How can molecular science help to understand mental health?

Mental health is the sum of our psychological, emotional, and social wellbeing. Combined, these help us cope with life’s difficulties. Yet a worryingly substantial proportion of the population will suffer from poor mental health at some point in their lives. This is the first in a series of blogs exploring the molecular basis of mental health, and how a molecular perspective can help develop new treatments.

by Naveesha Karunanayaka and Isabella von Holstein

According to NHS England, each year 1 in 4 people in the country suffer from poor mental health, and this figure is expected to rise – particularly due to the impact of the global pandemic. But in comparison to our physical wellbeing, our mental health can often be overlooked and neglected. The inability, or lack of motivation, to identify issues with our mental health means that they often go unacknowledged and untreated. Globally, more than 70% of people with mental health illnesses don’t receive treatment. This leads to severe consequences, such as self-harm and suicide. Men aged 45-49 have been found to be particularly vulnerable, having the highest suicide rates in the UK, according to the Office for National Statistics (2019 data). What causes these issues, and why are some people affected more than others?

The molecular science of mental health

The exact influence that different factors have on mental health have yet to be clearly understood. These include genetic influences and trauma. In particular, stress, and the ability to respond to it, has a significant impact on mental health. Neo-natal experiences in rats effect their long-term cognitive emotional response, suggesting that stress response is highly dependent on the individual and their background.

More often than not, ill health is caused by a combination of different effects.  Separating out the influence of different factors is challenging.


Currently the main method of diagnosis is via a healthcare professional’s observations and judgement. However, this is not a particularly accurate or precise method. Self-reporting by the patient is subject to recall and often provides only a snapshot of the problems. Another key issue in diagnosing these conditions is the lack of biomarkers presented. Biomarkers are biological molecules found in the blood or other bodily fluids. Analysis of biomarker molecules provides information that can aid diagnosis of diseases. If biomarkers could be identified that are linked to particular signs or characteristics of mental health issues, these would be identifiable in every patient and a consistent system of diagnosis could be developed. Universal tests or observations that indicate mental health problems could be created that would produce conclusive and accurate confirmation of a mental illness. This would increase accuracy of diagnosis as there would be a definite outcome to look for to diagnose a patient. But – so far – the lack of biomarkers associated with mental health conditions has prevented this method of diagnosis.

Genetics – the analysis of DNA to understand heredity of certain traits – may provide valuable insight. Various techniques are used to either isolate, grow or analyse genes of interest, however, it is difficult to apply this to psychiatric disorders. This is partly because the genetic locations associated with particular psychiatric disorders are usually unknown. In these cases, what’s known as linkage analysis can prove useful – the functionality of genes is observed in relation to their location on a chromosome. Genes that are physically close on a chromosome remain close during cell division, indicating that a certain psychiatric disorder could be passed down through the generations. Other investigative techniques under development will be discussed later in this blog series, including association studies, artificial intelligence and machine learning.

Using molecular science to develop effective treatments

A non-pharmaceutical intervention that has proven effective is psychotherapy, also known as speech therapy. However, most interventions involve drugs to medicate the patient. Medication is usually given using a trial-and-error system that may result in the patient getting worse before they get better.  Available pharmaceutical treatment methods can be unreliable and often have side effects.

Gene therapy, which works by altering, replacing or inactivating genes that cause illnesses, is a viable option currently being explored.  However, it is an invasive and expensive technique, and is not yet approved for psychiatric conditions. In addition, there are currently some discussions around the ethics of this therapy.

two human head silhouettes in different colours
Factors in Mental Health (Manhattan Medical Arts, 2019)

Other options for both diagnosis and treatment are active areas of research. These include the use of virtual reality (VR), artificial intelligence (AI) and even smartphones. Smart apps allow users to document daily life. These apps could also utilise facial recognition to detect slight changes in appearances, which can indicate mental health issues in conjunction with other symptoms. Wearable devices that contain sensors can record information about activity, and in future could measure the chemistry of skin, sweat or blood to monitor key biomarkers and provide deeper insight into the patient’s health. Clinical trials are investigating the efficacy of using the data provided by such devices, combined with other clinical information, to detect early changes in depression, schizophrenia, and posttraumatic stress disorder (PTSD).  We will delve into these technological developments in later blogs.


The stigma around mental health is gradually being alleviated as society opens up about the reality of the issues we are facing. With new technologies constantly emerging and increased focus on good mental health, there is hope that reliable systems for quantitative diagnosis and treatment of psychiatric disorders will be developed soon.

Blog posts coming soon: the molecular science of depression and anxiety, schizophrenia, diagnostic tools and digital twins.


Further Reading

Chakraborty, P.K., Vargehese, T. and Narayana, P.L. (2017). Molecular Genetics in Mental illness. Medical Journal Armed forces India. 50(3):211-214.

Department of Health and Social Care (2011), No Health Without Mental Health: a cross-government outcomes strategy.

Guk, K., Han, G., Lim, J., Jeong, K., Kang, T., Lim, E. K., & Jung, J. (2019). Evolution of Wearable Devices with Real-Time Disease Monitoring for Personalized Healthcare. Nanomaterials (Basel, Switzerland)9(6), 813.

Henderson, C., Evans-Lacko, S. and Thornicroft, G. (2013). Mental illness stigma, help seeking, and public health programs. American journal of public health103(5): 777–780.

Hirschtritt, M.E. and Insel, T.R. (2018). Digital technologies in Psychiatry: Present and Future. The Journal of lifelong learning in psychiatry. 16(3): 251-258.

Korf, B.R. and Liu, N. (2012). Human Genome Project, Genomics, and Clinical Research. Principles and practice of clinical research (Third Edition). 707-725.

Levine, S. (1957). Infantile experience and resistance to physiological stress. Science. 126(3270): 405.

Mental Health Taskforce (2016). THE FIVE-YEAR FORWARD VIEW FOR MENTAL HEALTH. NHS England.

ONS (2019). Suicides in the UK: 2018 registrations.

Schneiderman, N., Ironson, G. and Siegel, S.D.  (2005). STRESS AND HEALTH: Psychological, Behavioral, and Biological Determinants. Annual Review of Clinical Psychology. 1:607-628.

Thome, J., Hassler, F. and Zachariou, V. (2011). Gene therapy for psychiatric disorders. The world journal of biological psychiatry. 12(Suppl 1): 16-18.

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