Periodic Reporting for period 3 - GO-DS21 (Gene overdosage and comorbidities during the early lifetime in Down Syndrome)
Berichtszeitraum: 2023-01-01 bis 2024-06-30
The project aims to better understand the causes behind the development of obesity and intellectual disabilities (ID) in people with Down syndrome (DS). DS occurs in about 1 in 1,000 births and comes with a higher risk of severe obesity and ID. This increased risk suggests that certain genetic or environmental factors related to trisomy 21 (the extra chromosome that causes DS) may be responsible.
The project, GO-DS21, has the following goals: 1) To determine age-related comorbidity patterns observed over the early lifetime (before age 45) in persons with Down syndrome. 2) To identify specific physiological biomarkers and regulatory signatures in human, cellular, and animal models. 3) To decipher the contribution of environmental factors (stress, diet, exercise) to trisomy 21 obesity/ID comorbidities in preclinical models. 4) To investigate the effects of overdosage of three candidate genes to explain comorbid patterns in mouse models. 5) to integrate multilevel data from human patients and preclinical and cellular models using computational biology models and machine learning approaches across different spatial and temporal scales of biological complexity. 6) to design new therapeutic interventions to reduce the penetrance of comorbidities in preclinical models.
This is a short video explaining the project: https://www.youtube.com/watch?v=zhobkgkWh6A
The project, GO-DS21, has the following goals: 1) To determine age-related comorbidity patterns observed over the early lifetime (before age 45) in persons with Down syndrome. 2) To identify specific physiological biomarkers and regulatory signatures in human, cellular, and animal models. 3) To decipher the contribution of environmental factors (stress, diet, exercise) to trisomy 21 obesity/ID comorbidities in preclinical models. 4) To investigate the effects of overdosage of three candidate genes to explain comorbid patterns in mouse models. 5) to integrate multilevel data from human patients and preclinical and cellular models using computational biology models and machine learning approaches across different spatial and temporal scales of biological complexity. 6) to design new therapeutic interventions to reduce the penetrance of comorbidities in preclinical models.
This is a short video explaining the project: https://www.youtube.com/watch?v=zhobkgkWh6A
This research will improve diagnosis, help predict risks, and create recommendations to prevent or reduce these conditions in people with DS. Additionally, the findings may also benefit the general population
First, in our work with human data, we continued to study how and when different health issues (comorbidities) develop in people with Down syndrome (DS). We used electronic health records from the UK, covering data from 1990 to 2020, to compare the lifelong health problems of people with DS to those with other intellectual disabilities and the general population. We identified health issues specific to DS and grouped them by conditions like heart disease, autoimmune disorders, and mental health problems. We found that these conditions show different patterns of development based on age compared to others, which affects how and when healthcare should be given to people with DS. To be precise, DS comorbidities had distinct age-related incidence trajectories, and their clustering differs from those observed in the general population and in people with other intellectual disabilities, with implications for the provision and timing of healthcare screening, prevention, and treatment for people with Down syndrome. (Lancet Public Health https://doi.org/10.1016/S2468-2667(23)00057-9). So far, we’ve recruited 92% of the participants we aimed for and have seen 221 people for their first visit. We also followed up with 65 participants, collecting samples for further analysis using advanced techniques.
Second, we studied how environmental factors like diet, stress, and exercise affect health issues in DS. Using a specific mouse model of DS, we looked at how diet, gender, and genetic factors impact health, focusing on regular and high-fat, high-sugar diets. This research uncovered a new health problem also seen in humans with DS. We are continuing studies to explore how stress and exercise affect these issues in DS. Further investigations into genetic changes and biomarkers are ongoing in DS mouse and rat models, with some new health problems identified, such as higher death rates due to aneuploidy and brain-related issues, which are still being studied.
Third, some unexpected results challenged our approach of focusing on specific genes. We are now looking at how the overexpression of specific genes like DYRK1A and NRIP1 might lead to issues like stress hormone problems and obesity. Our detailed studies confirmed that too much DYRK1A affects metabolism and thinking ability, but NRIP1 was not as impactful. Another gene is currently being studied with promising results. New treatments to reduce DYRK1A overactivity in DS models have shown success, moving us closer to clinical applications.
Fourth, advanced analysis techniques (multi-omics) of blood and tissues, including studying metabolism, proteins, and gut bacteria, have provided new insights into DS. We’ve collected stem cells from eight individuals with DS to study how body weight affects health. Ongoing data analysis will help us predict health outcomes based on critical biomarkers. With one year left in the project and advanced bioinformatics tools available, we expect to gain new knowledge about the causes of health issues in DS and better support experimental modelling.
First, in our work with human data, we continued to study how and when different health issues (comorbidities) develop in people with Down syndrome (DS). We used electronic health records from the UK, covering data from 1990 to 2020, to compare the lifelong health problems of people with DS to those with other intellectual disabilities and the general population. We identified health issues specific to DS and grouped them by conditions like heart disease, autoimmune disorders, and mental health problems. We found that these conditions show different patterns of development based on age compared to others, which affects how and when healthcare should be given to people with DS. To be precise, DS comorbidities had distinct age-related incidence trajectories, and their clustering differs from those observed in the general population and in people with other intellectual disabilities, with implications for the provision and timing of healthcare screening, prevention, and treatment for people with Down syndrome. (Lancet Public Health https://doi.org/10.1016/S2468-2667(23)00057-9). So far, we’ve recruited 92% of the participants we aimed for and have seen 221 people for their first visit. We also followed up with 65 participants, collecting samples for further analysis using advanced techniques.
Second, we studied how environmental factors like diet, stress, and exercise affect health issues in DS. Using a specific mouse model of DS, we looked at how diet, gender, and genetic factors impact health, focusing on regular and high-fat, high-sugar diets. This research uncovered a new health problem also seen in humans with DS. We are continuing studies to explore how stress and exercise affect these issues in DS. Further investigations into genetic changes and biomarkers are ongoing in DS mouse and rat models, with some new health problems identified, such as higher death rates due to aneuploidy and brain-related issues, which are still being studied.
Third, some unexpected results challenged our approach of focusing on specific genes. We are now looking at how the overexpression of specific genes like DYRK1A and NRIP1 might lead to issues like stress hormone problems and obesity. Our detailed studies confirmed that too much DYRK1A affects metabolism and thinking ability, but NRIP1 was not as impactful. Another gene is currently being studied with promising results. New treatments to reduce DYRK1A overactivity in DS models have shown success, moving us closer to clinical applications.
Fourth, advanced analysis techniques (multi-omics) of blood and tissues, including studying metabolism, proteins, and gut bacteria, have provided new insights into DS. We’ve collected stem cells from eight individuals with DS to study how body weight affects health. Ongoing data analysis will help us predict health outcomes based on critical biomarkers. With one year left in the project and advanced bioinformatics tools available, we expect to gain new knowledge about the causes of health issues in DS and better support experimental modelling.
Overall, we have made a significant effort to communicate our new knowledge to the scientific DS community, the individuals with DS and their families, and the caretaker. The information about GO-DS21 is refreshed regularly on our website (www.go-ds21.eu) and a Twitter/X account. Almost all the GO-DS21 partners presented the GO-DS21 project and the first results at the T21RS scientific meeting in Roma in June 2024. Furthermore, we raised awareness about DS comorbidities and alternatives to mitigate them at the Science and Society session of the meeting with an easy-to-read and understandable GO-DS21 presentation and the FOODFALL application; both available online.