Across the western world, ageing is one of the most important health issues of our age.  Estimates suggest that the number of older people will double by 2050 around the world, so being able to age healthily is increasingly important.  Whilst I’ve discussed various strategies for maintaining activity levels as we age in the past, a new paper from the Moscow Institute of Physics and Technology suggests that big data can play a key role in developing new therapeutics and biomarkers to help slow down the ageing process.

The paper reveals that the mortality rate of people roughly doubles every eight years, with diseases such as cancer and strokes considerably more likely after 40 years of age.  Our physical decline should not be considered a fait accompli however, as many other animals seem to lack this accelerated risk of mortality as they age.  The paper argues therefore, that our own mortality rate should be subject to some manipulation.

Lessons from dynamic systems

The paper aims to take a cue from the way complex systems, such as financial markets, are modelled and suggests that this can be applied to the study of ageing.  As a result, it’s possible that we can use this big data approach to accurately predict someone’s biological age, their ageing rate and even possible targets for anti-ageing therapies.  The researchers have already applied this approach to identifying biomarkers for ageing and frailty after harvesting data from wearables and smartphone apps.

“The much anticipated 11th Revision of the International Classification of Diseases (ICD-11) introduced a number of aging-related conditions such as age-associated cognitive decline. This should facilitate new clinical trials and market authorization of therapies aimed at functional declines associated with aging. Among the most promising targets for the first anti-aging therapies are blood circulating molecules, since its vital role in aging is supported by experiments with young plasma transfusion,” the author explains.