Life history regulation in the Westendorp Group
Our primary goal is to understand human aging, to interfere in the bio-molecular process, and to prevent the occurrence of degenerative diseases. We hope to enable people to live healthier for longer.
Denmark has the advantage of having abundant data on the entire population as well as pathological specimens that go back to the beginning of the 20th century. Combining these exceptional sources will help identify different patterns of aging among individuals. Such a national approach overcomes typical pitfalls of surveys and cohort studies, which are often held back by low participation rates particularly among the very healthy and severely diseased.
“We use personal data to perform careful scientific explorations for the good of everyone,” says Professor and Group Leader Rudi Westendorp.
The techniques for handling and analysing large quantities of data have become mature using novel computational approaches. The use of personal data for research is only sustainable when it balances the rights and interests of the individual with that of society as a whole. Scientific explorations of personal data should be carried out in such a way that the intrusion of people’s privacy is minimal and appropriate.
We discovered a Danish longevity birth hotspot centered on a group of rural islands, with a 1.37 times increased chance of becoming a centenarian for the cohort born 1906-1915. The hotspot has lower post-70 year mortality for both men and women, although markedly more so for women. Mortality is lower for all those born in the hotspot, whether or not they are still living there by age 70. The difference in mortality is still observable and not substantially weakened for women born in the hotspot 1916-25 and 1926-35. We find two regions with significantly increased probabilities of reaching age 100 for those resident there at age 70, both with centenarian rates similar to those of the birth hotspot (https://www.ncbi.nlm.nih.gov/pubmed/30317223).
To determine the life histories of people with fast and slow aging trajectories
We will construct life histories from a very high number of existing sources of information, which cover most aspects of life ranging from living conditions, family relationships, education, work and working conditions, income, wealth and health. The possibility to follow cohorts of individuals over their life course enables us to study the interplay between various in- and extrinsic factors over time. We will study life courses using classic epidemiological tools on these trajectories followed by unsupervised learning applying machine learning algorithms to identify different patterns of aging.
To identify and understand pathological, morphological and molecular biomarkers of aging
Scanning techniques of tissue specimens have become mature and there is a broad consensus that digitization and computational analysis of histological images is expected to bring the objectivity, accuracy and speed of diagnoses to a next level beyond what is possible by specialist human observation only. Machine learning reduces inter-observational bias and outperform humans at recognizing various pathological changes. These novel methods appear to be cost effective but have relied on a limited number of organs, diagnoses and datasets and the true potential has not yet been fully explored.
To decipher the dynamic interplay between nuclei and mitochondria in aging cells
We will systematically analyse large datasets of multiple types to: (i) identify key components affected by age or experimental perturbation; (ii) establish networks of interaction; (iii) develop dynamic computational models based on these networks; (iv) use model selection methods to discriminate between alternative network topologies and generate predictive models. To characterise the data, we will apply an ensemble of methods, including frameworks in R/Bioconductor and toolboxes connected via APIs, algorithms for machine learning and deep learning, mutual information, Bayesian inference and various cluster analysis.