The Institute runs an annual 1,000 word essay prize competition named after psychiatrist Duncan Macmillan who helped pioneer a community-centred approach to mental health.

The competition is held in partnership with The Royal College of Psychiatrists and promotes the expertise and knowledge of psychiatry trainees nationwide.

The 2020 competition entries have now been judged and we’re delighted to announce this year’s winner is Dr Kathy Liu, MRC Clinical Research Training Fellow at University College London and the Camden and Islington NHS Foundation Trust.

Kathy’s winning entry answered the competition essay question “What’s the next big thing in psychiatry” by exploring why psychiatrists should have a more integrated understanding of the brain-body relationship.

Second prize in the competition was awarded to Dr Jalil-Ahmad Sharif from Solent NHS Trust, for his entry championing how Omic technologies could allow psychiatrists to delve further into the potential risks or biological causes of developing psychiatric diseases by pathogenic genes.

Read both of the winning entries below.

FIRST PRIZE: The next big thing in psychiatry research: A more integrated brain-body perspective

By Dr Kathy Liu (MRC Clinical Research Training Fellow at University College London and Camden and Islington NHS Foundation Trust)

As mental health professionals, we are likely to strive for a holistic approach to managing mental health conditions by considering biological, psychological and social aspects of diagnosis and treatment. But viewing physical and psychological as separate concepts tends to reinforce the division between mind and body, and contrasts with accumulating evidence that points to a closer connection between mental and physical dysfunction than we might have envisaged. Indeed, core symptoms of psychiatric illnesses may result from sustained disturbances of an individual’s cognitive-somatic integration, which can potentially be mitigated via novel prevention and treatment strategies. It looks like the next big thing in psychiatry research could be a more integrated understanding of the brain-body relationship, and how this is impacted by social context.

While it has been known for over 150 years that the heart and brain are anatomically and functionally connected, functional brain imaging has recently revealed specific neural regions, comprising a ‘central autonomic network’, involved in the autonomic regulation of the heart ¹. This network substantially overlaps with others involved in attentional control and emotion regulation, generating a neuro-visceral functional system equipped to achieve goal-directed and adaptive behaviours (for example, in response to stress). Heart rate variability (HRV), or the variation in time between each heartbeat, correlates with central autonomic network activity and is believed to index the integration between the central and autonomic nervous systems. Lower HRV, reflecting poorer neuro-visceral integration and adaptive capacity, has been linked to cognitive impairment and emotional dysregulation in major depression, anxiety, alcohol dependence and schizophrenia ², as well as worse cardiovascular function. As an easy-to-obtain measure, it has potential to be a biomarker of emotional health, particularly in people who are unable to accurately report their subjective emotional states. Crucially, from an intervention perspective, the cardiac neuro-visceral relationship appears to be bi-directional. Biofeedback exercises that involve controlled slow breathing have been shown to achieve increases in HRV, along with decreases in anxiety, depression and post-traumatic stress disorder symptoms ³.

The gut is another organ increasingly shown to be closely connected to brain function. Its anatomical connections are now implicated in the aetiology of Parkinson’s disease, where retrograde transport of ɑ-synuclein from the gut to the substantia nigra, via the vagus nerve and recently discovered nigro-vagal pathway, is suspected to induce degeneration of dopaminergic neurons ⁴. Whether treatment of gastrointestinal disturbances in prodromal Parkinson’s disease might alleviate disease progression has yet to be confirmed. Other research has shown that our gut physiology is associated with one of our core emotions, disgust. Alterations in gut motility secondary to domperidone, a peripheral dopamine antagonist, was related to reduced disgust avoidance in a recent study ⁵, revealing potentially novel pathways to treat maladaptive disgust, which can occur in anxiety and obsessive-compulsive disorders. Changes to gut microbiota diversity can also influence brain function and have been implicated in the development of abnormal visceral pain sensation, which occurs in conditions such as fibromyalgia and complex regional pain syndrome ⁶, possibly via aberrant inflammatory processes.

Brain-body interactions via chemical or humoral signals, related to infection and/or prolonged inflammation, have been implicated in the development of psychiatric disorders to an increasing extent. The rapidly emerging reports of Covid-19-related neuropsychiatric sequelae ⁷ have recently brought this to light, although the longer-term consequences of ‘Long-Covid’, where patients commonly experience fatigue, anxiety, depression and cognitive impairment, have yet to be fully characterised. Peripheral inflammation can activate cytokine-binding sites expressed along autonomic pathways such as the vagus nerve, rapidly inducing activation of specific brain regions and changes in behaviour, whilst some circulating inflammatory factors, such as interleukin (IL)-6 and interferon (IFN)-alpha, can directly trigger a cascade of microglial activation across the brain ⁸. There is convincing evidence that sustained inflammation due to, for example, chronic stress, plays a role in and is a potential treatment target for at least a subset of patients with major depression and bipolar disorders ⁹.

Functional neurological disorders (FND), widely perceived to be purely psychological in origin, are increasingly considered to be disorders of cognitive-somatic integration. Recent findings have shown that our brain’s perception and interpretation of bodily signals can influence emotion, via ‘bottom-up’ sensory signals and ‘top-down’ attentional and predictive processes that generate an internal representation of the body. There is evidence that these processes, including the ability to accurately sense internal signals, are abnormal in FND and associated with enhanced or diminished sensory perceptions or movements ¹⁰. These mechanistic underpinnings may also be shared with functional somatic syndromes such as chronic fatigue syndrome and fibromyalgia ¹¹, as well as eating disorders ¹². It is possible that the modulation of individuals’ internal bodily awareness may form the basis for effective treatments for these disorders in future.

Further research that establishes how specific brain-body integration deficits arise and develop within varying social contexts, whilst identifying predisposing, triggering and perpetuating factors, is necessary. Ageing is likely to be one of the factors that impacts the integrity of the brain-body relationship, and age remains the biggest risk factor for dementia. A recent study found that frailty, defined as a multisystem decline in reserve and function giving rise to physiological vulnerability, independently contributed to dementia risk ¹³. If the cognitive-somatic pathways underlying this association can be clarified, interventions that target frailty could form part of preventative strategies for dementia in future.

We are faced with mounting evidence that supports a deep level of brain-body integration. Our growing understanding has manifold implications for psychiatry and increasingly falls out of step with the convention of considering physical and mental health separately. The important question that remains on our minds and lips is, will a more integrated brain-body perspective deliver inclusive and meaningful benefits for patients?

References

1. Thayer, J. F. & Lane, R. D. A model of neurovisceral integration in emotion regulation and dysregulation. J. Affect. Disord. 61, 201–216 (2000).
2. Mulcahy, J. S., Larsson, D. E. O., Garfinkel, S. N. & Critchley, H. D. Heart rate variability as a biomarker in health and affective disorders: A perspective on neuroimaging studies. Neuroimage 202, 116072 (2019).
3. Lehrer, P. et al. Heart Rate Variability Biofeedback Improves Emotional and Physical Health and Performance: A Systematic Review and Meta Analysis. Appl. Psychophysiol. Biofeedback 45, 109–129 (2020).
4. Travagli, R. A., Browning, K. N. & Camilleri, M. Parkinson disease and the gut: new insights into pathogenesis and clinical relevance. Nat. Rev. Gastroenterol. Hepatol. 17, 673–685 (2020).
5. Nord, C. L., Dalmaijer, E. S., Armstrong, T., Baker, K. & Dalgleish, T. A Causal Role for Gastric Rhythm in Human Disgust Avoidance. Curr. Biol. (2020) doi:10.1016/j.cub.2020.10.087.
6. Crock, L. W. & Baldridge, M. A Role for the Microbiota in Complex Regional Pain Syndrome? Neurobiology of Pain 100054 (2020).
7. Varatharaj, A. et al. Neurological and neuropsychiatric complications of COVID-19 in 153 patients: a UK-wide surveillance study. Lancet Psychiatry 7, 875–882 (2020).
8. Savitz, J. & Harrison, N. A. Interoception and Inflammation in Psychiatric Disorders. Biol Psychiatry Cogn Neurosci Neuroimaging 3, 514–524 (2018).
9. Jones, B. D. M. et al. Inflammation as a treatment target in mood disorders: review. BJPsych Open 6, e60 (2020).
10. Drane, D. L. et al. A framework for understanding the pathophysiology of functional neurological disorder. CNS Spectr. 1–7 (2020).
11. Teodoro, T., Edwards, M. J. & Isaacs, J. D. A unifying theory for cognitive abnormalities in functional neurological disorders, fibromyalgia and chronic fatigue syndrome: systematic review. J. Neurol. Neurosurg. Psychiatry 89, 1308–1319 (2018).
12. Martin, E., Dourish, C. T., Rotshtein, P., Spetter, M. S. & Higgs, S. Interoception and disordered eating: A systematic review. Neurosci. Biobehav. Rev. 107, 166–191 (2019).
13. Wallace, L. M. K. et al. Investigation of frailty as a moderator of the relationship between neuropathology and dementia in Alzheimer’s disease: a cross-sectional analysis of data from the Rush Memory and Aging Project. Lancet Neurol. 18, 177–184 (2019).

SECOND PRIZE: The next big thing in psychiatry research: Omic technologies

Br Dr Jalil Sharif (Solent NHS Trust)

The human genome was mapped successfully in 2003 using Omic technologies. Since then, new waves of sequencing technology, allowed scientists to correlate the impact of genes on diseases, while simultaneously kick-starting a new branch of medicine called personalised medicine.¹

Oncology is at the forefront of harnessing personalised medicine. It is gradually permeating to other specialities, and its impacts on psychiatry will be groundbreaking.

Current challenges in psychiatry are the symptom-based diagnosis to provide caseness for patients; limitations are the inadequate response of psychotropic medications to their respective psychiatric disease. Taking aripiprazole as an example, only one in five patients responds to treatment. ¹

The RDOC framework developed by the US-based NIMH is an outstanding tool in neuroscience research to overcome this challenge. ^2–4⁠ It takes the bio-psycho-social model into account to integrate psychiatric diseases with neuroscience to research pathogenesis to establish a newer system of nosology. RDOCs framework biological model aims to research dysfunctional genetic, transcriptional, translational, cellular and biochemical processes, namely:

• genomics (DNA sequences)
• transcriptomics (RNA transcription)
• proteonomics (protein translation)
• metabolomics (metabolites). ^3,5

It focuses on individual and combined biological processes and integrates them systematically to establish causes for psychiatric illnesses. Studying the impact of psycho-social stressors using the RDOC framework is vital.

Intellectual disability (ID) and autism have multiple pathogenic genes. The dysfunctional SHANK3-gene, for example, implicated in autism and Phelan-McDermid syndrome, is potentially pathogenic in schizophrenia and other neuropsychiatric conditions. ⁶

In the UK, the STOMP initiative has improved prescribing for patients with ID. It aims to reduce the harm caused by polypharmacy - closely considering risk vs benefit, especially when prescribing off-licence. ⁷ The combining of Omics with the RDOC framework will further direct treatments to specific psychiatric diseases and reduce polypharmacy. Appropriate rationalisation of psychotropic medications is essential in minimising patients exposure to unnecessary drugs using outdated prescribing practices. Targeted medication reviews are critical in reducing the unwarranted impact of the multi-factorial side-effects of psychotropic drugs.

Targetting rare genetic causes of ID, where patients are more susceptible to psychiatric diseases such as schizophrenia allows their analysis in genome-wide association studies. Subsequently, stratifying these genes into polygenic risk scores will enable clinicians to develop improved diagnostic tools taking genes as well as symptoms into account. Improving psychiatric diagnosis, caseness and treatment, taking current prescribing practises to another level. ^8–10

An example of how this can be achieved is the breakthrough technology of induced pluripotent stem-cells (iPSC) alongside genomics, it enabled the culturing of human neuronal organoids. These "mini-brains" utilised in research are the closest to human in-vivo testing to study aberrant neuronal functioning and morphology, an excellent tool for psychiatric personalised medicine. ^11–15 Suppose genes show statistically significant pathogenic potential, splicing this distinct pathogenic gene into human iPSCs allows research of neurodevelopment and drug response to psychotropic medications.

It is evident that utilising this research methodology will enable a better understanding of the biochemical changes to inter-cellular and intra-cellular processes when modulating neurotransmission and neurotransmitters by psychotropic medications. Subsequently, this will enhance the targetting of psychotropic medications to the patient, called pharmacogenomics, thus offering personalised medicine. Pharmacogenomics will also help in repurposing the side-effects of drugs used for other conditions as a potential treatment option in psychiatric diseases. ¹⁶

Another layer of research in Omics is the study of epigenetics, exploring if specific genes are switched on or off. This regulatory mechanism impacts on protein synthesis, metabolism and signalling. ^11,17⁠ As epigenetics elucidates expression of distinct genes, it is vital in understanding the reverse effect of psychotropic drugs on the gene, proteins, and metabolism in the neurons but also other cells in the body. Interestingly, psycho-social factors reflexively modulate genes epigenetic properties. Research of psycho-social factors leading to diseases are priorities of the RDOC framework, as mentioned earlier.

For example, epigenetic research has challenged some firm-held beliefs of pathophysiology in dopaminergic and glutaminergic signalling in schizophrenia. A study by Skene et al. 2018 in mice focused on pathological messenger RNA transcription processes impacting on protein metabolism, highlighting an increased association of schizophrenia in the abnormal development of medium spiny neurons, pyramidal cells, and interneurons. Conversely, it did not find any association with dopaminergic neurons or glutaminergic communication pathways. ¹⁸⁠

Current challenges of genomic research are the underrepresentation of ethnic minority participants and researchers. The genetic reference genome for cross-referencing genetic variants comes from a White-American cohort. It undermines population-based genetic research from non-white ethnic backgrounds, potentially misattributing ethnic-specific genetic variants as pathogenic—reducing utility in these population cohorts. ¹⁹ The East London Genes and Health dataset is a step in the right direction as it uses genetic data from south Asian participants reducing reference genome bias, enriching genomic research by generating ethnic-specific variants. ²⁰⁠ Interestingly, the datasets may highlight founder effects and the impact of cultural practices on the prevalence of diseases, such as consanguineous marriages. In such population cohorts prevalence of disorders may be higher as genes are either recessive or knocked-out, highly advantageaous for studying rare diseases. ²¹

Currently, second-generation or next-generation sequencing technology analyse smaller fragments of the genetic code to provide a better understanding of the overall intronic or exonic regions. Despite being very sensitive and specific with a low error-rate, the bottleneck to Omics research is the cost and computational resources required. ^22,23

The third generation of sequencing technology aims to overcome some challenges by being able to capture longer sequences of patients genetic and epigenetic profile in parallel; however, it retains the financial and computational challenges. ²⁴ Proof-of-concept fourth generation sequencing goes further by visualising real-time protein synthesis in cells in-situ, vital in studying the effect of psychotropic drugs on cells. ²⁵

In summary, Omic technologies will undoubtedly allow us to delve further into the potential risks or biological causes of developing psychiatric diseases by pathogenic genes. Harnessing stem cells for pharmacogenomic testing will enable better targetting of medication to patients. In turn, this will reduce polypharmacy. Combining pharmacogenomics and the RDOC framework will enhance patient care by genetic - and symptom-based nosology enhancing psychiatric Personalised Medicine.

References

1. Schork NJ. Personalized medicine: Time for one-person trials. Nature. 2015;520(7549):609–11.
2. Heckers S. The Value of Psychiatric Diagnoses. JAMA Psychiatry. 2015 Dec 1;72(12):1165.
3. Cuthbert BN. The RDoC framework: Facilitating transition from ICD/DSM to dimensional approaches that integrate neuroscience and psychopathology. World Psychiatry. 2014 Feb 1;13(1):28–35.
4. Sisti D, Young M, Caplan A. Defining mental illnesses: Can values and objectivity get along? BMC Psychiatry. 2013 Dec 24;13(1):346.
5. Hasin Y, Seldin M, Lusis A. Multi-omics approaches to disease. Genome Biol. 2017;18(1):1–15.
6. Yi F, Danko T, Botelho SC, Patzke C, Pak C, Wernig M, et al. Autism-associated SHANK3 haploinsufficiency causes Ih channelopathy in human neurons. Science (80- ). 2016;352(6286).
7. Branford D, Gerrard D, Saleem N, Shaw C, Webster A. Stopping over-medication of people with intellectual disability, Autism or both (STOMP) in England part 1 – history and background of STOMP. Vol. 13, Advances in Mental Health and Intellectual Disabilities. Emerald Group Publishing Ltd.; 2019. p. 31–40.
8. Torkamani A, Wineinger NE, Topol EJ. The personal and clinical utility of polygenic risk scores. Vol. 19, Nature Reviews Genetics. Nature Publishing Group; 2018. p. 581–90.
9. Igo RP, Kinzy TG, Cooke Bailey JN. Genetic Risk Scores. Curr Protoc Hum Genet. 2019 Dec 1;104(1).
10. Khera A V., Chaffin M, Aragam KG, Haas ME, Roselli C, Choi SH, et al. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat Genet [Internet]. 2018;50(9):1219–24. Available from: http://dx.doi.org/10.1038/s41588-018-0183-z
11. Attwood S, Edel M. iPS-Cell Technology and the Problem of Genetic Instability—Can It Ever Be Safe for Clinical Use? J Clin Med. 2019;8(3):288.
12. Kim D, Kim C, Moon J, Chung Y, Chang M, Han B, et al. Generation of Human Induced Pluripotent Stem Cells by Direct Delivery of Reprogramming Proteins. Cell. 2010;4(6):472–6.
13. Chung SY, Kishinevsky S, Mazzulli JR, Graziotto J, Mrejeru A, Mosharov E V., et al. Parkin and PINK1 Patient iPSC-Derived Midbrain Dopamine Neurons Exhibit Mitochondrial Dysfunction and α-Synuclein Accumulation. Stem Cell Reports [Internet]. 2016;7(4):664–77. Available from: http://dx.doi.org/10.1016/j.stemcr.2016.08.012
14. Takahashi K, Yamanaka S. Induction of pluripotent stem cells from mouse embryonic and adult fibroblast cultures by defined factors. Cell. 2006;126(4):663–76.
15. Takahashi K, Tanabe K, Ohnuki M, Narita M, Ichisaka T, Tomoda K, et al. Induction of Pluripotent Stem Cells from Adult Human Fibroblasts by Defined Factors. Cell. 2007;131(5):861–72.
16. Doss MX, Sachinidis A. Current Challenges of iPSC-Based Disease Modeling and Therapeutic Implications. Cells. 2019;8(5):403.
17. Kim K, Doi A, Wen B, Ng K, Zhao R, Cahan P, et al. Epigenetic memory in induced pluripotent stem cells. Nature. 2010;467(7313):285–90.
18. Skene NG, Bryois J, Bakken TE, Breen G, Crowley JJ, Gaspar HA, et al. Genetic identification of brain cell types underlying schizophrenia. Nat Genet [Internet]. 2018;50(6):825–33. Available from: http://dx.doi.org/10.1038/s41588-018-0129-5
19. Ballouz S, Dobin A, Gillis JA. Is it time to change the reference genome? Genome Biol. 2019;20(1):1–9.
20. Finer S, Martin HC, Khan A, Hunt KA, Maclaughlin B, Ahmed Z, et al. Cohort Profile: East London Genes & Health (ELGH), a community-based population genomics and health study in British Bangladeshi and British Pakistani people. Int J Epidemiol. 2020 Feb 1;49(1):20-21I.
21. Erzurumluoglu AM, Shihab HA, Rodriguez S, Gaunt TR, Day INM. Importance of Genetic Studies in Consanguineous Populations for the Characterization of Novel Human Gene Functions: Consanguineous Populations and Genetics. Ann Hum Genet [Internet]. 2016 Jul 30;80(3):187–96. Available from: http://doi.wiley.com/10.1111/ahg.12150
22. Goodwin S, McPherson JD, McCombie WR. Coming of age: Ten years of next-generation sequencing technologies. Nat Rev Genet. 2016;17(6):333–51.
23. Katsanis SH, Katsanis N. Molecular genetic testing and the future of clinical genomics. Nat Rev Genet. 2013;14(6):415–26.
24. Schatz MC. Nanopore sequencing meets epigenetics. Vol. 14, Nature methods. Nature Publishing Group; 2017. p. 347–8.
25. Ke R, Mignardi M, Hauling T, Nilsson M. Fourth Generation of Next‐Generation Sequencing Technologies: Promise and Consequences. Hum Mutat [Internet]. 2016 Dec 10 [cited 2020 Nov 13];37(12):1363–7. Available from: https://onlinelibrary.wiley.com/doi/10.1002/humu.23051