Fight Aging!

You have at least two ages: your chronological age, how long you have lived, and what we might call your biological age - which is a measure of how damaged you are. Aging, meaning the process of physical degeneration, is really just a matter of damage at the level of cells and molecular machinery. The more damage, the greater your biological age. If you are 56, you might have the damage load of the average 50 year old or the average 60 year old. Or worse, or better - and in either case that will reflect in your current health and remaining life span.

In actual fact, biological age is probably far more complex than this. There is every reason to expect different systems in your body to suffer different rates of damage accumultion. Consider the immune system in AIDS patients for example, which is prematurely aged into exhaustion and frailty. That is an extreme example of differential rates of damage: you would expect to find smaller differences in levels of accumulated damage in the biological components of a healthy person. But the differences are there.

Recognize that a lot of what I have said above is theory. Anyone who claims to be able to measure your biological age is largely blowing smoke: there's no standard for such a thing, and not much in the way of biomarkers of aging. Biomarkers are measurable aspects of our biology that can be scaled against age or remaining life expectancy - and so might be used to determine a subject's chronological age, or how much longer they might expect to live. The absence of good biomarkers poses a strategic challenge for the ongoing development of longevity science, because in order to efficiently evaluate a potential therapy to slow or reverse aging, researchers need to rapidly understand its actual effects on healthy life span. Sitting around and waiting is the only presently foolproof strategy, and that is one of the reasons that even mouse studies of longevity therapies are very expensive. No-one wants to run an experiment for going on four years if there is a way to call a halt after a few months and some biomarker measurements.

That difference in experimental run time represents a large sum of money in every sizable study, not to mention the opportunity cost in research that might otherwise move ahead, but must wait for years for results to arrive. Further, when you stop to consider human studies, you'll see that that the present state of affairs rules out a wide range of possible trials - "wait and see" isn't viable when the time frame is decades. This is why we should all be interested in progress towards the establishment of biomarkers for aging, and today I'll point you to recent work on DNA methylation, undertaken with that aim in mind. You might recall that DNA methylation correlates with age and age-related frailty, and here researchers greatly improve upon the precision of that correlation.

UCLA scientists accurately predict age with saliva sample:

Using saliva samples contributed by 34 pairs of identical male twins ages 21 to 55, UCLA researchers scoured the men's genomes and identified 88 sites on the DNA that strongly correlated methylation to age. They replicated their findings in a general population of 31 men and 29 women aged 18 to 70. ... Vilain and his team envision the test becoming a forensic tool in crime-scene investigations. By analyzing the traces of saliva left in a tooth bite or on a coffee cup, lab experts could narrow the age of a criminal suspect to a five-year range.

Epigenetic Predictor of Age:

From the moment of conception, we begin to age. A decay of cellular structures, gene regulation, and DNA sequence ages cells and organisms. DNA methylation patterns change with increasing age and contribute to age related disease. Here we identify 88 sites in or near 80 genes for which the degree of cytosine methylation is significantly correlated with age in saliva of 34 male identical twin pairs between 21 and 55 years of age. Furthermore, we validated sites in the promoters of three genes and replicated our results in a general population sample of 31 males and 29 females between 18 and 70 years of age. The methylation of three sites - in the promoters of the EDARADD, TOM1L1, and NPTX2 genes - is linear with age over a range of five decades. Using just two cytosines from these loci, we built a regression model that explained 73% of the variance in age, and is able to predict the age of an individual with an average accuracy of 5.2 years.

There are some subtleties here. DNA methylation occurs in different regions of DNA at different rates (and probably at different rates in the same region of DNA in cells in different locations in the body). The researchers here have found a statistical model based on methylation of a few specific genes in one portion of the body that is a biomarker of chronological age. For our purposes that is the less useful biomarker: we want one that measures remaining life expectancy, or in other words a biomarker that is effectively a measure of present levels of biological damage.

We know that methylation patterns correlate with age-related frailty. There is every reason to expect that researchers will soon be able to build a range of statistical measures based on DNA methylation that will predict remaining life expectancy across most common states of health and ages. The research described above gives further weight to that expectation.