Deep longevity, a biotech company that aims to transform longevity R&D through AI, has released a paper in Nature Ageing, describing longevity medicine – a new form of study converging AI, basic research and medicine.
Ageing
Ageing is a universal feature shared by all living organisms. The rate of ageing differs among individuals and species. Yet the time elapsed since birth is a strong predictor of health status and mortality. Ageing plays a key role in the onset and progression of many human diseases and affects all organs. While researchers long to target ageing, pharma companies are still searching for compounds and interventions to treat individual chronic diseases, such as cancer. Estimates indicate that complete elimination of cancer would only result in a 2.3-year increase in life expectancy in the US at birth and 1.3-year gain at age 65. This is because there are many age-associated processes and diseases that manifest in later life.
Despite evidence-based medicine effectively reducing mortality, it has also increased the economic burden of disease in developed countries. This is because this increase in lifespan has not be met with an increase in healthspan. Improving the quality of life for older adults has failed. This is because the main driver of most diseases is the process of ageing. Therefore, the authors argue that targeting ageing would provide a more substantial benefit than reactive therapeutic approaches.
AI-powered tools in longevity medicine
The authors define longevity medicine as “a branch of precision medicine that is specifically focused on promoting healthspan and lifespan and is powered by AI technology.” They believe that AI-powered longevity medicine will facilitate the discovery of drug targets and the identification of tailored geroprotective interventions and ageing biomarkers to enhance the study of ageing.
Understanding the ageing process requires longitudinal monitoring of millions of parameters in many different types of datasets. No human doctor can currently accurately predict age using multiple different biological data types. This is where modern AI can play a role. AI systems can find complex patterns within large volumes of longitudinal data. Some AI models have already been found to outperform human experts in many tasks, including image recognition. Deep learning, in particular, was a big breakthrough for AI research. It has allowed experts to train deep neural networks on massive longitudinal datasets.
Deep learning was instrumental in developing Deep Ageing Clocks (DAC). DAC estimates an individual’s biological age state based on data from routine blood analyses. Using tools like DAC, clinicians can more accurately assess and monitor individual health risk and tailor interventions accordingly.
Future of longevity medicine
The authors noted that for longevity medicine to be formally seen as a branch of medicine, it needs to be practiced by physicians. They also suggested that ageing needs to be monitored and treated as a medical condition, with designated studies being conducted to demonstrate the efficacy and safety of specific interventions.
As longevity biotechnology and AI continue to advance and percolate through clinical research and clinical practice, physicians will increasingly need to navigate through various AI technologies and applications.
It is also important to consider the potential impact of longevity medicine on health equity. While the authors acknowledged that people may assume longevity solutions will only be available to the wealthy, they noted that longevity medicine is partly equipped with a low cost and minimally invasive collection, including wearable trackers and DAC.
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