Transforming how we see aging:

Biological age diagnostics

Table of Contents:

Foreword by Joanna Bensz

Executive summary

Introduction: Biological age diagnostics to transform how we see aging

Part 1: Classification system and parameters for assessing biological age diagnostic devices

Part 2: Aging clocks and biomarkers

Part 3: Physical and physiological tests and the microbiome

Part 4: Bio-imaging and genetic tests

Part 5: The future: Comparisons of age diagnostics and combinatorial diagnostics

Part 6: Investing in biological age diagnostics

Part 7: Trailblazers



Daragh Campbell

Head of Research (Former)
Market Intelligence Unit, Longevity.Technology

Girish Harinath

Scientific Editor
Market Intelligence Unit, Longevity.Technology

This report is one of the best and most practical summaries of the current availability and further potential of diagnostics and biomarkers in the longevity space.

When entering the sector a few years ago, we identified the need for more practical applications of longevity science and technology in the medical and clinical field in Europe. Our goal was to add healthy years and vitality to the lives of our clients and be able to measure it.

Two people born on the same day age very differently. We knew that, but now, with the use of biomarkers of aging, we can measure and quantify this and recommend a personalized care program and precise intervention strategy. Based on a variety of biomarkers, from clinical to generic, physical, cognitive and epigenetic, a personalized longevity plan, including an action plan can be prepared.

The quantification of individual aspects of health, using molecular and cellular biomarkers, and functional markers are necessary foundations for planning any lifestyle optimization interventions. In the future, they will also be required for longevity and body rejuvenation actions, as well as assessing their cumulative effectiveness and progress.

However, we have observed that there is very little collaboration and knowledge sharing between hands-on medical experience, preventive medicine, preventive cardiology, preventive radiology and the growing field of longevity science and technology.

This is work in progress and we are working with these technologies every day, acquiring knowledge and experience related to their usefulness, advantages and weaknesses.

Our Longevity Center clinics in Europe specialize in detailed health risk assessments using biomarkers of aging and biological age, followed by a personalized comprehensive health and longevity plan aimed at enhancing physical, mental, and social well-being for people of all ages. We use biomarkers of aging to personalize treatments and ensure the validity of interventions through regular checkups.

We consider biological age to be the most reliable determinant of the actual state of health of the body and set it as the basis of our overall activity.

Aging diagnostics as well as clinics focusing on preventive and personalized health are essential for a future of medicine and the only way to make it possible to shift from sick care to healthcare. But even in case of disease, our diagnostics and interventions help to faster recovery and work as a support to standard treatment.

The indications are that there will not be a single aging biomarker, but that there will be a constantly refined and best-performing set of parameters that will stand the test of clinical verification. We truly believe that aging diagnostics have the potential to influence every aspect of people’s health and wellness and we are very excited about the exponential growth and developments in this technology sector and looking forward to the future of personalized medicine.

Our experts in health optimization and longevity focus on the latest scientific developments in health and aging biomarkers, genetics, epigenetics, immune-psychology, nutrition, metabolic health, mitochondrial health, sleep quality, and age science to increase physical, mental and social well-being for people of all ages.

One of the most important aspects of successful therapies and interventions is the client’s active engagement and participation. In the shorter term, biological age can also provide a useful high-level indicator of health status. Increasing the number and methods of quantified self-reports or tracking physiological responses, for example, by using diet apps, sleep, stress and exercise tracking wearables, reinforce health-conscious lifestyle choices, and prompt lifestyle changes that lead to healthier outcomes.

To be of practical use, aging biomarkers should be accurate, reproducible, minimally invasive and performed rapidly at a reasonable cost.

Joanna Bensz
Founder and CEO of the Longevity Center

  • Biological age reflects the health and functioning of our body’s organs and tissues and explains why two people the same age may have drastically differing health statuses. In other words, biological age reveals that the aging process can be sped up or slowed down. Damage accumulation associated with aging takes place on the molecular and cellular level, gradually over the span of decades, culminating in the manifestation of disease. Transitioning from a “disease-care” system to a true “healthcare” system requires tracking these changes and addressing them before disease arises.
  • Biomarkers of aging represent foundational changes to the body that are shared between all individuals and characterise the rate at which an individual is aging. Biological age diagnostic tools use AI and big data to track changes in biomarkers of aging to predict biological age. Research is revealing that not only do individuals age at different rates, but each of the organs and tissues within our body have specific aging signatures (ageotype). To date, there is no comprehensive biological age diagnostic tool that is reflective of overall, systemic aging. Accordingly, each biological age diagnostic device is reflective of specific aspects of the aging process and predicts morbidity and mortality with varying degrees of efficacy.
  • Capturing the subtle age-related changes that define an individual’s ageotype can help optimise how they engage with lifestyle change or longevity interventions. Thus, biological age diagnostics operationalise features of health so they can be measured, stratified and appropriately addressed to slow down the process of aging and perhaps even achieve rejuvenation.
  • The biological age diagnostics’ market is wide open as each technology has its own strengths and weaknesses. This report looks at a classification system to help facilitate comparison between the various technologies so both investors and consumers can get a sense of the unique value each one provides; aging clocks, biomarker tests, physical/physiological, microbiome and genetic tests are discussed in a biological age diagnostic context.
  • There are several factors that influence the ultimate efficacy of a biological age diagnostic including the nature of the biomarker of aging measured, big data used to develop the tool, aspects of aging it captures and in which tissues, ease of use and cost, among other factors. In regards to big data, diagnostic tools that are trained solely on predicting chronological age are inferior to those technologies that are trained on risk factors, morbidity and mortality data. The efficacy of biological age diagnostic tools is specific to the population demographic information used in the training data set and will be further optimised by including data from longitudinal and interventional studies with clinical outcomes. By tracking several different biomarkers of aging with combinatorial biological age diagnostics we can create a future where everyone has their own “healthy aging dashboard”.
  • Most biological age diagnostics make commercial sense even without the tick of an FDA clinical approval due to the increasing size of the wellness market, set to reach $6 trillion by 2025. At home biological age diagnostic tests are attractive in terms of revenue due to the need for repeat purchases. Revenue can also be boosted by selling to longevity clinics and insurance companies – >80% of biological age diagnostics already sell B2B.
  • Biological age diagnostic start-ups experienced growth in the years of 2015-2018 following the publication of Horvath’s epigenetic clock paper in 2013 that showed that the clock has the potential to address many fundamental health questions in developmental biology, cancer and aging research. The bulk of the biological age diagnostics market is concentrated in developed countries, with the United States (53%) and the United Kingdom (35%) dominating the market. $589 million was raised in 2021 for biological age diagnostics, corresponding to 32% of total funding to date and demonstrating a step-change in investment into these biological age companies.
  • Investment at this point in the biological age diagnostics market currently still offers relatively early-stage investment into an exciting, opportunity-rich sector which has potential to influence every aspect of humanity’s health and wellness and to offer transformational returns from expected exponential growth.

Diagnostics in Longevity

  • Humans are living longer, but an increase in lifespan has not been accompanied by an increase in healthspan. If ignored, this trend promises to pose significant burdens on healthcare systems, the economy and quality of life.
  • Healthcare systems need to change from a treatment perspective to a preventative perspective. Diagnostic information will be invaluable for healthcare systems, healthcare providers, healthcare professionals and patients.
  • The aim of biological age diagnostics is to detect minimal fluctuation in biomarkers of aging and in doing so, provide an estimate of remaining healthspan and lifespan
  • By tracking damage to our bodies as we age, biological age diagnostics have the potential to allow us to control how we age and promote a healthier aging process.
  • Challenges to identifying biomarkers of aging include:

1) distinguishing age-related changes that cause no harm from those that drive the process of aging, and
2) finding biomarkers that reflect the aging of the entire organism and not just individual tissues or physiological processes.

The silver tsunami

The “silver tsunami” is upon us. The world is experiencing a rapid increase in its aging population and the global population of over 60s will surpass two billion by 2050: a 12-fold increase from 1950 (World Health Organization, 2021).

The seismic shifts that led to the rising tides of the silver tsunami stemmed initially from treatment of infectious diseases and subsequently cardiovascular disorders. Rapid advances in biotechnology and AI continue to fuel its momentum, but, these advances have created a disconnect in which the rate of increasing lifespan has outpaced that of healthspan. Individuals are now living longer lives but are also at a higher risk for a wide range of age-related diseases and co-morbidities (Khan, 2017). If ignored, this trend promises to pose a significant burden on the healthcare system, economy and quality of life.

Today, individuals live a significant portion of their lives with increased frailty, suffering from multiple morbidities and experiencing late-life disability.

It is becoming clear that our 20th century healthcare system is now serving as a 21st century “disease-care system”. Despite rapid technological advances and the emergence of personalised therapeutics, we remain stuck with an outdated operating system (OS) that was built on addressing symptoms and disease rather than preserving health. Updating our OS requires defining the transition point, within the continuum of the aging process, in which health ends and disease begins. This transition point largely resides within the realm of subtle molecular and cellular level changes within the body which can now be observed, tracked and measured. The geroscience community aims to use these insights to redefine the overall strategy required to promote healthy aging throughout one’s life.

The value of changing to a diagnostic information healthcare service system from the current “disease-care” system.
But what drives the aging process, how can we reliably measure it, and how does it relate to health and disease? Answering these questions could provide high resolution insights that we can use to optimise therapeutic interventions and lifestyle habits to transform the way we age. Ultimately, this tailored approach will help give everyone a “healthy aging dashboard” that they can use to truly optimise their longevity by preserving their health throughout their lifespan and in doing so, compressing the period of morbidity.

Chronological age, biological age and healthspan

It has always been apparent that different people age differently. What this really means is that the perceived age of an individual may vary significantly from their actual calendar age (or chronological age) in that two 65-year-olds may not only look different but present with drastically different health statuses. Chronological age is reflective of the number of times the earth has travelled around the sun since we were born. In terms of its utility as a standalone health metric, it knows no difference between two people born on the same day.

Indeed, as living matter constantly and dynamically changes with time, numerous age-related changes occur the longer we live on earth. On a population level, this lends significant predictive power to chronological age for predicting an individual’s health status. As it is universal and readily available, chronological age is a popular estimate of the rate of aging and health in daily life. But it is a far from perfect metric as chronological age does not measure any specific age-related changes taking place in the body (Ji, 2021). For example, older individuals of the same chronological age differ in their physical and cognitive functioning. This difference in health status is better reflected by an individual’s biological age. Unlike chronological age, the rate of biological aging is variable and often disproportionate with the amount of time passed (Ji, 2021). Having an accurate assessment of biological age is an invaluable diagnostic tool that can be used to assess the influence of various longevity interventions, lifestyle habits and behaviours on our health. Perhaps most importantly, it can do so well before disease or dysfunction arises. So, what influences the rate of biological aging, and how can we calculate biological age?

Two individuals with the same chronological age could have very different healthspans. This would be better captured with an accurate biological age, which is a better representation of the individual’s internal health status.

Biological age is a complex parameter that reflects the health and functioning of the body’s organs, tissues and physiological processes in composite. On a deeper level, biological aging is associated with a reduction in the reparative and regenerative potential of the cells that make up the body. This reduction manifests as a decreased resilience in response to everyday stressors as we age (oxygen, movement, wounds, refined and processed foods, toxins, etc) and a time-dependent failure of physiological processes that leads to vulnerability to disease and death. It is this link to physiological functioning and decline that makes the measurement of biological age so much more impactful and useful than chronological age. Importantly, no single factor or mechanism (to date) has been shown to drive the process of biological aging. As such, it is almost unanimously agreed upon that the process of biological aging is driven by integrated and networked interactions between various molecular and cellular phenomena (Khan, 2017).

The aging process results in multiple traceable footprints on the molecular, cellular, organ and functional level. These footprints can be tracked and quantified to estimate an organism’s biological age. Sensitive and quantifiable methodologies are required to precisely measure the rate of biological aging, especially since age-related changes primarily take place on the molecular and cellular level, gradually over the span of decades. The evolution of such methodologies almost exclusively relies on AI and machine learning algorithms to make sense of changes in large quantities of health data across age demographics (Galkin, 2020). This has led to the emergence of modern-day biological age diagnostics.

Aging leaves footprints that can be tracked and quantified…

Today, more than a dozen biological age diagnostic tools exist, each of them utilising unique data types (such as demographic information and biomarkers) and training procedures to measure the rate of biological aging. In general, calculating biological age requires the calendar age of a person, their health as relating to their age and some “traceable footprint” that is reflective of the aging process (biomarker of aging). With these parameters in place, biological age diagnostics are trained (through the analysis of massive amounts of health and demographic data) to give an accurate assessment of an individual’s functionality, survival/mortality and overall health status compared with other individuals of the same age demographic. Biological age diagnostics can at least partially predict lifespan, but this has limited utility. More importantly, it can quantify the differences between healthspan and chronological lifespan, especially when the models are trained on health parameters that are reflective of biological age as opposed to chronological age (Ji, 2021). The value of any biological age diagnostic tool resides in the ability to predict “trajectories of aging” where the accelerated ones would predict unhealthy aging and disease and decelerated ones would predict healthy longevity.

By calculating an individual’s “aging trajectory”, biological age diagnostics offer the ability to assess the potential of various longevity therapies and interventions for slowing down the process of aging and even achieving rejuvenation. Thus, the efficacy of any biological age diagnostic tool is dependent on finding those biomarkers that are the best proxies of the underlying process of aging. Further, they should be both sensitive and robust so insights can be gleaned, and interventions applied within an effective time frame .

Biomarkers of aging: biological age diagnostics

A biomarker is a characteristic that is objectively measured and evaluated as an indicator of some biological state, condition or process. In biomedical research and clinical practice, biomarkers include measurements that suggest the etiology of, susceptibility to, activity of, or progress of a disease (Strimbu, 2010). We will refer to these as “classical or first-generation biomarkers”. The behaviour of classical biomarkers is intricately intertwined with the activity or progress of disease rather than the cause of disease itself. As the manifestation of disease is different within individuals, these markers often only work well as averaged indicators in very large samples and can vary a lot between individuals and demographics. Accordingly, physicians are often forced to engage in cycles of “trial and error” that are centred on palliative treatment of symptoms rather than the root cause (age-related changes), which can give a varying range of treatment outcomes (Ferrucci, 2020).

Geroscience proposes that the underlying biological mechanisms of aging are a unifying driver of disease in all individuals and that it is central to the global increase in susceptibility to disease and disability. In fact, strong correlations have recently been revealed between various states of health and phenotypes that are typical of the aging process, especially autophagy, mitochondrial function, cellular senescence, microbiome changes and DNA methylation (Anton, 2020).

Biomarkers of aging represent foundational changes to the body that are shared between all individuals and characterise the rate at which an individual is aging. In other words, it is a biological parameter of an organism that offers a better prediction of the progression of the aging process, functional capability and disease vulnerability than chronological age can. The role of biological age diagnostics is to detect minimal fluctuation in these biomarkers of aging and in doing so, provide an estimate of remaining healthspan and lifespan (Fuellen, 2019).

The complexity, heterogeneity and integrated nature of the biology of aging

However, identifying biological age diagnostics is not a simple process as aging is not dictated by a single pathway but an accumulation and propagation of damage and dysfunction across cells and tissues of the body. There are many theories as to the processes and drivers of aging (see appendix 1.2 for full description) but not one has been identified as the main cause. What is agreed is that aging is plastic and depends upon a balance between quality control systems that maintain the integrity of our cells and the effects of “wear and tear”.

Aging process

Wear and tearProgrammed model
Gradual damage accumulationChanges occur at the level of gene regulation and expression
Quality control system are compromised with ageSynchronized by a “systemic” aging clock
Damage accumulates past a given threshold in tissuesThe influence of this systemic clock on the rate of aging trumps that of “local damage accumulation” in various cells and tissues.
“9 hallmarks of aging”
“7 deadly things”

Aging drivers

Disposable somaAntagonistic pleiotropy
Evolutionary trade of between reproductive fitness and longevityGenes and processes beneficial in early life become detrimental later in life
Each organism has finite resource to dedicate to maintenanceEvolution turns a blind eye towards any gene or process that may drive the aging process later in life if it helps propagate our genes early in life.
The body uses its resource to maintain reproductive system
This process leads to age-related damage accumulation and degeneration in other tissues
Summary of the main aging drivers (disposable soma theory and antagonistic pleiotropy theory) and aging processes (wear and tear model and programmed model).

As such aging, as a process, is not fixed to the pace of chronological time, but can speed up or slow down depending on the rate of intrinsic cellular clocks that are driven by environmental stressors and evolutionary pressures.

This plasticity of aging is based on genetic and lifestyle/behavioural predispositions as well as developmental programming and environmental exposures. Several studies suggest that our personalised “aging rate” is primarily driven by our lifestyle and environmental exposures (Passarino, 2016). Of course, biological factors that make each of us unique such as the specific genes we may possess, microbes within our biome and environmental toxins we are exposed to have a significant role to play in whether a given intervention is beneficial or detrimental to our health. But this is where detailed profiling and personalised biomarker tracking comes into play for the effective construction of lifestyle regimens and therapeutic design. Simply put, the process of aging occurs at different rates in different individuals and with the right information, we can choose to put our foot on the accelerator or the brakes.

Various organs and tissues of our body have specific aging signatures and rates. With the right information from biological age diagnostics, we can manipulate the aging process with a personalised approach dependent on these signatures.

However, this is more complicated than one might assume. Research is revealing that the various organs and tissues of our body have specific aging signatures and rates. This is an important point of consideration because there is no evidence (to date) of a biological age diagnostic device that captures the biological age of the whole organism. Rather, each biological age diagnostic is developed and trained to capture the health of various organs, tissues and aspects of physiology. This does not mean that our tissues age in isolation and that “a common denominator” for most age-related processes can’t be found. Specific tissue-related changes in aging rate often impact organic function leading to failures in other systems. For instance, structural aging of the cardiovascular system seems to influence neurodegeneration, cognitive impairment and kidney disease. Further, signalling factors (termed chronokines) released into the bloodstream likely serve to further coordinate the aging of tissues across the body. Some biological age diagnostic tools operate via assigning a weight to various “tissue specific biomarkers of aging” based on their perceived importance to form a final “age score” (Ahadi, 2020).

In conclusion, the cells and tissues of the body are intricately interconnected and, at least on some level, aging is coordinated by this vast network of interactions. It is a difficult business to reduce the complex state of an individual to one single number, and it goes without saying that this comes at a price. First and foremost, a lot of information is lost on the way – for example, we do not capture the state of single organs or organ systems (Ferrucci, 2020). Nonetheless, we must take this “tissue specific rate of aging” into consideration when evaluating the strengths and weaknesses of biological age diagnostic tools in existence today, as well as what the evolution of biological age diagnostics may look like in the future.

Ageotypes: the many flavours of aging

Another valuable principle to consider when evaluating the utility of a biological age diagnostic device is that the specific sequence of events that drive aging is unique to everyone. For instance, one individual may experience age acceleration through excessive oxidative stress in a given tissue (such as the liver) while the aging trajectory of another is dictated by insufficient clearance of cellular trash (aggregates) in the kidneys. This is reflected by the observation that within a population of individuals of the same age, there is considerable variation in the types and extent of disease as well as degree of functional impairment risk. This has led to the classification of ageotypes, a “deep learning” approach that generates a personalised characterisation of how an individual is aging. This characterisation relies on the identification of various trends and hierarchical clustering of biomarker changes within specific organs, tissues and cells of the body (Snyder, 2019).

The functional significance of biological age diagnostics is the ability to identify individuals that are “younger” or “older” than their chronological age within the same demographic cohort. For truly accurate biological age quantification, this demographic cohort may need to be further stratified by specific ageotypes. Classifying ageotypes gives us important information that can help guide how individuals engage with lifestyle change or longevity interventions to modulate the rate of biological aging.

Biological age diagnostics help pinpoint the rate an individual is aging irrespective of the amount of time they have spent on earth.

This information alone is a foundational starting point for one’s longevity journey as it offers great insights into how one has been aging to date based on their lifestyle habits, genetics and life history. Scientists have recently revealed that our lifestyle habits and environment (including socioeconomic status, drugs and technologies we engage with; collectively termed “the exposome”) have anywhere from 75-90% influence on our rate of aging, with the rest being dictated by genetics (Passarino, 2016). This means our environment and life experiences play a significant role in our rate of aging, but this has conventionally not been easy to reliably track and quantify. By its very nature, testing longevity interventions requires following subjects until the end of their lives. This can take an impractically long time, making it nearly impossible to test the efficacy of longevity interventions without first establishing how advanced the aging process is in an individual.

As such, the true potential of biological age diagnostics resides in the ability to test the efficacy of longevity interventions and lifestyle changes that influence the underlying process of aging itself within a tractable time-frame. This has profound implications for longevity therapeutics as there is no reference criterion for assessing healthy aging (Jazwinski, 2019). Biological age diagnostic tools can be utilised as a consumer product for individuals to optimise their lifestyles and longevity strategy, as well as surrogate endpoints and outcome measures for clinical interventions designed to extend healthspan.

  • Each biological age diagnostic has its own strengths and weaknesses based on the aging biomarker itself, the data used to develop the tool, the aspect of aging it captures and in which tissues, ease of use, cost and other factors.
  • The classification system used to define the different types of biological age diagnostic tools will help explain the nature of the aging biomarker and data, how it is obtained, how it may be best interpreted as a predictive measure for biological age and facilitate comparison between other biological age diagnostic technologies.
  • This section looks at the strengths and weaknesses of: aging clocks, biomarker tests, microbiome tests, bio-imaging tests and physical/psychological tests

Classification system and parameters for assessing biological age diagnostic devices

Biological age diagnostics will play a central role in the longevity industry and could become a primary metric in geroscience, regenerative medicine, AgeTech and personalised medicine. They also serve as a major channel for the implementation of AI within the longevity industry, translational research, drug discovery and development and clinical trials. This report highlights an inherent challenge in searching for the “best” biological age diagnostic device as “best” may not even exist due to the complexity, heterogeneity and integrated nature of the biology of aging. Instead, each biological age diagnostic has its own strengths and weaknesses based on the aging biomarker of focus, big data used to develop the tool, the aspect of aging it captures and in which tissues, ease of use, cost, among other factors.

As all of the biological age diagnostic tools being developed focus on capturing the aging process, there is significant overlap and redundancy in both the aging information captured as well as given strengths and weaknesses for the various devices (Macdonald-Dunlop, 2021). For this reason, it is important to define a classification system that stratifies the different types of biological age diagnostic tools into categories, as well as establish standardised criteria that can be used to evaluate and compare their unique value and challenges.

The classification system used to define the different types of biological age diagnostic tools will help explain the nature of the aging biomarker and data, how it is obtained, how it may be best interpreted as a predictive measure for biological age and facilitate comparison between other biological age diagnostic technologies. For example, aging clocks measure patterned changes in “omics” data that generally reflect aging at a molecular level, whereas physical and physiological biological age diagnostics reflect systems level decline and compromised integration between organ systems. Hence the former has greater utility as an early diagnostic tool and can provide more nuanced insights on aging mechanisms while the latter has greater predictive power in the elderly, is earlier to implement and provides greater functional insights (Solovev, 2020).

Due to the rapid pace of discovery and array of emergent biological age diagnostic devices and terminology, it can be a challenge making sense of both the underlying science and market potential. Having this classification system will add clarity and guidance for consumers and investors to optimally engage and invest in what is quite likely the most transformative sector of longevity.

Biomarker of aging criteria

Measurable Can also accommodate for age-related changes that occur with time. As such, this biomarker should be a process that is measurable early on in life and a shared mechanism across all aging individuals with the only variability being in the rate of change.
Pervasive Not only the predisposition to disease or the effect of pathology itself, but the ability to identify subclinical disease states can help ensure that data sets accurately classify successful aging, delayed morbidity and increased longevity rather than only age-associated disease states
Predictive This is reflected by its accuracy as a measure of predicting an individual’s risk for specific age related conditions (physiological, physical, cognitive) and diseases in a way that is correlated with, but independent from (and more accurate than) chronological age;
Evidence-based Within both model organisms and humans
Reliable It should exhibit predictable changes across large portions of the population, reproducibly measured during extremely short intervals, minimally sensitive to non-age related physiological changes/environ-mental conditions and exhibit some sensitivity to the influence of established longevity inventions

As mentioned, there is a wide range of biological age diagnostic devices in development that measure various omics and aging biomarkers. A good biomarker of aging should meet the biomarker of aging criteria stated above.

Parameters to evaluate efficacy and market feasibility of biological age diagnostic devices

Below is a classification system and list of parameters that will help compare the value of each technology to discern which have the greatest longevity market potential and impact on healthy aging. For a full description of each, please see appendix section 1.3.

Evaluation Parameter Description
Number of different biomarkers measured Single or panel;
Health of tissue/physiological process captured Age of specific organ system or cells, amount of chronic inflammation, etc
Prognostic and/or Predictive Prognostic: prediction of progression of a given disease

Predictive: helps identify which treatment individual is most likely to respond to or benefit from

Training data set used Demographics: sex, ethnicity, height, weight, smoking status, specific progeronic or healthy population

Predictive measure: chronological age, mortality, organ health, age-related disease

Accuracy Precision measurement of biological age that is reproducibly measured within extremely short intervals
Iterability It can be iterated upon and optimised within different tissue types, data types and synergises with other biological age technologies
Mechanistic insight Insights into specific ageotype, aging mechanisms, hallmarks
Cost Inexpensive, affordable, expensive
Technical simplicity and user interface Need for intensive protocols or procedures, specialised equipment or techniques; how information is communicated back to end user
Accessibility What and how specimens are collected or health gauged (invasiveness)
Targeted therapeutic potential Aging biomarker measured can be targeted for rejuvenation
Sensitivity Viability of biological age assessment in an immediate and effective time frame of implementation

Classes of biological age diagnostic devices

The biological age diagnostic devices listed below all measure aging biomarkers that are integrated into an AI/machine learning model that incorporates big data health information to predict biological age. It should be noted that there are composite biological age tests being developed that combine multiple biological age diagnostics (Zhavoronkov, 2019).

Aging clock

The measurement of a single molecular process or “-omics” signature that changes predictably (in a specific pattern) with age.

Examples: epigenetic (methylation) clock, glycation clock, transcriptomic clock, telomere clock.

Biomarker test

The measurement of distinct biomarkers (typically secreted factors or cells) that represent a matrix  of health and aging features from multiple tissues.

Examples: plasma lipids and proteins, blood count, glucose, albumin, liver enzymes and inflammatory stress factors, etc.


Measurement of various physical, cognitive and physiological functions that represent a matrix of health and aging features from multiple tissues. These tests are grounded in a systems biology approach and theoretically measure network interactions within the body that lead to emergent phenotypes representing functional health and decline.

Examples: gait speed, frailty index, cognitive function, psychological/subjective age assessment, cardiorespiratory parameters, vocal analysis, etc.


The measurement of changes in microbial species and function (typically gut microbiome).

Example: gut microbiome test.


The measurement of visual changes to various organs (typically skin).

Examples: skin aging, thymic involution, liver adiposity, etc.


The sequencing and analysis of panels of genetic polymorphisms (gene variants) that are predictive of aging features across multiple tissues as well as overall health and disease.

Examples: APOE-3, FOXO3, IL-6, IGF-1, SIRT6, etc.

Aging Clocks

Aging clock

The measurement of a single molecular process or “-omics” signature that changes predictably (in a specific pattern) with age.

Examples: epigenetic (methylation) clock, glycation clock, transcriptomic clock, telomere clock.

Cost – Moderate (few hundred dollars)

Predictive potential – High (multiple tissues, diseases, risk factors and all cause mortality)

Accessibility – High (blood sample, simple at home test kit)

Iterability – High (synergise with multiple tissues, data types, technologies)

Strength – High sensitivity, reflects fundamental biology of aging at molecular level (address root cause)

Weakness – Inherits training sets biases, variable across different demographics and based on measurement assays

Recently, advances in artificial intelligence have permitted the identification of robust aging biomarkers to be used in the development of medical and lifestyle interventions.

Researchers are now developing tools to accurately interpret biomarkers of aging known as “deep aging clocks” for applications in personalised medicine. These methods translate various interpretations of biological age based on the measurement of a single molecular process or “-omics” signature that changes in a specific, predictable pattern with age. Aging clocks come in many different forms depending on the aging biomarker being measured (Lohman, 2021). The clocks we will focus on within the report are: epigenetic telomere, transcriptomic and glycan-age clocks.

A degree of scepticism about clock algorithms

Many researchers are sceptical about using clock algorithms to evaluate antiaging interventions. One reason for this is that clocks are typically optimised to predict chronological age with higher and higher precision (higher correlation coefficient r). This means that a chronological age trained model, if done perfectly without error, is useless (unless for forensic purposes) because these predictors rely on the error between predicted age and chronological age to assess biological age and aging rate (Bell, 2019). It can no longer be used to tell a 60-year-old with the metabolism of a 70-year-old from another 60-year-old with the metabolism of a 50-year-old as both will register as 60 years on this “perfect” clock.

For those models that achieve the “sweet spot” of “close but not perfect predictive power”, it is hard to tease out which part of the deviation of the predicted and actual chronological age is truly due to the individual’s clock difference and which part is due to the prediction error.

Thus, only the outliers that significantly deviate beyond the prediction error range can be reliably determined as true biological age deviation from the chronological age. On the other hand, training a model on perceived age, morbidity and/or mortality partly overcomes such a “model error confounding factor”, making deviations truly biologically relevant when errors are minimised. As such, it should be noted that a clock can be a very good predictor of chronological age, but less good at predicting mortality compared with another clock that is not as good at predicting chronological age (Xian, 2021).

Another issue is that we need to ensure the target is causing aging and not just a response to the aging process. For example, white cell count is correlated to cancer; if we create a “cancer clock” based on white blood count and use it to evaluate anti-cancer interventions, an intervention which sets back the clock to zero and beyond would be determined the best intervention but would cause those who got this intervention to die rapidly. The issue here is that white blood cells are a response to cancer, not the cause (Mitteldorf, 2020).

Furthermore, as there is not a single definition or theory of aging universally followed within the industry, clocks will differ on what theory of aging the researchers believe and therefore will have different outcomes to what interventions are the best to “remedy” aging (Mitteldorf, 2020). For example, telomere clocks calculate biological age via measuring the rate of telomere attrition within cells over time. If these algorithms are used to evaluate antiaging interventions, the conclusion may be that the best aging treatments are those that extend telomeres. This is a dangerous game to play as telomere length is context dependent and can, in some instances, lead to tumour formation and growth.

The ideal clock

The ideal clocks will be of both high accuracy and interpretability and are predictive of aging related health status, health span, morbidity and mortality. With the growth of multi-omic technology and data – especially longitudinal and interventional data with clinical outcomes – more and more true biological age trained models will be available in the future to replace the error-dependent chronological age predictors. With these true biological age clocks, one can then thoroughly compare the biological and functional mechanisms driving the convergence and divergence of the clocks as well as design interventions to slow or even rewind individual or multiple clocks in different tissues and at different levels (Adav, 2021).

Epigenetic clocks

Of all the biological age diagnostics being developed, epigenetic clocks are garnering the most attention within academia and the biotech sector – with several products already in the consumer market space and several more in development.

Epigenetics translates to “above-genes” and analyses the molecules (methylation, acetylation, etc) that sit on our DNA and regulate the expression and maintenance of our genome. A wide variety of stimuli influence epigenetic changes, including intrinsic developmental programs, environmental exposures, diet, stress, exercise and drugs. In essence, our epigenome is the regulatory interface between our external world and our internal responses (Horvath, 2018).

The process of aging leads to epigenetic alterations, including changes in DNA methylation, through both multiple distinct and intersecting age-related mechanisms. DNA methylation occurs at specific sites called CpG islands. As each CpG site can be differentially methylated in different cells, the site‐specific percent methylation of each CpG across the genome can be quantified. The percentage of methylation at specific CpG sites can be used to derive an “epigenetic clock” that estimates chronological aging with unprecedented accuracy. Most importantly, deviations between epigenetic and chronological age identify individuals who are biologically older or younger than their chronological age. Epigenetic clocks predict a broad range of health outcomes better than chronological age alone. Consistent with this notion, “epigenetically older” individuals have a higher risk of developing several age-related diseases and premature all-cause mortality. Unsurprisingly, all DNAm clocks reveal accelerated aging in individuals with age-related diseases (Ferrucci, 2020).

Notably, DNAm clocks correlate with factors that are modifiable with lifestyle change such as BMI, obesity, metabolic syndrome and cognitive decline. In fact, various lifestyle changes have recently been revealed to slow the rate of epigenetic aging. For example, lower protein intake, caloric restriction and weight loss all associate with DNAm age deceleration (Hanjani, 2018). To appreciate the several different types of epigenetic (DNAm) clocks, it is important to understand epigenetic aging and why it is so heavily intertwined with the aging process, please see Appendix 2.1 for more information on epigenetic aging.

Many DNA methylation clocks have now been derived through the collection of DNA samples from saliva, whole blood and skin. Due to their individual strengths and weaknesses, explicit reference must be made to the specific clock that is being used. Each DNA methylation clock is unique to its method of calibration, the importance of tissue/s employed, number of samples, specific training data set and statistical methodology. Clearly, small sample sizes are more susceptible to multiple aging-related confounders, measurement errors and imperfect statistical predictions (Bell, 2019).

Below is a summary of the epigenetic clocks that have been developed to date. For further detail on each, please see Appendix 2.2.

Horvath Development: The “OG aging clock” developed from the analysis of 8000 samples across multiple tissue types – including cancer and stem cells and trained to predict chronological age


Predictive power: Strong predictor of compromised cognitive function, grip strength, lung function, Alzheimer’s and Parkinson’s, osteoarthritis, obesity, metabolic syndrome and all-cause mortality

Hannum Development: Analysed methylome of over 450,000 CpGs in a mixed population of 656 individuals. Trained exclusively on blood samples to predict chronological age


Predictive power: Strong predictor of motor coordination, cognitive health, immunosenescence and all-cause mortality

Zhang Development: Analysed DNA methylation in over 13,000 blood samples across 14 data cohorts consisting of a mixed population of individuals


Predictive Power: Strong predictor of cardiovascular disease, cancer and all-cause mortality. Known as the “cancer clock” as there is a dose response relationship between DNAm age and cancer prediction. One-year increase in Zhang epigenetic age = 6% increased risk of developing cancer within three years.

PhenoAge Development: Used clinical data from a diverse and nationally representative sample of nearly 10,000 adults with complete biomarker data. Combines a DNAm clock with 9 blood biomarkers (including reactive protein, glucose and albumin) to predict chronological age, disease and mortality.


Predictive power: Trained based on morbidity and mortality data rather than chronological age alone. One of the stronger predictors of age-related morbidity (Alzheimer’s, immunosenescence, frailty, etc). A one year increase in DNAm PhenoAge is associated with a 4.5% increase in the risk of all-cause mortality.

GrimAge Development: Analyzed over 7000 samples from diverse data cohorts (including Framingham Heart Study). Constructed as a composite of 7 surrogate DNA methylation based markers for plasma proteins (i.e. c-reactive protein and GDF-15), self-reported smoking pack years, gender and chronological age.


Predictive power: Strong predictor of cardiovascular disease as well as time to cancer, hypertension, and type 2 diabetes. Notably, GrimAge is the most robust predictors of time of death. Reliably outperforms all DNAm based clocks in predicting morbidity and mortality.

Comparing the predictive potential of epigenetic DNAm clocks

Maddock and colleagues compared Horvath’s, Hannum’s, PhenoAge and GrimAge biological aging acceleration rate using three physical (grip strength, chair rise speed and forced expiratory volume) and two cognitive (episodic memory and mental speed) measurements. They concluded that the second-generation estimators (PhenoAge and GrimAge) that were trained based on morbidity and mortality data outperformed the first-generation clocks that were trained based on chronological age (Maddock, 2019). In a subsequent study, McCrory et al examined the same four aging rate estimators on nine health related clinical outcomes and concluded that the biological aging rate estimated by GrimAge was associated with 8/9 phenotypes and hence the best one among those four (McCrory, 2021).

DNAm clocks are currently the most accurate diagnostic devices for predicting chronological age and the most popular biological age diagnostic tool in the market. Further, they have the greatest degree of large-scale cohort validation. Each DNAm clock has its unique value at predicting chronological age, biological aging rate as well as the incidence and progression of various chronic diseases of aging. DNAm clocks that are trained solely on chronological age have limited value and appear to be inferior to those clocks that are trained on morbidity and mortality data and/or utilise other types of aging biomarkers to strengthen predictive power for biological age rate. Another potential challenge is that changes in DNA marks often take a long time to emerge in response to aging interventions (Simpson, 2021). The continued use of DNAm clocks in studies testing longevity interventions will provide more clarity on its utility as a biological age diagnostic tool.

Predicting BA and CAQuality of training setsDNA screening and statistical methods
Best practice is to incorporate multiple measures of physical fitness apart from chronological age to derive biological age.

Epigenetic aging rates will differ in tissue/cell types. There currently a lack of evidence for morbidity and mortality prediction by DNAm clocks in tissues other than blood.

Biological mechanisms driving the clock to have a higher or lower biological age are unknown. When these mechanisms are identified, there could be more useful tools for clinical applications developed
DNAm clocks inherit all their training set’s biases, hence require careful sample selection and data annotation. Clocks trained on specific data types make predictions that are bound to the data types utilized within the training data set.

DNA methylation is a dynamic process; therefore, longitudinal studies are needed to understand how epigenetic clocks change within individuals’ life trajectories.

Current epigenetic clocks are trained with a small number of sample sets that bias the prediction error between the chronological and the biological age – larger-scale datasets from different populations, ethnicities, genders, tissues, cells, and diseases would be preferable
All DNA methylation based aging rate estimators use high-throughput DNA methylation data, typically Illumina arrays, and are trained to predict chronological age or aging related risk factors such as mortality by a linear regression model.

How DNA methylation is screened is critical for a clock’s relevance. For example, while CpG microarrays are highly reproducible, bisulfite sequencing approaches suffer from coverage issues and testing clocks using this method on independent data sets may be impossible due to missing values for important CpGs.

However, bisulfite sequencing is cheaper and could be better for commercial optimisation.

When comparing DNAm clocks and assessing their accuracy as a biological age diagnostic tool, the three principles described in the table above are important to keep in mind (Kudryashova, 2020). For further detail, please see Appendix 2.3.

Telomere aging clocks

Telomeres are regions of DNA at the end of chromosomes that can protect functional regions of DNA from damage. In somatic cells, telomeres become shorter each time a cell divides because they don’t have the enzyme telomerase that can lengthen telomeres. This characteristic shortening with each successive cell division is what comprises the basis of the telomere aging clock. Eventually, the shortening of telomeres leads to cellular dysfunction or programmed death (Dweck, 2021).

The relevance of telomere attrition as an aging biomarker is justified by studies displaying correlation between longer telomeres and increased lifespan in model organisms and in humans. Decreased telomere length in humans has been associated with age-associated conditions such as Alzheimer’s disease, female reproductive aging, metabolic and cardiovascular disease. In clinical studies, lifestyle behaviours such as smoking, sedentarism and a lifetime accumulation of stress has been associated with short leukocyte telomere length. Multiple researchers have estimated that the speed of telomere attrition can be between 40-50bp/year in blood cells (Wang, 2018). However, the initial measured telomere length and identified attrition rate vary greatly in different studies. For example, a 13‐year prospective study in the Baltimore Longitudinal Study of Aging reported that average telomere length shortens with aging, but the direction and magnitude of change are different in different circulating cells and extremely heterogeneous across individuals. In fact, a substantial percentage of individuals displayed an average lengthening of telomeres (Meier, 2019).

Telomere aging clocks remain a hypothetical concept for several reasons. Oxidative and inflammatory noise masks “replicative attrition signals” in blood samples and is a major obstacle to creating an accurate age predictor. Further, sub-populations of cells can have different telomere lengths which makes them much better aging biomarkers for individual cell types as opposed to system wide aging. For example, naïve T-cells are reported to have telomeres 1.4kbp longer than memory T-cells. When mixed populations of blood cells are tested for telomere attrition, it remains unclear whether the discovered changes are due to telomere attrition or just due to differences in naïve T-cell content within the sample. Tracking blood cell telomere length in individuals shows that it does not decrease at a constant rate and can in fact go up. Further, in post-mitotic cells such as neurons, telomere length remains stable throughout life, making it a negligible biomarker of aging for this important cell type. In dividing cells, telomere length typically fluctuates by ±2-4% per month, revealing its dynamic nature and indicating that its interpretation as a biomarker of aging requires longitudinal design (Vaiserman, 2021). Finally, activity of the enzyme telomerase (which serves to lengthen telomeres) has been found to increase in endurance training within lymphocytes, heart and endothelial cells, challenging the prior hypothesis that telomeres exhibit a steady shortening rate (Werner, 2019) .

Telomere measurement is extremely expensive and is performed via:

  • qPCR (only a relative measurement)
  • Terminal restriction fragment (introduces many errors)
  • STELA and TESLA (labor intensive and low throughput)

All these issues preclude the creation of a robust telomere-based aging clock that can independently predict the rate of biological aging (Dweck, 2021). But this doesn’t preclude telomere length from being one variable in a fully functional multi-dimensional aging clock.

Transcriptomic aging clock

Transcription is the process through which our genes get copied and expressed. As such, transcriptomic data is one of the most abundant but variable types of data. The evolution of microarray and RNA sequencing technology since 2000 has created millions of gene expression datasets from multiple tissues. Despite high variability, transcriptomic data is one of the most valuable types of data in research because it characterises gene expression and cellular activity and enables pharmacological target identification. This is especially important for the understanding of specific diseases such as cancer (Zhavoronkov, 2019). Today, it has been repurposed to create a powerful biological age diagnostic tool.

Transcriptome analysis enables characterisation of specific up- or down-regulated pathways and proteins associated with aging. Numerous studies have mapped aging phenotypes to changes in transcriptome and more than one thousand transcripts have been shown to be differentially abundant in people of different age groups. The quality and predictive power of the different types of transcriptomic age clocks depend on: cell types tested, number and types of genes tested, technology used to assess gene expression, number of samples from training data set, method of analysis (AI algorithm and statistical methods) and whether it is trained to predict chronological age, disease or mortality risk (Galkin, 2020).

In 2015, the first transcriptomic aging clock was published that was trained on blood RNA profiles from 14983 people. In this study, 1497 genes were reported to significantly change with age.

The clock achieved 7.8 years mean absolute error (MAE) and its biological relevance was further proved by displaying that increased predicted age (in relationship to chronological age) is associated with higher blood pressure, blood glucose, BMI and cholesterol levels. Encouragingly, it was also shown to correlate (albeit weakly) with the Horvath and Hannum epigenetic clocks (Peters, 2015). There are other transcriptomic age clocks that have been developed including the Healthy Aging Gene Score which ranked 150 preselected aging related genes in a cohort and showed association with cognitive impairment (Xia, 2021). RNAageCalc uses 1616 tissue specific and age-related genes to create a clock that has a higher correlation to chronological age and lower median error than other transcriptomic age clocks. Furthermore, it is highly associated with mutation burden, mortality risk and cancer stage in several types of cancers and offers complementary information to DNA methylation age (Ren, 2020) .

Optimising transcriptomic aging clocks for commercialisation

Transcriptome age predictors have not yet reached the “less than five years accuracy” milestone of their DNAm counterparts. With high-throughput RNAseq technology in place, accomplishing this feat is most likely only a matter of time. To date there is no transcriptomic age clock available outside of research and development (Solovev, 2019).

An accelerated transcriptomic age directly associates with higher blood pressure, cholesterol, glucose, smoking levels, BMI, as well as several chronic diseases of aging. Beyond this, transcriptomic age clocks provide invaluable functional insights including specific ageotype trajectories as well as pharmacological targets. (Jylhävä, 2017)

There are several unique strengths that transcriptomic age clocks possess that make them stand out in comparison to other biological age diagnostic devices. But there are also multiple aspects of this technology that are yet to be optimised. One challenge is that transcriptional expression is extremely variable across different individuals, contexts, as well as assays used to measure transcriptional changes. For example, Mamoshina et al examined blood samples from 6465 individuals and 17 data sets and found that variations in technical processing of sample sets influences blood expression profile more significantly than disease and age itself. Secondly, transcriptomics analysis is expensive and requires exclusive equipment as well as highly qualified personnel. Technological advances such as the development of high throughput sequencing technologies will lead to automation as well as the emergence of cheaper equipment that will help address these challenges. In fact, the future of transcriptomic biological age diagnostic devices resides in portable devices (Mamoshina, 2018).

There are several other challenges that transcriptomic clocks share with other aging clocks including inaccurate data source and problems with normalisation of sampling features which leads to the situation where algorithms trained on one data type do not fit with other independent samples, leading to irreproducibility. This is observed within the results of various transcriptome studies, where intersections in findings are rarely observed. Finally, different cell populations have different sets of genes that are differentially expressed with aging (for example, CD4 vs CD14 immune cells) (Solovev, 2020).

GlycanAge clock

Glycans are chains of monosaccharides (simple sugar molecules) that can attach to one another as well as other types of molecules (such as proteins) and go on to form quite complex structural chains. Glycation is a non-enzymatic process that is characterised by pieces of sugar molecules that spontaneously slough off and attach to molecules, cells and vasculature. Over time, this can lead to cellular dysfunction and disease. Glycosylation, on the other hand, is a byproduct of enzymatic reactions that leads to the “intentional” addition of carbohydrates (glycans) onto molecules and cells. Hence, glycosylation is an active, energy demanding and regulated process. Glycans are the largest class of organic molecules by weight and perform a wide variety of functions including regulatory and structural roles within the body (Barchi, 2021).

The GlycanAge clock is a biological age diagnostic tool that analyses patterns of glycans on immunoglobulin (antibodies). These glycan patterns that are enzymatically placed on antibodies control whether antibodies have pro- or anti-inflammatory roles in the body. When we are young, we have a systemic “immunosuppressive” glycan signature. As we age, this signature shifts to a pro-inflammatory one. In this sense, GlycanAge is useful due to the physiological information it provides – it is essentially a reflection of the health status of the immune system and extent of chronic inflammation in the body. In fact, GlycanAge is the best-known marker for assessing the extent of chronic inflammation within the body over time (Kristic, 2014).

Towards a unified theory of aging: GlycanAge and “Inflammaging”

Chronic inflammation is potentially contributing to every chronic disease of aging. This is because the presence of chronic inflammation leads to increased production and circulation of cytotoxic factors (cytokines) by our immune cells. These factors start damaging healthy cells and tissues, both unintentionally or by mistakenly recognising them as foreign invaders. Further, chronic inflammation is an energy demanding process and leads to the triage of vital blood flow, nutrients and oxygen away from organs that need them for daily maintenance (brain, liver, kidney, etc) and towards the sites of inflammation. Over time, this leads to the accumulation of damage in the organs and tissues that are deprived of these vital resources, leading to accelerated aging and the manifestation of disease. Finally, persistent chronic inflammation leads to a vicious loop of damage and repair which causes the formation of scar tissue or fibrosis, compromises tissue structure and function and leads to degenerative pathologies and frailty (Franceschi, 2018). GlycanAge ​​clock been shown to change well before disease arises and it can be used as an early diagnostic tool that can be acted upon to modify the trajectory of menopause and diseases such as type II diabetes, cardiovascular events and even COVID disease severity. Further, GlycanAge has been shown to change in response to lifestyle interventions such as weight loss, diet and meditation on the timescale of weeks to months (Kifer, 2021) . This makes it an ideal biological age diagnostic clock to be paired with therapeutics or lifestyle change.

GlycanAge has been shown to predict chronological age with an error of 9.7 years and increased GlycanAge is associated with dysregulation of biomarkers such as fibrinogen, hemoglobinA1c, BMI, triglyceride levels and uric acid after correction for age and sex. Finally, it is intriguing to consider the role of glycans as a therapeutic target. If we manipulate the glycan signature to reverse the progeronic glycation of antibodies, would we also be reversing chronic inflammation and its corresponding health consequences? Several associational intervention studies suggest that this is indeed the case. (Paton, 2021). The GlycanAge clock is a uniquely powerful biological age diagnostic tool due to its strong connection to biological age as well as its role in tracking chronic inflammation and the process of inflammaging – the closest phenomenon we have to a unified theory of aging. Further research and validation will play a pivotal role in optimising the predictive potential of GlycanAge clock, making it a leading-edge biological age diagnostic tool.

Biomarker tests

Biomarker tests

The measurement of distinct biomarkers (typically secreted factors or cells) that represent a matrix of health and aging features from multiple tissues.

Examples: plasma lipids and proteins, blood count, glucose, albumin, liver enzymes, oxidative and inflammatory stress factors, etc.

Cost – Low to Moderate

Predictive potential High (multiple tissues, diseases, risk factors and all-cause mortality)

Accessibility High (blood or urine sample, simple at home test kit)

Iterability High (synergise with multiple biomarkers, data types, technologies)

Strength Well established databases, reflects systemic aging, provides mechanistic information for therapeutic intervention

Weakness Sub-optimal and inconsistent isolation protocols, biomarkers exhibit dynamic fluctuations, small fraction of metabolome identified.

Biomarker tests measure a range of factors that reflect the constant changing molecular, cellular and/or physiological processes that maintain homeostasis in different environmental contexts and throughout the aging process itself. We have defined biomarker tests as a class of biological age diagnostic devices that involve the measurement of distinct biomarkers that represent a matrix of health and aging features from multiple tissues. Typically, the factors measured include a composite of classical biomarkers that are integrated into a panel to reflect changes in biological age. But there are also tests that can measure novel aging biomarkers that reflect various age-related changes including the hallmarks of aging. The efficacy of biomarker tests as biological age diagnostic tools depends on the integration of several biomarkers, or features of aging, that have to be weighted in order to be integrated into a single biological age score. Such weighting is dependent on population level data and defining thresholds that distinguish “healthy” vs “unhealthy”, as well as the personalised physiology of the individual being tested (Colloca, 2020).

There are several different types of biomarkers that can be measured and integrated into a panel in order to reflect biological age and age rate including: proteomic (measurement of proteins), nucleic acid (measurement of DNA or small RNA molecules), lipids and other byproducts of metabolism (i.e. reactive oxygen species or advantage glycation end (AGE) products). These biomarkers can also be measured using a variety of tools (nuclear magnetic resonance (NMR) imaging, mass spectrometry, various isolation and purification methods) and from a range of (typically fluid) specimens (blood, urine, serum, saliva, sweat, etc) (Adav, 2021). Due to the diversity of metabolic factors that can be measured (in various combinations) to derive a biological age score, this class of biological age diagnostics is the broadest.

Like transcriptomic age clocks, biomarker tests have the advantage of providing mechanistic insights on the types of physiological changes that drive the aging process, hence providing unique insights that can be used to design therapeutic or lifestyle interventions. For example, data from centenarian studies implicate proteins involved in healthy immune function, lower pro-inflammatory status, microtubule motor activity, growth factor signalling, angiogenesis, cholesterol metabolism, antioxidant capacity, nutrient transportation and of course, regeneration and repair. This data helps elucidate targetable candidates or biomarkers for the development of clinical interventions that could help achieve healthy aging (Balistreri, 2012).

Parsing the complexity of the “aging metabolome”

The advances in technology and significant breakthroughs in metabolomics research within the past decade offer invaluable insights into the correlation of metabolite level with aging and diseases. Although metabolomics technology has high potential to elucidate the aging process, it is handicapped due to a lack of comprehensive databases to support the identification of as many metabolites as possible. According to recent human metabolome database 4.0 (HMDB 4.0), the total number of metabolites is 220,945, of which 18,588 metabolites can be detected and quantified. This represents 8% of our total human metabolome and the more we study metabolomics, the smaller that number becomes (Wishart, 2018). Further, it is still difficult to assign and differentiate changes within identified metabolites that are solely due to neutral age-associated changes, age related damages, or distinct pathologies. Hence, in-depth research is still required to establish the contribution of metabolomics in the aging process.

Most biomarker tests measure factors that exhibit dynamic fluctuations based on internal and external stressors, hence they represent “snapshots in time” of overall health. This is one of the reasons why measurement of single factors can be misleading or provide an incomplete picture of biological age. Aging is a multi-level process, so markers of individual mechanisms cannot cover all its aspects. Therefore, constructing panels of biomarker changes – in a networked fashion – typically give a more complete picture of biological age. The number of biomarkers measured should be comprehensive but not excessive to the point of diminishing feasibility and increasing cost. Hence, there is a need for more longitudinal data within research to identify true chronokines – factors that stably change with age (Adav, 2021).

One example of a single factor that is predictive of biological age and age rate is NAD+. Decreasing NAD+ levels have been found to be significantly associated with increasing age due to its role in mitochondrial health, nutrient sensing, DNA repair and epigenetic maintenance. The evolution of AI and machine learning technologies will further accelerate progress in finding impactful chronokines that help minimise the number of biomarkers that need to be tested to produce an accurate reflection of biological age. High accessibility, convenience, standardised protocols and affordable price make clinical biochemistry tests a promising source of aging biomarkers that could be used independently or to complement other methods of age prediction in the development of biological age diagnostic devices (Schulz, 2016).

Plasma BiomarkersUrine Biomarkers
  • The cellular and physiological processes in the cells of different tissues and organs adjust in response to various factors.
  • This is reflected in the blood plasma metabolome.
  • Blood plasma contains thousands of metabolites from all different tissues of the body.
  • The first plasma biomarker test (Zhavoronkov et al) for biological age was based on a deep learning neural network that analysed basic blood test data of over 60,000 people and included: sex, geographical location and -40 plasma biomarkers that had a mean average error of 5.55 years for prediction of chronological age.
  • The researchers found that the people predicted to be older had higher mortality rates than those predicted to be in line with their chronological age, confirming the biological significance and suggesting clinical application.
  • Further, these studies also revealed that the five most important blood markers for predicting human chronological age are albumin, glucose, alkaline phosphatase, urea and erythrocytes.
  • Urine is a chemically complex biological fluid that is non-invasive, easily obtainable, and largely free from interfering proteins and lipids.
  • Urine analysis has revealed that factors involved in energy metabolism, lipids, metals, amino acid metabolism, and gut microbiota are key regulatory factors in the aging process.
  • One such study used NMR to measure 59 urine metabolites (including creatine and hydroxymethyl butyrate) from over 4000 individuals to calculate a biological age score.
  • The researchers found that this score was significantly associated with several age associated diseases (i.e. metabolic disorder, frailty, autoimmune disorders) and predictive of overall survival in a 13 year follow up period.
  • A lack of a well-established comprehensive, electronically accessible global database is a major limitation in the identification and quantification of urine metabolites. This must be addressed in order to develop robust BioAge diagnostic devices utilising biomarker tests of urine samples.

The promise and challenges of plasma and urine biomarker tests

Table of biomarkers tests (adapted using Adav, 2021 and Xia, 2021)

One of the major challenges in the development of biological age biomarker tests is the general lack of consensus regarding the relative contribution of each biomarker or combination of markers. This is based on conflicting results and different biomarker candidates emerging from different studies. Variations arise due to technology accessibility, detection limits for large molecules, tissue selection, differences in protein isolation protocols and the lack of well-conducted proteomic studies on different populations. This makes avoiding heterogeneous and non-comparable information very difficult to accomplish (Rivero-Segura, 2020).

Overall, metabolic biomarkers seem to be promising since they are feasible to perform among large population data sets. Further, metabolomics represents the result of a complex network of molecular processes including genomic, epigenetic, transcriptomic and proteomic events. This means that the metabolome may better reflect system-wide aging-phenotypes. However, the biggest issue of this approach is the data handling since several metabolites are small molecules contained in different biological matrices, biofluids, tissue or cells. Hence, they are commonly underestimated due to sub-optimal and inconsistent isolation protocols used for data handling and analysis (Rivero-Segura, 2020).

Physical and physiological function test

Physical/Physiological Function Test

Measurement of various physical, cognitive and physiological functions that represent a matrix of health and aging features from multiple tissues. These tests are grounded in a systems biology approach and theoretically measure network interactions within the body that lead to emergent phenotypes representing functional health and decline,

Examples: gait speed, frailty index, cognitive function, psychological/subjective age assessment, cardiorespiratory parameters, vocal analysis, etc

Cost – Low to Moderate

Predictive potential – Moderate to High (multiple diseases and all cause mortality, less predictive at younger ages)

Accessibility – Moderate to High (many tests can only be performed in clinic)

Iterability – Moderate to High (limited data on synergy with other biomarkers, data types, technologies)

Strength – Well established databases, reflects systemic aging, provides mechanistic information for therapeutic intervention

Weakness – Sub-optimal and inconsistent isolation protocols, biomarkers exhibit dynamic fluctuations, small fraction of metabolome identified.

Biological age diagnostics that are based on performing physical, cognitive and physiological function tests involve the measurement of various “macro-level” phenotypes which represent a matrix of health and aging features from multiple tissues. These tests are grounded in a systems biology approach and theoretically, measure network interactions within the body that lead to physical and physiological phenotypes. Due to the nature and resolution of these types of tests, they are typically more accurate within individuals that are already compromised by the aging process. Further, they are not particularly revealing in terms of specific aging mechanisms that may be playing out in the individual to drive accelerated biological age rate (Moskalev, 2020).

Typically, physical and physiological biological age diagnostic assessments measure multiple different aspects of functional fitness to derive an overall biological age. Examples of biological age diagnostic devices that fall into this category include frailty index, gait speed, various cardiorespiratory parameters and cognitive tests. Within this class, frailty index is the most prominent, accurate and comprehensive type of biological age diagnostic, so we will focus on its potential and pitfalls more than others.

Frailty Index

Frailty index (FI) refers to a method of quantifying frailty in older individuals, with the underlying mechanism being a measurement of deficit accumulation (deficits identified or measured). While there appears to be an organ-specific vulnerability to aging, frailty refers to the cumulative decline that precedes death. Since frailty is typically driven by age-related molecular, cellular and organ level dysfunction occurring over time, frailty index is a “late stage” measurement of the decline of multiple aging systems that operate in a network. Damage in this network, whether partial or complete, is propagated across the body because of the networking of our physiological systems resulting in measurable physical decline. The more interconnected a given parameter, the larger the effect on aging. It is hypothesised that frailty emerges from the increased energetic demands of an aging system that is increasing in entropy and losing integration. Thus, complexity decreases as biological age increases. This is the result of damage, dysfunction, as well as loss of components and the interactions between them (Jazwinski, 2019).

As such, the FI is developed using clinical information that is likely to capture an organismal perspective on aging that is closer to the clinical end-point of death compared with other biological age diagnostic devices (like aging clocks and biomarker tests) that measure molecular level phenomena that occur decades before frailty arises. For this reason, while the “functional” outcomes measured by FI are critical for quality-of-life assessment in the elderly, they occur late in life and fail to capture the initial changes of biological aging at younger ages. Therefore, FI is a poor biological age diagnostic to assess the aging process within younger individuals and a sub-optimal tool to use within preventative medicine (Moskalev, 2020).

Unlike other biological age diagnostic measures, we can say that FI is unequivocally measuring the byproducts of the aging process. This systems biology-based nature of FI distinguishes it from other quantitative measures of biological age. In addition, FI is uncomplicated mathematically and predicts mortality without the incorporation of chronological age as one of its parameters. Physical function and anthropometry are the most practical measurements among phenotypic biomarkers of aging. In this regard, walking speed, chair stand, standing balance, grip strength, body mass index, waist circumference and muscle mass are well known. These physical functional measurements, though simple, can perform better than DNA methylation in terms of relationship to health status in demographic research. There are several examples of the utility of FI as a biological age diagnostic (Ji, 2021).

In a study of 1788 community-dwelling elders, poor FI was associated with a 2.31-fold increased risk of all-cause mortality compared with those who scored robustly on the index. Interestingly, a meta-analysis examining FI scores between men and women found what the authors described as a “male-female health-survival paradox”. The paradox was that at all ages females displayed higher FI scores, despite males having higher mortality rates at each level of the frailty index. This coincides well with findings that women tend to live longer but have compromised healthspans. Overall, functional biological age assessments in humans tend to show less heterogeneity among the young. This makes physiological and functional assessments less ideal instruments for assessing the aging process compared with other molecular biological age diagnostic tools (Lohman, 2021).

Subjective age assessment

Another type of biological age diagnostic device that falls within this class of physical and physiological tests is the subjective (or psychological) age assessment. The subjective age test uses psycho-social questionnaires to assess various parameters of mental and emotional health, personal experiences, personality traits, cultural values and lifestyle parameters (such as sleep assessment) that reflect an individual’s perception of their age. The subjective age test has been employed to predict both chronological and perceived age to investigate psychological aging. Within these studies, the difference between perceived age and chronological age was significantly predictive of neurodegenerative and metabolic disease, liver damage, frailty and mortality rate (Zhavoronkov, 2020).

Subjective age may be associated with the rate of biological aging because our psychological state has been shown to directly influence neuroendocrine and metabolic dysregulation, pulmonary disease, muscular dysfunction, as well as the process of chronic inflammation. Further, our perception of our own age and health may influence our behaviours and the way we move through and engage with the world around us. This in turn influences our lifestyle habits, social connectivity and opportunities presented to us on a day-to-day basis. All these factors can both directly and indirectly influence our biological age and aging rate (Wettstein, 2021). Subjective age assessment is a particularly useful metric within the class of physical and physiological function tests as it can be successfully used in younger individuals to assess biological aging rate. Amongst all biological age diagnostic tests, subjective age assessment is the cheapest, least invasive and most convenient test available.

Microbiome tests

Microbiome Test

The measurement of changes in microbial species and function (typically gut microbiome)

Example: gut microbiome test

Cost – Moderate (few hundred dollars)

Predictive potential – Moderate (several diseases, not reflective of individual tissue health and significant interindividual variability)

Accessibility – High (fecal sample and/or microbial metabolites in plasma)

Iterability – Moderate (limited data on synergy with other data types, biomarkers and technologies)

Strength – Simple and convenient test, can inform interventions, microbiome has profound influence on systemic health

Weakness – Variability between demographics, not well characterised, does not inform health of individual tissues.

The human microbiota is composed of different phyla including: bacteria, archaea, fungi, protozoans and viruses, which in their entirety, have a commensal relationship with their human host. Our microbiota resides in different parts of the human body including skin, eyes, mouth, nose, reproductive organs and digestive tract. Notably, our microbiome’s composition varies among tissues and organs and exhibits dynamic changes throughout our lifespan based on environmental factors including lifestyle, stress, exercise, diet, drug consumption and age (Rivero-Segura, 2020).

Our gut microbiota has a profound influence on the functioning of various physiological systems beyond gut health including the health of the immune system, metabolic health, and the brain. In fact, over 90% of the factors in our blood stream are produced or modulated by our microbiome. In this sense, we may consider ourselves more of a “super-organism” that is composed of both our “human selves” and “microbial selves” which are constantly engaged in a dynamic conversation that influences our rate of aging (Adav, 2021).

Several studies have reported that older adults exhibit a specific microbiota phenotype. Specifically, with age there is an observed decrease in microbes that have anti-inflammatory effects and an increase in microbes associated with type II diabetes, colorectal cancer, inflammatory bowel disease, cardiovascular disease, frailty and other chronic diseases of aging (Agus, 2021).

Most biological age diagnostics that test gut microbiota involve genomic profiling of microbes within fecal matter. Through fecal matter tests, researchers have found that there is a decrease in the abundance of the genera Bacteroides, Bifidobacterium, Blautia, Lactobacilli, Ruminococcus and an increase in the genera Clostridium, Enterobacteria, Escherichia, Streptococci. Interestingly, one study found that microbial biodiversity declines as a function of biological age (as assessed by frailty index) while showing little or no difference with chronological age. But biodiversity alone is far from the complete picture (Solovev, 2019).

A big difference in the gut of elderly individuals is the low levels of short chain fatty acids. This reduction is associated with increased counts of pathogenic bacteria and early development of age-related diseases. Hence, to propose a microbiome panel as a biomarker of aging, current research in the aging field requires the development of a functional panel that combines measurement of both microorganisms as well as microbiota-derived metabolites. To date, few studies combine the omics tools to associate the human microbiome with the metabolites present in human blood samples (Huang, 2020).

Aging microbiota phenotype
  • Loss of microbial species
  • Reduction in biodiversity
  • Dysbiosis drives cellular and molecular aging
  • Production of microbiota-derived metabolites that influence waste disposal
  • Triggers chronic inflammation via increased inflammatory cytokine signalling
  • Low production of short chain fatty acids

A recent study used a deep neural network approach to calculate the rate of biological aging based on a metagenomic (microbial genome) dataset. The list of microbial species used to calculate age consisted of 1673 strains and it was able to predict chronological age with a mean absolute error of 3.94 years. This is very close to the precision of the Horvath clock which has a mean absolute error of 3.4 years (Solovev, 2019). This result highlights the promise of developing gut microbiota based biological age diagnostics. It could be even more useful if combined with plasma biomarker panels reflecting metabolic activity.

As with transcriptomic biological age diagnostics, microbiota tests seriously depend on robust methodologies of extraction and analysis. Further, microbiota studies should incorporate the influence of other phyla such as viruses, fungi and archaea, as well as other host’s characteristics such as ethnic origins, nutrition and genetics. All these factors play a crucial role in the immune response, which in turn shapes the composition and function of the microbiota. Moreover, since aging itself is a complex and heterogeneous process, finding a reproducible microbiome-based panel useful among different populations can help systematise sample collections (Rivero-Segura, 2020).


Bio-imaging Test

The measurement of visual changes to various organs (typically skin)

Examples: Skin aging, thymic involution, liver adiposity, etc.

Cost – Low to Moderate

Predictive potential – Low to Moderate (few diseases, chronological age, not reflective of individual tissue health)

Accessibility – Moderate to High (simple digital imaging or advanced imaging in clinic)

Iterability Moderate (limited data on synergy with other data types, biomarkers, and technologies)

Strength – non invasive, economic and large scale data collection in short period of time

Weakness – Not much data on prediction of morbidity and mortality, not reflective of individual tissue health, limited insights on biology of aging

The bio-image class of biological age diagnostics involves the measurement of age-related visual changes to various organs of the body, most notably the skin. This is typically done via taking photo images and analysing them via AI based algorithms. One such device – PhotoAge – is developed to predict chronological age using neural network analysis of 8414 images of eye corners. PhotoAge can predict chronological age with a median average error of 1.9 years with no health associations investigated (Bobrov, 2018).

Another new AI-based neural network model operates by collecting and analysing 3D facial images from a cohort of ~5,000 Han Chinese individuals, together with baseline information. This predictor achieved an error between chronological age and predicted age of only 2.90 years. They also found that the heterogeneity in aging rate peaked at middle age. Further, when this bio-imaging diagnostic was combined with transcriptomic data (from peripheral blood mononucleocytes) the researchers found mechanistic insights such as the fact that smoking cigarettes accelerates aging via elevated inflammatory cytokine expression.

This study highlights the unique potential for bio-image technologies to synergise with other biological age diagnostic devices to provide added value compared with either technology alone. Bio-image devices have the advantage of being non-invasive, economic and allow for large-scale data collection within short periods of time. In the instance of this study, the unprecedented accuracy of the bio-imaging device was combined with the unique mechanistic insights gleaned from the transcriptomic clock to provide added utility as a biological age diagnostic (Xia, 2021).

Finally, a novel bio-imaging device used an AI model trained on perceived age rather than chronological age. This AI demonstrated the ability to learn the human age-perceiving process, hence avoiding the often-problematic use of prediction errors as a surrogate for the rate of biological aging. Further, this training method makes it more precisely associated with lifestyle and health parameters than a training method based on predicting chronological age alone (Imai, 2019).

Genetic test

Genetic Test

The sequencing and analysis of panels of genetic polymorphisms (gene variants) that are predictive of aging features across multiple tissues as well as overall health and disease.

Examples: APOE-3, FOXO3, IL-6, IGF-1, SIRT6, etc.

Cost – Low to Moderate

Predictive potential – Low to moderate (not predictive of disease/age when considered in isolation)

Accessibility – High (blood sample, simple at home test kit)

Iterability – High (synergise with multiple tissues, data types, technologies)

Strength – Well characterised databases, easily accessible, powerful supplementary tool when combined with other BioAge technologies and biomarkers

Weakness – Predicts predisposition, not significantly predictive of health/biological age when considered in isolation

The human genome was comprehensively sequenced by the start of the 21st century. Since then, scientists have been possessed by the allure of what our genes can teach us about our health and wellness. It was believed that by knowing what gene variations an individual had, the better one could predict their disease risk. This remains true in individuals who have diseases caused by mutations in a single gene, but we have also come a long way in understanding the limitations of using genes as a predictive tool for chronic diseases, especially when applied in isolation.

Knowing what genes we possess can inform us about our risk for disease, but whether that disease manifests (as well as how and when) is largely influenced by activity of our epigenome (regulated by lifestyle and environmental factors).

This highlights the most significant limitation of genetic tests; they are static markers of the baseline rate of biological aging and do not capture any changes in the rate of aging that may occur through lifestyle changes or therapeutic interventions. Advances within the field of genomics are teaching us that the most powerful use of genetic tests is as a supplementary tool consisting of comprehensive panels of genes combined with lifestyle, environmental and other demographic information. The more information we have to combine with genomic insights, the more powerful genetic tests become at predicting health and disease.

Employing AI and machine learning to curate and analyse databases that comprehensively collect this type of information, such as the UK biodata bank, holds tremendous promise to create good predictors of biological age (Lohman, 2021).

Biological age diagnostic genetic tests involve the sequencing and analysis of panels of genetic polymorphisms that are predictive of aging features across multiple tissues, as well as overall health and disease. These panels of genes are typically stratified into progeronic and longevity gene variants, and it is the combinatorial analysis of these sets of genes from which a biological age score is derived. Progeronic genes (variants of APOE, BRCA, TNF-alpha, etc) are well characterised and have been researched and implemented within the clinic to assess predisposition to various diseases; they are now even being used as a marker for early intervention. Longevity genes (variants of FOXO, IGFR, AMPK, etc), on the other hand, are a relatively novel concept (Ukraintsteva, 2016).

Researchers – particularly Nir Barzilai and colleagues out of Albert Einstein Medical School – have made tremendous advances in this space by studying the genome of centenarians to characterise those genes that help drive extreme human longevity and the escape of age-related diseases. “Heritability” is a metric that reflects the amount of influence our genes have on the manifestation of a given phenotype or trait (such as eye colour, type II diabetes, depression). Studies estimate that the heritability of lifespan, up to ~70 years of age, ranges from 10% to 25%. However, to reach higher ages, we become increasingly dependent on the favourable genetic elements of our genomes. In fact, the heritability of becoming a centenarian has been estimated to be approximately 60% (Tesi, 2021). Using genes may not be as precise as other biological age diagnostic devices, but it can inform us whether we have genes that predispose us to age faster or slower than our chronological age.

A growing body of research highlights the utility and value of polygenic age scores (PAS). One study showed that a PAS which included 330 variants helped to discriminate between centenarians with “youthful” cognitive health compared with older adults with cognitive decline. This PAS was also associated with longer survival (up to a 4- year difference) in an independent sample of younger individuals (Tesi, 2021). Another recent study found that a panel of 150 genes (termed the “healthy gene score”) could accurately predict an individual’s biological age and was more closely tied to their risk of age-related diseases, such as dementia and osteoporosis, than was their chronological age (Lohman, 2021). A third notable study illustrates the utility of genetic information as a powerful supplementary tool to predict aging rate, delayed aging and healthspan.

This study used a sub-population of over 300,000 individuals from the UK biodata bank as a discovery cohort to identify 12 gene variants that serve as a genetic signature for healthspan. This genetic health signature was significantly predictive of chronic diseases such as metabolic syndrome, type II diabetes, coronary heart disease and all-cause mortality.

One of the most powerful findings from this study was that the predictive power of the genetic health score was significantly enhanced when coupled with other demographic information provided in the UK database ( for example, sleep, smoking status, ethnicity, BMI, etc) (Zenin, 2019).

Studies that use comprehensive health databases to supplement genomic information are quickly becoming “the norm” in genomic studies and will eventually assist in the discovery of many more genes implicated in the control of human aging and disease.

The results from these experiments indicate that an extended human lifespan is, in part, the result of a constellation of variants each exerting small advantageous effects on aging-related biological mechanisms that maintain overall health and the absence of progeronic variants that decrease the risk of age-related diseases. These studies reveal the promise of implementing genetic testing to construct comprehensive panels that can accurately predict a “baseline rate” of biological aging.

Ultimately, the PAS would be developed as a supplementary tool that synergises with other demographic health information and biological age diagnostic tests to optimise predictive power for biological age and rate of aging.

Each class of age diagnostic by specific parameters:

ClassAging ClockBiomarker TestMicrobiome Test
Biomarker of AgingHighHighMedium
Aspect of aging capturedHighHighMedium
Training data setHighHighMedium
Mechanistic insightMediumHighHigh
Technical simplicityMediumMediumMedium
Therapeutic potentialMediumMediumMedium

ClassPhysical/ Psychological Functioning TestBio-imaging TestGenetic Test
Biomarker of AgingLowLowLow
Aspect of aging capturedMediumLowMedium
Training data setMediumMediumMedium
Mechanistic InsightMediumLowMedium
Technical SimplicityHighHighMedium
Therapeutic PotentialMediumLowLow

Comparison of various biological age diagnostics

There are a handful of studies that compare biological age diagnostic technologies that are worth mentioning. One such study, conducted by Li et al, performed functional assessments and collected blood samples from 845 middle aged or older individuals from Sweden for 20 years. They measured their biological aging during this time frame assessing frailty index, function index, four DNAm clocks (Horvath, Hannum, PhenoAGe, GrimAge), cognitive function, physiological age (waist circumference, BMI, blood panels, etc) and leukocyte telomere length. They found that all nine biological age measures could be used to explain the risk of individuals in the group dying during the follow-up period.

In other words, when comparing individuals with the same chronological age, the person with a higher biological age measure was more likely to die earlier.

This supports that biological age diagnostic technologies (of different types and classes) are complementary in predicting risk for mortality, despite reflecting different aspects of aging (Li, 2020). When comparing the different biological age diagnostic devices, they found a low correlation between telomere, epigenetic clock, physiological/functional and biomarker-composite aging rate estimators when correlation with CA was minimised (regressed out or adjusted for). This could be since each diagnostic device reflects different aspects of aging or technical errors inherent in each modality of measurement.

Another noteworthy finding that is corroborated by other comparative studies is that biological age diagnostics of the same class are most highly correlated with each other. The Li et al study also found that the Horvath and GrimAge clocks were the least correlated among the epigenetic clocks, indicating that these two assessments captured uncorrelated information to a larger degree and might have the potential to inform aging-related outcomes independently.

Finally, the study concluded that GrimAge and frailty index were most accurate at predicting risk of mortality and telomere length was the worst (Li, 2020). Although insightful, keep in mind that predicting mortality alone gives limited information about healthy biological aging as one can have both a robust healthspan (predicted by reduced risk of morbidity) and truncated lifespan.

Other noteworthy insights from this comprehensive study revolve around the unique value of utilising the various biological age diagnostic technologies at various time points in the aging process, as well as interpretations of gender differences in age rate. Researchers observed that the aging acceleration of cognition (cognitive function) and physical function (FAI) seemed to take place before age 70, while frailty (FI) went up at an increased rate after age 75.

Further, molecular diagnostic technologies have been shown to capture age acceleration at even earlier time points. This evidence suggests that accelerated cognitive and physical dysfunctions are likely to arise prior to frailties and molecular aging occurs first.

This information implies that various diagnostic screenings and corresponding interventions could be tailored for people at different points in the aging process.

Regarding sex specific differences in aging, they found that women presented lower molecular ages (telomere length and methylation age estimators) but higher functional ages (FAI and FI) across the age spectrum. In parallel with this, women tend to present lower mortality but higher morbidity rates, especially at advanced ages, compared with men of the same age. This correlates well with findings that women tend to have longer lifespans but shorter healthspans than men, highlighting those different medical needs and therapeutic strategies are likely required for men and women (Li, 2020).

Another pivotal study tracked ~1000 participants for over a decade to create and compare the performance and efficacy of eleven -omics aging clocks that track biological aging. This study replicated many of the findings from the above study and provided many additional insights. The eleven clocks covered various classes and types of biological age diagnostic devices including: IgG glycomic clock, plasma proteomic (just proteins), metabolomic (consisting of many different plasma metabolites), lipidomic (just lipids), epigenomic (Horvath and Hannum), as well as physiological and functional clock (weight, blood pressure, fasting glucose, etc). In addition, they added two novel clocks that included a DEXA whole body imaging set of body composition measures and one based on all the assays combined (termed Mega-omics).

One of the major questions the researchers sought to address in this study is whether the clocks measure a single biological age in which differences arise from focus and accuracy, or whether they are measuring biological age of different physiological systems (Macdonald-Dunlop, 2021).

The major findings from this study are as follows (Macdonald-Dunlop, 2021) :

  • Most clocks were able to accurately predict chronological age in an independent (European) study cohort, except for one of the metabolomics clocks and the DEXA clock. This suggests that population demographic information (lifestyle, environmental factors, etc) can independently influence clock construction and efficacy. This finding serves as a warning to using aging clocks in new populations.
  • Interestingly, researchers found that 94% of the variance in chronological age prediction was shared among clocks, suggesting significant overlap in clocks between the information they provide. Ultimately, it was concluded that all 10 omics clocks track more common rather than complementary aspects of chronological age.
  • Although all clocks were predictive of risk factors (total cholesterol, blood pressure, c-reactive protein, etc) and disease, the physiological and functional aging clock showed the greatest predictive power. Further, clocks were shown to predict different types of diseases and risk factors with variable efficacy. For example, the Metabolomics clock had significantly higher predictive power in the case of cardiometabolic disease while the glycomics clock was a more effective predictor of type II diabetes. Notably, amongst all clocks there was more variation in predictive power across risk factors than across diseases.
  • Interestingly, the most accurate estimators of chronological age: Mega-omics, proteomics and metabolomics clocks were not strongly associated with subsequent hospital admissions or many other risk factors that influence healthspan and biological age rate. This does not mean the assays themselves cannot be used to estimate biological age but highlights how accurate chronological age prediction does not mean a given technology is superior at predicting biological age and morbidity.
  • To validate translational efficacy within the clinic (minimise cost and maximise efficiency), the researchers cut down the number of biomarkers measured for each clock to a “core subset” that were most impactful on age prediction. Surprisingly, they found that the biological age rate derived from this core subset of biological age markers appeared equally predictive for diseases as more comprehensive clocks and more predictive for health risk factors.

This information combined suggests that biological age diagnostic tools are effective predictors of chronological age, risk of disease, as well as biological age. Further, different biological age diagnostic tools capture different aspects of physiological aging (and corresponding disease risks) and those tools that fall within the same class perform similarly. Finally, although a comprehensive and integrated clock offers the highest predictive power, it must be trained on more than just chronological age and include metrics of disease risk as well as diverse demographic information. Further, it may not be the case that measuring more biological age markers is necessarily better as those clocks refined to a “core subset” of impactful biomarkers of aging performed equally and in some cases better, at predicting disease risk and biological age.

Combinatorial diagnostics

While much research has focused on the quantification of biological aging through the application of individual diagnostic tools that dissect and reflect some critical aspect of the aging process, a comprehensive and integrative score incorporating molecular biomarkers and physiological functional parameters is lacking. Current strategies to assess systemic biological age carry significant limitations as individual parameters to accurately reflect organismal aging that can be reliably applied on an individual level has proven elusive.

Further, biological plausibility suggests that no single biomarker or clock is likely to suffice given the underlying multifactorial and multisystem nature of the aging process, with variable changes occurring on a molecular and organ-based level.

This highlights the utility of striving for a “gold standard” biological age diagnostic tool that computes a (seemingly ever-expanding) aggregate score of biological aging.

This scoring system requires careful integration of molecular markers (surrogates of the hallmarks of aging, clocks and -omics methodologies) with longitudinal physiological functional measures that reflect aging across all aspects of physiology (biome, cognition, frailty, immune and endocrine health) (Khan, 2017). This would be implemented through the integration of epigenetic clocks with classical and innovative biomarkers, gut microbiota signature, GlycanAge, subjective age assessments, physiological and functional health assessments, genetic risk factors, etc.

The information extracted from these biological age diagnostic devices would be processed via AI to give a more accurate assessment of biological age and health status based on network interactions between the various layers of aging. Applying such a comprehensive methodology to characterise the aging process in an individual will allow for the generation of personalised insights for unparalleled tailoring of therapeutic interventions and preventive regimens.

These comprehensive tools are currently being developed and implemented, yet research regarding optimal methods for creation and evaluation of a composite biological age score is still in its early days.

As the fields of geroscience, AI computing and biotechnology continue to evolve together, this will lead to a renaissance in the way we understand, track and engage with the aging process.

Criteria for inclusion of molecular and physiological parameters into a composite score require that the individual parameters be independently associated with aging and provide additive information when combined. For instance, a higher level of biological age predictive precision and functional utility may be attained by checking the concordance of a transcriptomic clock with a DNA-methylation landscape.

Further, many biological age diagnostic tools capture overlapping aspects of biological aging, suggesting that there is no guarantee that integrating more aging markers will reflect more aspects of aging – this must be accounted for as to not distort final age scores. And of course, increasing the procedural complexity of a predictor inevitably raises the cost of measurement; from this point of view, biomarker and genetic tests offer the most cost-effective means to increase another model’s performance.

In some research studies, various biological age diagnostic tools have also been shown to provide contradictory information on the rate of biological aging (this may be due to the variable rate of aging in different tissues captured by these tools), highlighting the fact that simply incorporating more biomarkers into one model does not necessarily guarantee its increased performance. This must be accounted for in the development of an accurate composite tool.

Deep Longevity is one of the companies leading the field in creating a composite measure by combining several aging clocks and biological age diagnostic tools which captures information that is stored, tracked and updated in their longevity cloud platform.

Further, their user-friendly Young.AI app allows the user to keep track of every aspect of their aging process in real time (Zhavoronkov, 2019).

One of the most comprehensive efforts for developing a combinatorial biological age diagnostic panel is Oliver Zolman’s Biological Age Marker (Z-BAM) assessment. The Z-BAM assessment measures 100s-1000s of non-biopsy-based markers – in every demographic, adjusted for every confounder, using every class of biological age diagnostic device – to calculate the biological age of all 78 organs of the body. The Z-BAM methodology is based on the concept that true rejuvenation cannot be achieved without a reductionist approach of addressing aging in every single organ, tissue and cell type possible.

This epic effort literally creates thousands of personalised aging clocks that are methodically integrated together to provide unprecedented insights on aging throughout the body.

Whole body biological age is hierarchically calculated via the Zolman Organ Biological Age Algorithm which combines groups of sub-organ biomarkers to calculate organ biological age, which are then used to calculate organ system biological age and finally weighted together to calculate final biological age. Ultimately, this provides multi-decade or lifetime all-cause mortality risk encompassing all age-related diseases and clinical outcomes. This information will then be used within Zolman Rejuvenation Clinics across the globe to rejuvenate all 78 organs of the body in a personalised manner.

In doing so, Zolman Clinics strive to be the first practical therapeutic approach to help individuals reach longevity escape velocity – implementation of rejuvenation therapies so that for every year that passes, one year of life is gained. These leading edge, longevity focused clinics and tools will initially only be available for the affluent. But as with all technological advances, as time passes and our healthcare system evolves, cost will move in a direction that is more affordable for the public (Zolman, 2021).

Other biological age biomarkers (hallmarks of aging)

In the process of compiling this report we have extensively researched and curated the most relevant and intensively researched biomarkers of aging and biological age diagnostic tools that we believe will play the most significant role in extending human healthspan. Hence, these technologies represent some of the most impactful investments within the longevity market.

However, the realm of biological age diagnostics is both vast and rapidly evolving – there are many relevant biomarkers of aging for which biological age diagnostic tools are still being developed and still more yet to be discovered.

Many of these emerging biomarkers of aging are either still relatively invasive or elusive to measure, require expensive and labour-intensive equipment, chemically unstable or subject to rapid degradation upon measurement, biologically complex and/or human data is not easily accessible or available at all (Guerville, 2019).

We have created a table including some of these biomarkers – many of which are defined hallmarks of aging – and how they are being measured within model organisms in the lab. It must be said that this table is by no means comprehensive; it serves to provide a general sense of other emerging developments within the field that will soon be worthy investment opportunities. These biomarkers of aging and the technologies being developed to measure them, have tremendous translational potential and will likely be covered in more detail within subsequent versions of the biological age diagnostics report.  

Biomarker of agingHow it is currently being measured
DNA damage increases with ageMeasure phosphorylated H2A.X – critical protein for DNA damage repair – in circulating cells (red blood cells). Cost and labour intensive.
Senescent cell burden increases with ageSenescence Associated Betagalactosidase in circulating cells (T lymphocytes), skin and fat cells. Because of senescent cell heterogeneity, there is yet to be a single, effective marker of senescence. Other markers include secreted proteins, lipids and miRNAs, many of which make up the senescence associated secretory profile.
Mitochondrial health declines with age Oxidative stress increases with ageMeasure oxidative damage to macromolecules in blood (specific nucleic acids, proteins and lipids), antioxidant capacity, markers of mitochondrial physiology and function (mtDNA copy number, mtDNA mutation, amount of mitochondria with muscle biopsy. However, these require extremely careful standardisation. In particular, blood measurements may be affected by changes in circulating cells and high levels of mtDNA copy number (or other markers) can also indicate chronic tissue hypoxia and are inversely correlated to age in disease contexts.
Proteostasis (autophagy and proteasome) declines with agingCan measure LC3 (marker of lysosomal health and autophagy) using a fluorescent dye that marks particular chemical structure (cysteine thiols) of misfolded proteins.
microRNAs (miRNAs)Extracted from lipoproteins or extracellular vesicles. Changes in specific miRNas are correlated with inflammation, senescence burden and systemic aging (such as miR-34a, miR-21, miR151a-3p)
Stem cell exhaustionStem cells can be extracted and cellular health assayed, but they are not easily accessible. Further data on changes with human aging in stem cell numbers, characteristics and replication potential are still limited.
Advanced glycation end (AGE) productsOriginates from glycation of proteins, lipids, nucleic acids which leads to inflammation, oxidative stress, cellular senescence and cell death. Can be measured via cellular autofluorescence (directly correlated with AGE accumulation) in the skin or other tissues.
Progernoic factors increase with age and youth factors decrease with ageFactors secreted by cells, typically into the circulatory system, have been observed to increase (progeronic factors: IL-6, TNF-alpha, B2M, damage associated molecular patterns) or decrease (youth factors: NAD, GDF-11, oxytocin, estrogen) with age. Can measure with mass spectrometry and other plasma analytic techniques.
Rate of translation (elongation phase) declines with ageNo good measurement metric in humans to date. Extremely chemically unstable process to dissect and analyse. Data is conflicted because different cell types have variable rates of translation. Further, the rate of translation is extremely susceptible to a wide variety of stressors.

(Ferrucci. 2020) (Guerville, 2019)

Investing in biological age diagnostics

Data presented in this section was analysed using Longevity.Technology’s proprietary database. This database is regularly updated to track the evolving longevity market landscape. The data presented is as of 01 January 2022 and is based on 48 diagnostic companies that have been deemed “biological age” by First Longevity, using the descriptions provided in part 2 of this report on biological age diagnostics.

  • Biological age diagnostic start-ups experienced growth in the years of 2015-2018 following the publication of Horvath’s epigenetic clock paper in 2013 that showed that the clock has the potential to address many fundamental health questions in developmental biology, cancer and aging research.
  • Most biological age diagnostics make commercial sense even without the tick of an FDA clinical approval due to the increase of the wellness market, set to reach $6 trillion by 2025.
  • At home biological age diagnostic tests are attractive in terms of revenue due to the need for repeat purchases.
  • Longevity clinics and insurance companies will be lucrative target markets for biological age diagnostics. 81% of biological age diagnostics sell B2B.
  • The bulk of the biological age diagnostics market is concentrated in developed countries, with the United States (53%) and the United Kingdom (35%) dominating the market.
  • $589 million was raised in 2021 for biological age diagnostics, corresponding to 32% of total funding to date and demonstrating a step-change in investment into these biological age companies;
  • Investment at this point in the biological age diagnostics market currently still offers relatively early-stage investment into an exciting, opportunity-rich sector which has potential to influence every aspect of humanity’s health and wellness and to offer transformational returns from expected exponential growth.
Why are biological age diagnostics different?

Venture capital has tended to approach diagnostic companies with caution, particularly as their development does not follow the same, more predictable, pathway of pharmaceuticals.

Clinical diagnostics have been described as too capital intensive, too slow to commercialise, consequently generating lower returns. However, in the period 2014-2017, money invested in clinical diagnostics increased significantly and start-ups in this area soared. This was mostly due to the development and promise of Next Generation Sequencing (NGS), which can decipher huge amounts of biological information quickly and cheaply. This meant improved returns on investment for NGS companies and, of a dozen well-funded NGS companies, seven exited and returned, on average, 4.4 times invested capital within seven years (Winter, 2017).

We believe that venture investors should take a good look at the longevity diagnostics area, which could follow a similar pattern to NGS tests. NGS tests presented a wealth of knowledge as a diagnostic to many inherited disease areas, such as cancer predisposition and prenatal knowledge of chromosome abnormalities.

Longevity diagnostics could promise a similar wealth of knowledge of the diseases of aging. There was a huge increase in biological age diagnostic company start-ups post 2013, following publication of Steve Horvath’s ground-breaking paper on his epigenetic clock that could address many fundamental health questions in developmental biology, cancer and aging research.

% of total biological age diagnostic companies formed to date by year. There was considerable growth in 2015-2018 of biological age diagnostic start-ups.

Personalised medicine and personalised health initiatives are already underway and are expected to gain momentum in the near to medium term, as the pressures of an aging population put increasing strain on health services across the globe. This population will put unprecedented demands on the healthcare sector and deplete healthcare resources. Studies have suggested that there could be a worldwide net shortage of around 15 million healthcare workers by 2030 (Liu, 2017).

Better diagnostic tools could offset the increasing demand faced by the healthcare industry because of increasing aging population. Biological age diagnostics look at early-stage damage that occurs in the body that results in age-related disease at an older age. If biological age diagnostics can become validated diagnostics of these end diseases, or even predictions of onset of morbidity, they could provide cost-effective solutions and allow better health predictions and detection of disease at a much earlier stage (Liu, 2017).

Due to the different capabilities of biological age diagnostics (as explained in section 2), they may be better at predicting certain risk factors and disease states than others. For example, if used in healthcare, GrimAge may be chosen to look at heart disease risk, while those looking for more precise, mechanistic and wider insights may derive greater benefit from a biomarker test or transcriptomic clock. Another example is GlycanAge, already shaping up to be a robust diagnostic for predicting perimenopause in women. However, a truly transformative potential paradigm may be achievable through combining biological age diagnostic devices with one another (as well as other technologies that capture clinical health information) and processing all the data with AI and machine learning continuously in real time. By bringing different data types together, there will be much more information about an individual’s aging process, facilitating the offering of more precise, specific personalised ways to slow it down and to quantify the future effect of the relevant recommendations.

In addition, the use of biological age diagnostics within clinical trials will lead to better interpretation and enhanced standardisation of clinical trial results, personalised application of longevity therapeutics and therefore increased efficacy and accelerated clinical validation. This paradigm has unlimited potential because of the enormous flow of biological data that can be continuously extracted for each individual, to inform protocols for personalised lifestyle change, appropriate drugs and other therapeutic interventions.

The CEO of Deep Longevity, Alex Zhavoronkov, made this point elegantly: “We are standing before the inflection point. If you think about the internet or personal computer or the iPhone or Facebook, these exponential technologies that took over the world – with respect to PCs we are currently in the 60s, if you compare us to the internet, you are probably in the late 80s…” (Longevity blog, 2020).

Instantly commercial to the wellness industry

However, despite the many advancements in the area, the FDA has not approved any direct-to-consumer biological age tests, so the findings of these biological age diagnostics cannot formally diagnose a person with anything as yet. As aging is not a disease then biological age diagnostics are not classified as disease assessment. However, most of these longevity diagnostics make commercial sense even without the tick of an FDA clinical approval.

Recent research by McKinsey & Company revealed that consumers care about their wellness. In their survey out of 7,500 consumers across Brazil, China, Germany, Japan, the United Kingdom and the United States, 79% said wellness is important to them and 42% said it is their top priority (Callaghan, 2021). This point is further supported by the increasing popularity and usage of wearable devices such as fitness bands and watches, that allow people to monitor their health (GlobalNewsWire, 2021).

Worldwide, the health and wellness industry generate 5.3% of global economic output. As of 2019, the global health and wellness market was estimated at over 4.4 trillion US dollars. This figure is expected to increase to over six trillion US dollars by 2025 (Gough, 2021). Such large growth in the market in a relatively short time is supported by increasing consumer interest. Companies at the forefront of creating novel diagnostic tools are likely to benefit within this highly attractive market sector.

Providing diagnostic tools which allow people to monitor the projection of their aging and provide greater accuracy in assessing and monitoring health states has the potential to further boost the health and wellness market. Many of the biological age diagnostic companies are already selling their products to consumers, giving people more convenient, often more accurate and more customised solutions. As consumers become more accustomed to the benefits of a faster and more personal approach, they are unlikely to want to give it up. The “at home diagnostics market”, which refers to tests on human body samples such as blood, urine and saliva, is estimated to grow by more than 30% over the next few years to $6.53 billion by 2025 – 76% of biological age diagnostics are already being sold directly to consumers (Versace, 2021).

76% of biological age diagnostics are available as an at home test

Additionally, many at home longevity diagnostic tests are attractive in terms of revenue due to the need for repeat purchases. For example, the results of a genetic test from 23andme is unlikely to change significantly over time and many users will not retest. In contrast, longevity tests that respond to changes in the environment or to a person’s overall health will tempt the user to repeat test to track the changes in their health and ensure their biological age is managed/reducing.

The chart below shows the distribution of biological age diagnostic companies by type. Most of these tests will require repeat purchases monitor the effectiveness of health and lifestyle changes.

% of companies within the biological age market developing each biological age diagnostic category

Emerging target markets

There are additional emerging B2B markets that could increase sales and provide biological age diagnostics companies with development opportunities. 81% of biological age companies market their products to other businesses (B2B2C). As the longevity market grows there will also likely be an increase in the number of longevity clinics that specialise in precision medicine. Longevity clinics will benefit from developments in biological age diagnostic tools as they will allow them to provide more in-depth assessments, make more accurate predictions and construct more tailored programs for individuals.

Markets for BioAge Diagnostics

61% of biological age diagnostics are already marketing both B2B and B2C

Another emerging market for biological age diagnostics is the insurance market. As developed populations face demographic changes and as life expectancy continues to increase, longevity could prove a risk for insurance companies if they do not have the tools to make accurate predictions about the life expectancy of their customers. If a person was to live for 10 years longer in good health, this would mean they will pay life insurance premiums for longer before any claims are made. This would of course benefit the insurance company. However, the current forecast is painting a less attractive picture of an unhealthily aging population. In this case the health insurance companies may be at risk of having to pay claimants earlier and of paying out for longer than initially estimated. Therefore, insurance companies could greatly benefit from access to longevity diagnostic tools which could provide more accurate predictions about expected lifespans and healthspans over time.

Investment by location

The bulk of the biological age diagnostics market is concentrated in developed countries, with the United States (53%) and the United Kingdom (35%) dominating the market. These countries offer improved investment environment, infrastructure and legal systems for biological age diagnostic companies, as well as clusters of research units and staff that specialise in geroscience.

% of companies by location

Although the biological age diagnostics market in the US is not currently dominated by any single company there still exists, amongst the players in this field, a core of expertise such that a likely co-mingling of ideas and experience has the potential to lead to exponential growth, as evidenced by the similar circumstances for high-tech innovation in Silicon Valley in the 1980s which led to the huge expansion of their industry.

In the US, there is also a concentration of aging clock and biomarker/biomarker panels biological age tests being developed.

US analysis: % of companies in each biological age category

Since 1993, >$1.8 billion has been invested into biological age diagnostics (estimate based on publicly available data). Interestingly, $589 million was raised in 2021 for biological age diagnostics, corresponding to 32% of total funding to date and demonstrating a step-change in investment into these biological age companies.

Total funding by location ($M)

Investment raised in 2021 in biological age  diagnostics
RoundTotal funding ($M)Raised in 2021 ($M)% of total company funding raised in 2021
Post-IPO equity956.537539%
Series A13.51289%
Series A>1.8UnknownUnknown
Series C102.55453%
Series C20013065%
Venture – series unknown2.41.667%

Challenges for the market

Several limitations currently exist that diminish the quality and utility of biological age diagnostic devices and some of these are summarised in the table below.

Limitations to developing biological age diagnostics

Limitations based on complexity of aging
Small sample sizeVast variability seen among humans (ageotypes)
Limited testing of variablesVariability seen within differential tissue aging

Potential for confounding variables


No single agreed upon fundamental theory of aging
Omission of novel aging biomarkers that may interact with one another to influence aging signatures

Lack of information on unified mechanisms that drive systemic aging

Lack of longitudinal studies with repeated measures In the same individual 
Lack of population-level replication and validation In clinical settings for predictive ability
Heterogeneity, access and reproducibility of -omics methodologies and analytical protocols which lead to batch-to-batch variation

Lack of standardisation of protocols, good practice in bioinformatics and statistical methodologies


Substantial investment is necessary to develop an estimator of biological aging that is robust, precise, reliable and sensitive to change. Developing a biological age diagnostic index is perhaps the most critical milestone required to advance the field of longevity research and address the increasing burden of multimorbidity, frailty and disability in an expanding aging population.

A robust biomarker of biological aging would have benefits beyond the early identification of accelerated aging.

  • Firstly, the genetic, environmental and behavioural risk factors associated with accelerated aging could be identified. Then, longitudinal studies could be employed to identify specific time points and conditions at which the trajectories of aging change and relate those to other health‐related triggers, such as the exposure to pollution associated with moving to a different city. This will allow precise tailoring of lifestyle habits, environment and therapeutic protocols to significantly extend healthspan.
  • Secondly, as biological aging is the primary cause of compromised resilience, Biological age diagnostics can help differentiate and discriminate between interventions with potentially serious side effects for some individuals.
  • Thirdly, longitudinally, a marker of aging could be used to track whether interventions with similar target specificity and short-term efficacy on disease prognosis affect the rate of aging differentially. This approach could be used to both refine choices in alternative therapies and develop new medications in order to avoid damage accumulation and long-term side effects.

Ultimately, clinical trials can be designed to specifically target the rate of aging, the underlying causes of multimorbidity, or both as the primary outcomes of interest. With this personalised paradigm in place, the list of interventions is almost limitless, even without considering the many other applications that are currently unknown and will only become evident as the field progresses.

In conclusion, investment at this point in the biological age diagnostics market currently still offers relatively early-stage investment into an exciting, opportunity-rich sector which has potential to influence every aspect of humanity’s health and wellness and to offer transformational returns from expected exponential growth.

Deep Longevity

Company Profile

Deep Longevity is a longevity-focused AI company that will develop and provide the customised predictors of human biological age to the network of Human Longevity Inc (HLI) concierge longevity clinicians. Specialising in the development of deep biomarkers of aging using clinical blood tests, transcriptomic, proteomic, epigenetic, microbiome, behavioural, wearable, imaging and multiple other data types, Deep Longevity will provide a broad range of deep aging clocks to some of the world’s most advanced longevity clinics and physicians and is developing a range of simple consumer applications to track the rate of aging at the individual level. Deep Longevity developed the Longevity as a Service (LaaS)© solution to integrate multiple deep biomarkers of aging to create the deep aging clocks that will provide a universal multifactorial measure of human biological age.

Deep Longevity is a spin-off company of Insilico Medicine, a Hong Kong drug development company created in 2014 by Dr Alex Zhavoronkov and a group of like-minded AI enthusiasts. Insilico Medicine moved on to develop a suite of products to serve the drug development process: PandaOmics for transcriptomic analysis and target discovery, Chemistry42 for molecular design, InClinico for clinical trial optimisation and other tools. A special division of Insilico Medicine was tasked with the research of aging and geroprotectors. They developed a consumer antiaging platform: Young.AI. The division’s main research focus was the aging clock technology: a set of statistical approaches that allows for the quantification of the most basic aging processes. Aging clocks serve as a yardstick to measure the efficiency of geroprotectors, making antiaging drug design possible. In 2020, the biomarker division split into its own company, Deep Longevity.

“Biology of a living organism is a dynamic process with trillions of features changing in time at the atomic, molecular, cellular, tissue, organ, system, organismal and environmental levels. And the best way to study biology is to track all these features in time at the highest resolution possible to understand the causality and the intimate interplay of these features. In 2015 we were the first to realize that one of the most impactful applications of deep neural networks is the prediction of age using massive amounts of longitudinal time series data. Since then, we have developed a very large number of aging clocks using multiple data types, many of them published, patented and tested in a broad range of applications. This experience allows us to tap into a broad range of industries such as healthcare, clinical, consumer, life insurance and even psychology. Our mission is to extend healthy productive longevity and we are developing a new field of longevity medicine, in which the objective is not only to prevent disease, but also to keep the individual as close to the age of optimal performance during the entire life span as possible.” Alex Zhavoronkov, PhD, founder of Deep Longevity.

Deep Longevity has developed several aging clocks for all antiaging needs. It is the only company that measures the pace of aging using nine data dimensions. By bringing different data types together, Deep Longevity can tell much more about individual aging processes. All its aging clocks are developed using deep-learning approaches and its models take in the whole context of a person’s aging trajectory, offer insights into how to slow it down and, most importantly, quantify the future effect of any recommendation. Deep Longevity established a research partnership with one of the most prominent longevity organisations, Human Longevity, Inc. to provide a range of aging clocks to the network of physicians and researchers.

The company is developing a comprehensive decision support system for physicians to enable the development of personalised longevity protocols using the latest advances in longevity biotech. The products it has developed for clinics include comprehensive PDF reports that allow physicians to monitor their patients’ aging intensity, API access to Deep Longevity aging clocks and a soon-to-be-released service “Longevity Coach”, which will be a patient managing software and network platform for longevity-focused professionals. Providing these longevity services to clinics and hospitals aids them in providing tools to patients that will allow them to live longer and healthier.

Deep Longevity is also targeting the insurance sector by providing longevity services that will allow insurance companies to give more value to the customer and increase retention. Furthermore, global societal aging has generally been considered detrimental to a country’s economic health since it reduces the workforce and increases burdens on healthcare systems. A key part of longevity and increased healthspan is the freedom to work; when workers are living healthier, longer lives, an aging workforce can be an opportunity to boost the economic productivity of the employees.

Deep Longevity is also establishing a Longevity Network with Young.AI at its core. Young.AI is the first longevity cloud platform to connect all the stakeholders such as physicians, clinics, hospitals, insurance companies and wellness centres together, making it easier for end users to access longevity services. Young.AI helps people to discover the ways to preserve their health over long periods of time and to experience, achieve and finance superior longevity. Its PDF reports provide detailed information on current biological age and give personalised tangible recommendations.

Originally incubated by Insilico Medicine, Deep Longevity started its independent journey in 2020 after securing a round of funding from the most credible venture capitalists specializing in biotechnology, longevity and artificial intelligence.

Flagship Product Deep Dive


BloodAge is a neural network that estimates a person’s pace of aging from their blood panels. So far, this is the most popular aging clock among Deep Longevity clinical partners. The model that eventually received the name of BloodAge was initially released in a 2016 publication by Putin et al. This publication showed that it was not necessary to employ high-end lab equipment, such as sequencing or genomic array platforms, to determine one’s rate of aging. The blood panels that BloodAge uses are relatively inexpensive (<$200) and can be prepared in any clinic within days compared with >$350 per sample for epigenetic aging clock (dominant aging clock in the field of age prediction) and usually comes in tow with a prohibitively long turnaround time of over 30 days. BloodAge uses only 30 blood biomarkers, shown to be associated with mortality in different populations and can estimate the survival time of hospitalised COVID-19 patients.

BloodAge reports give a personalised longevity plan with just a simple blood test. Deep Longevity tests a minimal of 30 blood biochemistry and cell count markers to calculate the end user’s biological age. People with a BloodAge that is 5 years higher than their chronological age have double the mortality rate, compared with “normal agers”. Conversely, slow pace of aging, as reflected by a biological age 5 years younger than your chronological age, is a protective factor. Deep Longevity’s deep aging clocks can provide information on how each individual marker contributes to the final age prediction in years and identifies the optimal path for lowering BloodAge. The BloodAge report then returns personalised longevity plans to help clients reach their optimal biological age

Product efficacy

BloodAge’s efficacy is verified by a peer-reviewed publication by Putin et al, “Deep Biomarkers of Human Aging: Application of Deep Neural Networks to Biomarker Development”. In this publication, over 50K blood samples from one lab service provider in Moscow (In Vitro Labs) were used to train the neural network to estimate patients’ age. The aging clock was validated in a cohort of over 6K patients and predicted age with a mean absolute error (MAE) of 5.6 years. The medical relevance of BloodAge has been shown in several follow-up publications. In a further 2018 study, BloodAge was tested in three populations: Canadians, Eastern Europeans and Koreans. In all these cohorts, high predicted age was recognised as a significant mortality risk factor. Other evidence of BloodAge’s clinical relevance was presented in a 2019 study, in which smokers displayed higher predicted age compared with non-smokers.

Product Developement

All Deep Longevity aging clocks are developed using deep-learning approaches. This method allows Deep Longevity to see age-related trends that escape the attention of other, less sophisticated methods. One of the key advantages of neural networks is the level of personalisation. The Deep Longevity model takes in the whole context of a person’s aging trajectory, offers the ways to slow it down and, most importantly, quantifies the future effect of any recommendation. Patients can easily see how much biologically younger they will become if they lower their cholesterol level, reduce fast food intake, or take up dance lessons.

Currently, Deep Longevity holds a patent for the application of deep learning to the aging clock problem (US10325673B2). To date, it has released an mHealth app, “Young.AI”, launched several online educational courses and is partnered with longevity clinics in the US and EU. Patents for more specific microbiome and psychological aging clock are pending. In the US, Deep Longevity has filed for “NMN Clock” and “Mind Age” trademarks.
In 2022, Deep Longevity is planning to release several new products, including a mental health application, an EHR system for longevity medicine and new clinical pipelines based on new data types, such as epigenetics. It is also planning to expand its presence in Asia.

Target market

Deep Longevity’s services are targeted to all people to discover how much they can do to preserve their health over long periods of time, to experience, achieve and finance superior longevity. An increased healthspan is the freedom to work; when workers are living healthier, longer lives, an aging workforce can be an opportunity to boost economic productivity from employees.

Channels to market

Deep Longevity plans to market through clinics and diagnostics centres. Deep Longevity partner clinics will be able to provide patients with the tools they need to live longer and healthier lives. Deep Longevity already has several package options that work for any size clinic. Deep Longevity already has a longevity medical collaboration with LifeHub and Life Clinic, the largest functional medicine-based medical wellness and medical clinic facilities in Hong Kong. By taking a science-based approach to health optimisation and longevity, they can address physiological imbalances that could stem from nutritional deficiencies or other factors like hormonal imbalances or the effects of environmental pollutants.

Deep Longevity also plans to market to insurance companies; including longevity services in an insurance package will allow the insurance company to give more value to the customer and increase retention.

Success Factors

Team and Reputation

Deep Longevity’s team of scientists and AI researchers invented the industry’s first-ever aging clocks using deep neural networks and identified aging-related biomarkers for diseases like diabetes, sarcopenia and non-alcoholic steatohepatitis (NASH). After it exited stealth and announced its partnership with Human Longevity Inc (HLI), Deep Longevity was acquired for US$3.79 million by Hong Kong-based Endurance RP, an investment firm specialising in healthcare and late-stage life sciences. This announcement meant that Deep Longevity firmly positioned itself as the gold standard in multi-modal aging clocks. As well having as the largest number of clocks, the company has developed a wealth of experience in building them and applying them to clinical practice. The combined company is set to refocus on longevity and intends to build the first longevity conglomerate working with insurance companies, pharmaceutical companies, healthcare providers and clinics (the proof of concept was previously completed with HLI). After the acquisition in late 2020, Alex Zhavoronkov stepped down as the CEO of Deep Longevity to assume a new role of a Chief Longevity Officer (CLO) at the company. Jim Mellon is now the Chairman of the combined company, and Dr Wei-Wu He has joined as the Director. These changes mean that Deep Longevity is now run by some of the most credible experts in aging and longevity, and together these individuals are well set to create a dedicated and focused longevity company.

Intellectual Property

All Deep Longevity aging clocks are developed using deep-learning approaches and Deep Longevity holds a patent for the application of deep learning to the aging clock problem (US10325673B2). Patents for specific clocks related to the microbiome and psychological aging have been submitted and are pending approval.
Using its IP, Deep Longevity has a range of products such as, health applications, an EHR system for longevity medicine, and new clinical pipelines based on new data types that can penetrate different markets from clinics to insurance companies.


Led by ETP Ventures and Human Longevity and Performance Impact Venture Fund (“HLPIVF”), Deep Longevity’s series A investment round includes participation from BOLD Capital Partners, Longevity Vision Fund, Oculus co-founder and former chief software architect Michael Antonov through Formic Ventures and LongeVC. Deep Longevity also claims celebrity investors in AI and prominent US biotech investors among its undisclosed investors.

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Company Profile

AgeRate is a direct-to-consumer longevity which aims to unlock the secret to living a longer and healthier life by redefining AgeRate. Founded in 2018, AgeRate has spent the last 3 years creating and developing a novel at-home blood test and mobile app that allows users to discover how well they are aging and what actions they can take to improve. The company has leveraged the expertise of world leading researchers to develop a custom epigenetic analysis and proprietary algorithms to reveal a user’s biological age and up to 20 additional health and longevity insights.

AgeRate is currently focused on launching its flagship product, an at-home blood test, in addition to growing its user-base with an objective of 10,000 users by 2023. The goal is to begin to quantify true improvements in aging with these users. The company is on a mission is to be a global leader in longevity research within the next 7 years. Using de-identified data from consenting users, AgeRate is striving to amass a large longevity dataset, allowing for new breakthrough discoveries into how we can slow or reverse aging.

In the future, AgeRate’s research team will be developing new algorithms to unlock deeper insights into their customers’ health and longevity using epigenetic information. In addition, the team is investing in new testing methods and expanding beyond epigenetics, this will include proteomics and mitochondrial health.

Flagship Product Deep Dive

AgeRate’s at-home blood test for longevity health and insights

With its at-home blood test, AgeRate’s flagship service allows users to monitor their rate of aging and discover how they can alter their lifestyle habits to age better. The test reveals a user’s true biological age and up to 20 health and lifestyle indicators that help users not only know how well they are aging but understand areas of improvement. To achieve this, the service uses a custom and cost-effective epigenetic analysis combined with proprietary algorithms. The results are provided through a mobile app with a built-in longevity coach that provides users with tailored lifestyle challenges to help them improve, forming a full package longevity service.

AgeRate uses their DNA methylation analysis and proprietary algorithms to reveal meaningful insights into health and longevity. The algorithm used to determine biological age using DNA methylation data is commonly referred to as an epigenetic clock. Epigenetic clocks are valuable in the sense that they allow us to quantify how well someone is aging without waiting for them to die. The performance of an epigenetic clock is validated by comparing the biological age and the chronological age. The best epigenetic clocks have a strong correlation between biological and chronological age.

Product Development

AgeRate’s epigenetic clock has been validated in a data set of ~13,000 samples and demonstrates high performances. The difference between biological age and chronological age is strongly associated with disease and mortality for each epigenetic clock. AgeRate’s Chief Science Officer has contributed to large scale research studies on biological age and its relation to health outcomes such frailty and the risk of incident atrial fibrillation.

Target market

Although aging is a condition that affects everyone, AgeRate defines its target audience as health-conscious consumers looking to take a proactive approach to their health and longevity. Healthcare has succeeded in keeping people alive longer, but not healthier for longer. The approach has been reactive, treating the signs of and conditions of aging as they manifest. The growing number of people spent in poor health in their later years is putting strain on our healthcare system. A proactive approach towards aging is needed to solve this, which allows consumers to quantify the early signs of aging and target the root causes before age-related diseases begin to manifest.

Channels to market

AgeRate will initially offer its technology directly to consumers, providing a direct service to people to analyse their health whilst at home. In the future, they plan to outsource the service to clinicians through business-to-business channels, allowing healthcare providers who specialise in longevity to help their patients benefit from AgeRate’s service, widening their outreach. In addition to this, the company plans to partner with intervention providers, such as supplement companies, to conduct research and support in validating the effectiveness of their products in slowing aging.

Success Factors

Team and Reputation

AgeRate is currently based in Canada and is formed of a small diverse team of scientists and engineers who have a shared vision of a world where our physical capabilities are not defined by our chronological age. The founding team met at McMaster University in 2018 and all have an educational background in life sciences. AgeRate has had over 5000 users sign up to be one of the first to try their service once they launch. In addition, the company has formed strategic partnerships with 10 health companies interested in using the service to validate their antiaging interventions and provide the option to their customers to get tested.

AgeRate’s CEO, Cole Kirschner, has experience managing teams of 20+ sales professionals, winning multiple sales & leadership awards.

AgeRate’s Chief Science Officer, Dr Guillaume Paré, is University Scholar and Professor of Pathology and Molecular Medicine at McMaster University and Director of the Genetic and Molecular Epidemiology Laboratory. A world-leading physician-scientist in the area of genetic and molecular epidemiology, his research has achieved critical advances in our knowledge of the genetic causes of heart attack and stroke and he is establishing new ways to identify high-risk patients, modifiable risk factors and preventive therapies.

The CTO, Kevin Peters, was a management consultant at Accenture, leading teams of 40+ engineers in the Enterprise Transformation and Emerging&Growth departments. In addition, he owned and operated a restaurant of 10+ employees.

Intellectual Property

AgeRate’s advantage lies in the use of epigenetics to reveal long-term health and longevity insights in addition to biological age. Unlike most at-home health testing companies which use traditional laboratory tests that provide a snapshot of a user’s health, AgeRate uses epigenetics to reveal the long-term impact that lifestyle has had on health and longevity. This allows AgeRate to provide users with accurate insights into how well they are aging and provide a comprehensive picture of how they can improve.


AgeRate has raised $350k in pre-seed funding from world leading start-up programs such as Founders Factory, Start-Up Health and leAD Sports & Health Tech Partners. AgeRate is currently raising a Seed fundraising round to launch their flagship product and expand into other areas of testing. The primary focus will be on developing robust biomarkers of aging and understanding how certain lifestyle interventions can

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Company Profile

GlycanAge, founded by Professor Gordan Lauc, aims to calculate a person’s biological age using glycan analysis. Glycans are important, but often overlooked building blocks of life; the surfaces of all cells are covered with a thick layer of glycans and there is no cell on this planet that can survive without this thick coating. The same is true for most proteins, including the infamous S-glycoprotein of the SARS-CoV-2 virus. SARS-CoV-2 depends on its S-glycoprotein for virus entry and cell fusion, but without it, is merely a non-functional backbone that cannot perform its function. Clearly glycans are important to all life, and it has been nearly a decade since a comprehensive policy document, endorsed by the US National Academies, concluded that “glycans are directly involved in the pathophysiology of every major disease”.

Glycans are linked to the aging of our immune system and they can reveal how we are changing on the inside. Glycans attached to immunoglobulins are one of the major features that differentiate immunoglobulins from young and old people. This difference is partly defined only by our chronological age, but even more with our biological age determined by our lifestyle choices. GlycanAge is the only company that analyses the level of low-grade chronic inflammation in order to calculate a person’s biological age, by analysing glycans on the main protein of the immune system – immunoglobulin G.

Figure 1. SARS-CoV-2 virus is reliant on its S-glycoprotein for function.

Changes in glycan composition can regulate inflammatory responses, aid viral immune escape, regulate apoptosis and promote cancer cell metastasis. Glycans are the ultimate layer of complexity of life and they integrate genetic, epigenetic and environmental information into physiological processes that are vital in all aspects of health and disease. They are the first messengers of any change in the homeostasis of the body. By unlocking the potential of the human glycome, GlycanAge is enabling their customers (both individuals and clinical partners) to better navigate healthy aging and disease prevention.

GlycanAge was incorporated in 2016 as a commercial spinout of Genos, the largest research institute and pioneer of high-throughput glycomics and precision medicine. Genos is the world’s leading laboratory in high-throughput glycan analytics and, in total, has analysed over 150,000 individual glycomes, with a current pace of over 30,000 analyses per year. Led by Professor Gordan Lauc, Genos was the first to perform large scale studies of the human plasma glycome (in 2009) and human IgG glycome (in 2011), which were the basis for the subsequent first genome-wide association studies of the human plasma and IgG glycomes. Genos received 23M euro of non-dilutive grant funding, of which 15M was used for IgG research over the span of 10 years, in which over 100+ scientific papers were published.

The research team behind Genos is combining glycomic data with extensive genetic, epigenetic, biochemical and physiological data in a systems biology approach. GlycanAge was established as a commercial spinout of Genos with the intention to translate research discoveries into products for the healthcare market to improve quality of life for all.

Gordan Lauc: “I have worked in the field of glycan biomarkers for 30 years and finally we are at the stage where we can offer reliable tests for the healthcare market. GlycanAge is the first of the series of glycan markers, it can be used not only by clinicians but also end customers as well. While medicine is still focusing on treating disease, GlycanAge is helping people to navigate their own healthy ageing and prevent diseases”

The majority of chronic diseases are non-communicable diseases that are formed by often years of exposure to inflammation. The main protein of our immune system is immunoglobulin G and different glycans attached can change how it functions, which can be analysed using IgG glycomics. As we age, glycans become increasingly pro-inflammatory, and an increase in low-grade chronic inflammation and progressive inflammaging is one of the key hallmarks of aging that has not been researched enough due to lack of adequate biomarkers. In a research study on how different aging clocks are associated with health outcomes, IgG Glycomics was one of the best at predicting future hospitalisation due to the broadest range of diseases including influenza, pneumonia, circulatory diseases, diabetes and metabolic diseases, outperforming 11 other molecular aging clocks.

GlycanAge has so far managed to experience 300% year-on-year growth with only one product, GlycanAge. This was achieved with almost no marketing spend, one salesperson and minimal investment from the CEO alongside a few strategic angels, including Tim Marbach from Asia Venture Group and Maud Pasturaud from the Atomico Angel Group.

GlycanAge is also expanding in the perimenopausal and menopausal health markets with a study that showed that changes in estrogen levels change the composition of the glycans on IgG. Upon FDA approval, GlycanAge will release MenoAge, one of the first tests that will serve as the most reliable indicator of average estrogen levels over the past three months. Further products also include DiabRisk, which will predict the risk of developing type II diabetes and is also still awaiting regulatory approval.

With access to new markets, the company will position itself as a global leader in prognostic and diagnostic testing with a potential exit through an IPO or by becoming an acquisition target for large diagnostic companies.

The company has incredible room for growth with little or no competition. The company is seeking outside capital to cover regulatory costs and serve as fuel for marketing efforts which will bring predictable and scalable revenue increase in the next 2 years. GlycanAge will open a $5M seed round in 2022, allowing investors to unlock new market growth opportunities and invest in menopause R&D, building a world-class customer-obsessed sales and marketing team to deliver exponential growth and dominate the menopause space. GlycanAge are raising US$5M to setup the first of its kind high throughput glycomic lab in the US, scale up operational and sales capacities, invest further into menopause R&D and launch MenoAge.

Flagship Product Deep Dive

GlycanAge background

All autoimmune and many chronic diseases are often preceded by years of gradual inflammation. That inflammation slowly accumulates small damages that become disease-causative. By monitoring an individual’s internal state of health, GlycanAge hopes to ensure long-term health and lower the risk of encountering non-communicable diseases. Type II diabetes, atherosclerosis and rheumatoid arthritis are all diseases that can often be avoided by ensuring a proper lifestyle that promotes health and rejuvenation rather than inflammation.

As we age, glycans change from a more anti-inflammatory profile to a pro-inflammatory profile. GlycanAge analyses the composition of glycans or sugar molecules found on IgG in order to determine a person’s biological age based on their inflammatory environment. Epigenetic changes in the DNA have shown little correlation to incidence of diseases, and whilst traditional blood markers may be useful for diagnosing certain specific diseases, only GlycanAge focuses on analysing the state of the immune system that can lead to chronic and non-communicable disease.

Since GlyanAge analyses the glycans that can change how IgG functions in the body – either inflammatory or anti-inflammatory – increase in GlycanAge directly correlates with an increase in systemic inflammation, which is the underlying root cause of most chronic and autoimmune disorders and thereby serves as a prognostic tool.

GlycanAge reports 24 directly measured glycans using 30/50 different glycan structures. To simplify this for the user, Glycan Age has developed three key indexes or scores, G0, G2 and GS, which are reported to the user when they complete the GlycanAge test.

  • G0 are glycans without galactose, which are the most proinflammatory.
  • G2 are glycans with two galactoses, which are suppressing inflammation.
  • GS are glycans with sialic acid, which also suppress inflammation

After grouping into these categories, GlycanAge ultimately simplifies into one simple number, biological age, which reflects a person’s inflammatory status. This number correlates highly with chronological age but not perfectly. The difference is determined by the person’s lifestyle.

GlycanAge has analysed IgG glycan profiles of more than 150 000 people to build this model and compare an individual with this model to determine their GlycanAge. A higher biological GlycanAge indicates more inflammatory glycans are present which can lead to disease. The direct link to inflammaging is why GlycanAge has the greatest value for predicting the risk of developing autoimmune and chronic diseases in the future.

GlycanAge results correlate with other, traditional biomarkers of an unhealthy lifestyle such as: glucose levels, HbA1c, triglycerides, cholesterol, LDL, BMI and others. GlycanAge can extract structures that may also be specific to certain diseases in the future. Glycans also strongly respond to lifestyle interventions that are positively linked to the biology of aging including diet, weight loss, physical activity and changes in the microbiome.

Following a GlycanAge test, consumers have the option to have a consultation with a clinician who goes through the user’s health profile and makes a conclusion on what may be the main reason for the inflammatory glycans. Based on this advice, the user can make lifestyle changes and assess whether they have an impact on their GlycanAge by taking another test.

Unlike other tests that focus mostly once the disease is already evident, GlycanAge focuses on prevention and timely action before the illness manifests. This enables users to use GlycanAge as a personal navigator that can identify interventions that are most beneficial, but at the same time as a motivation tool that provides feedback in real-time.

The company possesses IP on the product, with no competitor capable of emulating its technology due to lack of infrastructure and human capital. GlycanAge is currently marketed as a non-diagnostic test. However, together with Genos which leads the Human Glycome project, the company is collaborating with instrument providers to develop IVD certified instruments and reagents that will enable IVD certification of glycan profiling. Upon certification, due to specific glycans relating to certain health outcomes, GlycanAge believes that glycan reports have the potential to be used as clinical diagnostics for diseases such as cardiovascular risk.

Proof of efficacy

Increased GlycanAge is akin to a more inflammatory environment and accelerating GlycanAge predicts chronic disease incidence and hospitalisation better than any clinical biomarkers and other clocks, as demonstrated by an independent, peer reviewed study of 11 different molecular aging clocks. While there are other biological age testing kits on the market – there is none that analyses the main actor of the immune system. IgG is the main culprit behind chronic-low grade inflammation that leads to autoimmune and chronic diseases, proving the clinical relevance to long term health outcomes.

Product development: MenoAge and DiabRisk

GlycanAge is continually improving and updating the value of its offering through the continual research efforts of its parent company, Genos, which is the main driving force in the advances of knowledge of human glycome in the past 10 years. With each study, more information is gained about the correlation of specific glycan structures and the development of specific diseases.

Interestingly, in women, a sharp change from a more anti-inflammatory glycan profile to a more inflammatory one occurs around the age of perimenopause. Subsequent studies have shown that this phenomenon is due to estrogen.
High levels of estrogen correlate with “younger” glycans, which have an anti-inflammatory profile and consequently lower glycan biological age. In females, the change from a young to old IgG glycan profile is particularly pronounced in the time preceding the average age of menopause. This observation led to the theory that estrogen may be regulating IgG glycosylation, which was proven in a study where gonadal hormones were deprived and estrogen provided transdermally. This finding led to GlycanAge building a new product, MenoAge, that can serve as a prognostic test for diagnosing perimenopause and menopause.

“After analysing the IgG N-glycome in over 100,000 people we developed the GlycanAge test of biological age that is helping people to manage their health better. Recently our focus has shifted to perimenopause since our initial data suggested that women experience an increased pace of biological ageing in this period. In this current large study, on a unique cohort of twins, performed in a collaboration with Professor Tim Spector and his team, we managed to confirm this observation,” says Gordon Lauc.

The perimenopausal and menopausal market is underserved, and there is currently no test that can give complete hormonal insight. Blood tests are unreliable as long-term indicators of hormone levels since hormones fluctuate daily. MenoAge is a test that will serve as the most reliable indicator of average estrogen levels over the past three months and will open the peri/menopausal market. The test itself is pending FDA approval.

DiabRisk will be the company’s third product and will analyse total plasma glycome to predict risk of type II diabetes. As diabetes will soon become the most prevalent modern disease, GlycanAge aims to expand into the prognostic as well as diagnostic market.

Target market

As well as targeting older consumers that wish to modify their current GylcanAge, the company also targets young individuals, though before mid-thirties it is sufficient to have one test every few years. Subsequently, testing frequency should increase to at least once per year, while women approaching perimenopause should get tested every few months.

Individual clients and healthcare providers are GlycanAge’s target customers. The company’s biggest client base to date is providers of antiaging interventions since the GlycanAge test can be used to objectively confirm success of their interventions.

For the B2C market, the biggest opportunity lies in women aged 40/55 who are undergoing hormonal changes, athletes, people with existing health issues as well as those interested in the prevention of illness. The global menopause market is forecasted to reach 22.7 billion by 2023 and preventative and personalised medicine is to hit 575 billion. GlycanAge encompasses all of these markets.

Channels to market

For the B2B market, GlycanAge’s biggest opportunity lies in the wellness industry, from longevity clinics to functional medicine practitioners. It is partnering up with longevity clinics as well as influential figures in the longevity space to increase its exposure in the wellness markets.

However, the future route to expansion will be aimed more towards the B2C market as it will provide a more scalable source of revenue as well as an affiliate program. The affiliates, as well as partners, receive a commission from each test, so GlycanAge aims to increase its marketing spend via social media channels to grow its audience and market to consumers directly thus increasing the profit margin.

With access to new markets, the company will position itself as a global leader in prognostic and diagnostic testing with a potential exit through an IPO or by becoming an acquisition target for large diagnostic companies.

Success Factors

Team and Reputation

The scientific aspect of the GlycanAge is led by prof. Gordon Lauc, a pioneer in high-throughput glycomics while the commercial aspect is led by Nikolina Lauc, an entrepreneur with over 10 years of experience.

As GlycanAge is a spin-out of Genos, the scientific team boasts 10 postdoctoral scientists and many Ph.D. students which have contributed greatly to the field of glycomics. Through participation in 6 FP7, 5 H2020 and one IMI2 grants (direct funding of over 8M EUR) and some other national and international grants (direct funding approx. 5M EUR) Genos has accumulated significant material and human resources that enabled successful integration of glycomics into multiple genetic, epidemiological and clinical studies.

The team have already made relationships with multiple longevity and menopause clinics, that use GlycanAge in assessments of their patients.
GlycanAge is structured and positioned in such a way to use the intellectual capital to create products that will be the spear-tip of new advances in personalised medicine.

Intellectual Property

As a spinout of Genos, GlycanAge continues to gain information about the correlation of specific glycan structures and the development of specific diseases. This is invaluable IP that can be used to generate further glycan tests for specific conditions/diseases that are related to inflammaging.

The company possesses IP on its product GlycanAge with no competitor capable of emulating its technology due to lack of infrastructure and human capital. This product alone managed to experience 300% year-on-year growth. GlycanAge is currently marketed as a non-diagnostic test, however, together with Genos which leads the Human Glycome project, the company is collaborating with instrument providers to develop IVD certified instruments and reagents that will enable IVD certification of glycan profiling. Upon certification, due to specific glycans relating to certain health outcomes, GlycanAge believes that glycan reports have the potential to be used as clinical diagnostics for diseases such as cardiovascular risk.

GlycanAge is also expanding in the perimenopausal and menopausal health markets with a study that showed that changes in estrogen levels change the composition of the glycans on IgG. Upon FDA approval, GlycanAge will release MenoAge, one of the first tests that will serve as the most reliable indicator of average estrogen levels over the past three months. Further products also include DiabRisk, which will predict the risk of developing type II diabetes and is also still awaiting regulatory approval.


GlycanAge has had pre-seed investment from the CEO alongside a few strategic angels, including Tim Marbach from Asia Venture Group and Maud Pasturaud from the Atomico Angel Group.

GlycanAge will open a US$5M seed round in 2022, allowing investors to unlock new market growth opportunities. The capita raised will be used to invest in menopause R&D and to build a world-class customer-obsessed sales and marketing team to deliver exponential growth and dominate the menopause space.

GlycanAge are raising US$5M to setup the first of its kind high throughput glycomic lab in the USA, scale up operational and sales capacities, invest further into menopause R&D and launch MenoAge.

With access to new markets, the company will position itself as a global leader in prognostic and diagnostic testing with a potential exit through an IPO or by becoming an acquisition target for large diagnostic companies.

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Company Profile

InsideTracker was founded in 2009 by leading scientists in aging, genetics and biometric data from MIT, Tufts and Harvard. Its mission is two-fold: first, to offer its users a clearer picture (than they’ve ever had before) of what’s going on inside their bodies and then to provide them with concrete, personalised, trackable action plans for living a longer, healthier life.

“InsideTracker is a truly personalized nutrition and performance system. Our mission is to help people add years to their lives and life to their years by optimizing their bodies from the inside out”.

The idea for InsideTracker can be traced back to a childhood experience of its founder, Gil Blander. When Gil was a young boy, he lost someone close to him, his aunt. Gil was only 12 at the time, but her passing left a permanent mark. “I never thought about death before. I thought everybody lived forever. I was so sad about my aunt and so sad for myself, too. Why couldn’t life go on forever? Why did it have to end?” This event was the spark that led to Gil’s lifelong passion and pursuit of the answer to how to delay the onset of age-related diseases and increase lifespan. “I see the human body as a machine,” says Dr Blander. “Fine-tuning it will not only allow it to perform better now, but also to perform longer into the future.”

This early passion for longevity has driven Dr Blander throughout his career and ultimately led to the idea that spawned InsideTracker. During his time at computational systems biology company Genstruct, he conducted extensive research into drugs that mimic the effects of caloric restriction and concluded that nutrition had much greater potential for delivering results than drugs. Through his work at the Weizmann Institute of Science and MIT, Dr Blander is now recognised worldwide for his research in the biology of aging and translating research discoveries into new ways of detecting and preventing age-related conditions.

Flagship Product Deep Dive


InsideTracker’s mission is to help people realise their potential for long, healthy, productive lives by optimising their bodies from the inside out. InsideTracker’s proprietary algorithm analyses its users’ biomarker and physiomarker data to provide a clear picture of what’s going on inside them. Based on this analysis, InsideTracker offers its users ultra-personalised, science-based recommendations for positive changes to their nutrition, supplementation, exercise and lifestyle, along with a plan of action to track their progress toward their goals.

InsideTracker’s team of scientists, bioinformaticians and technologists built a powerful engine called SegterraR that mines and visualises data. Leveraging the power of complex computer programming, SegterraR can mine vast amounts of biometric and scientific data, preparing it for use by InsideTracker’s automated algorithmic engine, SegterraX.

SegterraX, is a patent-pending, automated algorithmic engine that runs the InsideTracker platform. It generates ultra-personalised interventions for each end user by integrating the full range of user inputs (biochemistry, demographics, profile, habits, genetics) with rules developed by InsideTracker scientists based on their analysis of over 2,500 peer-reviewed scientific publications, a demographic database of over 180,000 healthy individuals, a database of over 8,000 unique foods and the 200+ combined years of scientific experience across its team and scientific advisory board. SegterraX is continuously refined, drawing on cutting-edge research and technological advances.

To insert their biomarkers for analysis, users can either order their blood and DNA tests from the company’s web site or upload their results from recent tests by a third-party. They then fill out an online questionnaire to provide additional information relating to their nutrition, habits and lifestyle. All this data, including up to 45 key blood biomarkers, is then analysed using SegterraX and results in a detailed action plan delivered via the web site and a mobile app. The mobile app also allows integration with real time physiomarker data from the end users fitness tracker, such as a Fitbit or Garmin. Combined with the blood and DNA biomarker data, this gives end users an exponential level of precision and customisation to their action plan.

“When it comes to understanding what is going on inside your body, blood + DNA is a powerful one-two punch. Your blood data is dynamic, changing over time with various inputs – from your environmental exposure and dietary choices to your sleep and stress levels. Your DNA is hard-coded into each cell and may provide a high-level picture of your body’s genetic potential – that is, how you might be predisposed to respond to certain inputs, like food, sleep, exercise and stress. Taken together, your blood and DNA can help steer personalised guidance generated by the InsideTracker platform to ensure it is as accurate as possible and giving you an advantage in reaching your wellness goals.”

The product contains a lot of personal data, which InsideTracker takes seriously. The company is HIPAA compliant, and even with 10 years in business with the handling of thousands of DNA and blood tests, the company’s reputation remains untarnished. “Security and vigilance do not stop. We have implemented best practices for security and are continuously improving them to meet the highest industry standards.”

Efficacy of product

A 2018 paper in Nature’s Scientific Reports journal demonstrated the validity of InsideTracker’s approach to using blood biomarkers to develop customised intervention strategies.

The population (1032 across a broad age range) was apparently healthy individuals that used Inside tracker and received at least two blood tests. Each individual was presented with a variety of interventions relating to food, supplements and lifestyle, that were generated based on the end users baseline biomarkers and using the broad scientific literature base generated through SegterraX.

To understand whether there were improvements in biomarkers, the longitudinal changes for individuals whose baselines values were out of the clinically acceptable range were assessed. There were notable improvements in most of the biomarkers analysed. However, as this was an observational analysis, one cannot establish the causality of platform use on biomarker changes or resolve which component of the intervention may have been related to the results. Overall, the study used a rich longitudinal dataset of clinical biomarkers to uncover novel biological relationship whilst validating known ones. It also demonstrated that InsideTracker is associated with improvements in health parameters.

Product Development

While he was sure that InsideTracker was realising its mission and people to live a longer, healthier life, Dr Blander, a self-described “longevity freak”, wanted to be sure. Working on this challenge with David Sinclair and Leonard Guarente led to the development in 2015 of InnerAge – a measure of biological age. Having collected a wealth of data from its predominantly healthy users, the company released InnerAge 2.0, which now incorporates 14 biomarkers for women and 18 biomarkers for men that are closely correlated with age.

InnerAge 2.0 is a new and improved ultra-personalised nutrition system focused on optimising the end users healthspan. InnerAge 2.0’s advanced data-driven model first calculates biological age, then provides an action plan of science-backed recommendations with the goal of improving the quantity and quality of the years ahead of you.

Target market

People want to take charge of their health and wellness. Biased, misleading, impersonal information creates doubt and confusion obscuring their way forward. As a result, they lack three important things to help them get a clear picture of what their bodies look like on the inside, a clear measure of whether their diet and exercise choices are helping or hurting and clear idea of who or what to trust when it comes to health, wellness and performance guidance.

This is exactly what InsideTracker has been designed to solve. InsideTracker is a platform for people of any age to optimise their health goals.

Success Factors

Team and Reputation

Gil Blander, founder and Chief Scientific Officer, is internationally recognised for his research in the basic biology of aging and translating research discoveries into new ways of detecting and preventing age-related conditions. He leads the team of biology, nutrition and exercise physiology experts and computer scientists at InsideTracker and has been featured in CNN Money, The New York Times, Forbes, Financial Times, The Boston Globe.

Ram Mester, Executive Chairman, has over 25 years of experience building tech companies and leading executive teams. As CEO, he led Guardium from start-up to a dominance position in the database security market and its acquisition by IBM. As co-founder and CEO at Telekol, he led its growth from start-up to top supplier of enterprise grade intelligent communications systems and its acquisition by Nokia. Additional past and present engagements include executive management, advisory and board roles with technology start-ups in security, cloud computing, database software and SaaS based e-commerce and behavioural analytics.

InsideTracker’s scientific advisory board has over 200+ years of combined experience and includes David Sinclair, Lenny Guarente, Jeffery B. Blumberg and David Katz.

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Company Profile

Jinfiniti was founded in 2018 by renowned scientist Dr Jin-Xiong She who revolutionised the fields of diabetes and cancer therapy by applying a strategy of predictive, preventative and personalised medicine. Jinfiniti is laser focused on solving “the aging problem” by helping individuals make informed health decisions by tracking changes in an array of the most impactful and modifiable biomarkers of aging within their body. Jinfiniti understands that every individual ages differently and believes that by taking a holistic and quantitative approach, each individual can make sense of the unique factors accelerating or slowing down their unique aging process. It is Jinfiniti’s mission to empower individuals to use their tests within a “test-intervention-retest” paradigm in order to construct a personalised longevity strategy that transforms their healthspan. This focus on improving healthspan places Jinfiniti outside of the disease diagnosis domain, a well established and competitive environment and firmly in the health and wellness space where their company can truly thrive.

“I have worked in the disease therapeutics space for several decades and it is well occupied by several remarkable companies developing impactful technologies. Jinfiniti is not in the business of addressing disease, we are in the business of promoting wellness and preventing disease. We seek to marry rigorous science with actionable insights to empower individuals to make a real impact on their health and quality of life.”

The idea for Jinfiniti had been incubating in Dr She’s mind for several years and has an origin story that very much aligns with the company’s philosophy today. Dr She has had a long history of interest in the nutraceuticals space, especially the prospects of improving the efficacy of nutraceuticals by bringing evidence-based science to a realm that is currently poorly regulated and chock-full of “snake-oil” salesmen. In 2010, Dr She challenged his nephew, who had a supplement company in China, to break out of this paradigm and prove his supplements work by testing what people need and measuring how his supplements were working for customers. A strategy that even today remains largely ignored by supplement companies. Dr She decided to apply his extensive scientific expertise to revolutionise the way the supplement field operated by making this rigorous strategy “the norm” within the field. Dr She discovered that the longevity field was the ideal space to implement these principles when he met a functional medicine doctor that requested he design a test that could be used to validate the efficacy of longevity interventions by measuring how patients were aging on a cellular/molecular level. It was this serendipitous meeting that led to the birth of Jinfiniti and their powerful health paradigm: “Age related diseases have a common set of root causes that, before manifesting as disease, result in sub-optimal health that can be measured, tracked and remedied”. With this single paradigm Jinfiniti addresses both the core of preventative medicine and the ultimate goal of geroscience.

Nearly 20 years ago, Dr She started the first research program that united bioinformatics and scientific benchwork under the same roof at the Medical College of Georgia of Augusta University. Since then Dr She has been on the leading-edge of applying multi-factorial analysis and expertise in integrating different data types to understand and therapeutically address complex disease processes. There is no process that is as multi-faceted and deserving of a holistic approach than the process of aging, the rate of which is influenced by nearly everything we are exposed to in our environment (collectively called our “exposome”). Jinfiniti is applying this quantitative approach to make sense of how each individual is aging (within the context of their unique exposome) by developing novel technologies and assays that help their customers measure classically immeasurable biomarkers of aging and use this information to help them make the right decisions to preserve their health. Highlighting the unique value that Jinfiniti provides within the BioAge diagnostic space is the fact that they are the first – and only – provider of NAD and cellular senescence testing for consumers. Two of the most powerful biomarkers (and drivers) of aging, both of which can be influenced by various supplements, lifestyle changes and drugs.

“Aging is an extremely complex process. Hence within the longevity field, no single company alone is going to succeed at solving the aging problem.” Dr She is steadfast in his intent for Jinfiniti not to compete with any other company. This is exemplified by the astounding number of collaborations Jinfiniti has established within the field. “I feel a sense of urgency to “solve the aging problem”, both for selfish and altruistic reasons, in which collaboration is going to be key. There is plenty of space for innovation within the longevity field and Jinfiniti will continue developing and producing things that are unique. I believe rapid progress within the longevity field will be made through mutually beneficial collaboration and not cut-throat competition. This is one of the reasons why we don’t operate in secrecy or have extensive IP protection. This being said, if anyone wants to compete – I welcome it”.

Flagship Product Deep Dive


Jinfiniti’s mission is to help individuals make wise healthcare decisions by measuring the molecular and cellular “health biomarkers” that are the root cause of aging and using this information to engage with the world in a way that maximises their healthy longevity. This is one of the key characteristics of Jinfiniti that makes them stand out from other companies developing BioAge diagnostic devices. Jinfiniti is not focused on addressing disease, they are focused on promoting health.

“Life isn’t about your lifespan, it’s about your healthspan. It’s about being able to outrun your grandkids. It’s about playing tennis until you’re 94 or beyond. It’s about feeling alive for as long as you live.”

This is no minor distinction as their philosophy influences every aspect of how Jinfiniti operates including: the types of biomarkers they measure, the metrics and thresholds they use to define “healthy” vs “unhealthy”, product user interface, as well as the strategic collaborations they establish. Through their every action Jinfiniti exemplifies the idea that “how we frame our strategy for addressing healthy longevity, our mindset, is just as important as the product or solution itself”.

Jinfiniti’s assays are rooted in a data driven approach as Dr She comes from a background of collecting and analysing data from 20-30 year long studies and has unprecedented access to biobank data which includes biochemistry, demographics, genetic information, etc. Due to the dozens of collaborations and extensive experience Dr She has garnered through his decades of academic experience working with biobank data, Jinfiniti is constantly iterating and evolving their platform technology and products.

Jinfiniti uses AI technology to analyse biomarker data in its own database as well as data available in the public domain to define different reference ranges: optimum, suboptimal, moderately and severely deficient. The “optimum range” is derived from data for a youthful population. This is a key characteristic of Jinfiniti’s approach to biomarker reference ranges as diagnostic biomarkers are usually derived from comparison of disease versus “healthy” individuals. Such cut-offs completely ignore early signs of sub health and risk for developing clinical symptoms in the future.

“Jinfiniti’s role is to track health biomarkers rather than age biomarkers. If a biomarker is correlated with age, it may not be measuring health very well as it could be measuring natural, progressive changes with age. Further, if you are looking for changes that are correlated with disease, you’re intentionally focusing on a specific demographic, a very small portion of the population, and you’re excluding a lot of relevant data in the process.”

Jinfiniti’s two main products is its intracellular NAD test and AgingSOS panel which measures myriad longevity biomarkers. NAD is one of the most important molecules for maintaining cellular health and the most powerful, single biomarker of aging. NAD levels characteristically decline with age, likely driving several pathological aspects of the aging process. This is supported by the fact that research has shown that boosting NAD levels has been shown to improve healthy longevity, but there has never been a way for consumers to measure NAD levels until the development of Jinfiniti’s novel assay. Users can use Jinfiniti’s intracellular NAD test to measure the amount of NAD within their blood cells with a simple “finger-prick” blood test which is collected on a filter paper and sent back to the lab for analysis. To date, no other company has been able to capture this information because NAD is extremely unstable and degrades rapidly when exposed to the environment. A key aspect of Jinfinti’s intracellular NAD assay is the use of a stabilizing/fixing buffer which preserves the NAD until it reaches the lab for analysis. This reflects a key strength of Jinfiniti’s team as they combine the finest minds in biology, chemistry and bioinformatics to deliver a never before seen product and service. “Jinfiniti’s intracellular NADTM test is the critical starting point to help you create an effective program for boosting NAD levels and improving your healthy longevity”.

Jinfiniti’s AgingSOS™ is a first-of-its-kind biomarker panel, a comprehensive tool to maximise healthy longevity. This assay addresses several layers of physiology and hallmarks of aging to holistically address the complexity that encompasses the aging process. Recent research has revealed that aging occurs on several different levels and at different rates within the various tissues of our body. This further emphasised the idea that we’re only as strong as our weakest link, hence the importance of assays that reflect the health of the “several different layers” of the aging process. “If you try to understand one piece of the longevity puzzle in isolation, the solution will prove elusive. We need network thinking to address the root cause of aging”

AgingSOS™ analyses innovative longevity biomarkers – with a simple “finger-prick” – including intracellular NAD, circulating NAD, cellular senescence, chronic inflammation, oxidative stress and antioxidant capacity, tissue stress and damage, among other important biomarkers of health. Of particular note is Jinfiniti’s novel cellular senescence assay, the first of its kind to be available commercially. Cellular senescence is widely recognised as a primary driver of the aging process as it drives chronic inflammation and the process known as “Inflammaging”. Reducing cellular senescence (with specific supplements, exercise regimens, etc) can help reduce chronic inflammation and the chances of developing many age-related diseases. Jinfiniti provides an added component of “gamification” and helps their users make sense of their biomarker data by providing them with a “wellness index” (ranging from 0–100) which gives them a sense of their health status compared with “youthful” levels of the various biomarkers. The wellness index helps individuals make sense of what could otherwise be an overwhelming amount of data and provides an easy metric to track their progress and help figure out what factors are influencing their score. “I don’t believe in assigning individuals a biological age, as most other companies in this space do, because the age clocks do not provide precision actionable guidance, at least not with currently available age clocks. The wellness index is a reflection of your health or sub health and is one number to keep at the front of your mind to guide you in your health journey, if nothing else.”

Jinfiniti’s AgingSOS™ biomarker panel allows individuals to understand what is going on within the “black box” of their body. Most importantly, each and every biomarker in the panel is actionable and points to mechanisms for healthy lifestyle change, making it an extremely powerful tool for cultivating healthy longevity. Dr She takes the relationship he forms with his customers extremely seriously. “For Jinfiniti, our customers are more than just customers. They are our friends and they are our family”. This is more than just talk as Dr She goes out of his way to personally research and respond to questions from his customers that helps them make sense of their data and engage with healthy lifestyle change, all at his own time and cost.

Efficacy of product

Jinfiniti’s NAD test has been used in several clinical trials to assess how individuals respond to different therapeutics. Jinfinti recently conducted two studies using its NAD test but the results have not yet been published. They also have two more studies underway.

Through the formation of various strategic collaborations, Jinfiniti’s major focus for the next year is to take their tests through FDA approval. This will expand the amount of data supporting the efficacy of their tests as well as its scope of application within the longevity field.

Product Development

Jinfiniti has been evolving at a rapid pace since it officially began operations in 2019. It has established early proof of concept of its tests within the lab, defined and tested its assays within human subjects and established viable commercial products that have been used to collect health data that can be used to further iterate on and improve their product. Jinfiniti’s R&D team is extremely strong and have developed several assays (that are yet to be commercially released) that measure other longevity biomarkers. This includes assays that measure the activity of NAD degrading enzymes, another extremely valuable insight that can be used to optimise an individual’s NAD regimen. Further, Jinfiniti is currently developing a subscriber based platform in which they can establish long term relationships with their end-consumer and help them create actionable longevity strategies based on their data by providing personalised lifestyle recommendations and longevity coaching within a “test-retest” paradigm. This relationship would help consumers derive more value from their health data and allow Jinfiniti to further evolve their machine learning platform by building on their reference datasets. Dr She also has a strong drug development background and plans to apply his skill sets and experience to develop and improve the efficacy of various nutraceutical formulations. Jinfiniti is currently laser focused on increasing revenue, manpower and infrastructure in order to accomplish all of these product development milestones.

Ultimately, Jinfiniti strives to become the “one stop shop” that provides critical tools for individuals seeking to optimise their longevity strategy through tracking longevity biomarkers, developing longevity supplements and providing longevity education.

Target market

As biomarker testing is critically important for the success of many different longevity realms, Jinfiniti is a central node within the longevity space. This is especially the case as Jinfiniti has developed assays that have never before reached the market, including its NAD and cellular senescence tests. Therefore target markets for Jinfiniti include:

Nutraceutical and drug development companies for both the formulation and evaluating efficacy of their products

Biomarker companies that seek to use Jinfiniti’s tests to expand their assays and provide end consumers with more information for health and wellness promotion

Functional medicine clinics for validating the efficacy of longevity interventions and health recommendations (dietary, supplement, exercise, stress reduction, sleep, etc).

Athletes that seek to optimise their performance with various training and dietary regimens

Most importantly, Jinfiniti’s tests are for anyone and everyone seeking help in navigating the ocean of “snake oil supplements and health gurus” in order to take charge of their health and wellness with personalised, precise and actionable insights on the unique factors that slow down or speed up their aging process. Jinfiniti is intent on transforming the health and wellness space by helping individuals preserve their health before disease arises and prides itself in making its tests as simple as possible with cheap shipping and quick responses. In service of this mission, one of Jinfiniti’s major goals is to continue working on ways to bring the price of its assays down to eventually make their tests available to everyone.

Channels to market

Jinfiniti has focused on B2B channels (such as supplement companies) to help businesses improve their products, B2C channels (individuals) to help consumers optimise their health and B2B2C channels (functional medicine doctors, for example) to provide clinicians tools to improve the health outcomes of their patients and make their job more energy efficient.

Dr She insists: “Jinfiniti is not just about the science or the business. It is about forming friendships and collaborations with others working towards a common goal.”

Success Factors

Team and Reputation

Dr She was the founding director of the Center for Biotechnology and Genomic Medicine at the Medical College of Georgia. He is internationally recognised for his studies in a broad range of fields including diabetes, cancer, immunology, genomics, proteomics, bioinformatics, biomarker and drug development. Dr She is well known for providing the foundation for a 20 year long international consortium known as The Environmental Determinants of Diabetes in the Young (TEDDY), which aimed at identifying novel ways to prevent diabetes. He has also conducted extensive research to identify biomarkers and therapies for various types of cancer using a combination of big data, machine learning and deep learning. Dr She has secured more than $100 million in research grants over the course of his career and authored 400 peer-reviewed papers with more than 15,000 citations. Dr She has also collaborated with over two dozen companies within his academic career and has carried this momentum forward with Jinfiniti as they have established numerous partnerships with companies such as Alive by Science, Quicksilver, Quicksilver Scientific, Donotage, TruDiagnostics, GlycanAge, Bioenergy Life Sciences, BioViva and a number of functional/integrative medicine clinics.

A critical member of the team is Dr Boying Dun, who received a BS degree in food science and a PhD degree in analytical chemistry. Dr Dun was instrumental to the development of the lab assays and the formulation of nutraceuticals. She serves as Jinfiniti’s Chief Innovation Officer.

Two business veterans recently joined the Jinfiniti management team. Both were former Jinfiniti customers. Bob Thordarson is a serial entrepreneur and helped companies to scale up business; he serves as Jinfiniti’s Chief Technology Officer, in charge of information technology and D2C marketing. Kurt Johnsen is also a serial entrepreneur including founder and CEO of Simplified Genetics; Kurt has extensive experience in marketing and serves as Jinfiniti’s Chief Growth Officer.

Jinfiniti has just moved from a business incubator to their own independent facility and its team currently consists of eight employees as well as consultants. Jinfiniti was selected as a top 5 life extension blood test company in 2021 by Longevity Advice and a market leader in longevity biomarker testing by Longevity International and a 20 most innovative companies to watch 2021 by Business Worldwide Magazine.

Intellectual Property

Jinfiniti possesses a number of proprietary technologies, know-hows and information, but chose to not apply for patent protection for many reasons. Dr She wants to focus their time and resources on business operation and believes that Jinfiniti can maintain a competitive edge by continuing innovation of services and products.


Jinfiniti received start-up funding and is now cash positive. Jinfiniti plans to start raising more capital in the second half of 2022.

Jinfiniti’s funding is undisclosed.

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Company Profile

Founded in 2020, TruDiagnostic has already been making headway in the longevity industry with a mission to improve people’s lives. TruDiagnostic wants to arm patients and physicians with information, enabling them to make the right decisions at the right times through insights found in the fluid epigenome. The company’s core belief is that methylation is a biomarker with robust potential because it can be crossed-trained to other biomarkers in a multi-omic framework. With a market currently made up of physicians in the integrative, cash-pay medicine space, TruDiagnostic have a clear pathway to profit, with immediate plans to offer direct to consumer kits and insights starting in 2022. Currently TruDiagnostic reporting is mostly for age-related diagnostics through their TruAge test, but this will be expanded as more data is collected for additional health verticals.

In addition to the clinical test offering, TruDiagnostic also provides laboratory services to research institutions. This includes proteomic analysis, RNA-Sequencing and genomic processing services. It has a robust bioinformatics department that can analyse large datasets for new predictive health algorithms. While TruDiagnostic aims to continue to produce the best aging algorithms to date, it is also expanding into other areas of epigenetic interpretation with an overall goal to become a leader in its clinical service.

TruDiagnostic’s unique ability, making it stand out from the competition, is the ability to leverage the benefits of its large cash pay provider database. The physicians, nurses and other healthcare providers in this space are uniquely interested in aging and preventative medicine. This allows the company to use these to distribute testing, but more importantly, it also helps gather unique and in-depth covariates that can be obtained from direct-to-consumer testing.

These covariates are incredibly important, especially as the field of methylation continues to expand, as methylation data is useless without the outcomes and phenotypes of patients. Through TruDiagnostic’s physician relationships, it are able to conduct low-cost clinical trials and gather health history and data such as blood chemistries, immune profiles, microbiome testing and more. This data allows investigation into the connections of these phenotypes to the methylome which then allows TruDiagnostic to be uniquely suited to create new algorithms, offering even more value.

TruDiagnostic will be updating its unique rate of aging algorithm every 5 years as the cohort ages enabling even more accurate rate of aging determinations. It also plans to create saliva conversion methods of all published aging clocks to make saliva collection a viable and accurate option. Further, the company plans to release a skin aging algorithm; this will provide the first epigenetic algorithm to quantify skin again based on phenotypic measurements. They believe this technology will be heavily adopted by skincare brands and medical spas in the future. Lastly, TruDiagnostic plans to release a custom research array to investigate the imprintome. With collaborators, TruDiagnostic has identified for the first time, all areas in the genome which are imprinted and inherited by a single patient. These areas have a large relationship to many diseases, and will be offered as an array of research investigations for private research groups and universities studying autism, Alzhiemer’s, schizophrenia and many other imprintome associated disease

TruDiagnostic’s key performance objectives are:

  • Developing new intellectual property through our testing and analysis programs
  • Identifying new partnerships
  • Increasing our testing volume with additional lower cost reports.
  • Providing medical interventions based on these reports from supplements to telemed and EHR access

TruDiagnostic’s additional opportunity for growth is the development of new IP through building out their phenotype and multi-omic database, enabled by partnerships with academia and an extensive provider network. It seeks to develop new IP that assess epigenetic changes and cellular senescence and other hallmarks of aging through methylation testing.

Flagship Product Deep Dive

TruAge Test

TruDiagnostic’s flagship product is the TruAge test: an exclusively licensed phenotypic-trained age algorithm. This test provides a snapshot of an individual’s intrinsic or biological age and can quantify age via advanced epigenetics using TruDiagnostic’s exclusive, best-in-class algorithm. TruDiagnostic is the only company that has published algorithms, with their testing method, which assesses 850k CpGs, being the most advanced analysis currently available on the market. In addition, this test uses blood, which is the only validated method for biological age testing. TruDiagnostic is able to offer testing at a reduced price given the high volume of its lab, which currently serves three markets: direct to consumer, lab services and corporate health.

In its early experience the company has learned many useful lessons. One of the most important being the wish to be platform agnostic. As the technology for investigating methylation adapts, TruDiagnostic wants to be flexible to the best and least expensive method, all while prioritising not sacrificing the depth of the measurements. The private database has been a huge asset to research collaborations as TruDiagnostic now has more samples and phenotypic data than almost any database which is publicly available. It hopes to continue to build out a software and commercial platform which can use this data for drug discovery, health insights and the ability to publish papers

TruDiagnostic’s product is an epigenetic change test that assesses 850k CpGs and so, directly assesses an individual’s biologic age. However, the ability to analyse these samples goes way beyond age-related diagnostics. With custom deconvolution algorithms TruDiagnostic can report on:

  • Environmental exposures
  • The impact of early life and nutrition
  • The mitotic clock history
  • Telomere length
  • Immune cell subsets
  • Exposome features; smoking and drinking behaviours
  • Epigenetic risk factors for obesity and diabetes

The company aims to continue to expand aging insights with the multi-omic dataset to build the best database of interventional epigenetic changes and how these changes relate to disease prevention. Using this epigenetic database, it is uniquely suited to continue to build out scientifically validated and well powered algorithms and reporting features beyond aging. TruDiagnostic is the only company to test above 120,000 CpGs (testing over 850,000) and is the only company to have their own published trials and interventional investigations.

Product Development

The company’s technology is currently in its pre-clinical phase. The TruDiagnostic suite of proprietary algorithms have all been validated. Most studies have used TruDiagnostics’ own DunedInPACE Rate of Aging, for which they have exclusive licence, this is the only 3rd-generation clock currently available. TruDiagnostic has several large, funded partnership studies with Yale, Harvard and UPenn which will establish the efficacy of their own novel aging, death predictor and immune cell subset deconvolution algorithms which are expected to be published in 2022.

Target market

While TruDiagnostic does have channels that directly target consumers, its biggest target markets are healthcare providers and antiaging therapy companies. Providers can offer TruAge testing, as well as their additional tests, to patients making changes (whether medication, lifestyle, or behavioural) in order to impact overall health and aging. TruAge tests can detect the impact of these changes on biological aging. TruDiagnostic has been approached by many antiaging therapeutic companies whose market products such as diet or nutritionals need to be validated through TruDiagnostic tests and algorithms.

If everyone in the world could reduce their epigenetic age by 7 years lower than their chronological age, prevalence of age-related disease would halve. TruDiagnostic has begun to find interventions which have objective data on reducing aging. It aims to help its consumers test, but also implement interventions to reduce their epigenetic aging. This would have a massive positive impact on reducing disease and helping our rapid growing aging populations. The company plans to expand to a direct-to-consumer market in niche areas such as pregnancy and fertility planning, neurocognitive diagnostics, nutrition and fitness recommendations and research services. These goals will become easier to obtain as a greater understanding of how to interpret the methylome data is gained, which currently is in its infancy.

Channels to market

TruDiagnostic plans to continue marketing tests through three markets: direct-to-consumer, lab services and corporate health. Currently, the focus is on keeping a deployment plan in the space of healthcare providers. This is due to the benefit of gaining very unique datasets in this space. In mid-2022 TruDiagnostic also plans to increase consumer reporting and advertising significantly in the direct-to-consumer space as they develop lower cost and more target testing such as exposome testing, fitness and nutritional testing and even aesthetically trained skin aging algorithms.

Additionally, through partnerships with academia, the company can continue to offer more 3rd party services and analytics. These include the creations of many custom methylation arrays to focus on research in certain areas such as the impritome which will change the way we look at preconception and diseases such as autism and Alzheimer’s. It also includes transcriptomic, genomic and proteomic processing services.

Success Factors

Team and Reputation

The company was founded by a group of investors with experience in the healthcare space. Prior to the founding of TruDiagnostic, members of mgmt. and the board had experience in the integrative space which had massive growth. The previous company and pharmacy was the 4th fastest growing healthcare business in the world in 2019. The vision for TruDiagnostic was created in late 2019. While epigenetic aging technology had previously been used, it had not been applied to interventional health studies. That changed in September of 2019 when the TRIIM trial was published. This trial was the first proof of concept that epigenetic age could be reversed with intervention. The team saw the opportunity to offer this testing to their network of cash-pay medicine providers to effectively quantify aging.

Beyond the goal to bring this testing to clinical practice, the TruDiagnostic team also decided to also use this as an opportunity to research connections of health and aging to larger portions of the epigenome. Since that time, TruDiagnostic has started Research collaborations with Harvard, Yale, Duke, Cornell, The Van Andel Institute, UCSF and more.

Intellectual Property

TruDiagnostic has multiple lines of investigation ongoing for development of new IP. Through academic partnerships, they are exploring development of tests to assess markers of Alzheimer’s disease, development of cardiac disease and rejection of liver transplant. TruDiagnostic is expected to publish a highly accurate, multi-omic informed biological age clock and death predictor in the summer of 2022.

TruDiagnostic currently has 10 proprietary or exclusively licensed algorithms for aging, cellular deconvolution, rate of aging, telomere length and mitotic clocks and has over 39 research trials ongoing. Through its partnerships, it fosters publication of study findings and pursues federal grant funding opportunities. Both activities provide the company with a competitive edge, as it gains recognition as a leader in epigenetic methylation analysis. TruDiagnostic regularly seeks new partnerships. As a result of the company’s fast growth and early success, it believes that it is well-positioned for an IPO within the next five to seven years.


In TruDiagnostic’s first 18 months, over $3 million had been invested. Amongst investors is the well-known Dustin Cappelletto who serves as Tru’s Chairman; he is also the Founder and CEO of LIV Health. For the next 24 months, the company’s goal is to raise $8 million in capital. This capital will be used to improve the bioinformatics platform, research datasets and covariate data collection; it will also be put towards monetising the death prediction algorithm; monetising the skin age algorithm in the aesthetics market; continuing to publish results from interventional trials and increasing reporting and algorithm outputs, as well as the lab services business. TruDiagnostic is also intending to build out its C-Suite team.

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ZiO Health Ltd

Company Profile

ZiO Health is a VC-backed biotechnology company that has developed pocket-sized technology, bringing laboratory-based testing to the home. Founded in 2016, ZiO Health has spent the last 6 years developing biosensor technology to be used in various point of use analytical devices. The company’s core belief is that everyone should have access to molecular level testing to optimise long term health and prevent sickness. To maximise impact on society, ZiO Health has partnered with multiple firms allowing them use of Zio Health’s patented technology to develop customised solutions.

As a doctor, CEO and co-founder, Neel Patel was frustrated with the inefficiencies associated with the blood testing available in a hospital setting and wanted to find a solution to accelerate the full testing process. Alongside this co-founder, Dr Shaolin Liang had recently become a father and discovered a desire to bring lab testing into the home with the initial goal being to gain confidence that his baby was receiving the correct nutrients. Liang wanted to use his technical knowledge to develop a solution to this. Together they worked with a shared goal of bringing hospital lab testing into the home allowing greater accessibility to molecular testing, providing a new route to optimising health and sickness prevention.

One of the applications for ZiO Health’s proprietary biosensor technology is the ‘home health hub’ – a home testing device with instant results with personalised nutrition and wellness advice. The product, described below, has allowed ZiO to take their first steps towards providing individuals with new technology that will not only allow a greater understanding of their health, but give insights into what can be done to prolong good health.

ZiO Health will continue to fine-tune their home health hub with the focus set on improving health and wellness, aiming to expand their test range to maximise user value. The next steps for ZiO Health medical solutions (Therapeutic drug monitoring) include a progression onto a trial with a larger sample size prior to initiating the FDA regulatory process in the US, while the next steps for the company’s customised solutions centres around a new partnership involving water quality testing. This new product will pilot in New Zealand specifically focusing on Paralytic Shellfish Toxins.

Flagship Product Deep Dive

The Home Health Hub developed by ZiO Health has the potential to change how individuals view their personal health. Allowing greater personal responsibility into taking care of ourselves leading to earlier intervention when health declines, giving peace of mind to individuals moving through life.

ZiO Health places great importance on empowerment, to encourage individuals to take control of their health, optimise their well-being and prevent sickness with easily accessible home diagnostic technology and personalised testing solutions. Standing above the competition, ZiO Health avoids the pitfalls many “mail to lab” services encounter as sending samples to the laboratory can be expensive and time consuming, leaving individuals waiting days for results and advice. The Home Health Hub is low cost and instantaneously gives a quantitative result, allowing instant advice to be given through the accompanying software app. The Home Health Hubs sensor is functional with blood, urine, saliva & breast milk, allowing ZiO Health to offer a significant range of testing and diagnostics.

ZiO Health are currently testing their technology in third party labs and institutions in various geographical locations. A recent trial includes tests that are being conducted in the US at a university hospital, showing positive results thus far, with the device being able to give quantitative results within the clinical range required. The Home Health Hub is currently at technology readiness level (TRL) 5-6 where the platform technology has been established to work and is ready to be tested in a simulated environment. The software component is easily adapted to integrate into both consumer health and hospital platforms.

Target market

The Home Health Hub has an initial target market focussing on the health-conscious consumer:

This is envisioned as 25-45 year-olds that exercise 4+ per week. They are always seeking new health-related products to expand their knowledge of health. Users are trying to receive a deeper understanding of themselves and gain confidence on what works on them. By monitoring specific markers in bodily fluids, the user is given the opportunity to optimise their health through nutrition & wellness advice received. This monitoring allows lifestyle changes to be made, reducing the risk of developing chronic conditions.
With the global health & wellness market is worth $4.5 trillion, ZiO Health are entering the UK market at a promising time, where their initial obtainable market is valued at £90m. Whilst up-front costs for personalised drug therapy is more costly than traditional medicines due to the need for companion diagnostics; according to the National Academy of Medicine, personalised drug therapy could generate $100 billion in added value from longer, healthier patient lifetimes showing a worthwhile investment in shifting healthcare responsibility to allow individuals to understand and take better care of themselves.

Channels to market

ZiO Health is optimising several channels to market through both business-to-business relationships and business to business to consumer relationships. Initially Zio Health will be targeting companies that have the same target user as themselves. Through current R&D partnerships the company will be launching certain applications into the market space. Further, ZiO Health are partnering with insurance companies & healthcare institutions to aid the overall goal of bringing disease monitoring and medication monitoring into the home. In regard to ZiO Health’s customised solutions, the company is forming licensing agreements to allow other companies to utilise their technology within their own products.

Success Factors

Team and Reputation
  • ZiO Health was founded in 2016 by Drs Neel Patel and Shaolin Liang
  • The company has gained support from SOSV, HAX and J&J Innovation
Intellectual Property
  • PCT patents in US & Europe
  • ZiO Health is collaborating with University Hospitals which are running trials with the company’s technology.
  • Through collaborations with private companies, ZiO Health’s technology is being developed to be used for customised solutions – for example water quality testing.
  • ZiO Health’s competitive advantage is its proprietary biosensor technology. It is able to rapidly develop new tests to increase test ranges within existing devices and to develop new devices for various applications.
  • With multiplexing, ZiO Health is able to add multiple tests on the same cartridge, enabling rapid analysis, diagnosis or monitoring at the point of use. The affordable technology can be mass manufactured at a low cost.
  • Another major benefit of the technology is capability of continuous monitoring, a feature that the company will incorporate for some applications.
  • ZiO Health’s largest backer is SOSV, a VC based in San Francisco.
  • The company is also associated with other well-known investors that strategically benefit the company, however those will currently remain undisclosed.
  • ZiO Health is raising $3 million that will be utilised to complete trials in the US.
  • Further, the funding will allow for investment into company growth with a focus on expanding research and development partnerships and the ability to increase internal capacity to accelerate research. ZiO Health will see growth in their teams in China and the UK.

Adav, S. S. (2021, April 1). Metabolomics signatures of aging: Recent advances. Aging and disease. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7990359/

Aging Analytics Agency. (2019). Home | longevity int. Biomarkers of Longevity Analytical Report. Retrieved from http://data.longevity.international/Biomarkers-of-Longevity-Report.pdf

Agus, A. (2021, June 1). Gut microbiota-derived metabolites as central regulators in metabolic disorders. Gut. Retrieved from https://gut.bmj.com/content/70/6/1174

Ahadi, S. (2020, January 13). Personal aging markers and ageotypes revealed by deep longitudinal profiling. Nature News. Retrieved from https://www.nature.com/articles/s41591-019-0719-5

American Federation For Aging Research. (2016). Afar biomarkers of aging – american federation for aging … Biomarkers of Aging. Retrieved from https://www.afar.org/imported/AFAR_BIOMARKERS_OF_AGING_2016.pdf

Anton, S. D. (2020, October 22). Innovations in geroscience to enhance mobility in older adults. Experimental Gerontology. Retrieved from https://www.sciencedirect.com/science/article/pii/S053155652030471X

Balistreri, C. R. (2012, April 23). Genetics of longevity. data from the studies on Sicilian centenarians. Immunity & ageing : I & A. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3402998/

Bell, C. G. (2019, November 25). DNA methylation aging clocks: Challenges and recommendations – genome biology. BioMed Central. Retrieved from https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1824-y#Sec17

Bobrov, E. (2018, November 9). Photoageclock: Deep learning algorithms for development of non-invasive visual biomarkers of aging. Aging. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6286834/

Callaghan, S., Lösc, M., Pione, A., & Teichner, W. (2021, April). McKinsey & Company . Retrieved from Feeling good: the Future of the $1.5 trillion wellness market: https://www.mckinsey.com/industries/consumer-packaged-goods/our-insights/feeling-good-the-future-of-the-1-5-trillion-wellness-market

Colloca, G. (2020, August 22). Biological and functional biomarkers of aging: Definition, characteristics and how they can impact everyday cancer treatment. Current oncology reports. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7442549/

DeVito, L. M. (2021, September 8). NYAS Publications. The New York Academy of Sciences. Retrieved from https://nyaspubs.onlinelibrary.wiley.com/doi/10.1111/nyas.14681

Dweck, A. (2021, July 1). The advancement of telomere quantification methods – molecular biology reports. SpringerLink. Retrieved from https://link.springer.com/article/10.1007/s11033-021-06496-6

Fahy, G. M. (2019, September 8). Reversal of epigenetic aging and immunosenescent trends in humans. Wiley Online Library. Retrieved from https://onlinelibrary.wiley.com/doi/full/10.1111/acel.13028

Ferrucci, L. (2020, February). Measuring biological aging in humans: A Quest. Aging cell. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6996955/

Franceschi, C. (12, March 2018). The continuum of aging and age-related diseases: Common mechanisms but different rates. Retrieved March 12, 2018, from https://www.frontiersin.org/articles/10.3389/fmed.2018.00061/full?utm_source=ad&utm_medium=fb&utm_campaign=ba_sci_fmed

Franceschi, C. (2018, July 25). Inflammaging: A new immune–metabolic viewpoint for age-related diseases. Nature News. Retrieved from https://www.nature.com/articles/s41574-018-0059-4

Fuellen, G. (2019, October). Health and Aging: Unifying Concepts, Scores, Biomarkers and Pathways. Health and aging: Unifying concepts, scores, biomarkers and pathways. Retrieved from http://www.aginganddisease.org/EN/10.14336/AD.2018.1030

Galkin, F. (2020, April 6). Biohorology and biomarkers of aging: Current state-of-the-art, challenges and opportunities. Ageing Research Reviews. Retrieved from https://www.sciencedirect.com/science/article/pii/S1568163719302582

Gough, C. (2021, February). Wellness industry – Statistics and Facts. Retrieved from Statista: https://www.statista.com/topics/1336/wellness-and-spa/#topicHeader__wrapper

Guerville, F. (2019, December 16). Revisiting the hallmarks of aging to identify markers of biological age – the journal of prevention of alzheimer’s disease. SpringerLink. Retrieved from https://link.springer.com/article/10.14283/jpad.2019.50

Hanjani, N. A. (2018, June 29). Protein restriction, epigenetic diet, intermittent fasting as new approaches for preventing age-associated diseases. International journal of preventive medicine. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6036773/

Horvath, S. (2018, April 11). DNA methylation-based biomarkers and the epigenetic clock theory of ageing. Nature News. Retrieved from https://www.nature.com/articles/s41576-018-0004-3

Huang, S. (2020, February 11). Human skin, oral and gut microbiomes predict chronological age. mSystems. Retrieved from https://journals.asm.org/doi/10.1128/mSystems.00630-19

Imai, T. (2019, February 13). Facial cues to age perception using three … – plos. Facial cues to age perception using threedimensional analysis. Retrieved from https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0209639&type=printable

Jazwinski, S. M. (2019, March 26). Examination of the dimensions of biological age. Frontiers. Retrieved from https://www.frontiersin.org/articles/10.3389/fgene.2019.00263/full

Ji, L. (2021, September). Frailty and biological age. Annals of geriatric medicine and research. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8497950/

Jin, K. (2010, October 1). Modern biological theories of aging. Aging and disease. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2995895/

Justice, J. N. (2018, December). A framework for selection of blood-based biomarkers for geroscience-guided clinical trials: Report from the Tame Biomarkers Workgroup. GeroScience. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6294728/

Jylhävä, J. (2017, April 1). Biological age predictors. EBioMedicine. Retrieved from https://www.sciencedirect.com/science/article/pii/S2352396417301421

Khan, S. S. (2017, May 23). Molecular and physiological manifestations and measurement of aging in humans. Wiley Online Library. Retrieved from https://onlinelibrary.wiley.com/doi/full/10.1111/acel.12601

Kifer, D. (2021, January 1). Immunoglobulin G glycome composition in transition from pre-menopause to Menopause. medRxiv. Retrieved from https://www.medrxiv.org/content/10.1101/2021.04.10.21255252v1

Krištić, J. (2014, July). Glycans are a novel biomarker of chronological and biological ages. The journals of gerontology. Series A, Biological sciences and medical sciences. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4049143/

Kudryashova, K. S. (2020, March 11). Aging biomarkers: From functional tests to multi‐omics approaches. Analytical Science Journals. Retrieved from https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/10.1002/pmic.201900408

Larocca, D. (2021, April 21). No time to age: Uncoupling aging from chronological time. MDPI. Retrieved from https://www.mdpi.com/2073-4425/12/5/611/htm

Li, X. (2020, February 11). Longitudinal trajectories, correlations and mortality associations of nine biological ages across 20-years follow-up. eLife. Retrieved from https://elifesciences.org/articles/51507

Liu, J., Goryakin, Y., Maeda, A., Bruckner, T., & Scheffler, R. (2017). Global health workforce labor market projections for 2030. BioMedCentral, Retrieved from https://doi.org/10.1186/s12960-017-0187-2.

Levine, M. E. (2018, April 18). An epigenetic biomarker of aging for lifespan and healthspan. Aging. Retrieved from https://www.aging-us.com/article/101414/text

Lohman, T. (2021, September 26). Predictors of biological age: The implications for wellness and aging research. Gerontology & geriatric medicine. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8477681/

Longevity blog team. (2020, September 5). DEEP LONGEVITY, BIOLOGICAL AGE AND THE FUTURE OF LONGEVITY AS A SERVICE – AN INTERVIEW WITH ALEX ZHAVORONKOV. Retrieved from Longevity blog : https://www.nickengerer.org/longevity-and-wellness/deep-longevity-biological-aging-clocks-interview-alex-zhavoronkov

Macdonald-Dunlop, E. (2021, January 1). A catalogue of OMICS biological ageing clocks reveals substantial commonality and associations with disease risk. bioRxiv. Retrieved from https://www.biorxiv.org/content/10.1101/2021.02.01.429117v2.full

Maddock, J. (2019, October 20). DNA methylation age and physical and cognitive aging. OUP Academic. Retrieved from https://academic.oup.com/biomedgerontology/article/75/3/504/5601224

McCrory , C. (2021, April 30). Grimage outperforms other epigenetic clocks in the prediction of age-related clinical phenotypes and all-cause mortality. The journals of gerontology. Series A, Biological sciences and medical sciences. Retrieved from https://pubmed.ncbi.nlm.nih.gov/33211845/

Meier, H. C. S. (2019, December). Cellular aging over 13 years associated with incident antinuclear antibody positivity in the Baltimore Longitudinal Study of Aging. Journal of autoimmunity. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6878149/

Mitteldorf, J. (2020, December 22). What to look for in a biological clock. Josh Mitteldorf. Retrieved from https://joshmitteldorf.scienceblog.com/2020/12/21/what-to-look-for-in-a-biological-clock/

Moskalev, A. (2020, February 11). Mortality: The challenges of estimating biological age. eLife. Retrieved from https://elifesciences.org/articles/54969

Parks, T. (2016, December 9). Treat aging, not the diseases of age, says geneticist. American Medical Association. Retrieved from https://www.ama-assn.org/delivering-care/population-care/treat-aging-not-diseases-age-says-geneticist

Passarino, G. (2016, April 5). Human longevity: Genetics or lifestyle? it takes two to Tango. Immunity & ageing : I & A. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4822264/

Paton, B. (2021, May 28). Glycosylation biomarkers associated with age-related diseases and current methods for glycan analysis. International journal of molecular sciences. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8198018/

Protsenko, E. (2021, April 6). “Grimage,” an epigenetic predictor of mortality, is accelerated in major depressive disorder. Nature News. Retrieved from https://www.nature.com/articles/s41398-021-01302-0#:~:text=’GrimAge%20Acceleration’%20is%20defined%20as,self%2Dreported%20smoking%20packyears15.

Rivero-Segura, N. A. (2020, September 23). Promising biomarkers of human aging: In search of a multi-omics panel to understand the aging process from a multidimensional perspective. Ageing Research Reviews. Retrieved from https://www.sciencedirect.com/science/article/pii/S1568163720302993?casa_token=lHHv7KK6yHIAAAAA%3AQEtSAZi_f4rt0RKE6y1LjLWKslD2d3kLXRvcq3HMlJqf5syH5rSVu2mM7pUIk7O33qYpOx2Pnw#tb0005

Schultz, M. B. (2016, June 14). Why nad(+) declines during aging: It’s destroyed. Cell metabolism. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5088772/#:~:text=Unfortunately%2C%20NAD%2B%20levels%20steadily%20decline,of%20sirtuin%20and%20PARP%20activity.

Simpson, D. J. (2021, September 6). Cellular reprogramming and epigenetic rejuvenation – clinical epigenetics. BioMed Central. Retrieved from https://clinicalepigeneticsjournal.biomedcentral.com/articles/10.1186/s13148-021-01158-7

Simpson, D. J. (2021, September). Epigenetic age prediction. Aging cell. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8441394/

Snyder, M. (2019, August 29). Big Data and Health. The Lancet Digital Health. Retrieved from https://www.thelancet.com/journals/landig/article/PIIS2589-7500(19)30109-8/fulltext

Solovev, I. (2019, November 28). Multi-omics approaches to human biological age estimation. Mechanisms of Ageing and Development. Retrieved from https://www.sciencedirect.com/science/article/abs/pii/S0047637419301976?casa_token=D4qauroXt6EAAAAA%3AmFG0fXh32R888eR7K4Ktex7033G0jSs7pX_nEj4d3aG-NKAfWwOzhRNCN0DzSvIWJaGML-_92w

Strimbu, K. (2010, November). What are biomarkers? Current opinion in HIV and AIDS. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3078627/

Ukraintseva , S. (2016, February 17). Puzzling role of genetic risk factors in human longevity: “risk alleles” as pro-longevity variants. Biogerontology. Retrieved from https://pubmed.ncbi.nlm.nih.gov/26306600/

Vaiserman, A. (2021, January 21). Telomere length as a marker of biological age: State-of-the-art, open issues and future perspectives. Frontiers in genetics. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7859450/

Versace, C., Hawkins, L. E., & Abssy, M. (2021, October). Why investors need to pay attention to the at-home diagnostics market. Retrieved from NASDAQ: https://www.nasdaq.com/articles/why-investors-need-to-pay-attention-to-the-at-home-diagnostics-market-2021-10-28

Wettstein, M. (2021, May). Feeling younger as a stress buffer: Subjective age moderates the effect of perceived stress on change in Functional Health. Psychology and aging. Retrieved from https://pubmed.ncbi.nlm.nih.gov/33939449/

Winter, A. d. (2017, August). good year for diagnostic startups. Retrieved from Med city new: https://medcitynews.com/2017/08/good-year-diagnostics-startups/

Wishart, D. S. (2018, January 4). HMDB 4.0: The Human Metabolome Database for 2018. Nucleic acids research. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5753273/

WHO. (2021, October 4). Ageing and health. World Health Organization. Retrieved from https://www.who.int/news-room/fact-sheets/detail/ageing-and-health

Xia, X. (2021, April 30). Ageing research reviews – PICB.AC.CN. Assessing the rate of aging to monitor aging itself . Retrieved from https://www.picb.ac.cn/hanlab/paper/XianXia.AgeingResearchReviews.2021.pdf

Zenin, A. (2019, January 30). Identification of 12 genetic loci associated with human healthspan. Nature News. Retrieved from https://www.nature.com/articles/s42003-019-0290-0

Zhavoronkov, A. (2019, July 3). Deep aging clocks: The emergence of AI-based biomarkers of aging and longevity. Trends in Pharmacological Sciences. Retrieved from https://www.sciencedirect.com/science/article/pii/S0165614719301142

Zhavoronkov, A. (2019, November 25). Deep biomarkers of aging and longevity: From research to applications. Aging. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6914424/

Zhavoronkov, A. (2020, December 8). PsychoAge and subjage: Development of deep markers of psychological and subjective age using artificial intelligence. Aging. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7762465/

Zhavoronkov, A. (2021, January 14). Artificial Intelligence in longevity medicine. Nature News. Retrieved from https://www.nature.com/articles/s43587-020-00020-4?fbclid=IwAR1uQRAnR7aj3riqLhCVSMY_lzUybrA6PFxUcotQw4G3FFQjxTAoAh9wOXI

Zolman, O. (2021, August 18). The Zolman Biological Age Marker (Z-BAM) Criteria. The Zolman biological age marker (Z-BAM) criteria. Retrieved from https://www.oliverzolman.com/blog/the-zolman-biological-age-marker-z-bam-criteria

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