People with severe mental illness (such as schizophrenia, bipolar disorder and major depressive disorder) have a reduction in life expectancy of up to 25 years. That is the difference between having a retirement and not.

The main cause of early death in the UK is heart disease, and this has historically been attributed to the shared risk factors, such as low socioeconomic status, alcohol and smoking, which are more common in mental illness and increase with severity of disease. Medication for mental illness further increases risks of heart disease through increased weight gain.

What if this is overly simple? What if the same mechanisms cause mental illness and heart disease? This would fit with shared risk factors, and if the mechanisms could be identified, maybe medication for heart disease could be repurposed to improve symptoms of severe mental illness and reduce the long-term complications.

The growing availability of large datasets such as the UK Biobank, discoverable via the Health Data Research Innovation Gateway, with information on mental and physical health traits and genetic data is crucial to increasing our understanding of the impact of mental illness on risk of heart disease.

Genetic data is particularly important, as it is fixed from conception and it not influenced by disease processes, therefore a single measure can be useful for a lifetime. The open science initiative is equally important, as the publicly available results of genetic analyses can be hugely powerful tools for further analysis:

  • As genome-wide association analyses identify more and more genetic regions associated with mental and physical illness, it is clear that many of them are implicated in both. Detailed candidate loci analyses can start to determine whether the same genes are involved. Combining the genes into network maps can highlight mechanisms for further investigation.
  • Genetic correlations comparing effects of genetic variants across the whole genome on mental and physical illness can determine which conditions might be more genetically similar. High genetic similarity could mean the same mechanisms being involved, and as the mechanisms underlying heart disease are better understood than those for severe mental illness, this can point to potential mechanisms for further investigation in mental illness.
  • Genetic risk scores, (i.e., the sum of risk-variants) for one illness (e.g., schizophrenia) can be assessed for impact on other illnesses (e.g., heart disease), with extensions of the methods being able to determine which condition is driving which, or whether the is a bi-directional effect.

All of these approaches are now possible because of the public availability of results of most genome-wide association studies. It is worth noting that these methods are all providing evidence supportive of shared mechanisms underlying mental and physical illness.

A further question is why mental illness is so heterogeneous? For example, most depressive episodes are accompanied by weight gain (typical depression), but a small proportion are accompanied with weight loss (atypical depression). Could there be genetically different groups, within a mental illness diagnoses?

One recent study suggests that this is indeed the case, with genetic variation implicated in both schizophrenia and heart disease identifying three groups of individuals, with subtle differences in heart disease risk. No grouping was identified when using genetic variation implicated in depression or bipolar disorder and heart disease. The grouping and between-group differences were repeated in an independent dataset, but the between-group differences are too small to be clinically useful.

In summary, large population studies with a wide range of information and genetic data are crucial to understanding of comorbidities of mental illness. Currently evidence suggests that there are shared mechanisms for mental and physical illness, but the details are not yet clear.

I aim to expand on this work by investigating the early life and longitudinal impact of the genetically defined groups, as well as extending this approach to non-European ancestry data.


Dr. Rona Strawbridge is UKRI Innovation Fellow at University of Glasgow.

Follow Rona on Twitter @RJStrawbridge.

Read this interview with Rona from last year’s Festival of Genomics

Explore the mental health Collection on the Gateway.