Individualized Healthcare, AI, and GLP-1s w/ Mariette Abrahams

Mariette Abrahams joins the GLP-1 Hub podcast to explore how artificial intelligence and personalized nutrition are converging to transform healthcare for people on GLP-1 medications, including what true tech-enabled personalization looks like across genetics, microbiome, metabolic flexibility, wearables, and lifestyle data, how to separate evidence-backed tools from hype, and why stronger longitudinal data is essential to close access gaps and improve outcomes.

About Mariette Abrahams:
Mariette Abrahams PhD MBA RD is the CEO and Founder of Qina, a nutrition innovation consultancy and market intelligence platform that operates at the intersection of food, health, technology, and society. She has worked in the clinical and medical nutrition industry for over 25 years, leveraging her combined expertise in nutrition, business, and research to help businesses create the next generation of nutrition solutions rooted in science. She is an entrepreneur, regular international speaker, and published researcher in the area of tech-enabled personalized nutrition and health.

Guest Links:
Precision health and nutrition for weight management
About Mariette: Mariette Abrahams Founder of Qina, Thought leader and entrepreneur

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Understanding Hypophosphatemia: Recognition, Diagnosis, and Treatment
Endocrine experts distinguish Hypophosphatemia from osteoporosis & osteomalacia

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• 25:22 (Cont.) Individualized Healthcare, AI, and GLP-1s w/ Mariette Abrahams

Transcript

Mariette Abrahams PhD MBA (00:00)
I think we are at such an exciting time right now. And it started off last year. Last year was a huge...

time, I guess, in the industry because we can see now that technology, food, health and pharma are all coming together. You can add wellness in there as well. They're all converging. And so when you look at GLP-1, the impact that that has had has been tremendous.

Ana Reisdorf, MS, RD (00:18)
Mm-hmm.

Ana Reisdorf, MS, RD (00:27)
Welcome to the GLP-1 Hub podcast. I'm Ana Reisdorf, registered dietitian, GLP-1 user. And today I'm sitting down with Dr. Mariette Abrahams. She is a registered dietitian, PhD, and AI expert. And we are combining the two hottest topics in the world right now, AI and GLP-1s. We're gonna talk about how artificial intelligence is changing personalized nutrition, why your genetics and microbiome might explain how you respond to the medication, and what you actually need to know for trusting any

health app with your data. You don't want to miss this one. And if you're enjoying the show, please take a second to leave us a review on Apple podcasts or Spotify, or drop us a comment over on YouTube. It really helps us reach more people in this community. Now let's get on with the show.

Ana Reisdorf, MS, RD (01:12)
I want to welcome Mariette Abrahams today, founder of Qina Health. Or is it just yeah. Okay, just Qina, no health behind it. And she is a registered dietitian, but also a PhD and an AI expert, would we say, for helping integrate AI into healthcare? Okay, we're gonna go with that.

Mariette Abrahams PhD MBA (01:18)
Qina Qina, just Qina.

Yes, I would say.

Ana Reisdorf, MS, RD (01:37)
So today we are combining the two hottest topics in the world, AI, GLP-1. All right, everybody, those are the two topics. So can you introduce yourself a little bit and tell us your professional background, Mariette?

Mariette Abrahams PhD MBA (01:43)
Correct.

Yes, yes. And thanks for thanks so much for having me. And so I'm Mariette and I started off my career as a registered dietitian working in clinical nutrition. And ⁓ actually, my specialization was in inflammatory bowel disease. So I really started, you know, really becoming very interested in why do people get flare ups and why does it differ so much and why do people end up having surgery and others are not. So I really got very interested in inflammatory bowel disease from

very early on in the clinical arena. And that then kind of sparked this very early science called Neutrogenetics, ⁓ which is then looking at, is there kind of a genetic component in inflammatory bowel disease, which was really, really early at that time. And so at the same time, I studied towards an MBA because I felt, well, I'll probably not be in clinical for the rest of my life. So might as well do something that, you

know,

we'll add some skills and I did that and which I don't regret at all. And then I ended up working in medical nutrition as a medical affairs director, manager in four specific products that were used in the hospital for inflammatory bowel disease. So that was quite a nice, you know, segue into industry then. And then I ended up, of course, ⁓ being more interested in this emerging area of technology in nutrition and health, which was

Ana Reisdorf, MS, RD (03:00)
Mm.

Mariette Abrahams PhD MBA (03:13)
then coined personalized nutrition. And so that was around 2012. And then since then, I have combined kind of my love for nutrition and then this, you know, in the use of technology with this emerging area in personalized nutrition. And that's when I started consulting and set up the company. And so now we've really, really moved on almost more than 10 years now in the personalized nutrition industry, really being a

a company and consultancy that not only helps companies develop products and solutions within personalized nutrition, but also we track the industry. So we also provide market intelligence. So it's a combination of domain expertise and then market data as well as our digital tools, including AI that we then use to help companies to innovate.

Ana Reisdorf, MS, RD (04:02)
Cool. So what is personalized nutrition? Like what does that mean exactly?

Mariette Abrahams PhD MBA (04:07)
Yeah, that's a good question because I think from a dietitian's point of view, we've always been doing personalized nutrition. We've always kind of delivered personalized nutrition. But I guess what is new is that when we were training is that there wasn't this technology aspect to it. So we did PDFs, we wrote meal plans, we nutritionally analyzed, you know? And so that's really what it was about. Whereas now, of course, with the advent of wearables and more devices and

machine learning and AI, that brings the technological aspect. in essence, personalized nutrition is the ability to personalize the recommendation for an individual based on their goals, know, with their beliefs, maybe their budgets, their values, but also their preferences. But you can do that in a more maybe precise way or more technologically advanced way. So you can reach more people and still use the evidence base, but it's

technology part that enables collection of data, curating the data, synthesizing, analyzing and then providing a recommendation and that's where personalized nutrition So I like to call it tech enabled personalized nutrition so people don't think personalized nutrition is you know just something.

Ana Reisdorf, MS, RD (05:20)
Right, and something new, because technically we have been practicing like that, and we were practicing like that for a long time. Like we didn't just give everybody the same food all the time. We were trying to like make it for them. So would genetics play a role in that? Would that be a data piece of data that you would put in too?

Mariette Abrahams PhD MBA (05:25)
Exactly.

Correct.

Exactly. So we're looking at genetics. So that could be, you know, specific genetic variants that maybe influence not only what your nutritional requirement could be, but it could also impact how you actually utilize that, you know, vitamin or mineral. And so that can pinpoint, but that's a very, I would say, reductionist view. I mean, the science has moved on a lot since then, of course. But now if you're looking at kind of a GLP

one, example, or this whole weight management area, there have been some genetic variants identified that actually demonstrate that these people actually have maybe a slower metabolic rate. They may need a higher protein intake. So you can actually imagine if you knew that upfront. That could make a difference to somebody providing the recommendation, right? so that is one piece of the puzzle. Genetics is just one piece of the puzzle. Then you could have the microbiome, where we also know

that people have, know, your microbiome shifts all the time with your age, your lifestyle, with the environment that you live in, but then also can determine how you respond to medications such as GLP-1 as well. in that, not only is your dietary intake reduced a lot, which will change your microbiome, but also your pre-existing before you start can then also impact how you could potentially respond to the medication. So there's genetics, there's the microbiome, there's also something like

flexibility, so your metabolic rate, and how quickly do you fall back into your, you know, your baseline normal. And so people, some people are very flexible and some people are...

very inflexible. And so it's how do you challenge your body and how have you lived your life as well so that we can see how you respond. So if you understand, for example, like a glucose tolerance test would be something like testing your metabolic flexibility. Does your blood sugar come back to normal again or do you stay up there for a long period of time? So that also provides some insight as well. so dietary preferences would be a point. Taste preferences would be a point.

Ana Reisdorf, MS, RD (07:28)
Mm-hmm.

Mariette Abrahams PhD MBA (07:40)
Cultural and ethnicity would be a data point. Physical activity, your emotional stability or mental health would also be a data point. So there are so many different things including wearables that could all be factored in to what we like to call an engine. Like all the data goes in there and then you apply your algorithms for example to then output the actual recommendation. And of course different companies use different kind of data sets.

the most common ones would be either survey-based or DNA-based, some biological data such as DNA or a stool sample or a blood test for example, hair sample, things like that. And then of course you have the healthcare professional that can also put in their data points as well.

Ana Reisdorf, MS, RD (08:24)
Sure, so I've seen over the last,

I don't know, 10 years or so, a lot of different testing, at-home testing companies popping up. So there's microbiome ones, there's genetic ones, I did a hair test with some supplement company, they gave it to me, all sorts of things. But I have not seen anybody bring all that together. Because you get your results, and I'll be honest, for the vast majority, was like,

Mariette Abrahams PhD MBA (08:31)
Yeah.

Yeah.

Yep.

Yes.

Ana Reisdorf, MS, RD (08:48)
Like one of them sent me something back said, don't eat donuts. And I was like, well, I didn't need to pay $300 for you to tell me not to eat donuts. Like I knew that, not ideal food, right? So are you trying to maybe synthesize all of that? Because those are all factors in my health.

Mariette Abrahams PhD MBA (08:54)
Yeah, exactly. Exactly. Exactly.

Correct.

Correct. And this is where actually Qina was born, right? Because what we were seeing is that a lot of the companies that were coming onto the market were saying that they were personalizing and then using different, you know, whether it's questionnaire or biological data. And then we were interested in, what science is that based on? And then what recommendation are you saying then based on that? So that's really what we're interested in. And so of course, what we found was actually,

A lot of them will say, so you can fill in hundred question, question and answer. You think, wow, this is really going to be, and then you get exactly what you got. more broccoli, eat fatty fish.

Stay away from alcohol and avoid sugar. so really there was very little to personalizing. And I actually think we are now further along the way. are still far away. Yeah, we are still far away from deeply personalizing. However, because of more scientific research that has shown, for example, that people might respond better to a particular kind of a dietary pattern, there is a bit more personalization coming in the products now.

wanted

to do is say okay well if you say that you can personalize based on xyz have you actually tested it in the human study to prove that you do actually xyz and actually they weren't that many so what we said was actually now what we're going to do is create a Qina score so we look at different components we look at not only the composition of the team do you have like you know a dietitian and a tech person on the team or the leadership advisory board and what

What kind of behavior change is incorporated into the solution? Or are you just printing out an 80 page report and then people are left to their own devices? But also, have you validated your solution to actually prove? Because some people just say, well, this study, it's like a combination of vitamins or minerals or herbs or something. And then you say, well, that one does that and that one does that. But then you don't do the actual study that does the group. And so it's the same.

Ana Reisdorf, MS, RD (10:52)
Mm-hmm.

Right.

Mariette Abrahams PhD MBA (11:11)
So what we now have creators is this Qina score to say, well actually there is a difference. Some companies do invest in research and development and actually prove the solution and others that just say, hey, fill out my 150 questions and you know, here's three apples a day. They would be graded lower. And so that's where we kind of started because we kept on being asked, you know, okay, if we want to partner with companies, which ones would you recommend? And we were like, yeah, well these companies do and they go, no, but which exactly?

So now we've got the Qina score, we can say, now you look at it. And here are all the links and here it provides a bit more of a structure to it. So that's kind of what we've done. But to your point.

the number of evidence-based tools out there is really a few. If you think about the 380,000 apps that are available in the app stores, we have got about 700 of tools on the Qina platform that we say, okay, this is actually based on personalization and not marketing, so to speak.

It's moving along. It's not as far as we would like it to be, but at least, you know, it's getting better.

Ana Reisdorf, MS, RD (12:20)
So on your platform, are you making recommendations for companies that are doing it correctly? Is that what, like you're trying to consolidate all these companies that are already doing it or are you gonna provide all those services?

Mariette Abrahams PhD MBA (12:31)
Now, so what we do is we've created this database of all the companies of what's happening in the land. The goal of the database, so to speak, was to track what the industry was actually doing. So we can now say, ah, we can say, see that between 2022 and this year, image logging is flying. So we can see, we can track that against what's happening in the actual science. Now we can see, for example, that AI agents have tripled in the last year. And so

Ana Reisdorf, MS, RD (12:43)
Mm.

Mariette Abrahams PhD MBA (12:59)
Yeah, so we can provide those kind of insights to companies. So if companies come to us and say, you know, we're thinking of playing in this segment, what is the ideal combination of features or tools that we should have or who should we partner with who provides this to us? And that's what we can essentially provide. So we are completely B2B. We don't provide this kind of solutions to consumers, but to companies looking to innovate within the space.

Ana Reisdorf, MS, RD (13:14)
Yeah.

Mariette Abrahams PhD MBA (13:27)
and then who are looking to partner as well.

Ana Reisdorf, MS, RD (13:28)
Right. So let's talk a little bit about the dangers of AI. When the AI first came, I spent a lot of my time in an existential panic. It has improved, mostly because I've checked out, like not really constantly listening to what's coming, just a little bit, just to keep my eye on it.

Mariette Abrahams PhD MBA (13:46)
Yep.

Yep.

Ana Reisdorf, MS, RD (13:48)
So

in terms of healthcare though, or health information, what are some of the red flags or ways people are going in the wrong direction? Or what are some of the things that you're seeing with people using AI more and more to get health information?

Mariette Abrahams PhD MBA (13:58)
Yeah.

Yeah, I think it's a very, very interesting topic because AI has been around for quite a long time. It's only recently in the last, I would say, four to five years where it's really kind of made its way into health and wellness and nutrition. And of course, the first thing always is, well, it's so easy. I just ask a question or I just click a button and then it gives me an answer. Right. And now we're at the stage where actually there's been so much AI integration and evolution

that people aren't saying, look at it, I read it, but I don't trust it. And so that's the interesting era that we're in now, or this interesting time we're in now, that the tools are available, but the people or the recommendations that are trusted are still from the healthcare professional, whether that be the dietitian and the doctor. So we have more and more tools and only because people are so inundated with data that there's actually very little insight that is provided. So you can have charts and graphs

until the cows come home, that doesn't mean that you're actually going to change your behavior, right? Because you want to know, what do I need to eat today? What do I need to feed my family tomorrow? And how do I know that what I'm doing today is impacting my health at the end of the year? And that's the stage where people are now starting to question, well, actually, how accurate is it?

from where we sit in the company, we look at, okay, what kind of AI technology is it? Because there are different kinds. You can kind of brush it all with, you know, it's AI, but there's image recognition, there's text, you know, there's voice recognition, there's machine learning, there's generative AI. There's so many different AI technologies that are implemented and combined in different ways to provide that recommendation. So where we sit, we say, okay, well, we would want to know where did it get the data from to provide

Ana Reisdorf, MS, RD (15:49)
Mm-hmm.

Mariette Abrahams PhD MBA (15:49)
the

recommendation. And so if you think something as simple as a product recommendation, you scan a barcode and then it combines the different databases. Which database did it use? Did it use the American one? Did it use the UK one? Where was the produce grown, for example? So you need to really understand where did it get the data from and if it's making you some kind of health recommendation.

Which research was that based on? And what population was that even based on? So you kind of have to always think of the next step. Well, is that, you know, heart health recommendation, blood sugar lowering recommendation? Is that really for me? Was that based on people like me or persons like me or groups like me? And that is the stage where we are now where people are

Ana Reisdorf, MS, RD (16:30)
Mm-hmm.

Mariette Abrahams PhD MBA (16:38)
we are past the hype cycle where people know AI exists. And now we are like, how accurate is it? And we see a growing divide, not only in the trust in AI solutions, but we also see a growing divide in the inequality of the solution because of the people that are using the AI tools and can afford AI tools are the people that can afford the wearables and the rings and the so on and that. And so if you're driving that data source, you get more and more tools that can pull data.

Ana Reisdorf, MS, RD (16:50)
Hmm.

We do.

Mariette Abrahams PhD MBA (17:08)
data from that source of that kind of groups of people. And so we continuously need to check and say, okay, how accurate is it? Where did you get the data from? And who is this impacting? And that's really important.

Ana Reisdorf, MS, RD (17:12)
Mm-hmm.

So

how do we make it like such this vast amount of information, how do we make it more equitable to the people who can't? how do we get it to the people so they can benefit from this knowledge, this data, this health information?

Mariette Abrahams PhD MBA (17:36)
Yeah. Yeah.

Yeah. Yeah.

Yeah, I think and that's where we are now, especially if we just kind of use GLP-1 as an example, right? Because what we are seeing now is that it's available, yeah, it's available, it's accessible, it's becoming more affordable as well. And so more people will use it, right? But what we don't have is the access to a dietitian, for example, a personal trainer, and then all these other tools, all these

Ana Reisdorf, MS, RD (17:46)
Yeah.

Mariette Abrahams PhD MBA (18:09)
data points that we mentioned at the beginning that can provide insight into what you should be doing to optimize your health in the long term because that's essentially what we are interested in right. So we can say okay well you need to adopt the behaviors and the knowledge to be able to incorporate it as you are ramping off, titrating down, that you now have adopted these new behaviors through the use of digital tools which can then help you to track and monitor and support you in the long

Ana Reisdorf, MS, RD (18:19)
you

Mariette Abrahams PhD MBA (18:37)
term. That is essentially the goal. What we are seeing now though is that there is a growing divide between those who can afford the GLP-1s and then those who cannot but who would benefit from it. So at Qina we believe there's now a great opportunity. This is like an inflection point in the industry where we can really make a difference that

the power and the money is not just in the hands of a few companies, whether that's tech, that's pharma, whether that's food, but actually that we can make it equitable and then make sure that the lines between food and health become increasingly blurred. So we see it as...

People who start on the medication need to be provided with a structure that they can access the tools, the digital tools and the healthcare professional in a way that is so low and cost effective that actually that quality data can feed back to the industry and the companies who need to innovate new products. And that's how we see this kind of network effect that is possible to bridge this gap between what is food and how food can

use to improve health. We are not there yet. We are not there yet. But this is where we need to get to because otherwise the inequality is just going to be widening. And then we're going to sit with an even bigger problem in the end where people really struggle to afford to pay health care. But as the science advances, they won't be able to benefit from these AI tools because they can't. So there needs to be a better balance between

the haves and the has nots and the ones who generate the data but are not benefiting from the data. That's essentially what I do.

Ana Reisdorf, MS, RD (20:12)
Mm-hmm. Yeah.

Absolutely. I mean, I think that even being able to provide more widespread support for people on GLP-1 even if they can't afford the medication, like you said, they maybe can't afford the dietitian or the trainer or the this or the that,

Mariette Abrahams PhD MBA (20:26)
Yeah, and that's exactly the issue that we need to find a better way. I think the tide is changing. Companies can also see that consumers want more control over their data. They're willing to share their data also, but they want to get something in return. They don't want to see companies making billions and billions and billions. And you gave me a product for $300 and then told me to eat more fish. So it's really a... Do you see there's an imbalance there?

When everybody needs to benefit, otherwise we're not going to benefit society as a whole.

Ana Reisdorf, MS, RD (21:01)
Right, right, interesting. So for the future of AI and healthcare, do you think that when we go to the doctor now, we're gonna have a little robot come in and take our assessment and then the doctor like looks so the doctor's appointments are gonna be even like shorter now?

Mariette Abrahams PhD MBA (21:16)
You know, I don't have that-

vision. I don't have that vision and I hope it doesn't come true. No, because I see AI as a very, very useful tool to be used in combination with the healthcare practitioner. But actually the healthcare practitioner or clinician can actually spend more time listening rather than taking notes and filling in the EHR and then doing this, doing that for documentation purposes. Because those things can actually be done with good AI, with you can actually spend

Ana Reisdorf, MS, RD (21:19)
Nope.

but absolutely not be a particular relation.

Mariette Abrahams PhD MBA (21:45)
I'm talking because what we see more and more, no matter how much technology has evolved, has become easier to use and better to use and nicer to use.

People want more human contact. That's the bottom line. People want to see a human. They want to interact with a human. They want to get advice from a human. And so we need to work together in collaboration and in tandem with technology that can make the lives easier and lower the burden on both sides on the patient end or consumer end and the healthcare practitioner end. Because AI is not going away. It's only going to get better and it's going to get better fast. But it's how do we integrate the two so we don't lose people and just

people talk to a robot for intake for example. I hope that's not the future for sure. But I do see there's a lot you can learn and do by you know sending people information ahead of time you know so that they they can think about more of the questions that they want to ask before they see the doctor or a dietitian. They can fill in validated questionnaires before they see the dietitian. You know there's so much that we can do that hasn't been done because what has happened is that especially in nutrition is that

the paper-based records has just moved to online.

But actually the practice hasn't changed. We haven't really evolved to integrate AI tools and how can we check, how can we close that gap between your next visit and monitor you remotely, give you feedback remotely, give you access to an AI agent and see what feedback, preempt what you're going to do and how you're going to behave, what decisions you're going to make rather than paper-based keeping a diary, see you next time. Those times are gone. And I think it's the adoption

And that was my whole PhD was the adoption of technology, of technology amongst dieticians. And so I think we need to think about the workflows and how we integrate new tools rather than saying, well, they're not there yet, they're inaccurate, da da. No, they're gonna get better, but they're gonna get better with scientific and expert input, not by themselves.

Ana Reisdorf, MS, RD (23:47)
So your overall perspective is positive in that it could help like continuation of care. And so you don't like drop off, know, you know how patients like drop off the face of the earth. I might be guilty of that, you know, like the doctor keeps emailing me and I'm like, I'm busy. Don't make the appointment, you know, that kind of thing. So you think the AI will be beneficial for that.

Mariette Abrahams PhD MBA (23:52)
You ready?

Exactly.

Yep, yep. I can listen.

Totally, I do think so. And I think it's the, if we can close the gap between, you know, like the 200 food decisions that we make in a day, it's the real life situation, right? Like, what am I gonna eat for dinner? Like, what do I buy when I'm at the store? How can I make an info? Because we are so overwhelmed with decisions every day. We are so overwhelmed with, know, adverts, messages, notifications, nudges, but...

we can leverage the technology. I think it is the only way future. It's the only future that we need to be able to find what is the entry point and personalize that to the individual. Not give everybody the same tool because you've got a contract with that company, but we need to be able to personalize which tool based on your digital skills, based on your capability, based on your willingness to share information. There's so many ways that we can personalize what's already there.

in a way that suits the individual to want to change their behavior in the longer term.

Ana Reisdorf, MS, RD (25:07)
Interesting. That's so interesting. think it's fascinating that you have such a positive spin on it. mean, you own a technology company. So for the future, if you want to answer either for GLP ones or for AI and healthcare, what are you most excited about that you know might be coming down the pipeline?

Mariette Abrahams PhD MBA (25:14)
Yeah.

Yeah, I mean, I think we are at such an exciting time right now. And it started off last year. Last year was a huge...

time, I guess, in the industry because we can see now that technology, food, health and pharma are all coming together. You can add wellness in there as well. They're all converging. And so when you look at GLP-1, the impact that that has had has been tremendous. If you think about our impact of five a day, reduce exercise, 150, I mean, you laugh. It's reality. How long have we been saying that? And it's just like all of a sudden,

Ana Reisdorf, MS, RD (25:44)
Mm-hmm.

Mariette Abrahams PhD MBA (26:03)
consumers are switched on, you all of a sudden talk about gut health and the microbiome and people are switched on, you know? And so there's this new language that people tune into and the form that it's delivered in that people are engaging more with their health and prioritizing it more, which I think is super exciting, which we've never had before. And like I said, I've been in personalized nutrition now for 14 years. This is the year, or last year was the year where you see, wow, this is now going to take off. And so for GLP-1, like I said, I think it's just a perfect match.

where we can match the nutrition side, the health side, the tech side, all together. So what we are seeing is people are changing their shopping behavior, they're changing their eating behavior, they're exercising more, know, they're taking more gym, taking up more gym memberships, they are tracking their health more. And generally, the biggest shift that I see is that they see GLP-1 as an investment in their long-term health. So this is not about weight loss, this is about

improving your metabolic health in the longer term. And this opens up new opportunities because people are going traveling, know, people are now attending spas. And so all of a sudden...

All industries are affected, all of them. And so you can't deny that this has not had a huge impact. And so what I'm really excited about is how do we now as a company that is really at this intersection of food and health can help companies create better products where we know that people are invested in their health spine. And on the other hand, how can we help consumers to be able to leverage these newer technologies and bridge the world in a way that everybody can benefit all the players along.

I ⁓ would call it the value chain, but everybody can then benefit in a way that we can actually have quality longitudinal data that we actually can say, well, if you cannot afford all these tests, basically we have this huge data pool where we see people with this kind of profile, behavior, baseline, blood, this is what you need to do. And we don't have any of that data. So I believe that the future is about quality data that can feed AI algorithms, which is diverse, consented.

actually provide us with long insight, not eight-week studies, not three-month trials, long, so that we can actually have an impact and advise people accordingly based on either their unique biological footprint, but also in terms of the behavior, so that we know what works for whom and why. And that's the thing that I'm most excited about.

Ana Reisdorf, MS, RD (28:31)
⁓ you're just a beacon of hope. Thank you so much. Where can people find out about your work and your company?

Mariette Abrahams PhD MBA (28:33)
Ha ha!

Yeah, so my company is called Qina.

And for of course the listeners, the site where ⁓ we say we can download maybe a free guide is called Qinahub.com. So that will be our kind of consumer arm where people can find out about webinars or we are trying to organize an event also for GLP-1 in Portugal. So beautiful side, the beach side of Portugal in the Algarve where we're looking at kind of education

especially because now, course, I said, wellness and travel is huge. And so we think this is an ideal. usually people go to university summer camp, to learn at university something. We think spa is a great way. think about, yeah, yeah. So think about that in terms of the longer term. And then for companies that are listening, we have Qina.tech ⁓ and that's the B2B site where we provide strategy, innovation, and also marketing intelligence and research as well. So Qina.tech

Ana Reisdorf, MS, RD (29:22)
Meet you.

Mariette Abrahams PhD MBA (29:38)
and QinaHub.com is where you can find us or of course you can follow me on LinkedIn

Ana Reisdorf, MS, RD (29:42)
Awesome, well, I will put all those in the show notes because I wanna keep track of all the things you're doing. It's so, so fascinating. And I really, really appreciate your time and your expertise. This has been such a lovely conversation.

Mariette Abrahams PhD MBA (29:54)
Thank you, Ana. Thank you. Bye-bye.

Ana Reisdorf, MS, RD (29:56)
I'm so grateful Mariette was willing to come on to talk about these two hot topics, AI and GLP-1. It was such a fascinating conversation. In the next episode, we'll have Dr. Aaron Hartman, who's gonna talk about how he's using GLP-1 to manage a variety of medical conditions, way outside of just weight loss alone. And if you wanna stay connected and know about all the great content we're creating here at GLP-1 Hub, make sure you are on the newsletter. I send an email every Tuesday with

⁓ maintenance tips, mindset, everything that you need to know to support your journey. And you can find that link in the show notes and I'll see you in the next episode.