Interview with Ken Raj
We tend to think of age in terms of the number of years we have been alive — meaning our chronological age. But the year that you were born is not necessarily an accurate measure of your health or your life expectancy. We are coming to realize that a better predictor is your biological age – and that can be quite different from your chronological age. So how do you learn your biological age? And what can you do with this information?
In this episode of humanOS Radio, Dan Pardi speaks with Ken Raj. Ken is a Senior Scientific Group Leader at Public Health London, and has worked extensively with Dr. Steve Horvath of UCLA in developing and interpreting genomic biomarkers of aging. They are best known for developing the “epigenetic clock,” a tool that predicts life expectancy by examining age-related changes to DNA methylation, then using that information to calculate biological age in relation to chronological age. The epigenetic clock is able to predict life expectancy with remarkable accuracy, with a margin of error of plus or minus three years.
The New Age of Healthcare
The world has changed, and so has the way technology is integrated with healthcare. The current pandemic has reinforced the belief that through the use of AI and machine learning technologies, will we be able to predict, detect, and diagnose healthcare conditions rapidly and accurately. There is a need to build better, accurate & reliable technologies that help us make a symbiotic leap in health-tech.
The Sheekey Science Show
What are the hallmarks of aging? Why is it over time cells become less functional? This video will introduce you to the nine different hallmarks of aging including the primary causes of damage, the responses to the damage and the systemic hallmarks.
A key question in biology is to understand why and how we age. Alongside this, the unprecedented gain in the average lifespan in humans, since the mid-twentieth century, has dramatically increased both the number of older people and their proportion in the population. This demographic phenomenon is changing our societal make-up, from only ~130 million being 65 years or older (~5% of the world population) in 1950, to a predicted ~1.6 billion people (~17%) by 2050.
However, the success in reducing mortality has not been matched with a reduction in chronic disease. This leads to the undesirable outcome of many years of this prolonged lifespan being spent in ill health, with an associated massive health care burden. Increasing the productivity and reducing the disease affliction in these extended years would be clearly beneficial for both the individual and society.
This aim of maximizing the “healthspan” makes obtaining accurate measures of aging-related pathology essential, to gauge its speed, decipher the changes that occur, and potentially unlock how aging acts as a disease risk factor. There is considerable population variation in the rate at which people visibly age as well as become impaired by age-related frailty and disease. Measurement of this relative “biological” aging may allow pre-emptive targeted health-promoting interventions, perhaps in a personalized and disease-specific fashion. It would also aid in testing interventions that attempt to modulate the aging process.
The cellular and molecular hallmarks of aging include changes associated with cell senescence, dysregulated nutrient sensing, and stem cell exhaustion, among others. Therefore, many biological measures, such as p16ink4a tissue levels, circulating CRP, creatinine, and fasting glucose, as well as telomere length all correlate with aging.
In this decade, we have discovered the remarkable power of epigenetic changes to estimate an individual’s age. Epigenetics encapsulates the chemical modifications and packaging of the genome that influence or indicate its activity, with strict definitions requiring inheritance through mitotic cell division. Observations of age impacting on this mechanism have been reported for more than 50 years and suggested a role in age-related disease.
However, the association between epigenetic modifications and age became most starkly apparent with the arrival of the first high-throughput arrays measuring DNA methylation. These high-resolution data enabled the construction of extremely accurate age estimators, termed “Epigenetic” or “DNA methylation clocks”.
FULL TEXT: BMC Genome Biology
In the life extension movement, longevity escape velocity (sometimes referred to as actuarial escape velocity) is a hypothetical situation in which life expectancy is extended longer than the time that is passing. For example, in a given year in which longevity escape velocity would be maintained, technological advances would increase life expectancy more than the year that just went by.
Life expectancy increases slightly every year as treatment strategies and technologies improve. At present, more than one year of research is required for each additional year of expected life. Longevity escape velocity occurs when this ratio reverses, so that life expectancy increases faster than one year per one year of research, as long as that rate of advance is sustainable.
The concept was first publicly proposed by David Gobel, co-founder of the Methuselah Foundation. The idea has been championed by biogerontologist Aubrey de Grey (the other co-founder of the Methuselah Foundation), and futurist Ray Kurzweil, who named one of his books, Fantastic Voyage: Live Long Enough to Live Forever, after the concept.
These two claim that by putting further pressure on science and medicine to focus research on increasing limits of aging, rather than continuing along at its current pace, more lives will be saved in the future, even if the benefit is not immediately apparent.