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Aging Insights: Dr. Jesse Poganik and Dr. Mahdi Moqri on Biomarkers and Longevity Research – The VitaDAO Aging Science Podcast

December 18, 2023

In this podcast with Dr. Jesse Poganik (@jpoganik) and Dr. Mahdi Moqri (@mahdi_moqri) we had a chat about their upcoming symposium and the importance of biomarkers. Our topics ranged from omics over to liquid biopsies and clinical studies. We talked about walking speed, epigenetics, longitudinal studies and prizes (yes, you can win money!)

Hope you will enjoy this podcast and our brief shownotes.

Short Bio of the speakers

Dr. Jesse Poganik is an Instructor in Medicine at Brigham and Women’s Hospital and Harvard Medical School. His research focuses on understanding the most fundamental aspects of aging: what is the essence of the aging process, and what features define the biological nature of aging? He leads several projects in the Gladyshev Lab to understand the temporal dynamics of biological age, the inter-cellular and inter-tissue dynamics of aging, and how aging may best be quantitatively assessed.

Dr. Mahdi Moqri is a joint Research Fellow in Aging Research at Harvard (Gladyshev Lab, Genetics) and Stanford (Snyder Lab, Genetics). He is leading the executive committee of the Biomarkers of Aging Consortium, to establish reliable biomarkers for longevity interventions and mentoring non-profits and young researchers in omics techniques.

Important links and upcoming events

The consortium website with relevant information and news

2023 Biomarkers of Aging Symposium
December 4th 2023

ClockBase: here you can check whether your favorite dataset, drug or intervention affects epigenetic age

What is a clock and what is a biomarker?

“You know when I was talking to people in Vadim’s lab when I was considering joining they were saying clock, clock, clock and I was just… I had no idea…[and] in many ways clock is not a good term”

Basically, a clock tries to measure biological aging or something related for newer clocks. A somewhat more technical definition would understand the clock as a mathematical algorithm, based on molecular data, that has some relation to biological age.

The problem with the word clock that Jesse alluded to is reflected in current papers. These days many clocks are designed not to predict age anymore, rather they are trained to predict age-related outcomes. For instance, the GrimAge epigenetic clock predicts mortality risk. At this point it might be better to just stick to the word “biomarker” instead of clock.

Even worse, as Mahdi mentioned, we do not want a clock that accurately predicts calendar age in most cases, because then we might as well check your birth certificate. We really are interested in outcomes, which are predicted by your “biological age” so to say — i.e. the overall state and health of a person.

A biomarker is an indirect measure of any biological process. As aging researchers we want this kind of indirect measure for aging. We could use this word instead.

Nevertheless, while inaccurate I like the term clock from a popular science perspective. Everyone understands that “the clock is ticking” towards undesirable outcomes or some well-defined end. The word and the idea captures the imagination of people and this is the reason why the term became so popular.

Clinical studies and data sharing — two pressing issues

We all agreed that biomarkers are needed to facilitate clinical studies. Having a marker that can predict drug success in advance would be a great boon to aging researchers. Although I did caution that even the best biomarkers for other diseases rarely if ever replace large studies with hard outcomes. Nevertheless, such markers can save billions by helping us to choose the best possible interventions to pursue in large studies!

Secondly, we all agreed that there are many reasonable and, unfortunately, also unreasonable obstacles hindering our work on biomarkers: “there are many barriers that make data sharing harder than it should be from a scientific point of view” (Jesse)

Hence, I am looking forward to the work of the Biomarker Consortium to improve datasharing and accessibility.

The importance of functional markers

Although neither of us is a clinician and this is more the purview of doctors, I encouraged the speakers to take a brief detour and talk about functional markers. I remain very excited about functional markers because they are relatively inexpensive. Measuring improvements in walking speed, hair loss, wrinkling, libido etc. is so much easier than doing thorough long-term mortality follow-up. (Also as you can see the distinction between marker and outcomes is quite blurry when it comes to clinical or functional markers. All of these can be considered markers of aging but are also valuable outcomes by themselves.)

The reason I am excited about these is that some drugs like rapamycin have shown rejuvenation-like effects in mice and there may exist more such drugs. Not only are they in a way more attractive than drugs which merely slow aging, they would be also cheaper to study since you could design a study based on functional biomarkers to efficiently measure rejuventation!

Jesse and I agreed that emerging digital biomarkers are very suited to study some of these functional markers (e.g. heart rate variability, sleep quality, movement patterns).

This topic then led to a great discussion about the difference between cross-sectional and longitudinal studies. Jesse mentioned that the latter are obviously more suited to study a continuous process like aging, although unfortunately such studies are difficult and expensive.

What do we mean by these terms anyway?

Cross sectional — single measurement.

Longitudinal — follow the same group of people or animals over time.

With biomarkers longitudinal can mean more than one measurement, or we measure a biomarker at point T1 and then outcome and outcome at a later point T2, e.g. does cystatin predict kidney failure and death. This in a nutshell is what a prospective cohort study does, which is a longitudinal study design.

The importance of omics

For those who do not know, “omics” refers to a collection of fields in biology that study various sets of molecules within cells, tissues, or organisms. The defining characteristics is usually a high throughput approach where many of these molecular species are studied at the same time. The study of small metabolites is called “metabolomics” and the study of transcription “transcriptomics”, etc.

The really cool thing about omics is that markers based on these techniques could predict changes in functional markers many years in advance.

We speculated which omics field is the best for predicting age-related outcomes. It appears that at this moment in time epigenetics takes the lead. However, this may be because the field had a head start and enjoys a large level of standardization which is essential for clinical studies and cohort studies.

The big disadvantage of epigenetics is lack of “interpretability” which means that given a result with some specific epigenetic methylation pattern it is hard to make sense of it in terms of causality. We simply do not know how these marks affect protein levels, which are the ultimate effectors. That is why the different omics layers are so important.

Mahdi also alluded to another issue. We have very little head to head comparison data and when they looked at this in preliminary work, it seemed like, for example, metabolomics was also performing very well for the prediction of age-related outcomes.

The host — brief bio

Kamil Pabis, MSc is an aging researcher and longevity advocate with several years of experience in the aging field that spans multiple countries. Among other projects, Kamil worked on long-lived dwarf mice in Austria, on mitochondrial disease and aging in the UK, and finally on the bioinformatics of aging in Germany and Singapore. Presently, he is involved in several projects related to science communication and translational aging research.

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