Could the genetics between aging and pathologies provide a common ground leading to an understanding of how to improve healthspan?

As time passes, our body’s integrity begins to fail us, making us more vulnerable to age-related diseases (ARD). Medical science and longevity researchers have pushed back on our lifespan limit – we are living longer than even a decade ago! But sadly, these developments have not translated as well into how healthy we are.

Longevity.Technology: Although ARDs involve different organs and pathologies, all of them show strong dependence on age. Now a research team has analysed 116 diseases grouped by age-of-onset profiles and studied their genetic associations.

As the researchers say: “It is therefore important to understand whether the aging process itself leads to different age-related conditions through common pathways, or whether the age dependency of different diseases has independent, time-dependent causes [1].” The study found higher genetic similarity between diseases with similar age-of-onset profiles and also that diseases with age-dependent profiles had an association with known aging-related genes.

Aging is an evolved protective mechanism. With time we accumulate mutations that often have negative consequences to our health, however by having a set mortality rate it may be that these mutations are prevented from being passed on. This is known as the mutation accumulation theory of aging.

The antagonistic pleiotropic theory of aging, differs by proposing that the natural selection of aging arises from genes that are pleiotropic, meaning that they influence two or more phenotypes that at appear to have no relation to each other.

What the theories have in common, is that they suggest that ARDs are influenced by genetic variation. While there have been reports emerging in support of this statement, the molecular basis for age and disease remains elusive.

In a collaborative effort between the Wellcome Trust Genome Campus, The Institute of Healthy Aging at UCL and the Max Planck Institute for Biological Aging, a large bioinformatic study compared genetic association with the age-of-onset of the disease in 116 self-reported diseases from the UK-BioBank database.

The results found that indeed ARDs share genes and common pathways of pathogenicity. Four main clusters were identified: “(1) diseases that rapidly increase after the age of 40, (2) diseases that increase after the age of 20, (3) diseases with no age-related pattern, and (4) diseases that peak around 10 years of age.”

Even by statistically accounting for co-occurrences of diseases, there was greater genetic similarity within a cluster than between clusters, suggesting a similar etymology of the diseases in a cluster. One example of a shared gene is HELLS in cluster 1, where HELLS gene disruption has been shown to cause premature aging in mice [2].

Environmental effects were studied to see how lifestyle of a participant affected the outcome, but it was found that this was not a factor affecting the genetics involved in the aging cluster classification.

Despite this study presenting very convincing data based on just under 500,000 participants, the authors outline various things that were not considered. Firstly, there was a limit of age of up to 65 years, meaning that late-stage diseases, such as Alzheimer’s and Parkinson’s disease, could not be analysed. Furthermore, disease landscape may shift if you a larger age population is considered. Secondly, cancer and immune-related diseases were not considered due to added complexity.

Thirdly, participants of the study face a “UKBB healthy volunteer bias”, whereby those reporting to the database are healthier that the true cohort, hence skewing accurate representation.

Ultimately, what the study shows is that there are common pathways to multiple pathologies which should be focused on by the pharmaceutical industry. Targeting the root of the aging diseases could ease comorbidity and lower the need and toxicity caused by polypharmacy [1].

[1] https://www.nature.com/articles/s43587-021-00051-5
[2] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC406293/
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