Description
Comments & Resources
Limitations of BMI and the Need for Phenotyping:
BMI alone is an inadequate measure of obesity as it does not accurately reflect body composition. Individuals classified as without obesity by BMI may have excess adiposity, highlighting the need for improved body composition assessments.
Obesity as a Heterogeneous Condition:
Obesity is not a single disease but a collection of diseases with varying metabolic, mechanical, and mental complications. Individuals with similar levels of adiposity can experience vastly different health outcomes, necessitating a more nuanced approach to classification and treatment.
Personalised Treatment Strategies:
A phenotype-based approach to obesity management allows for more targeted interventions. Body fat distribution, metabolic status, genetics, and functionality are key factors to consider when assigning phenotypes to patients. Identifying biological pathways associated with treatment resistance can improve outcomes for individuals who do not respond to conventional therapies. Comprehensive phenotyping, including assessment of comorbidities, is necessary for improved patient classification and care.
Challenges in Obesity Treatment:
Weight loss alone should not be the sole measure of treatment success, as pharmacological and surgical interventions offer benefits beyond weight reduction. Understanding why some individuals do not respond to treatment remains a key research priority.
Next Steps and Future Research
- Continue researching obesity phenotypes, particularly in younger populations, to refine and enhance patient classification and improve treatment strategies
- Study the biological pathways that contribute to poor treatment responses, identifying potential targets for intervention
- Promote the integration of body composition assessments into routine clinical practice to improve obesity diagnosis and management
- Explore personalised interventions, including pharmacotherapy, surgery, and dietary strategies, based on patient-specific obesity phenotypes
- Educate healthcare professionals on the heterogeneity of obesity and the importance of phenotype-based treatment to enhance patient care
Transcript
Transcripts are auto generated, if you spot an error, please email enquiries@easo.org
Hi everybody and welcome to the first ESO Comms Network webinar of this year. This monthly Comms webinar aims at enhancing our knowledge on obesity management and patient care. I am Mar Maragon, co-chair of the ESO basic science group here and I would like to thank you for attending our webinar today on obesity phenotypes understanding differences and implications for management.
In this webinar we will have two excellent speakers, Dr. Javier Gomez Ambrosi and Dr. Karel Leroux. They will tell us about the most recent scientific advancements in obesity classification by phenotypes going beyond the BMI of course and we will also learn on current phenotyping methods including genetic, metabolic and body composition analysis. This webinar is also aimed at providing insights into practical strategies for implementing personalized obesity treatment, especially focused in this case today on recent research into cardiometabolic disease and muscle disease classification.
Just a couple of messages before presenting our speakers. First, the webinar is being recorded and the recording and related resources will be available in the ESO video archive soon after this session. And second, you can or we encourage you to ask questions in the chat and this will be addressed after the presentations in the last 15 minutes.
And a final request, please complete the anonymous feedback form as this is much appreciated for improving future webinars. And let’s start with the scientific content of the webinar. Our first speaker is Dr. Gomez Ambrosi, as I mentioned before, and he’s professor at the University of Navarra and works at the metabolic research lab department of endocrinology and nutrition in the biomedical research institute of Navarra and Clinica Universidad de Navarra in Pamplona, Spain.
So Javier, the floor is yours. Thank you very much, Mark, and welcome everyone. Can you see my slides? Yes.
Okay. First of all, I’d like to thank the ESO for inviting me to this webinar and I have to say that I have no conflict of interest regarding this presentation. So the prevalence of obesity has increased alarmingly in the last decades, as you can see in this slide, and the prevalence is most of the European countries, so 20%.
And obesity is a serious health concern, increasing the risk of cardiovascular disease, fatty liver disease, type 2 diabetes, reproductive disorders, and cancer. One of the biggest problems for the phenotyping of obesity is that we still are using the BMI, and in many cases only the BMI to diagnose obesity, and as you know, the WHO defines obesity as a BMI equal or over 30, being the most frequently used tool for the diagnosis of obesity. It was proposed by Adolf Gekkelev, was known also as Gekkelev index more than a century ago, and it was another until 1972 that was renamed as BMI by researchers.
BMI is only a surrogate measure of body fatness and does not provide an accurate measure of body composition, and you can see in this slide these two drawings, the men on the left have a BMI of around 42, while the men on the right has a BMI between 40 and 45, but there is a huge difference in the body fat between them, women on the left has less than 5% of body fat, and women on the right has over 50% of body fat, so although BMI is easy to calculate and inexpensive, it can be also very highly intercised. So we need to pay more attention in body adiposity, which is what defines obesity, and is responsible for most of the problems associated with most of the comorbidities associated with obesity. So in this study we wanted to determine the degree of misclassification by using the BMI in the diagnosis of obesity, we included more than 6,000 people with body fat by a displacement plethysmography, and as you can see here, this is the data for women, more than 4,000 women, and vertical lines denote the cut-off points for defining overweight and obesity according to BMI, while the horizontal lines represent the cut-off points to define overweight and obesity according to body fat, and as you can see here, those women considered as without obesity or with normal weight according to BMI, most of them were incorrectly classified, or many of them were incorrectly classified if we look at the body fat, and the same being even higher in those women considered as with overweight by BMI, but many of them are considered as with obesity according to body fat percentage, while in those classified as with obesity according to BMI, only a few are incorrectly classified.
Similar data were found in men, so in summary, we found that in those people considered as with normal weight according to BMI, 20% of them, if we look at the body fat, will be classified as with obesity, while in those considered as with overweight according to BMI, 80% of them will be considered as with obesity according to body fat, and while this was well-known, but we also aim to analyze the impact of this classification, let’s dimensionally look at the metabolic risks in those people, so we compare people matched by body fat, but considered with obesity by BMI or without obesity by BMI, and compare with normal weight people, both men and women, and we observe that people without obesity by the BMI criteria, but with obesity according to body fat, has increased blood pressure, glucose levels, insulin levels, BPFs, C-reactive protein, and fibrinogen concentrations as compared to the normal weight, very similar to those with obesity by both BMI and body fat, and we also found a reduction in men’s skin cholesterol, so we are underestimating or even ignoring the metabolic risks of these patients. Most recently, we aim to perform a similar study in children, in more than 500 children and adolescents aged between 6 and 17 years, and were classified by the code criteria according to BMI, and we also found a high number of subjects misclassified using the, you know, the degree of misclassification for girls and for boys, and again, summarizing, we found that from those considered as with normal weight according to BMI, 7% were actually with obesity according to body fat, while in those considered with our weight according to BMI, 62% of them could be considered as with obesity according to body fat percentage. Again, we wanted to estimate the metabolic risk of this misclassification, comparing the two groups, both with obesity according to body fat, but only the last with obesity according to BMI, and we found in boys an increase in both groups regarding c-reactive protein and in blood pressure, while in girls, we found increased blood pressure, glucose levels, acid, white blood cells, c-reactive protein, and a decrease in the similar increase in both groups with obesity according to body fat and c-reactive protein, and a decrease in In addition, we performed a glucose tolerance test, and we found a similar increase in glucose response after the challenge.
So again, in children and adolescents, we are also finding the same or similar findings in adults, that some of them may be, we are underestimating the metabolic risk in these patients not considered with obesity according to BMI. Well, since not all health workers have access to body composition, we have developed a tool for the estimation of body adiposity that we call Globale, stands for Global Body Adiposity Estimator, that is valid for adults and mostly for people not very active. Well, we have seen that to look at the amount of body fat is very important, but also we need to focus on not only in the amount, but also in the distribution of body adiposity.
So we know that in the expansion of adipostasis that takes place in obesity, the B-sperella adipostasis, which is the more pathogenic one, expands mainly through hypertrophy of the adipocytes, which is accompanied by a recruitment of pro-inflammatory cells, as well as our dysregulation of adipokines and the presence of insulin resistance, while in the expansion of subcutaneous adipostasis, which takes place mainly through hyperplasia of adipocytes, there is an equipment of anti-inflammatory and macrophages, and there is insulin sensitivity. So we need to pay attention not only to the amount of body adiposity, but also to the distribution. In this sense, we have very easy-to-calculate tools to anthropometric tools, such as the waist circumference.
You can see here the cut-off points to define increased risk in females and males, and also the cut-off points to define high-cutting metabolic risk in females and males. And regarding the waist to height ratio, we know that, or it has been described that a ratio of the waist circumference to the height, both expressed in centimeters, when it’s equal or higher to 0.5, we may see that there is an increased metabolic risk. For example, in the IBS study, including more than 168,000 people from 63 countries, they observed an accumulative effect of the waist circumference and the categories of body mass index for the prevalence of cardiovascular disease in men, as we can see here, and in women, and a more marked prevalence of type 2 diabetes in men and women.
And regarding the waist to height ratio, in this study involving more than 4,000 patients with hypertension, we can see that the incidence of cardiovascular events in people for five years is highest in the waist to height ratio of 0.6, as compared to the waist to height ratio between 0.4 and 0.5. So we can see that these tools, which are very easy to measure, may be very useful in the medical practice to use them in the last years, has gained popularity, the use of the metabolic and health definition. In my opinion, I don’t like this definition, because what would be the presence of obesity without other metabolic signs, such as glucose or lipid metabolism, or hypertension. And I was saying that I don’t like this time, because I consider that including the words obesity and health in the same definition may be misleading to most populations.
In addition, there is no clear classification criteria for the definition, but not only until recently, that has been considered that the definition should be the presence of obesity without any other signs of symptoms related to metabolic signs. So we can see here that the characteristics of the metabolically unhealthy obesity would be characterized by a reduced amount of subcutaneous adipose tissue, and a higher amount of visceral adipose tissue, together with the presence of insulin resistance, inflammation, and the accumulation of lipids in the organs, such as the heart, the pancreas, the liver, or the skeletal muscles. Well, in this study, we want to analyze the comparison of people with metabolically unhealthy obesity against those with metabolically abnormal obesity, and we found that if we perform a glucose tolerance test, we found that around 30% of people exceeded in glucose tolerance of type 2 diabetes.
In addition, we found altered levels of adiponectin, leptin, the pro-inflammatory adipoprotein insulin, and metaboloproteinase 9, which is involved in the extracellular matrix remodeling. And furthermore, we found that the expression of mRNA in the visceral adipose tissue in both groups of patients with obesity were very similarly altered regarding the expression of pro-inflammatory cytokines, such as phosphoponitin or TNF, or tetanustin, for example, while the glucoformatory protein SFRPCA5 was very similarly decreased. Well, when we pay attention to when we are dealing with the phenotype of patients with obesity, we must also pay attention to skeletal muscle, but not only to the amount of skeletal muscle, but also to the functionality.
And in this sense, we can find people with what has been called sarcopenic obesity, which is defined as the concomitant presence of obesity, so an excess of mass, but at the same time, a reduced amount or functionality of skeletal muscle mass that would be associated with an increase in the cardiovascular risk. For example, you can see here in a study performed in 25,000 Japanese people between 40 and 80, followed by 24 years, and they found that those patients with obesity and the lower hand grip strength were those with the higher incidence of cardiometabolic cardiovascular events. So, by means of a very simple determination, such as a hand grip strength dynamometer, we can estimate the cardiometabolic risk of the patients.
So, in the last year, it has been published evidence of the utility of the morphofunctional evaluation, which includes the functional evaluation of the skeletal muscle, also to measure the amount of skeletal muscle bone and adipose tissue, and other measures, such as barometric parameters or the face panel, which will be very useful in order to determine the health of the patients, not only of these patients on the basis of obesity, but any other diseases. Well, in the obesity phenotyping, we should also pay attention to the fitness of the patients, and in this sense, it has been published that those patients classified by the BMI or the body part, and the cardiometabolic risk of death by cardiovascular disease is similar in those who are fit, those without obesity, but also fit and very much lower than compared with those who are fit. So, we also need to pay attention to the fitness of the patients.
Well, and given the importance of better from a good phenotyping of the patients, we propose a matrix in order to determine phenotypes of the patients regarding body amount, body part percentage, and a good distribution with circumference, and we prefer different metrics for males and females, since they are quite different, both in the body part percentage and with circumference, and we have nine different phenotypes, which were grouped in five different phenotypes, following a traffic light course classification assistance, and we validated the classification in our study, including more than 12,000 people, and according to the five phenotypes, most of them were with or were obesity, according to BMI, and we found 7% in the red group, absence of risk, 5% in the yellow group, slightly increased risk, 8% in the orange group, increased risk, 15% in the dark orange group, higher risk, and 67% in the red group, with very high risk. So, with this classification system, more patients were considered as having high risk, as compared to a similar matrix, including BMI and with circumference, so we are going to be able to detect more patients with cardiometabolic increased risk. We studied the metabolic syndromes in order to have a continuous value of the cardiometabolic risk, better than the dichotomic classification of metabolic syndrome, so we found that in the five different cardiometabolic risk groups, we had a continuous increase in this variable, and finding significant difference between all of them.
In addition, we found differences also for other variables, such as insulin, triglycerides, uric acid, for instance, proving that this tool may be useful in order to estimate the metabolic risk of the patients. Well, the Advant of Obesity Stating System is a clinical classification tool that may assess the impact of obesity beyond BMI, considering physical, mental, and functional health factors. It facilitates obesity screening by test stratifying patients according to the severity of complications, helping to identify cardiovascular, metabolic, and psychosocial risks, and as you can see here, the Advant of Obesity Stating System predicts mortality much better than the stratification by BMI in the reference study.
Genetics is also fundamental in obesity genotyping, as it may allow the identification of genetic variants, which are associated with a particular condition. So this may help to classify individuals according to their risk of developing obesity, all the complications, such as diabetes or cardiovascular disease, in addition to studying specific genes, facilitating the understanding of how patients, for example, respond to different and more diverse interventional treatments. So it may provide true information for the development of targeted therapies and prevention strategies.
Phenomics, which studies the global expression of different phenotypes, is also key, or may be key, to obesity phenotyping. Integrating data at the molecular, cellular, and clinical levels may allow the identification of complex patterns of how genetics, environment, and many other factors interact with the development of obesity and its complications. In addition, it may facilitate the identification of patients according to specific phenotyping characteristics, helping to personalize treatments.
Phenomics also may allow to identify predictive biomarkers, including obesity prevention and management. Well, in the last years, it has been an increasing interest in machine learning techniques. And, for example, in this study, by using these machine learning techniques, they were able to identify four in a big cohort.
Well, they used different cohorts, they identified four different metabolic phenotypes, as you can see here. And these different phenotypes may be very useful in order to identify or estimate the metabolic risk, and be a guidance for the treatment of these patients. In September, the ESO published in Nature Medicine a framework redefining the diagnosis, specification, and management of obesity.
This approach moves away from the exclusive reliance on BMI, and also highlights the importance of body fat distribution, especially abdominal or waisted fat, which may be a more accurate indicator of health risk. They propose to use, to include people with a BMI between 25 and 30, and consider also the ways to hide the ratio, in particular if it’s equal or higher than 0.5. And this approach, I think, promotes personalized and long-term treatment strategies, and more focus on the general health of the patients, and not only on the anatomical or metabolic risk. And very recently, in the last 14th of January, was published a new consensus in Ancient Diabetes and Diabetes Independently.
They agree with what has been said in this presentation, that BMI may be very useful, but it’s not a direct measure of fat, and that’s the distribution of fat around the body, and cannot determine if there is a health problem. So they propose a new classification system, aiming to provide more precise assessment and personalized care. However, in my opinion, patients with obesity who fall outside the clinical definition of obesity, for example, those considering this classification as clinical obesity, it is the consensus that we can lose access to necessary medical interventions and support, as the condition might not be formally recognized.
So, in conclusion, it’s necessary to go beyond BMI to phenotype the patient with obesity. There are different approaches to phenotyping patients in relation to obesity that take into account body composition, and the positive distribution, metabolic status, genetic functional status, among others, and the pains of phenotyping, including assessment of body fat and depression of comorbidities, will help in better identification of people with cardiovascular disease. And finally, thank you very much for your attention, and I would like to express my gratitude to all the people involved in the studies that I have shown.
Thank you very much. Thank you very much, Javier. Well, I encourage all the participants to ask questions in the chat, and we will address these after the next speaker presentation.
And I have the pleasure to present you Dr. Karel Leroux. He is Professor of Experimental Pathology at the School of Medicine, University College Dublin, and lead of the adult comms at Vincent’s Private Hospital in Ireland. So, the floor is yours, Karel.
I was able to share my slides, but not my screen. So, let’s try again. Can I just see, can you see the screen now? We can see the screen.
Is it now correct? Correct. Okay, perfect. Well, listen, thank you so much for this opportunity, and it’s wonderful to follow on Javier and how he sort of set the scene.
And I’m sort of starting with this Hans Christian Andersen picture of the ugly duckling, and Eustace is about identifying this phenotype. And, you know, you can see everybody is looking at this ugly duckling, and it doesn’t look like the others. And it’s actually trying to understand, you know, how can we understand obesity, and also, you know, is obesity and fat one disease? These are my conflicts of interest.
And I work with lots of different companies, with pharmaceutical companies, with nutrition companies, and also with surgical companies, because we do not actually think obesity is one disease. And therefore, it’s very, very unlikely that we’re going to find one silver bullet. So, that’s a different view to thinking about the heterogeneity of obesity, but rather thinking it as different diseases that actually contribute to the same adiposity.
Now, already there’s been mention made of the Edmonton Obesity Staging Score, and Arya Sharma was really one of the leaders in this field to actually think about obesity and the complications of obesity, not as a single and on a linear line, but really thinking about these complications, more than 220 complications, and we can group them either as metabolic or mechanical or mental. And important to say that somebody with obesity can have zero metabolic complications, but may have a large number of mechanical complications, or may have no mechanical complications, and may have mental complications of the disease of obesity. So therefore, it also tells you that somebody with exactly the same adiposity mass can have completely different complications of this disease.
And of are we treating the disease of obesity, or are we treating the complications of obesity? And because there’s more than 220 complications, both the EASO framework that was published in Nature Medicine, as well as the Lancet Commission that was already referenced, they have focused on the complications of the disease of obesity, because it’s a lot easier to actually create a phenotype around that and to work out how the treatments may differ. So somebody with type 2 diabetes who have obesity, the treatment is very different to somebody who have no metabolic complications, but may have osteoarthritis as a complication. So therefore, it’s been the easiest for us as clinicians to focus on the complications of the disease and try and phenotype accordingly.
But, as this famous painting that hangs in the London Gallery shows, this creates a lot of discussion. This is how society reacts, and you would have seen the discussion recently after the very good EASO suggestions, as well as the Lancet suggestions. You know, some people are deeply thinking about this, you know, some people are really frightened, you know, about these concepts, and some people don’t care.
So what I would like you to do is to be like the little boy on the right, to open the window and shine light on this, so that we can discuss it, because there are multiple ways to think about it. Now this has been a project that I’ve been involved with, and also EASO is involved in this, and also many centers across Europe, more than 34 centers, and this is the SOFIA project, where we try to get to stratifications of obesity phenotypes. And the principle is exactly as I’ve already illustrated, if you just look at the x-axis and you take a measurement of obesity, be it BMI, be it waist circumference, be it, you know, adiposity mass, you will see that most people behave in a linear fashion.
So the higher the measurements of obesity, the higher the risk, for example, of cardiovascular disease. But, as you will see, there are also these outliers. There are some people that are very high levels of obesity, but low levels of cardiovascular risk, and other way around, patients with low levels of obesity, with high levels of cardiovascular risk.
We identified these outliers as patients who are discordant, and we use this word discordant to try and study and understand this phenomenon. And this has been created in this two-dimensional map, where most people in the middle are those people who are concordant. So they are the ones that behave exactly as you would expect from their adiposity measure.
However, you have patients here who are discordant, who have a hypertensive profile, patients who are discordant, who have high transaminases, patients who are discordant, who have high lipid levels, patients who are discordant with hyperglycemia, and patients who are discordant from inflammation. And actually, the suggestion has been that if we look at these patients, they behave as different diseases. So they have the same body mass index, but they have very dramatically different risks when we actually look, for example, here at the group that were discordant for inflammation.
So their risk of developing cardiovascular heart disease, or type 2 diabetes, or hypertension, or even rheumatoid arthritis is much higher in comparison to those patients who are concordant, who are in the middle. So let’s see what happens if we take these patients who are discordant and we map it onto the patients who were treated in the scale study from Novo Nordisk. And what you will see is that these patients also map exactly as you would expect, you know, and these are the red dots mapped onto the black background.
The black background is our base case, and the red dots are the patients in the scale. And you can see how many of them were also discordant for these different interventions, both the males and the females. And what happens if we take the Rewind study? Now you’ll remember Rewind from Eli Lilly, and that was a patient group that had type 2 diabetes.
And you can see how those patients mostly map onto the group that are discordant for hyperglycemia. So that allows us to now see the risks of these patients, but can that help us also illustrate the response to treatment? And it’s when we actually look at bariatric surgery, and this is the SOS study, and again the black is the base case and the red dots are the patients from the SOS in usual care, how they map. And now I’m showing you those caught that are discordant for inflammation and discordant for hyperglycemia.
And now you see at the bottom those patients who had bariatric surgery who were also discordant. And it’s when we look at these, and does this predict response to treatment? To our great pleasure and surprise, it showed that those patients who are discordant for inflammation actually had a better result after bariatric surgery. So now you have a group that is at highest risk of developing complications, and they also have a better response to treatment.
Unfortunately, those patients who were discordant for the hyperglycemic profile, they had a worse outcome after bariatric surgery in comparison to those people who are concordant. So now we are thinking about obesity not as one disease, but as multiple diseases, and therefore we can see which disease will respond to which treatment. Now this is very early days, but it lays the foundation for us to start to understand.
Here I’m showing you the ultimate case of understanding the phenotype. This is a case that was diagnosed by Professor Steve O’Reilly and Sadaf Arouki, a young patient who had leptin deficiency. And by understanding the phenotype of this patient, why does this patient have so much adiposity, and understanding the behavior, and therefore actually understanding the phenotype that we have now classified as the syndrome of leptin deficiency, by giving this patient a specific treatment, leptin, which most other people won’t respond to, you can see just how well they responded.
So again, leptin deficiency is not average obesity. It is a completely different disease. It’s very rare of course, but it allows us to understand how we can, if we can actually get better phenotypes and understand obesity as multiple diseases, we can actually be much better.
But this also helps, because if we think about the common pathway, that although different diseases can generate very different risks, can have very different responses, if you think about the common pathway that many of these diseases work through, it appears to be the subcortical areas of the brain. I’m highlighting the hypothalamus, but also the areopistrema, the nucleus tractus solitarius. So even if you don’t know what the phenotype is of this patient, or what type of disease this patient has, very often if you target this part of the brain, be it with nutritional therapies, pharmacotherapies, or surgical therapies, then we can actually have a success.
And we now think about obesity and this common pathway almost as the regulation of temperature in your house, because in the past we thought about treating obesity as energy in versus energy out, and we tried to apply that to all forms of the disease of obesity. So it didn’t matter what type of obesity you had, we asked you to eat less food and do more exercise. But that didn’t work very well, because we didn’t understand the common pathway.
And actually the common pathway in this house to the temperature regulation is the thermostat. What is the temperature that you decide to make this house? And can you turn it up or can you turn it down? And now we think about the common pathway of many of the diseases of obesity as a pathophysiological dysregulation of adipocyte mass. That means we can change that subcortical areas of the brain with our interventions, and therefore we can reset where the adipocyte mass is being regulated.
That’s very well illustrated by the new treatments that we have. Here I’m showing you the data from this amount one three-year extension with dizepatide, and I want you to focus on how patients went from 108 kilograms when after one year went down to 83 kilograms, but they maintained the weight in 81 kilograms for three years if the medication was continued. And I think it does not matter what phenotype it is we are treating.
I think we need to be humble enough and honest enough to say that we cannot cure the disease of obesity, but you and I can control the disease quite well with nutritional therapies, pharmacotherapies, and surgical therapies. And here’s a very good illustration of how we can maintain that in the long term, provided we continue the intervention. But what we see when we stop the intervention, and here I’m showing you the step four study in patients who had somatoletide, and you can see the minute you stop the treatments in the dark blue line, patients regain their weight.
However, in those patients that were continued with the medication, they continued a very substantial amount of weight. But this is the final challenge for you and me. Very often you and I would like to examine a patient, speak to a patient, understand their readiness for change, and then make a suggestion and predict how much weight they will lose.
But you could see here that there were about 12 percent of patients who lost less than five percent of their weight, and that’s on the far left hand side and above the bar. So 12 percent of patients that lost less than five percent of their weight on the active medication, and on the far right you can see there’s 40 percent of patients who lost more than 20 percent of their weight. Now when we look at all the regular blood tests, all the regular psychological markers, you and I and anybody that was involved in all of these studies are unable to predict who is going to lose less than five percent of their weight and which patients are going to lose more than 40 percent of their weight.
Again, important for us to remain humble and never to blame our patients if they don’t respond to our good treatments, because we are not good enough yet to predict who will respond or who will not respond even when we have these great treatments. Of course what has happened in obesity, we’ve had bariatric surgery for more than 50 years as illustrated by the top line over there, that has provided more than 25 percent weight loss over the last 50 years. We’ve had lifestyle, intensive lifestyle changes that over the last 50-60 years has provided about 5 to 10 percent weight, and doesn’t matter if you have the best apps in the world and the most readiness for change, the treatment is about the same.
However, what has changed is medications and this is dramatically improved and therefore we are now able to actually scale our treatments for obesity. But I will warn you that we are going through this very usual Gartner hype cycle curve at the moment, and what you will see is we are at, we were now finished, and I probably think we’re right at the top of this curve, which is called the peak of inflated expectations. We’ve had such great treatments but our expectations were inflated, and now what we are starting to see is we’re starting to see more and more people that don’t respond to these treatments, and we don’t know why that is because we are thinking about obesity as one disease, and now we’re coming down on this curve to the trough of disillusionment, and we’re going to need to deal with this and also the side effects that we’re uncovering, so this is now our road.
But I would like us to make that as shallow and as short as possible to go up again on the slope of enlightenment, so that we can get to a plateau of productivity. This is usual for all medical interventions, for all technology, this is not new, but we are going through these processes and therefore it’s more important for you and me to really understand obesity and understand the we are going to treat this. So also now allow me just to conclude and suggest to you that the future of obesity care will be to have chronic treatment that aims at health gain, not only weight loss.
It’s recognizing the biological basis of this disease and we’re needing more and different treatments for the subtypes of this disease, and we need to never blame our patients if they don’t respond and if our treatments are not the right treatments for their disease. So thank you very much also to the team at home and happy to take some questions together with Javier. Thank you very much and the presentations are open to questions or questions.
There are no questions yet at the chat. I think there is one question. Oh yeah, sorry I haven’t seen it.
Thank you very much Javier. So should the response to treatment be measured by degree or weight loss? This is a question by Christian Broad. So maybe I should go first and suggest that, you know, when I treat somebody with type 2 diabetes, I do measure glycemic control, but the purpose of my treatment is to reduce microvascular and macrovascular complications of diabetes.
I treat diabetes as a systems disease. So, you know, when I am treating somebody with obesity, my objective is not weight loss. However, we can use weight loss as a surrogate marker for the effectiveness of the treatment and we know that many of our treatments, bariatric surgery, as well as our medications, have weight loss independent benefits.
So even those patients that do not lose weight also have benefits, you know, when it comes to cardiovascular disease, prevention of diabetes and others. But Javier, what do you think? How would you approach that question? Well, I think that I agree with you that we should also measure the improvement of comorbidities, but I don’t know if the question is more related to body composition or because if we look only in the body weight, we may have changes in skeletal muscle, reduction in skeletal muscle, or even a small reduction in body fat. So I don’t know if the question is going more related to the potential changes in body composition that we should pay attention.
Of course, the more easy and the more useful, well, more frequently used measures to measure only weight, but we think that it has many benefits to look also to other compartments, such as the skeletal muscle, more in these days that with the bigger, the other strong effects of the drugs on body weight, they are also being accompanied by reduction in skeletal muscle. So I think that we should go further and go beyond and to look also in body composition, not only in body weight. There was a comment that I think that it has been already addressed because this additional comment addressed whether it should be the degree of adiposticium has lost, which we should be monitored.
So I think that you have already addressed this question. Let’s move to the second one. It was, well, Loutarkos Tsoulis said, excellent talk.
Please comment on the lower response rate of males compared to females to GLP agonist in ACT-CTs. Yeah, I think that’s fascinating, you know, and I really didn’t believe that data initially, that females lost more weight than males biologically. And we published a paper originally with loraclitide and showed that plasma levels of loraclitide in females were higher than plasma levels of loraclitide in males.
And that’s merely because males are taller than females. So even for the same body mass index, the volume of distribution is more in males, and hence the plasma levels were lower. However, with semaglutide, as well as with dizepatide, that has been studied and is not found to be quite as clear cut.
And it does appear that females do lose more weight than males. Now, males lose a very substantial amount of weight and do very well and have tremendous benefits, as we would see in studies like SELECT, where mostly males were recruited, because they had a higher burden of cardiovascular disease. However, I think we do not understand quite why females lose more weight, but it does seem to be a robust outcome for pharmacotherapy, but not for surgery.
So bariatric surgery, males and females lose the same, but in pharmacotherapy, it appears that females lose more than males. I don’t know if you want to add anything to this, Javier, or we can go to the next question. No, no, I agree with Karel, and I don’t know if there is a physiological explanation other than the, no, that’s just in my body weight when you give the dosage to both in the same time.
Okay. In relation also to the treatments, the next question is, do you think that the patients that do not respond as suspected to pharmacotherapy or bariatric surgery should be considered also a phenotype of obesity? Should we understand then the molecular level in order to spot the biological pathways associated with resistance to obesity? Javier, do you want to take that first, and then I can comment? You have the question there. Joana Trigo.
I have a response. Probably those who don’t respond to the surgery, there is behind some alteration in some, an entire storm or some problem, but it’s not related to the failure of the surgery itself. I don’t know if you agree, Karel.
Yeah, and I would agree with that. I think it’s very important not to blame the patient, not to blame the practitioner, you know, the surgeon or the doctor, or not to blame the treatment, but to actually understand that this is just a very rare form of the disease. And, but it also is true that if patients don’t respond even to surgical treatment, that does not mean they won’t respond to medication.
And Alex Meris has done that in the Gravitas study, taking patients with a poor response and showing that they actually respond to medication. And now we are seeing data from real life that, from real world data, also showing that patients that don’t respond to medications may respond to surgery. And the same is true for nutritional therapy.
So, so I would say if somebody doesn’t respond to one treatment, it doesn’t preclude trying another treatment. And that’s what we would do in clinical medicine. So, so that would be my suggestion.
Okay, so again on treatment, but in this case, in treatment seeking patients with preclinical obesity and a family history of obesity complications, and thus high future risk, how should treatment success be framed for that patient? So, so the way we looked at patients with pre-diabetes in this amount one three-year extension study, you know, and also the scale pre-diabetes studies, what you will remember in those is those were patients specifically chosen because they were at risk of complications, in this case, diabetes. And what we were able to show is with effective treatment loraclitide or with zepatide, the same is true for somatolatide as well, you can prevent the disease from happening, in this case, diabetes. Now, that’s a little bit the way I would bring the same philosophical approach to patients who have increased adiposity, but do not yet have complications of obesity.
That our purpose, you know, always prevention is better than cure. So, can I prevent these complications? Of course, the issue that we all need to discuss is that, you know, can we afford it? What is the numbers needed to treat, you know, to actually afford this, you know, prevention? Now, for example, for cardiovascular disease, we know we need to treat 67 people with somatolatide to prevent one heart attack, and therefore that’s very worthwhile. But if we’re going to talk about preventing obesity, what’s the numbers needed to treat and can our healthcare systems afford it today? And maybe the answer is no, they cannot afford it today, but that does not mean we can’t treat the patients, you know, with treatment.
So, it’s a complex economic argument, it’s not a biological argument, because we know we can prevent the disease, you know, and the complications from occurring. But Javier, what do you think, you know, from those patients that you also refer to as patients who have obesity, but don’t yet have metabolic complications, can we treat them and prevent these metabolic complications from happening? Well, I’m not sure if they are referring to the definition, the clinical definition in the last consensus. And I think that we must be very implicative, because the definition, you may have clinical obesity being diabetics, because of the definition, because they cluster, you need to have a glucose metabolism, and reduce SGLL.
So, we need to have a very complete profile of this patient. And if he or she has some of these values out there, I think he probably should be treated more intensely than being only considered as with the clinical obesity. So, we need to be very thoughtful.
This next question, I think that can be addressed by both two, because Cornelia Pottery asked whether the new phenotyping of obesity, I think that the two frameworks referred to the two frameworks, considers different ethnicities. Those were certainly questions, I was part of the Delphi process, but not an author on the ER, so a consensus paper, and I was part of the steering committee on the Lancet. And that was a very important discussion point, because these definitions have to be globally applicable.
And therefore, you can see that certainly in certain ethnicities, you know, there’s higher risks of developing the complications of obesity. So, you can see that certain ethnicities will develop many of these complications at much lower BMI, and therefore, BMI per se may not be that valuable. But as Javier has pointed out, thinking about body composition, especially visceral adiposity, becomes much more useful.
So, measurements of waist to height ratio, waist to hip ratio, waist circumference, in those instances, those are ways that we can actually mitigate and make these approaches internationally viable and sensible. I’m sorry, Javier, go ahead. The BMI cut-offs are practically the same, but for example, in the consensus published in Nature-Driving Technology, I think it was in 2022, the international waist consensus, they published specific cut-off points, as Carl said, regarding adipose tissue distribution, paying more attention to visceral adipose tissue.
So, I think that the ethnicities should be taken into account more specifically in visceral adipose tissue. There was an additional question on these new frameworks, on how we understand obesity right now, and if it is possible to inform efforts to prevent obesity regarding this new framework. My views are that the frameworks are a massive step forward.
Both the IASA as well as the Lancet Commission have really taken a lot of time and thought through this, but it’s a very difficult problem because we don’t yet understand the disease of obesity sufficiently, but our knowledge is improving dramatically. So, I think these frameworks are really helping us to identify those patients that may benefit the most from interventions, because we know our interventions can not only prevent complications, but can reverse the complications in people who already have them, and I think that is, at the moment, the major contribution. But if we think about the disease of obesity, or the multiple diseases of obesity, we are not there yet, and therefore, preventing them remains a challenge.
However, I really think we are close to making those scientific breakthroughs, but we have to have an open mind to actually look at the new data and look at our own patients in a way that says, and we need to be curious, why is it that a patient with exactly the same body mass index, exactly the same visceral adiposity, two of these patients can have completely different complications, but also the same type of patients can respond very differently to the same treatment. So, an understanding that is that heterogeneity of one disease, or in fact, is it multiple different diseases, and those are the big breakthroughs that I think is going to happen. Okay, let’s change a little bit.
So, this question comes from a desperate in pediatric obesity, and his question is on when the different phenotypes, and I guess this is discordant or concordant, diverge. And he comments on the observation that in youngest children, we only see simple obesity without metabolic divergence. And perhaps there is a concomitant tendency for these other metabolic disturbances that is also sensitive to the unhealthy lifestyle brought about by excess adiposity.
So, when? Yeah. Yeah, I think younger patients, you know, are very interesting when we study them, because they have this incredible symptom burden, because many of them will report excessive hunger and excessive appetitive behavior, and much more so than people who develop obesity maybe at a later stage. So, I think that’s quite interesting to see from a symptom burden.
You are correct that also it takes time to develop these complications, and therefore what we now need to understand is, is some diseases, does that immediately when it happens, trigger these complications later on, or is it in fact, you know, that you have to have time, and over time everybody will actually develop complications. So, those are important questions, and we are not yet good enough to actually differentiate those out, but Javier, how would you approach that? Well, I think that may depend also in how we measure obesity, because as I have shown, if we only pay attention to BMI, maybe ignoring some problems that may be related more to an excess of body adiposity. And on the other side, I agree, I totally agree with you that it’s a matter of time that the problems appear, and in this sense, I would like to say again what I have said in the presentation, that talking about metabolically healthy obesity may be very dangerous.
In particular, if we translate this message to the patients, you can say to a patient that he or she has obesity and mishealth, and in this sense, the same message to our children or their parents, if we are saying, you know, it has obesity, but has any other problem, I think it’s very dangerous, because the patient is going to do anything to solve the obesity, and the problems sooner or later will appear. Well, thank you. I think the time is over.
There are still a few questions left, but we have to finish right now by thanking, of course, Xavier and Karel for being with us, and also all the participants who have really enriched the discussion. Let me finish by inviting you to come to the next European Congress on Obesity in Malaga, who will be held in May in 2025. We look forward to seeing you there, and thank you very much.
Thank you very much. See you next COMS webinar. Thank you, everyone.
Bye.