Created from one of the largest multi omic aging datasets ever developed, the new clock outperforms previous age calculating methods.
A study published today by scientists from Harvard University and epigenetic research company TruDiagnostic has shed light on the reasons why our bodies are aging on a cellular level, laying the foundations for medical based treatment options to reduce the risk of age-related death and disease in highly targeted ways.
Longevity.Technology: Age is the number one risk factor for most chronic diseases and death across the world. Epigenetics (or the way our genes are put to use throughout our bodies) has emerged as a crucial method of evaluating health, and while previous DNA methylation clocks could determine how advanced one’s body has aged, they have not yet been able to provide information to the reasons why someone might have accelerated or decelerated aging outcomes.
“In our research, we set out to create the best method to quantify the biological aging process. However, aging is extremely complex,” explains Harvard Medical School Associate Professor Dr Jessica Lasky-Su. “To solve this issue of complexity, our approach was to gather data across multiple sources of information. We chose to do this by building one of the most robust aging datasets in the world by quantifying patients’ proteomics, metabolomics, clinical histories and DNA methylation.”
Findings from the OMICm Age study, published today as a preprint and led, in part, by Dr Lasky-Su, showed that the new OMICm Age clock was able to predict death with approximately 90% accuracy over 10 years  – an impressive level of predictive accuracy. Relying on someone’s chronological age to make such a prediction would only produce a correct result 75.6% of the time, and while not yet peer reviewed, the OMIC Age clock appears to outperform existing methylation clocks such as GrimAge and PhenoAge.
Using this data, it is also possible to predict how changes in OMICm Age might impact an individual’s total lifespan. For instance, if someone with an OMICm Age of 60 was able to reverse their age by one year, their projected survival time would increase by 1.7 years. For someone with an OMICm Age of 70, it would provide a 1.35 year estimated lifespan increase. According to recent economic analysis of aging, increasing national life expectancy by one year would cause a $38 trillion economic boost to the US economy.
OMICmAge can also highlight which organ systems might be the most accelerated in aging or dysfunction, paving the way for earlier intervention or strategies that could improve an individual’s aging biology.
The new clock uses DNAm-based biomarkers instead of plasma biomarkers, meaning the biomarkers used represent longer average estimate of the biomarker concentration and are not as affected by day-to-day variations that could bias the results.
New hallmarks need new clocks
In 2020, there were nine universally recognized hallmarks of aging; earlier this year that was revised to 12, with another couple in the wings waiting to join the official party . Aging is enormously complex, and we can see change in almost every organ and biological process as we get older, so given the importance of quantifying the biological aging process, TruDiagnostic decided to collaborate with Harvard University researchers to create the most robust and informative biological age measurement to date.
The complexity of the aging process makes measurements of this process difficult. However, TruDiagnostic and Harvard’s approach was to measure as much of this variation in aging through large-scale, high-powered, multi-omic analysis. By quantifying each omic, they believed they could uncover insight into a wide variety of hallmarks of aging and other unique biological aging processes.
Also of note in the OMICm Age study, is the creation of novel methylation algorithms that can now quantify other important clinical biomarkers.
“In order to incorporate important factors of aging into a single diagnostic, we created predictors of metabolites, proteins, and clinical values using epigenetic methylation data,” explains TruDiagnostic’s Head of Bioinformatics, Varun Dwaraka, PhD. “For instance, these algorithms are able to predict values you might have taken at your doctor’s office like your fasting glucose or triglycerides from a simple finger stick blood sample.
“By developing these clinical and molecular predictors, we can achieve greater resolution of the multiple changes that occur with age and can identify why people age in different ways.”
These scores, termed Epigenetic Biological Proxies (EBP) by the paper, will be used in other epigenetic algorithms in the future to add more resolution to complex diseases such as neurodegenerative diseases, cardiovascular and pulmonary diseases and more.
“We believe this work is exciting because we are improving researchers’ ability to quantify the biggest risk factor for almost all age related diseases,” says Dr Lasky-Su. “However, it is also exciting because now we provide information which individual factors and biological processes are contributing to an individual’s current biological age.”