Interview with Prof. Slagboom Part 2: Metabolomics and healthy ageing

Nightingale recently interviewed Prof. P. Eline Slagboom (Leiden University Medical Center, The Netherlands) about her pioneering research on biological ageing. In part 1 of our interview, Eline explained why genetics and lifestyle factors play key roles in age-related diseases. In this second part of the interview, Eline discusses healthy ageing and "age-predictors."

How do we define unhealthy ageing? 

I would say unhealthy ageing is due to certain aspects of cellular and tissue ageing. You could also say that unhealthy ageing is characterized by having problems with your insulin signaling, blood pressure, lipid metabolism, and stress responses. In a deeper fashion, part of that systems failure can also be brought back to the fact that cells have accumulated damaged proteins and cells and can’t clean them up. 

In aged populations found in isolated regions, (so-called “Blue Zones”), molecular evidence has supported the theory that high vegetable intake, physical activity, low stress and a sense of community, promotes living to extreme ages. Is there comparable evidence from studies in Dutch cohorts?

We have not focused on any such groups of individuals in Dutch cohort studies. Blue zones are typically isolated, both genetically and also in terms of lifestyle. I think the biggest challenge for us is how to carry out our lives healthily and not get stressed. When we did a Dutch intervention study (the Growing Old Together Study), people aged between 60-70 reduced their calorie intake by 12.5 % and carried out 12.5% more exercise, which was a very feasible adjustment for them. It was amazing what a difference just 3 months of intervention made to their metabolic health, for example their capacity to remodel muscle. You can apply lifestyle interventions like these to non-isolated populations. 

At your Molecular Epidemiology section, you are focusing on using a variety of omics data, not solely genomics approaches. What do the different omics tell us and where do you see metabolomics aiding our understanding of human ageing?

I think omics can help in any area where you want to prove some pathway is causal to an outcome. It’s fantastic that you have genomics data you can use to make polygenic risk scores. On the other hand, your germline genetic background doesn’t change with age. I’m convinced that if you want to monitor people in the second half of life then all health aspects become important. You need quantitative omics data; the epigenome, metabolome and proteome. Metabolomics are a proof of principle, they help us to consider the different aspects of health and biological age before providing clinical advice. 

Why did you choose Nightingale’s metabolomics platform to support your research? 

We chose Nightingale very early on in the process when we wanted to work on metabolomics with our biobanking consortium BBMRI.Nl to investigates metabolites for a range of diseases. There were already a couple of papers out and I had the impression that the domains Nightingale’s platform captures were interesting. Whilst there are 220+ variables, they are very much related. I thought it was interesting that the platform already showed associations for different diseases. We have now submitted about seven papers about this work with our consortium and had about 50,000 samples measured in five waves. The quality of the measurements was good, they were delivered in time and the team kept us posted. 

Long-living families express favorable immune-metabolic health profiles that seem to be the opposite to metabolic syndromes. What can we do with this information and how can we translate other findings from ageing studies into effective medical interventions?

We can conclude that immune and metabolic health represents a large proportion of the physiological heterogeneity of elderly. In the clinic, we need improved biomarkers that are affordable and standardized, so that you can measure anybody turning up in hospitals and report on the status of multiple organs. Essentially, we need to establish more effective baseline measurements for each elderly person in the clinic. 

What would you consider are the most effective multi-biomarker indicators for unhealthy ageing? (e.g. Frailty Index) 

It depends on where you are in the life course, to make a distinction between healthy and unhealthy ageing, whether you want to estimate the health of a forty or 80-year-old. If you have a patient over 80 years, then establishing the frailty index is very important, based on health deficits and disabilities of daily living. If you really want to establish something more, we think you need molecular markers. Frailty can’t tell you everything about somebody’s response to intervention. I do think you need more of a molecular basis for that, which is what we’re been working on in the clinic. 

Four circulating biomarkers predicted the short-term risk of all-cause mortality among participants from the Estonian Biobank (after adjusting for conventional risk factors). How can findings like these be used to improve our assessment and treatment of ageing populations?

Firstly, those observations had to be replicated and we did that and even improved the prediction in a study based on a diversity of cohorts with data on prospective mortality (including many BBMRI.Nl cohorts). The question is however, where in the clinic do you really need a mortality predictor? It depends on what age frame you are looking at. It can only be useable if the prediction is good enough for the patient group that you want to take decisions on. I think it has to be shown to work in practice in order to establish to what extent clinicians can really use it. If during treatment, a participant’s metabolic profile changes to a more beneficial profile (or a profile that’s less associated with mortality) indicative on health at an individual basis, then it could be useful as a surrogate marker to test the outcome of different treatment strategies. 

What can you tell us about the “age predictor” you are developing?

Out of 100 people with the same calendar age, one is going to die tomorrow, and another is going live for 20 years longer. Calendar age doesn’t really say that much about the second half of life. An age predictor based on someone’s metabolic age is much more interesting and is now being tested for its associations with a number of diseases. Such a marker has already been established with respect to DNA methylation changes with age. For example, people with a relatively old DNA methylation profile have a higher mortality risk. 

Extra reading

Find out more about how metabolomics is helping researchers to unravel the complex biological mechanisms underpinning ageing in our article, Measuring Youth.