Integrating Biological Vulnerability, Social Context, and Neurodevelopment to Understand Inequalities in Adolescent Depression Risk and Treatment

Background

Adolescence is a sensitive developmental window in which depression and related mental-health difficulties often first emerge. This is also a period of rapid neurodevelopment and substantial social transition, making adolescence a time of heightened vulnerability and a key opportunity for prevention.

Recent advances in psychiatric genetics mean we can increasingly quantify aspects of biological vulnerability to adolescent depression. Genetic liability, for example, contributes to individual differences in risk, and longitudinal evidence shows higher genetic risk is associated with distinct symptom trajectories across adolescence (Kwong et al., 2019; Grimes et al., 2024). Yet many adolescents with elevated genetic risk remain well, while others with low risk develop clinically significant symptoms. Crucially, genetic risk itself is not modifiable—its value lies in understanding under what conditions it becomes clinically meaningful.

This indicates the importance of social and environmental contexts in which adolescents develop. Socioeconomic disadvantage, family adversity, and food insecurity are robustly associated with poorer youth mental-health outcomes, including our recent work (Xu et al., 2025). But here too, outcomes vary widely: some adolescents show resilience despite high adversity, implying that protective factors can buffer vulnerability. The challenge however is that these buffers are harder to measure, and are not evenly distributed across the population.

This creates a widening gap: biological risk tools are developing rapidly, while our ability to characterise and respond to the social contexts that amplify or buffer that risk is lagging. Precision mental health that focuses solely on biological stratification risks widening inequalities. This PhD will integrate genetic liability and social context—including their interaction—using an equity-by-design approach to identify which social determinants most strongly amplify biological vulnerability, which protective factors buffer risk, and how these are reflected in the developing adolescent brain and depression outcomes. Critically, the PhD will also test whether young people most affected by these social determinants are equitably represented among those seeking and receiving timely, effective support, using health records to examine social patterning in pathways into care as well as depression outcomes.

Overall, this PhD will integrate biological measures of vulnerability with social determinants to map inequitable pathways to adolescent depression. Using longitudinal population cohorts the project will quantify biological vulnerability (e.g., polygenic risk) alongside social adversity and protective factors (Xu et al., 2026), and test how these combine to shape neurobiology and adolescent depression trajectories. Analyses of health records will assess whether access to care and support is socially patterned in ways that reinforce risk. 

Aims

This project will use large adolescent cohorts with approved or exclusive access, including NextGenScot (1.2K), the Avon Longitudinal Study of Parents and Children (2K), the ABCD study (9K with locally processed and harmonized neuroimaging data).

1. Identify the most influential modifiable social determinants for adolescent brain development and depression risk.
   Quantify the independent and combined contributions of key social factors (e.g., socioeconomic disadvantage, food insecurity, family stress/adversity, neighbourhood/school context, supportive relationships) to neurodevelopmental and depression outcomes, using machine-learning approaches that allow comparison of relative importance and robustness across cohorts.
2. Test how these social factors interact with biological vulnerability to shape neurodevelopmental risk and resilience.
   Examine gene–environment interplay by assessing whether specific social determinants amplify or buffer associations between biological vulnerability and adolescent brain development and depression trajectories—pinpointing contexts in which biological risk becomes most consequential.
3. Translate equality implications: determine whether the most impactful social risks and buffers align with access to support, and prioritise policy-relevant targets.
   Using health/service records and cohort measures of help-seeking and service use, assess whether young people most exposed to the high-impact social determinants are less likely to reach/receive timely support, and generate a prioritised set of modifiable, policy-relevant factors for prevention and service planning.

PPIE will be embedded throughout using the supervisors’ longstanding collaborations with youth and teachers—co-producing priorities and informing interpretation of findings, and shaping dissemination for youth, families, services, and policy.

Training Outcomes

The PhD will be supported by a strong, interdisciplinary supervisory team consisted of biostastisticians, a clinical academic and a genomics epidemiology expert at Karolinska Institute. Together, the project will provide interdisciplinary training in psychiatric and genetic epidemiology, longitudinal population health, social determinants of health, and secondary analysis of large-scale cohort, neurodevelopmental, and health-record data. Training will cover longitudinal modelling, polygenic risk methods, brain connectome, harmonisation of social exposures, and equity-aware precision approaches (including ancestry-aware prediction, calibration, missing data, and bias/inequality implications). PPIE and youth co-production will strengthen skills in integrating lived experience and translating findings into policy- and practice-relevant outputs.

References

1. Kwong ASF, et al. Genetic and Environmental Risk Factors Associated With Trajectories of Depression Symptoms From Adolescence to Young Adulthood. JAMA Network Open. 2019;2(6). doi:10.1001/jamanetworkopen.2019.6587. 
2. Grimes PZ, et al. Genetic Architectures of Adolescent Depression Trajectories in 2 Longitudinal Population Cohorts. JAMA Psychiatry. 2024;81(8):807–816. 
3. Xu EY, et al. Childhood food insecurity and youth mental health trajectories in two UK longitudinal cohorts. Under review: preprint at medRxiv. 2025. doi:10.64898/2025.12.10.25341979. 
4. Xu EY, et al. Longitudinal Prediction of Adolescent Depression from Environmental and Polygenic Risk Scores. Accepted at Mol Psychiatry