Age is the largest risk factor for many of the chronic diseases that affect us all. This includes cardiovascular disease, different types of cancer or neurodegenerative diseases such as Alzheimer’s. In order to develop new interventions to slow down ageing (or even reverse it one day), quantifying ageing at the molecular and cellular level is a requirement. The need to establish reliable biomarkers of ageing has led to the creation of novel scientific efforts focused on this and has been a central topic of discussion in the largest conferences focused on extending human healthspan. 

Biomarkers of ageing are needed in different scenarios

For personalised preventative health

Consumers are overwhelmed with the amount of lifestyle programmes, apps and nutraceutical (supplements) interventions available in this space. Teasing apart what works for you using solid scientific evidence is far away from trivial. That is why having access to biomarkers that objectively quantify improvements of health (many years before a disease is diagnosed) is a huge step forwards.  

For drug development

Running clinical trials with ageing as an outcome would be a very costly and lengthy process (since it would require waiting until participants die or at least develop an age-related disease). Pharma companies need new biomarkers that serve as surrogate endpoints for ageing (so assessing whether a drug works or not can be done in a few months rather than many years). 

 Human ageing is a very complex biological process and measuring it requires combining the latest biomarker technologies (that generate large amounts of biological data) with AI or machine learning approaches. In our latest work we demonstrate how it is possible to build novel biomarkers of ageing hallmarks using our multi-omics and epigenetics biomarker discovery engine. 

NOTE: if you want to go deeper into the technical details, you can read the latest research on this topic in our new scientific publication: “A novel framework to build saliva-based DNA methylation biomarkers: quantifying systemic chronic inflammation as a case study”. This work was done by Hurdle’s scientists in collaboration with Bayer Consumer Health and academic collaborators from the University of Birmingham (inc. Professor Janet Lord) and the University of Edinburgh (inc. Professor Riccardo Marioni).

Accessible saliva-based biomarkers

At Hurdle, we have pioneered saliva-based biomarkers since we were founded in 2017 (you can read more about our first-in-class saliva-based biological age and our saliva-based COVID-19 PCR test). Saliva is very easy to collect in a non-invasive manner, which makes it an ideal biological sample type for preventative healthcare use cases at population scale. However, many biomarkers are not readily available in saliva but rather require a venous blood draw, which is more invasive and normally needs to be performed in a clinic, increasing the cost and time involved in the process. 

It is possible to predict the concentrations of certain biomarkers in blood using blood epigenetic (DNA methylation, DNAm) data. These DNAm proxies of the biomarkers are called EpiScores. For example, previous research has shown that it is possible to predict CRP levels (one of the gold standard biomarkers for inflammation) using blood DNAm data. Interestingly, the CRP EpiScore has a stronger association with some clinical outcomes (like cognitive ability) than the direct blood CRP measurement, highlighting that maybe EpiScores are more robust and reproducible metrics for medium and long term disease risk (as opposed to the acute fluctuations of CRP, for example due to recent infection or other stresses). 

DNAm data from saliva and blood is well correlated in human populations, probably due to the fact that 70-80% of the cells in saliva are actually immune cell types that infiltrate from the immune system. Given the utility of EpiScores, we decided to use this multi-omics approach to build a machine learning framework that translates the knowledge from blood DNAm to saliva DNAm and demonstrate that we can build saliva-based biomarkers that are reproducible. 

In order to do this, we built a novel dataset with matched saliva and blood DNA methylation samples, collected at the same time in the same individual. We ran different replicates per sample and were able to demonstrate that many EpiScores are reproducible (as measured with ICC values) between blood and saliva and also within saliva (which is important for longitudinal testing). EpiScores are therefore good building blocks (or features, in machine learning terminology) to train novel saliva-based biomarkers. Furthermore, because the EpiScores represent the concentrations of specific biomarkers in blood, this allows to build more interpretable biomarkers for specific diseases and biological processes. We decided to demonstrate that this was the case for chronic inflammation.

Quantifying chronic inflammation (InflammAge)

Ageing can be broken down into different molecular processes, known as the ‘hallmarks of ageing’, with one of them being systemic chronic inflammation (SCI). As time passes by, our immune system becomes less efficient at mounting the adequate immune response. This is known as immunosenescence and it is why older people have more infections and more serious health problems derived from them. Acute inflammation is part of a normal physiological response of our bodies to help fight infections. However, as we get older, our inflammatory baseline levels go up (even in the absence of an infection), which causes damage in our cells and tissues. It is the latter that is referred to as SCI. 

Breaking down biological age into its components (such as SCI) can help to better capture the impact of personalised interventions. We used the multi-omics biomarker discovery framework described above to create the first saliva-based epigenetic biomarker (or epigenetic clock) for chronic inflammation in humans (which we have named InflammAge). The difference between InflammAge and chronological age (known as InflammAge acceleration) is able to capture whether you have higher or lower levels of SCI than the average person for your age.

Using different cohorts (including Hurdle’s users and Generation Scotland, with N>18,000 samples), we demonstrated that InflammAge acceleration captures SCI status and immunosenescence, that is associated with all-cause mortality and also with how likely you are to develop many age-related diseases in the next few years. InflammAge also outperforms the other main published saliva-based clock while matching blood-based CRP associations with all-cause mortality. So a big big thank you to all the Hurdle users and Generation Scotland participants that consented for their anonymised data being used for research! ❤️

This figure shows how InflammAge acceleration is associated with all-cause mortality and the risk of developing many age-related diseases, when compared with other epigenetic clocks and CRP. 

 

But what is the point of InflammAge if we don’t know if we can action it? Fear no more, because in this study we demonstrate that different lifestyle and environmental factors (such as smoking, alcohol and specific diet variables) are associated with InflammAge:

• Some variables that increase InflammAge (bad): smoking, drinking and daily eating meat (other than poultry). 
• Some variables that decrease InflammAge (good): higher consumption of oily fish (at any frequency vs never), of brown bread (5-6 days/week vs never), of fruit (daily vs. once per week) and of green vegetables (5-6 days/week vs never).

While these are cross-sectional associations (and not longitudinal interventions or data from randomised clinical trials), they validate expected impacts of lifestyle factors on chronic inflammation at the population level. We hope to update our knowledge on which interventions work for different groups of people as more of our users test for InflammAge and collect information on their lifestyles. 

Together with researchers from the University of Birmingham, we also have preliminary evidence that a 3-month nutraceutical intervention with a Bayer formulation can potentially reduce InflammAge in those with a high InflammAge baseline level. While this was an open-label trial (info presented in this conference, publication coming soon), it is a huge step forwards in validating the actionability of InflammAge, showing how it outperforms traditional blood chronic inflammation markers and it provides an additional solution for consumers to improve their health.

What is next?

We have deployed InflammAge in our cost-effective Hurdle epigenetic platform, which will further democratise access to the biomarker for consumers and healthcare practitioners. We are also really excited to make InflammAge available in different geographies through our diagnostics-as-a-service (DaaS) infrastructure. Our strategic partnership with Bayer will be key in ensuring that testing for chronic inflammation (and other processes that affect biological age) becomes mainstream. Furthermore, we are currently exploring how our multi-omics biomarker discovery engine can be applied to drug discovery and patient stratification with our biotech and pharma partners, so stay tuned!