Neil. This was really interesting.

SPEAKER_00 30:59

I definitely Yeah, thanks thanks so much for having me.

SPEAKER_03 31:01

Yeah, and if your other research gets published, let me know. I'd like to share that with my readers and listeners too.

SPEAKER_00 31:08

Sure, yeah. We have a uh like a preprint on MedArchive, but of course it's not it's not been peer reviewed or published.

SPEAKER_03 31:13

But it's uh it's gonna get peer reviewed soon, right? Thanks so much, and uh thanks for tuning in.

SPEAKER_00 31:20

Talk to you and thanks for being interested in the study.

SPEAKER_03 31:23

Absolutely, it's very cool stuff. Very cool stuff. So all right, enjoy the rest of your day.

SPEAKER_00 31:27

And I say bye-bye.

SPEAKER_03 31:29

Bye. All right, everyone. Thanks for tuning in. Let me know what you thought. Uh stick around, subscribe to the podcast, and definitely check out some of the older episodes. Also, on my YouTube channel, I started doing longer form videos for WTF Health News, um, where I talk about a study that made me go WTF in the health realm and break it down totally organic, totally chill, totally laid back, but definitely check it out. Uh yeah, just experimenting, doing new things, trying things out, trying to build up the YouTube channel. All right, and now I'm going to end with the quote here. And sorry, I turned a fan on if you can hear it. It's really loud. I think something's wrong with it. Here's the quote. This one's from Richard Feynman from uh the Caltech Commencement Address 1974. The first principle is that you must not fool yourself, and you are the easiest person to fool.

SPEAKER_02 32:37

I like that one. What do you think of that one?

SPEAKER_03 32:40

All right, that's it. I hope you tune in next time. Sorry for my voice.

SPEAKER_02 32:44

It's uh laryngitis, uh something um but something lingering in my throat for a while now. That's all. Go out there and, you know, have fun and do good things and uh goodbye for now.