MEPERTROBE

Artículos científicos

• Gómez-Ambrosi J. Adipose tissue inflammation. Cells 2023;12:1484.
• Zapata JK, Azcona-Sanjulian MC, Catalán V, Ramírez B, Silva C, Rodríguez A, Escalada J, Frühbeck G, Gómez-Ambrosi J. BMI-based obesity classification misses children and adolescents with raised cardiometabolic risk due to increased adiposity. Cardiovasc Diabetol 2023;22:240.
• Gómez-Ambrosi J et al. Cardiometabolic risk stratification using a novel obesity phenotyping system based on body adiposity and waist circumference. Eur J Intern Med. 2024 Jun;124:54-60.
• Gómez-Ambrosi J et al. The evolution of the understanding of obesity over the last 100 years. Int J Obes 2025;49:168-176.
• Salmón-Gómez L, Victoria Catalán V, Frühbeck G, Gómez-Ambrosi J. Relevance of body composition in phenotyping the obesities. Rev Endocr Metab Disord 2023;24:809-823.
• Gómez-Ambrosi J. Recent progress in the management of obesity. Nutrients 2023;15:2651.
• Gómez-Ambrosi J. Recent progress in the management of obesity: Advances and insights from the latest research. Nutrients 2025;17:1225.
• Zapata JK et al. Childhood obesity: The threatening apprentice of the adiposity empire. Rev Endocr Metab Disord 2025;26.

Tesis doctorales relacionadas con este proyecto

• Rocío Marugán (ongoing). La titulada superior contratada por UNAV continua en el Programa de Doctorado en Investigación Biomédica de la Universidad de Navarra realizando sus estudios de doctorado.
• Alvaro San Martin Bernal (ongoing).

Deciphering Obesity: A voyage towards personalised medicine

Obesity is a complex illness that affect millions of people around the world. The treatment approaches are often impersonal, the same for all patients, which means they do not work the same way for everyone. That is why the MEPERTROBE project embarked on an ambitious mission, which is to better understand how each person responds to dietary treatments and lay the groundwork for personalised medicine for treating obesity. This study, which recruited 123 patients, sought to go beyond generic diets and research the individual characteristics of each participate in depth to discover which factors influence the success of a treatment.

What was done?

To achieve the goal, the MEPERTROBE team did a deep immersion with each patient and collected a large amount of data on every patient over the course of several months.

First, anthropometric and body composition measurements were made. In regards to basic anthropometrics, weight, height and waist, hip and neck perimeters were measured. That gave us a general idea about the shape of the body. In addition, a detail analysis was done with InBody. That advanced bioimpedance device provides precise information about body fat, muscle mass, fat percentage, segmented fat (in different parts of the body), water inside and outside of cells, basal metabolism (how many calories the body burns at rest), and body phase angles (indicators of cellular health). Air displacement plethysmography (BodPod) was also done. That technology measures body composition in a very precise way, differentiating between fat mass and mass with no fat. Lastly, visceral fat was studied using the ViScan device, which is a tool centred on quantifying one particularly important kind of fat, visceral fat, that accumulates around internal organs and is strongly related with health problems.

In addition to physical measurements, the study explored the daily routines of the patients. An accelerometer was used to measure physical activity and sleep. Physical activity and sleep patterns were monitored using portable devices (accelerometers). That revealed that most of the time was spent on low-intensity activities, and a higher sleep fragmentation (frequent wake-ups) was associated with more time awake.

Exhaustive blood tests were done to obtain a complete health profile. Variables like glucose, cholesterol, triglycerides, kidney and liver function, among others, were measured. Some cardiovascular risk factors were also identified. Specific biomarkers in blood were analysed, like adiponectin, leptin, TNF-α, etc., that are related with inflammation and the risk of heart disease. Those measurements were made at the start, and at two and four months of treatment, making it possible to observe how they changed over time.

Lastly, to understand the biology better, advanced studies like transcriptomics (RNASeq) were done. Genetic material was extracted from blood cells (PBMCs) to analyse which genes were being “activated” or “deactivated”. That gave us some clues about biological processes that occur in the body. Intestinal microbiota (metagenomic) studies were also done. Intestinal microbiota, or “intestinal flora”, is the sum of microorganisms that live in our intestines and that play a crucial role for health. The composition of the microbiota of each patient was analysed to see how it varied between individuals and how it could influence the response to the diet.

How was the data analysed? Artificial intelligence to the rescue

With such an immense amount of data for each patient, the key to finding patterns and relationships was the use of machine learning (artificial intelligence) models. The algorithms can process large amounts of information and learn from it to make predictions. The goal was to identify which characteristics of the patients was associated with a better response to the diet.

What results were obtained? Towards a deeper understanding

The study showed that not all the patients responded to the dietary interventions in the same way. Of the 97 patients who completed the study, we found 33 responding patients (34%) who lost more than 5% of their initial body weight after the first two months of dietary treatment. Another 64 patients (66%) were non-respondents, not reaching that threshold. Within the respondents, 22% (21 patients) were able to maintain their weight loss in the long term. A surprising group was also identified. They were “late responders” (11 patients) who did not respond initially, but they could benefit from a less restrictive diet followed over a longer period of time. That finding is crucial, because it suggests that there is not a single path to success in treating obesity.

The MEPERTROBE project culminated successfully. It produced an enormous amount of information that is stored in robust databases ready to be used with future artificial intelligence models. Insofar as the findings of this study, eight international scientific articles have been published, showing its impact on the research community.

What does that mean for the future? The promise of personalised medicine

The results of MEPERTROBE are a notable advance towards precision medicine for treating obesity. Until now, dietary protocols have usually been generic, without consideration for the particularities of each person. Thanks to the information obtained, in the future health care professionals will be able to design dietary interventions that are much more personalised, optimising the results for every patient. The vision of MEPERTROBE is clear: revolutionise the treatment of obesity going from a general focus to an individualised one where every patient gets the treatment and plan that is best adapted to their unique characteristics. This study brings us one step closer to a future where obesity is managed in a much more effective and personalised way.