A Biomarker of Aging Based on Blood Protein Levels

A robust, reliable, low-cost biomarker of aging that measures the burden of damage that causes aging would be of great value to the field. It would allow rapid testing of potential rejuvenation therapies, given the capacity to show how effective a treatment is in only a short period of time: test once, apply the therapy, test again a few days or a month later. Most of the work aimed at producing and proving such a biomarker is focused on assessment of epigenetic changes that are characteristic of aging. This is not the only approach, however. Research groups are also attempting algorithmic combinations of very simple assessments such as grip strength and skin elasticity, while others, as is the case here, are focused on measuring protein levels in blood samples.

At the end of the day, however, it is still far from clear as to how all of these potential biomarkers relate to the underlying damage that causes aging. It is quite possible that they are strongly dependent on only a fraction of the full range of types of damage, for example. A rejuvenation therapy might not change the biomarker as much as it should. Or perhaps more than it should. Thus proving out biomarkers must proceed in parallel with proving out rejuvenation therapies based on damage repair. At the present time one cannot just blindly use any of the existing biomarkers and assume the results to be useful in the matter of assessing interventions.

One interesting outcome from the work noted here is that it shows staged alterations in the biomarker, rather than a smooth progression of changes. The first such change occurs quite early, in the 30s. One might compare that result with recent work on changes in the gut microbiome that also shows alterations in gut microbe populations that are relevant to health, due to a loss of beneficial compounds produced by these microbes, taking place during the 30s - at exactly the same average age in the mid-30s, in fact, which is most intriguing.

Stanford scientists reliably predict people's age by measuring proteins in blood

Researchers analyzed the levels of proteins circulating in plasma - the cell-free, fluid fraction of blood - from 4,263 people ages 18-95. On measuring the levels of roughly 3,000 proteins in each individual's plasma, researchers identified 1,379 proteins whose levels varied significantly with participants' age. A reduced set of 373 of those proteins was sufficient for predicting participants' ages with great accuracy. In fact, a mere nine proteins were enough to do a passable job, and adding more proteins to the clock improves its prediction accuracy only a bit more.

The study's results suggest that physiological aging does not simply proceed at a perfectly even pace, but rather seems to chart a more herky-jerky trajectory, with three distinct inflection points in the human life cycle. Those three points, occurring on average at ages 34, 60 and 78, stand out as distinct times when the number of different blood-borne proteins that are exhibiting noticeable changes in abundance rises to a crest. This happens because instead of simply increasing or decreasing steadily or staying the same throughout life, the levels of many proteins remain constant for a while and then at one point or another undergo sudden upward or downward shifts. These shifts tend to bunch up at three separate points in a person's life: young adulthood, late middle age and old age.

The investigators built their clock by looking at composite levels of proteins within groups of people rather than in individuals. But the resulting formula proved able to predict individuals' ages within a range of three years most of the time. And when it didn't, there was an interesting upshot: People whose predicted age was substantially lower than their actual one turned out to be remarkably healthy for their age.

Undulating changes in human plasma proteome profiles across the lifespan

Aging is a predominant risk factor for several chronic diseases that limit healthspan. Mechanisms of aging are thus increasingly recognized as potential therapeutic targets. Blood from young mice reverses aspects of aging and disease across multiple tissues, which supports a hypothesis that age-related molecular changes in blood could provide new insights into age-related disease biology. We measured 2,925 plasma proteins from 4,263 young adults to nonagenarians (18-95 years old) and developed a new bioinformatics approach that uncovered marked non-linear alterations in the human plasma proteome with age. Waves of changes in the proteome in the fourth, seventh and eighth decades of life reflected distinct biological pathways and revealed differential associations with the genome and proteome of age-related diseases and phenotypic traits. This new approach to the study of aging led to the identification of unexpected signatures and pathways that might offer potential targets for age-related diseases.


Producers of supplements could use it and write on the bottles how effective they are. They should be forced by regulatory bodies to do so. In the same way as light bulbs, LEDs have graded A-F in green-red colors how energy efficient they are.

Posted by: thomas.a at December 21st, 2019 7:46 AM

This route seems more promising to me than either telomeres or methylation changes.

Posted by: JohnD at December 21st, 2019 1:22 PM

I concur, JohnD. This is the only known/feasible way to comprehensively capture most of the hallmarks of biological aging.

Posted by: Attila Csordas at December 22nd, 2019 3:39 PM

I assume these is one or more chemicals that are produced when a senescent cells dies. So how about this as a strategy to measure aging: Collect a sample of fatty tissue (who needs it anyway?), expose it to DQ in vitro, and then measure the chemical(s) associated with cell death. Why not measure those cell death chemicals in vivo after a hearty dose of DQ, both measuring AND rejuvenating?

Posted by: Thomas Schaefer at December 23rd, 2019 8:12 AM

Are there any commercial quality plasma protein tests that would simulate or approximate the Stanford paper results?

Posted by: Stuart Berkowitz at January 27th, 2020 10:16 AM

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