Welcoming ERA Chair holder to the Centre of excellence in nutrigenomics to optimize health and well-being
Identyfikator grantu: PT01001
Kierownik projektu: Carsten Carlberg
Instytut Rozrodu Zwierząt i Badań Żywności PAN
Data otwarcia: 2022-11-10
The key hypotheses of this proposal are i) that lifestyle changes can be monitored through the analysis of the epigenome of immune cells and ii) that in silico digital twin interventions are able to suggest individual-specific lifestyle changes that prevent the onset of the metabolic syndrome. In four work packages (WPs) we will address in this study how the epigenetic memory:
1. accumulates information of daily lifestyle decisions;
2. is well represented by cells of the immune system;
3. allows to distinguish individual’s responses to lifestyle changes;
4. is a major determinant for the susceptibility for the metabolic syndrome.
Addressing these objectives, we will perform a lifestyle intervention (increasing of the average number of daily steps to at least 10,000 as well as supplementation with a known modulator of the epigenome of immune cells, vitamin D3, to a serum level of 40 ng/ml) with 60 individuals showing first symptoms of the metabolic syndrome (pre-diseased) over 3 months (phase I). Blood samples will be taken at the beginning and at end of the intervention. Ten most committed individuals will continue the intervention for 3 years in total (phase II) and blood samples and clinical data will be collected every 3 months (WP1). The serum of these in total 230 blood samples will be analyzed for metabolites (fasting glucose, triglycerides, cholesterol, HbA1c, fasting insulin and vitamin D status), while the peripheral blood mononuclear cell (PBMC) fraction will be investigated for epigenetic memory, i.e. i) different layers of the epigenome (WP2) as well as the epigenome’s functional readout on the level of single cell transcriptomes (WP3). These large-scale data will be integrated longitudinally for each individual (intra-individual) as well as for inter-individual differences using bioinformatic approaches (including machine learning). We expect to identify hundreds of genomic regions with epigenetic signatures, the changes of which represent general responses to lifestyle decisions (doubling daily physical activity and supplementing vitamin D to sufficiency) as well as individual-specific reactions. In addition, the large-scale data will be used to create a mechanistic computational model (based on known pathways), which will be individualized for each participant serving as his/her digital twin (WP4). The digital twins of each of the 60 individuals will be used for in silico interventions with different dietary exposures as well as other aspects of the exposome, such as stress, infection and inflammation. Respective personalized advices will be given to all participants. Digital twin model refinement will be performed with data of phase II and iterative in silico interventions are expected to avoid the onset of the metabolic syndrome. The advantage of our intervention design is that the clinical effects (reducing symptoms of the metabolic syndrome) can be monitored easily and it is reachable in the study period. Improved metabolic and molecular profiles of individuals of phase II will serve as a proof of the successful application of digital twin models. Furthermore, based on a positive psychological effect personal recommendations are more likely to be taken over by the patient than general recommendations.