On Building Measures to Link Aging and Disease
In this popular science article on the relationship between aging and age-related disease, a researcher discusses one of a number of approaches to producing a biomarker of aging, a sensitive measure of the degree to which an individual is impacted by the cell and tissue damage of aging. A good biomarker should predict the onset of disease and remaining life expectancy to the degree that these are determined by damage:
In epidemiological studies, particular those focused on the molecular mechanisms of 'growing older', loss of function and the emergence of 'biomarkers' of disease, even in young middle-aged 'healthy' adults, are often presented as diagnostics for human ageing. From my perspective, this is almost certainly misleading as it implies that health, disease and longevity are all interchangeable synonyms for ageing. If we wish to identify a definitive 'ageing' molecular programme (e.g. biological age), one that is independently informative for future health and life span then it is critical that we clearly define what is meant by the term 'ageing' and appropriately develop an assay that measures this parameter. We also have to consider if the developed diagnostic, while statistically significantly related to biological age, is sufficiently sensitive and specific enough to be considered a useful diagnostic (most will fail this final criteria e.g. telomere assays).The other major consideration relates to how a novel diagnostic of 'biological age' would be used. If it were to be used as an independent diagnostic of longevity then it would be combined with other factors and behaviours that determine life-span, such as smoking and obesity. One could imagine the generation of an integrated risk 'score' utilised to determine insurance premiums for healthcare or to calculate pension requirements. These may seem controversial examples, but in reality our chronological age (birth year) and behaviours are already judged and used for these purposes. Why not have a more accurate 'diagnosis' of the contribution 'age' makes to these decisions? For example, if you are a poor 'biological age' (for your chronological age) then your breast-cancer or prostate-cancer screening might be scheduled 5-10 yr earlier than average.
Variation in the human transcriptome (RNA) has proven particularly powerful for identifying the huge variations in human physiology and physiological responses to environmental influences. So it is not surprising it has been used to develop diagnostics of human ageing, including our own model. While you can't use chronological age to diagnose the health status of an individual - the relationship between chronological age and disease is an epidemiological one - existing RNA or DNA methylation assays represent composites of ageing, disease and drug-treatment and not chronological age. We believe that 'biological' age will determine when you show clinical symptoms of disease and that we need an assay which accurately reflects your underlying 'rate of ageing' or 'biological age'. Which 'age associated' disease an individual then develops will depend on their genetic, epigenetic and environmental risks factors (and stochasticity).
To produce this new diagnostic of 'biological age' we had the hypothesis that we can find a set of RNAs in the tissue that was diagnostic for telling tissue from healthy old from healthy young people apart. In our study healthy old people were living a normal sedentary lifestyle, did not have type II diabetes and importantly had good fitness levels. By applying machine learning to this 'special' healthy ageing cohort, we found 150 RNA markers. In fact we could see that these 150 RNAs were either up or down regulated in tissue from healthy old people and we reasoned that activation of this gene expression 'programme' may help explain why these 65 year old people achieved good health despite living a sedentary life style. In fact, when we then applied the 150 RNA assay to a group of 70 year old people (people with the same chronological age) we found that their 'biological age' score varied dramatically and for those that failed to switch the gene expression pattern "on" as much died sooner and had a greater decline in organ function (kidney).
Link: http://www.labnews.co.uk/features/ageing-become-disease-08-02-2016/
Horvath's epigenetic clock seems to be the best biomarker of aging currently available. It would be helpful if more studies used it when testing in humans medicine or anti-aging therapies which could possibly extend lifespan such as rapamycin and senescence cell clearance.
Horvath's DNAm clock relies on 450K Illumina chip data - an assay that is very far from being a reliable LBT or diagnostic. The statistical correlations are highly significant (driven by a small relationship and very high sample sizes). In studies examining its utility for forecasting long term health or mortality (e.g. Lothian Study) the 450K assay was poorly related to outcomes, at a level the would not be useful, clinically. A key point is clinical relevance versus statistical significance. The latter does not ensure the former.