Fight Aging!

If the research community had a reliable, low-cost method of quickly assessing biological age, the burden of damage and dysfunction, a measure that is distinct from chronological age, then progress towards rejuvenation therapies might be accomplished more rapidly. At present the only reliable way to determine whether or not a given therapy produces a slowing of aging or rejuvenation is to run expensive, slow life span studies in mice. Even when taking the approach of starting the study with old mice, this is still quite a lengthy undertaking. Being able to apply a putative rejuvenation therapy to mice (or dogs, or non-human primates, or people) and then a few weeks later run a brief test to see how well it did would revolutionize the pace of progress.

Based on the past decade of work on biomarkers of aging, it seems plausible that a diverse weighted combination of measures will eventually prove to be good enough to greatly improve the economics of development for rejuvenation therapies. That good enough combination has yet to be established robustly, however. The various epigenetic clocks are promising, but not yet actionable, as researchers cannot say how exactly these clocks relate to the specific forms of molecular damage that cause aging. If the aggregate measure is higher or lower or unchanged following a given treatment, what does that mean? There is as yet no good answer to that question, and the answers will no doubt differ by class of therapy. Different biomarkers will react differently to various forms of biological repair. The same issue also applies to the other approaches beyond epigenetic measures.

Meanwhile, researchers continue to add new biomarkers and new combinations of existing biomarkers to the growing stack. The number of possible options grows on a month by month basis, but it may be that, at this stage, more effort should go towards calibrating the behavior of an existing biomarker approach, following use of interventions to slow or reverse aspects of aging, rather than continuing to pile additional markers onto the heap.

Age is more than just a number: machine learning may be able to predict if you're in for a healthy old age

Researchers focused on a type of skin cell called dermal fibroblasts, which generate connective tissue and help the skin to heal after injury. They chose this type of cell for two reasons: first, the cells are easy to obtain with a simple, non-invasive skin biopsy; second, earlier studies indicated that fibroblasts are likely to contain signatures of aging. This is because, unlike most types of cells that completely turn over every few weeks or months, a subset of these cells stays with us our entire lives.

The investigators analyzed fibroblasts taken from 133 healthy individuals ranging in age from 1 to 94. To get a representative sample, the team studied an average of 13 people for each decade of age. The lab cultured the cells to multiply, then used a method called RNA sequencing (RNA-Seq) to look for biomarkers in the cells that change as people get older. RNA-Seq uses deep-sequencing technologies to determine which genes are turned on in certain cells. Using custom machine-learning algorithms to sort the RNA-Seq data, the team found certain biomarkers indicating aging, and were able to predict a person's age with less than eight years error on average.

Researchers detect age-related differences in DNA from blood

Researchers have discovered age- and health-related differences in fragments of DNA found floating in the bloodstream (not inside cells) called cell-free DNA (cfDNA). These differences could someday be used to determine biological age - whether a person's body functions as older or younger than their chronological age. In a proof-of-concept study, researchers extracted cfDNA from blood samples from people in their 20s, people in their 70s, and healthy and unhealthy centenarians.

They found nucleosomes - the basic unit of DNA packaging in which a segment of DNA is wrapped around a protein core - were well-spaced in the DNA of the volunteers in their 20s but were less regular in the older groups, especially the unhealthy centenarians. Additionally, the signal from nucleosome spacing for the healthy centenarians was more similar to the signal from the people in their 20s than people in their 70s. Nucleosome packing is one aspect of the epigenome. Scientists first found cfDNA in the blood of cancer patients, and the fragments can be useful for diagnosing cancer. Earlier research has found that cfDNA is produced by dying cells, and as the cells die, the DNA is cut in between nucleosomes. "cfDNA is somewhat like a message in a bottle that captures what the cell looked like, epigenetically speaking, before it died."

Blood Markers in Healthy-Aged Nonagenarians: A Combination of High Telomere Length and Low Amyloid-β Are Strongly Associated With Healthy Aging in the Oldest Old

Many factors may converge in healthy aging in the oldest old, but their association and predictive power on healthy or functionally impaired aging has yet to be demonstrated. By detecting healthy aging and in turn, poor aging, we could take action to prevent chronic diseases associated with age. We conducted a pilot study comparing results of a set of markers (peripheral blood mononuclear cell (PBMC) telomere length, circulating Aβ peptides, anti-Aβ antibodies, and ApoE status) previously associated with poor aging or cognitive deterioration, and their combinations, in a cohort of "neurologically healthy" (both motor and cognitive) nonagenarians (n = 20) and functionally impaired, institutionalized nonagenarians (n = 38) recruited between 2014 and 2015.

We recruited 58 nonagenarians (41 women, mean age: 92.37 years, in the neurologically healthy group vs. 94.13 years in the functionally impaired group). Healthy nonagenarians had significantly higher mean PBMC telomere lengths, this being inversely correlated with functional impairment, and lower circulating Aβ40, Aβ42, and Aβ17 levels, after adjusting by age. Although healthy nonagenarians had higher anti-Aβ40 antibody levels, the number of participants that pass the threshold to be considered as positive did not show such a strong association. There was no association with ApoE status.