Early-onset depression: characterising development and identifying risks

Major depressive disorder (MDD) is the most common mental illness and is the single leading cause of years lived with disability. It is the top cause globally of disability in adolescents and young adults. The transition between adolescence and young adulthood is important because most new episodes of depression begin at this time. This transition period is the focus of our investigation. Depression that begins early (by the early 20s) predicts particularly poor mental health and social outcomes and is associated with a chronic and relapsing illness course which is difficult to treat. The strongest and most common risk factor for early-onset MDD is depression in a parent which increases the risk of depression in offspring by 3 to 4 fold. Many scientific and policy reports have identified the offspring of depressed parents as meriting special consideration so that those at ultra-high risk for depression can be identified early and receive preventive interventions. For these early intervention and prevention strategies to be effective in reducing the burden of depression, it is important to understand how depression first develops and what it is preceded by. It is also important to acknowledge that not all individuals with a depressed parent will go on to develop depression themselves so we need methods of predicting who is at highest risk so that those at very high risk can quickly receive the help they need. That is what we aim to do in this study which will use the largest existing sample of the offspring of recurrently depressed parents. These participants have been assessed repeatedly throughout adolescence and we now propose to undertake a fourth assessment timed to coincide with them making the transition to adult life. We will 1) characterise the long-term trajectory of depressive symptoms and 2) identify the factors that predict those who have a chronically depressed symptom trajectory as well as a diagnosis of depressive disorder. 3) We will develop and validate a risk prediction calculator that can be used to quantify individual risk for early-onset depression. Such tools are used routinely in general practice for predicting physical health problems such as cardiovascular disease but are not currently available for monitoring mental health. These kinds of tools are useful for ensuring that those individuals most in need receive the support and care they require and are targeted for prevention. Collectively, our study aims to identify the antecedents of early-onset depression and to develop clinical tools that will ultimately help individuals and families affected by chronic and impairing early-onset depression.

Major depressive disorder (MDD) is the most common mental illness and is the single leading cause of years lived with disability. Depression that begins early (by the early 20s) predicts particularly poor mental health and social outcomes and a chronic and relapsing course of symptoms over time. The strongest and most common risk factor for early-onset MDD is depression in a parent which increases the risk of depression in offspring by 3 to 4 fold. When depression arises in the offspring of depressed parents, it is likely to begin early and herald a malignant course of symptoms and impairment. Nevertheless, high-risk individuals vary considerably in their absolute level of risk. For preventive and early-identification strategies to be effective in reducing the burden of depression, it is important to understand how depression first develops, to characterise the antecedents of depression and to use this information to develop methods to rapidly and reliably predict who is at highest risk. That is what we aim to do in this proposal. Longitudinal data with repeated assessments of depressive symptomatology, hypothesised antecedents and risks enables us to characterise the early developmental trajectories of depression and its predictors. This proposal takes advantage of the largest existing prospective high-risk sample of the offspring of recurrently depressed parents. We will reassess individuals (4th assessment) in early adult life which is when the incidence of depression peaks. We will characterise the early trajectory of depressive symptoms between adolescence and early adult life in this group. We will identify the factors that predict a chronic long term trajectory of symptoms and early-onset major depressive disorder. We will develop and validate (in an independent sample) a risk prediction algorithm that can be used to quantify individual risk for early-onset depression.

This study will generate clinically relevant knowledge about how to identify early onset depression (chronic trajectory of depressive symptoms and depressive disorder). Predicted beneficiaries include:

Individuals affected by depression and their families.
Practitioners in health (primary and secondary care), education and social care who come into contact with depressed young people and their families as part of their professional work.
Agencies with roles in providing education for clinicians (e.g. Royal College of Psychiatrists)
Academics involved in basic and applied fields.
Researchers working on the project who will gain new skills and training.

How will they benefit from this research?

Understanding and quantifying risk for the development of depression is essential for effectively targeting preventive and therapeutic interventions. This quantification of risk for depression cannot currently be achieved accurately. This study is uniquely placed to generate clinically relevant knowledge about the development of depression and to profile levels of individual risk. The knowledge generated from this proposal can be used in the development of empirically-based guidelines and psycho-educational materials. These in turn can be used to support affected individuals and to aid and inform the professionals involved in helping these individuals and their families (e.g. in school, in health services). Basic science findings are essential for guiding the development of effective intervention and prevention and identifying risk groups. The development of an individual depression 'risk calculator' will be useful as a 'pre-emptive' medicine tool which can ultimately be used to guide clinical decision making and interventions and to provide personalised information to individuals about their absolute levels of risk. Findings will be shared with academics working in relevant scientific and applied fields.

What sorts of impacts are anticipated?

We anticipate that this research will generate empirical evidence that can feed into psycho-education resources about depression. This has the potential to improve mental health literacy about depression in families affected by this condition. This is important because depression in young people is under-recognized and most affected individuals do not receive any intervention. This is the case even when depressed individuals possess well known risk factors for depression such as having a parent with depression or having had a previous episode. Disseminating evidence on risk profiles of depression could therefore help ensure appropriate help is obtained within the constraints that health care services are working under. Improved psycho-education materials, and effective dissemination of these, could also potentially impact upon the barriers that prevent people seeking help when it is needed.

We will contribute to the expertise and knowledge of depression nationally and internationally.
Basic scientific findings, particularly from longitudinal studies, can feed into the enhancement of early identification, prevention and early intervention programmes. We will disseminate results to practitioners and those working in prevention and/or intervention. We will also seek to disseminate findings to policy makers both locally in Wales and nationally in the UK and with third sector organisations such as Youth in Mind and MIND.

A team of young researchers will be trained as part of this proposal. They will develop a range of transferable skills as a result of working on the project and we will provide mentorship to those that wish to consider careers in science. This research combines skills in longitudinal analysis and developmental psychopathology. The proposed study would therefore contribute in important ways to capacity building in this area.

The timeline for these impacts is 3-5 years.