By Jasmine Smith, CEO of Rejuve.AI, a firm supporting the progress of longevity research globally.
The pursuit of longevity – longer and healthier lives – for self and others has defined humanity for thousands of years.
Whether seeking elixirs or entire lakes, there’s no shortage of historical legends depicting the search for longevity secrets across ancient civilisations, from Mesopotamia to Egypt, China, Greece and beyond. It’s not unfounded to say that this desire for longevity is what laid the groundwork for medicine as a discipline, which started then with herbal remedies and is now taking previously inconceivable forms such as gene therapy.
Enormous progress in biomedical research facilitated this monumental shift. Discoveries were made. Institutions were built. Most importantly, standards of evidence were established.
Jasmine Smith, CEO of Rejuve.AI.
For most of the past century, Randomised Controlled Trials (RCTs) have been considered the gold standard of evidence-based medicine. Nearly every medical intervention you’ve ever been prescribed was first tested in an RCT before receiving approval. These trials are designed to minimise bias and provide reliable data on whether a treatment is both safe and effective for a specific condition on a population level.
In a typical RCT, participants are randomly split into two groups. One group receives the treatment, while the other—the control group—receives either a placebo or the current standard of care. The goal is to ensure both groups are as similar as possible so that any differences in outcomes can be attributed to the treatment itself.
One of the most significant ways to increase the statistical power of an RCT is to increase its sample size, referred to as the “N” in research papers. A larger N provides more data points, increasing the likelihood of detecting a true effect while reducing the margin of error. In other words, it helps determine, with greater precision, whether a treatment is safe and effective—for “the average patient.”
The problem is that no matter how large the N is, it can’t guarantee a tested treatment and dose will be safe or effective for you. That’s because you’re not average and this average patient doesn’t exist.
Variability in treatment response from one person to another is a pervasive reality, shaped by genetics, psychosocial aspects, environmental exposures, microbiome composition and countless other factors.
We’re now realising that this variability may not be the exception but the norm. Take conditions like depression, for example. Even with medications that have successfully passed RCTs, a significant portion of patients – between 30–40 per cent -don’t respond to treatment.
A major reason health has become “in” today is that people are growing more aware of these differences and that hardly anybody is average. So, they want to take control of their longevity in a way that reflects their unique biology.
N-of-1 trials can make that possible.
As you can tell from the name, N-of-1 trials study a single person. That individual serves as both the treatment and control arms of the study.
Rather than comparing groups of people, an N-of-1 trial involves the individual switching between the intervention and control multiple times, often with washout periods in between.
By analysing changes in key metrics across these phases, the trial determines whether an intervention is beneficial.
But, wait. That feels counterintuitive. Earlier, we mentioned how larger sample sizes increase the statistical power of a study and make results more generalisable.
So how can a trial with only one person be useful? Well, unlike RCTs, N-of-1 trials aren’t designed for generalisation. They produce high-quality evidence that applies only to the individual being tested, not a hypothetical average person.
This level of precision is why the Oxford Centre for Evidence-Based Medicine ranks N-of-1 trials as Level 1 evidence—higher than traditional RCTs.
While RCTs still occupy a crucial role in research and shouldn’t be expected to fade away anytime soon, N-of-1 trials have been successfully used for conditions ranging from cancer to asthma.
But they’re also primed for longevity studies, which require long-term data collection to optimize healthspan—the portion of life spent in good health, free from debilitating conditions.
For now, the most promising and accessible longevity interventions remain lifestyle modifications. But we all know that diet, exercise and other lifestyle changes don’t work the same way for everyone. That’s exactly where N-of-1 trials can help.
Want to lose body fat but unsure whether a keto or plant-based diet is more effective for you? Run an N-of-1 trial and compare both by tracking your waist circumference. Trying to build muscle but torn between two different resistance training programmes?
An N-of-1 measuring your progress via InBody readings can tell you which approach works better for you.
You can make it as simple or as complex as you like. As long as you know what you’re testing and track the right metrics, an N-of-1 trial can give you answers.
For N-of-1 trials to take off in that longevity capacity, the most significant hurdles might be logistical. With potential mass adoption, how could every N-of-1 trial have a dedicated healthcare professional overseeing it?
The answer is simple: eliminate the need for one. Empower people to conduct their own studies and focus on making the process more accessible and user-friendly.
Platforms like Rejuve.AI are being developed to simplify N-of-1 trial setups, making them practical and understandable with AI. A person would choose which interventions they want to test, and the system would generate a list of relevant biomarkers.
From there, all they’d need to do is upload their data—AI would handle the rest, analysing results over time and presenting them in a meaningful way. This wouldn’t just improve personal longevity outcomes.
It would also educate people on good research practices, enhance scientific literacy and contribute to the broader democratisation of health.
Longevity began millennia ago as a universal dream. Now, it can be a universal mission. We have the tools and technology to move beyond the “average person” paradigm and create a future where longevity is truly for everyone.
Jasmine Smith is CEO of Rejuve.AI. Prior to that she served as Memorial Hermann Health System’s Senior Medical Coding Specialist and oversaw community management and marketing at SingularityNET.
