It is Easy to Produce Omics Data, Harder to Achieve Useful Progress Based on that Data
The enormous reduction in cost and increase in capacity for analysis of living biochemistry over the past 20 years has led to vast warehouses of omics data: information on genomes, epigenomes, expression of transcripts and proteins, and more. Making something of this data in a reliable way is a more challenging proposition, and remains a work in progress. More data is almost always good in the long run, but the goal of science is understanding, not implementation. The data revolution in biotechnology may not greatly change the nature of the fastest path to human rejuvenation, which is to implement the SENS proposals for damage repair and see what happens. In the case of removing senescent cells, we can see that this produces rapid rejuvenation in mice, to a degree that is dramatic in comparison to any other approach to aging tested to date. If a tenth of the effort that goes into producing omics data went into furthering the SENS research agenda, we'd be much further along the road to radical life extension.
Biogerontologists are nowadays struggling with identifying actionable mechanisms of aging, with the goal of extending the time individual lives in good health, possibly delaying age-related diseases, and therefore reaching longevity. The issue is not simple to solve. In fact, although our understanding of aging biology in model systems has increased dramatically, thanks to the possibility to model the effect of single variants on the probability to extend our lifespan, Human aging and longevity are complex polygenic traits. They are influenced by the inheritance pattern of multiple genes/variants, each one with pleiotropic protective roles across several age-related diseases, and their interaction with environment. People can achieve older age while suffering major age-related diseases, because of their capability to survive those disorders, or they can escape entirely some of the most frequent causes of death and impairment, thus living not just a long but also a healthy life. The difference between these two aging trajectories and phenotypes is greatly discussed and investigated.
Biomedical innovation, and in particular research into "omics technologies," offers the promise of monitoring, preventing and treating age-related disabilities and diseases. Progress in genomics and functional genomics in the past decades have significantly supported our understanding of the molecular mechanisms associated with aging. However, it is nowadays clear that the complexity of aging requires a huge effort into data integration, building a broader omics profile, including genomics, proteomics, lipidomics or metabolomics, transcriptomics, etc.
Although the capacity to produce big data drastically increased over the years, integration, interpretation and sharing of high-throughput data remain major challenges. This seems even more challenging in the field of aging, because such an effort requires a more holistic view. Aging is not just the progressive decline of different functions, but rather a well-described phenotype, characterized by a complex remodeling across the whole organism. This is the key reason why omics technologies may greatly improve the definition of different aging phenotypes, and the classification of individuals with features ranging from the very frail, with a poor quality of aging, to the most extreme, the centenarian's phenotype, characterized by a long life.