Longevity Bottlenecks

How we can accelerate the longevity field:

The longevity field has received an influx of capital and talent over the past 5 years, but it is unclear where directing these resources would result in the biggest positive impact. We aimed to establish a systematic, rigorous and unbiased way to identify the areas where increased investment would accelerate progress across the whole longevity field the most. To do so, we surveyed ~400 participants across various sectors of longevity, asking them to 1) identify the major bottlenecks they are experiencing, 2) list their most needed solution, and 3) rate the potential efficacy and barriers to development of various aging intervention strategies. We built a classification system of Bottlenecks and Solutions based on grouping related answers and found the most frequently listed bottlenecks to be 1) lack of validated aging biomarkers; 2) an overall lack of funding; and 3) lack of good models for aging studies. Surprisingly, the most wanted solution was greater availability of large public datasets. Indeed, a common theme across all answers was the need for a new data-centric structure of scientific research, where large datasets are routinely gathered and made available, access walls are removed, protocols are standardized, negative and unpublished data are shared, and AI systems are released on the data for automated discovery. Finally, a lack of regulatory clarity was listed as the biggest barrier to development across all interventions, whereas cellular reprogramming, organ replacement, and genetic medicine (gene therapies and gene editing) were perceived as the intervention strategies with the highest potential for increasing healthy lifespan. We provide these data as a resource for funding agencies, philanthropists, entrepreneurs and newcomers to the field as a means to identify high impact areas to fund and work on.


Yes, we need a big centralized database where all datasets are stored and not on a specific researchers computer and which by time is deleted. The data should be open and possible to be combined between studies to find interesting new insights etc. This is a must thing to do to take research to the next level.


Biomarkers and funding were my top two. Iā€™m not surprised the general consensus is in line with that.

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Unsurprisingly I think a useful change would be the acceptance of the merits of multiple N=1 biohacking tests where a number of people experiment with the same interventions and aim to identify any consistent patterns.

Unsurprisingly I think off label usage of Rapamycin is a good candidate for that as are other interventions discussed on this forum.

It may be that the forum could approach this in a more systemic manner and identify particular experiments for people to do.

For example another good experiment is to measure vitamin D serum levels and then record vitamin D supplementation for a week or two and then measure vitamin D serum levels. Obviously there are large numbers of variables including UV B exposure, but this would at least give a developing database of what are reasonable outcomes to expect from a particular level and mode of supplementation.

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