WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
Aging populations face diminishing quality of life due to increased disease and morbidity. These challenges call for longevity research to focus on understanding the pathways controlling healthspan. We use the data from the UK Biobank (UKB) cohort and observe that the risks of major chronic diseases increased exponentially and double every eight years, i.e., at a rate compatible with the Gompertz mortality law. Assuming that aging drives the acceleration in morbidity rates, we build a risk model to predict the age at the end of healthspan depending on age, gender, and genetic background. Using the sub-population of 300,447 British individuals as a discovery cohort, we identify 12 loci associated with healthspan at the whole-genome significance level. We find strong genetic correlations between healthspan and all-cause mortality, life-history, and lifestyle traits. We thereby conclude that the healthspan offers a promising new way to interrogate the genetics of human longevity.
The project of Quantum Pharmacutical company (q-pharm.com)
It has long been suggested from the animal data that human locomotion may be related to health. Recently, we identified a generic framework to model and infer critical markers of age-related diseases from biometric signals for example, see our research using genetic networks (http://arxiv.org/abs/1408.0463). Our plan is to use the same approach to develop and test novel approaches to extract and analyse features from the physical activity data (as measured by accelerometry) and to identify associations between these physical activity features and age-related diseases including neurodegenerative, oncological and cardiovascular diseases. UK Biobank is concerned with understanding the determinants of diseases as well as identifying opportunities for prevention. We believe the proposed research addresses these aims by investigating the potential role of physical activity as a source of non-invasive exploration of a biological state. This will provide novel insights into the determinants and markers of common/age related diseases such as neurodegeneration, adult cancers and cardiovascular diseases and identify novel preventative and diagnostics approaches. We plan to analyse the raw accelerometer data to derive certain features, such as patterns and power spectrum distribution. We aim to investigate how these features are associated with the diseases of interest. The features that will be found to be most associated with the specific diseases will be nominated for further research as potential biomarkers of corresponding diseases. Participants that already have physical activity measurements (approx. 30 000 individuals) and all physical activity data as they will be collected and included in UK Biobank database (70 000 individuals more, 100 000 in total).