PI: Oliver Fiehn
A robust metabolism is the main driver of human longevity. Very old humans have reduced ability to balance calorie intake with use of nutrients, which is accompanied by less cell renewal and an increased burden of muscle breakdown and metabolic damage, from lipid deposits to inflammation. This project will detail the exact metabolic markers and mechanisms by which some humans reach a very old age while staying relatively healthy, and compare these findings with changes in organs and cells of long-lived animal models. We will interpret our findings in collaboration with the other Longevity projects and corresponding genetic, genomic and protein data.
We will use state of the art metabolomics assays and bioinformatics tools to characterize metabolic phenotypes in humans, mouse models and cells. These assays also detect environmental and food compounds that inform on exogenous factors that might impair longevity. All data will be standardized by internal stable-isotope labeled marker compounds, and quality controlled by method blanks and suitable pool control samples. We will annotate all metabolites and exposome chemicals by database reference identifiers to enable integrating metabolic data with text mining and genomic information. For human cohort studies we will use association modelling for longevity, using mixed effect , generalized linear, logistic and cox proportional hazards regression models with an adjustment for a priori identified confounding variables such as gender, comorbidities, smoking status, body mass index and medication. We plan to validate mechanistic models by animal models and cells.