A New Frontier for Life Optimization and Healthspan — Meet Ageotypes

Ron Gutman
9 min readFeb 28, 2020


Photo by Emma Simpson

Healthy curiosity and a real passion for exploration / adventure has been in the DNA of our Live Long & Flourish Club (LL&F) since its inception.

When we first heard about Dr. Michael Snyder’s work (the renowned chair of the Department of Genetics at Stanford University, and the director of the Center for Genomics and Personalized Medicine) we were thrilled. We thought: here’s a great fellow explorer who would be super-interesting to learn from about his work in academia and beyond, and a lot of fun to hang out with too.

Fernanda Gandara welcoming Dr. Michael Snyder to the LL&F Club

When we read about his very recent breakthrough publication in Nature Medicine on Ageotypes, we knew that it was the perfect timing to invite him to lead a discussion in our LL&F event at Stanford University. We were thrilled to have him join us last week, as our main guest, and to hear him present his version of (what we call) a Science Map^, which can guide people everywhere towards living healthier, happier, longer lives.

A day before Valentine’s Day, we gathered at Stanford University with a group of LL&F Club members who were quick to sign up before the event “sold out.” We charged-up with some super-tasty/healthy eats and drinks sourced from local farmers and food artisans and began our journey indulging a timely relevant discussion on relationships, love and good connections, and their impact on human flourishing and longevity (under our Club’s Brain and Mind Pillar.) After our Club members presented a bunch of Healthy HACKS⁺ and Motivating TRICKS* (under our Nutrition and Physical Activity Pillars), we continued with an expert-led timely conversation on How to Protect Against Coronavirus (under our Risk Mitigation Pillar.) When it was time for Dr. Snyder to share insights from his most recent work, the meeting was packed with standing-room only. The intellectual energy in the LL&F Community was high in anticipation of the conversation with Michael about his recent research and findings on aging types and aging trajectories.

Dr. Snyder started his talk introducing a similar framework to the one we’re using at the LL&F Club. He showed how healthcare today is mostly reactive, infrequent, focused on illness, measures very few things, and is mostly population-based.

Dr. Snyder on what health should be

On the flip side, Dr. Snyder explained that he believes (just like we do) in measuring and optimizing the quality of life continuously, or in other words, focusing on ongoing human flourishing. We entirely identify with Dr. Snyder’s view that we should actually zoom-in on health and not just care, and that we should be proactive, measure as many health-related indicators as we can, and do so as frequently as we can without interrupting our lives. In his introduction, Michael also advocated a more individualized approach to health and well being. Such personal approach, he said, means more targeted and more precise interventions that treat each individual and their situation as unique, rather than using “blunt tools from big averages.”

To demonstrate the importance of the last point Dr. Snyder shared with us a simple yet very revealing graphic on individual variation from “normal” in a simple test of Oral temperature in 2749 healthy individuals:

He continued to explain that over many years of extensive research he has realized that health is the product of both the Genome and the Exposome (pathogens, stress, exercise, nutrition, and the environment). His addition of the environment to the nature+nurture duo helped us update the framework we’ve using under the Four Pillars of the LL&F Club:

Dr. Snyder told us that his studies convinced him that people are unique and that they differ from one another on several dimensions that are very important to understand when determining how to optimize health and well being. The combination of these distinctive characteristics creates a metaphoric unique “fingerprint” and people with similar “fingerprints” can be clustered in distinctive groups (or types). These types seem to be consistent over time and they can help us understand people’s health and aging trajectories better, so we can tailor them the right interventions, which would in turn result in healthier happier longer lives.

But to get there, Dr. Snyder and his group had to first figure out what exactly makes us unique and what creates the basis for our distinctive health “fingerprints.” They were also curious to observe whether a particular “fingerprint” changes over time, and how it compares to and clusters with other people’s “fingerprints”. All of this required Dr. Snyder and his team to design a research project connecting subjects to an efficient monitoring system that measures signals on an ongoing basis. One can think about it almost as a metaphoric portable and non-interruptive “mini-ICU.” Despite the proliferation of tracking devices and tests in recent years, such undertaking was no easy feat.

To do so, Dr. Snyder and his group initiated a longitudinal study to follow more than 100 people over 7 years based on continuous, ongoing monitoring and measurement. To get a more comprehensive view of the aging process and to find clues on how to optimize health individually, their ambitious research project measured a very broad range of indicators. They collected numerous blood and stool samples, used advanced imaging technology and stress echo tests, and collected large amounts of sensor data from wearables, Continuous Glucose Monitoring (CGM), personal exposome monitors, and even analogue questionnaires (with self-reported health status.)

They made billions of measurements to identify patterns (including: activity, heart rate, calories, location, acceleration, skin temperature, sleep, SpO2, radiation, weight and more…) Over time, they carefully looked into changes in these measures and the levels of particular molecules. They also observed the creation and progression of tumors and of plaque in arteries. They used this huge battery of tests/devices and sensors to look into a variety of systems including the:

During the study, the researchers identified 49 major health discoveries in the subjects (in the areas of cardiovascular, metabolic, hematology, oncology, sleep, and infections). They showed that by tracking multiple signals (such as skin temperature and heart rate) they were able to anticipate the occurrence of a health condition before its formal diagnosis

Early signals of Lyme Disease using Data:

The researchers also showed that before the study started, many of the subjects were not aware of the true state of their health and well being. In fact, quite a few of them didn’t even know that they were pre-diabetics or even diabetics (!)

The researchers used the measurements and tests they administered to both diagnose the subjects appropriately, as well as observe how their conditions manifested and progressed throughout the study. Once again, by following multiple variables they were able to show, for example, that a viral infection was a precursor for developing type 2 diabetes, even when other factors remained constant.

Rose, Contrepois et al. Nat. Medicine 2019

Another good example was a glucose tolerance test (OGTT test) administered by Dr. Snyder’s team, which showed that different people responded differently to glucose stress. The researchers were able to demonstrate how multiple factors affected how one’s body responds to a surge of glucose. The determinants for the variability in the individual responses ranged all the way from genetic factors, to exercising just before consuming glucose (just like our own LL&F Member Sebastian Caliri discovered in his own CGM experiment, which he presented in our previous meeting at Stanford).

The researchers were also able to demonstrate that people’s biochemical profiles were quite stable and different from one another:

Zhou et al Nature 2010

In this Multidimensional-Scaling graph each dot is a different visit and each color is a different person. In each case (cytokines, which are immune markers, clinical labs, and metabolome), with the exception of transcript RNA, people’s samples tended to be more similar to their own samples rather than anyone else’s, even after 6 years of sample collection. Moreover, if one person got sick or underwent a perturbation (weight gain or exercise for example), her or his samples shifted, but they still looked more like their own rather than anyone else’s. Thus, one’s personal “molecular signatures” are very robust and stable and suitable to define that person.

The researchers also looked at the subjects’ insulin secretion levels and their glycemic response to certain foods. They were able to show that different people responded to the same foods differently. In other words, the same meal triggered different glycemic responses in different people.

Hall et al. PloS Biol 2018

They also identified clustering of different types of people who responded in certain ways consistently (in this glucose case, they called them Glucotypes).

Hall et al. PloS Biol 2018

Finding types of people was not limited to the molecular level. Dr. Snyder’s team also followed metabolic pathways in the subjects (like blood coagulation.) They were able to show that those metabolic pathways changed over time, and that different pathways had different correlations with age.

Combing through all this rich big data, Dr. Snyder and his team looked for clusters that could represent different expected health trajectories for people, which in turn could become the basis for a roadmap to personalized life-optimization plans.

Bringing all these studies together, Dr. Snyder and his group were able to identify four general classes of aging trajectories:

  • Immunity
  • Metabolic
  • Kidney (Nephrotic)
  • Liver (Hepatic)

They observed how these four “fingerprints” clustered in individuals at certain points in time, and named these groups “Ageotypes”. Using this framework, it’s now possible to construct more specific multivariate aging trajectories and interventions based on Ageotypes and how they manifest in different people.

In a new company he started recently (Q Bio) Dr. Snyder uses such multi-variate longitudinal measurements, as well as rate-of-change measurements for early detection of conditions and for health monitoring. “Multi-variate” means that by taking many measurements the researchers are better able to see abnormal conditions. “Longitudinal” means that by following individual change researchers can detect conditions at a personal level.

Benefits of measurements for early detection

In the future, a natural extension of this model could also help experts tailor dynamic individualized plans for health-optimization (from predicting risks, to early diagnosis, and from monitoring to treatment.) Dr. Snyder explained that Qbio does not currently make recommendations but rather empowers people to make their own decisions by themselves or with their physicians.

We’re definitely in the early days of continuously collecting and analyzing signals to optimize health and wellbeing, but Dr. Snyder’s work is off to a very promising start. It would be very interesting to follow some of the subjects of his ongoing studies (and similar ones) as well as the users of his services (and comparable ones) and conduct a cost/benefit analysis of the results. In particular it would be fascinating to see how this new classification and insights can help slow down and reverse aging processes in individuals. And even more so, it could become game-changing if it can also drive short-term and medium-term impact on people’s day-to-day lives as measured by energy levels, stamina, sharpness of brain, happiness, and other factors that many of us value in our everyday lives.



Ron Gutman

Inventor, investor, serial technology & healthcare entrepreneur, Stanford lecturer. Constant learning smiling and caring for others help me remain an optimist