Computational approaches to investigate multimorbidity in depression

Summary

Background:

Depression is a complex heterogeneous disorder. In severe depressive episodes, patients experience feelings of hopelessness/worthlessness, and suicide is a prominent risk. As the leading cause of global disability1, 300 million people are affected by depression and public health figures indicate depression accounts for 76.4 million years lost to disability; more than any other condition2. NI has one of the highest rates of depression in Europe with a lifetime prevalence rate of 16.3%3 and continues to have the highest rate of suicide of any UK country, at 16.5/100,000 (NISRA, reported by BBC News). NI has a 20-25% higher prevalence rate of mental health problems than the rest of the UK, with associated costs of £3.5 billion. The cost of depression to the UK economy is £70-£100 billion/year. Depression is often co-morbid with chronic physical conditions, particularly those with an inflammatory aetiology including CVD, diabetes and rheumatoid arthritis. In fact, a recent analysis of primary care patients reported that individuals with depression were more likely to have any one of 32 physical health conditions examined than those with no history of depression4. The concurrent occurrence of multiple chronic diseases in a single patient is known as multimorbidity and it is the most common chronic condition affecting more than 50% of people over the age of 65. Inflammation has been reported as a common occurrence in many patients exhibiting co-morbid or multimorbid diseases. Patients with multimorbidity have complex treatment needs that are not met by the single index disease-oriented approach. Thus there is an unmet need for better diagnostics and tailored therapies for patients with multimorbidity. In this project, our focus will be to investigate multimorbidity, specifically in depression.

Dataset:

Northern Ireland Centre for Stratified Medicine (NICSM) has recently procured access to the UK Biobank dataset, including, genomic, biochemical, diagnosis, medication/treatment, demographic/local-environment data of 500,000 participants. Approximately 20,000 of these participants are diagnosed with depression according to International Classification of Diseases Tenth Revision (ICD10). Of these, approx. 4000 are also diagnosed with asthma, approx. 1000 with chronic kidney disease, approx. 3000 with diabetes and approx. 1700 with stroke.

Aims:

Using advanced computational methods, such as Machine Learning, we aim to stratify mental health patients based on associated comorbidities and identify biomarkers (genomic, biochemical, demographic, etc.) which can help in better diagnosis of those stratified groups.

Prospective candidate:

The project will be entirely computational. Thus, we are seeking a student having a strong interest in computational approaches evidenced by good programming skills (preferable in Linux, MATLAB, C/C++, Python or R) and knowledge in biomedical/biological sciences, computational biology and statistics. However, students from more biology oriented background but strong interest to learn bioinformatics and programming are also encouraged to apply. Appropriate training will be provided during the course of PhD study. For any informal enquiry and/or to discuss more about the project, please contact the lead supervisor or any member of the supervisory team. Contact details of the supervisory team are mentioned on the right hand side of this webpage.

Researcher will be based at C-TRIC (Altnagelvin Hospital site).

References:

1. World Health Organization. (2018). Depression. [online] Available at: http://www.who.int/mediacentre/factsheets/fs369/en/

2. Global Burden of Disease Study 2013 Collaborators. (2015). Global, regional, and national incidence, prevalence, and years lived with disability for 301 acute and chronic diseases and injuries in 188 countries, 1990-2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet (London, England), 386 (9995), 743-800.

3. Bunting, BP, Murphy, SD, O'Neill, SM & Ferry, FR. (2012). 'Lifetime prevalence of mental health disorders and delay in treatment following initial onset: evidence from the Northern Ireland Study of Health and Stress', Psychol Med, vol. 42, no. 8, pp.1727-39.

4. Smith, DJ, Court, H, McLean, G, Martin, D, Langan Martin, J, Guthrie, B, Gunn, J & Mercer SW. (2014). 'Depression and multimorbidity: a cross-sectional study of 1,751,841 patients in primary care', J Clin Psychiatry, vol. 75, no. 11, pp. 1202-8.

Essential criteria

Applicants should hold, or expect to obtain, a First or Upper Second Class Honours Degree in a subject relevant to the proposed area of study.

We may also consider applications from those who hold equivalent qualifications, for example, a Lower Second Class Honours Degree plus a Master’s Degree with Distinction.

In exceptional circumstances, the University may consider a portfolio of evidence from applicants who have appropriate professional experience which is equivalent to the learning outcomes of an Honours degree in lieu of academic qualifications.

  • Sound understanding of subject area as evidenced by a comprehensive research proposal

Desirable Criteria

If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.

  • Completion of Masters at a level equivalent to commendation or distinction at Ulster
  • Experience using research methods or other approaches relevant to the subject domain
  • Work experience relevant to the proposed project
  • Publications - peer-reviewed
  • Publications record appropriate to career stage
  • Experience of presentation of research findings
  • A comprehensive and articulate personal statement
  • Use of personal initiative as evidenced by record of work above that normally expected at career stage.
  • Relevant professional qualification and/or a Degree in a Health or Health related area

Funding and eligibility

The University offers the following levels of support:

Vice Chancellors Research Studentship (VCRS)

Full award (full-time PhD fees + DfE level of maintenance grant + RTSG for 3 years).

This scholarship will cover full-time PhD tuition fees and provide the recipient with £18,000 (tbc) maintenance grant per annum for three years (subject to satisfactory academic performance).

This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.

Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

Vice-Chancellor’s Research Bursary (VCRB)

Part award (full-time PhD fees + 50% DfE level of maintenance grant + RTSG for 3 years).

This scholarship will cover full-time PhD tuition fees and provide the recipient with £8,000 maintenance grant per annum for three years (subject to satisfactory academic performance). This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.

Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

Vice-Chancellor’s Research Fees Bursary (VCRFB)

Fees only award (PhD fees + RTSG for 3 years).

This scholarship will cover full-time PhD tuition fees for three years (subject to satisfactory academic performance). This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.

Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

Department for the Economy (DFE)

The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £18,000 (tbc) per annum for three years (subject to satisfactory academic performance).

This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.

  • Candidates with pre-settled or settled status under the EU Settlement Scheme, who also satisfy a three year residency requirement in the UK prior to the start of the course for which a Studentship is held MAY receive a Studentship covering fees and maintenance.
  • Republic of Ireland (ROI) nationals who satisfy three years’ residency in the UK prior to the start of the course MAY receive a Studentship covering fees and maintenance (ROI nationals don’t need to have pre-settled or settled status under the EU Settlement Scheme to qualify).
  • Other non-ROI EU applicants are ‘International’ are not eligible for this source of funding.
  • Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

Due consideration should be given to financing your studies. Further information on cost of living

The Doctoral College at Ulster University

Key dates

Submission deadline
Friday 7 February 2020
12:00AM

Interview Date
9 to 20 March 2020

Preferred student start date
Mid September 2020

Applying

Apply Online  

Contact supervisor

Dr Priyank Shukla

Other supervisors

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