Novo Nordisk Fonden Challenge

Novo Nordisk Fonden Challenge

The Novo Nordisk Fonden Challenge project “Harnessing the Power of Big Data to Address the Societal Challenge of Aging” is a joint investment combining the strength of Danish data repositories and scientific research into ageing, embedded within an appropriate socio-ethics-legal framework. Overall, the aim of this project is to get a comprehensive understanding of the ageing process at a population, organismal, and molecular level.

Studying Population Ageing

First, we will create and analyse the life histories of people with fast and slow ageing trajectories in the general population. The life histories cover most aspects of life ranging from living conditions, family relationships, education, work and working conditions, income, wealth, biomedical conditions, geographical location, and the use of welfare services. This will make it possible to systematically trace individuals over time and to identify key events during the life course that result in different patterns of ageing and degenerative diseases.

Studying Organismal Ageing

Second, we will examine the pathological biomarkers of ageing in samples of human tissue by using data from The National Pathology Register. It is Denmark’s nationwide digital archive of biological samples and diagnostic information including numerous reports on five million unique individuals, representing approximately one third of the Danish birth cohorts. We will perform an in-depth investigation of the interplay between tissue damage, organ function, and age-related degenerative diseases.

Studying Molecular Ageing

Third, we will perform computational assisted analyses to dissect the complex relationship between proteomic hallmarks and biological behaviour of ageing cells that cannot be detected with standard pathological examinations. Many human and non-human omics datasets in the public domain provide a rich resource on system-wide changes taking place in different cells across the life course. Using these exceptional datasets, we will analyse the dynamic transition from normal cells to malfunctioning tissues and age-related degenerative diseases.

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