Psychological Distress, Socioeconomic Position and Inflammatory Biomarkers Across the Life Course:Elucidating the Interplay Using Causal Inference...

Mental health issues belong among the primary contributors to the disease burden. Depression and anxiety are linked to deteriorating physical health, which amplifies their impact on individuals and the health economy. Due to high population levels of psychological distress, research has focused on identifying biological pathways that underpin its negative effects on health. Chronic systemic inflammation is a major suspected mechanism, as pro-inflammatory biomarkers can increase both directly through stress system activation and indirectly through unhealthy behaviours.
Psychological distress often reflects underlying adverse socioeconomic conditions. Consequently, although psychological interventions can mitigate some of the impacts of psychological distress, they alone might not be sufficient in addressing the broader mental health crisis. Inflammatory biomarkers also show a social gradient, with studies finding higher inflammation levels in those with a lower socioeconomic position. Although psychological distress might partly explain this association, other mechanisms are plausible, including limited access to resources and different social environments. Additionally, increased access to resources might also enable individuals to cope better with mental health issues, suggesting possible interactive effects between socioeconomic indicators and psychological distress on health outcomes.

The relationships between socioeconomic indicators, psychological distress, and inflammation likely evolve throughout an individual's life. Plasma inflammatory biomarker levels increase with age, and socioeconomic disparities in their concentration also change over time. Despite advancements in life course modelling, traditional analytical methods are not fully equipped to elucidate these relationships, as they cannot handle time-varying confounding or integrate mediation with interaction. Addressing these limitations necessitates the use of causal inference approaches
This project aims to apply causal inference approaches to:
1. Assess the time-varying impacts of psychological distress on biomarkers of systemic inflammation.
2. Estimate the causal interactive effect of socioeconomic indicators (education, income/wealth) and psychological distress on biomarkers of systemic inflammation.
3. Decompose the average causal effect of socioeconomic indicators on biomarkers of inflammation into the effect mediated by psychological distress, the effect due to exposure-mediator interactions, and the effect operating through other pathways.
To meet these aims, the project will use three comprehensive cohort datasets spanning various life stages: The Avon Longitudinal Study of Parents and Children, the National Child Development Study, and the English Longitudinal Study of Ageing. To achieve greater comparability of findings across cohorts, measures of psychological distress will be calibrated using item-response theory-based linking. Socioeconomic indicators will comprise financial and educational metrics that best reflect one's access to resources at different life stages. These will encompass parental or household income, accumulated wealth, and either parental or personal educational qualifications. The main biomarker of interest will be the plasma concentration of C-reactive protein. Analyses will be conducted separately on each dataset, resulting in a qualitative synthesis of findings.
The findings will advance the understanding of the mechanisms connecting social conditions, mental health challenges, and physical health. As many interventions target either psychological issues or social adversity separately, combined approaches may be essential to change the health trajectories of socially disadvantaged individuals. Moreover, integrating a life course perspective within a causal inference framework may highlight specific periods in the mental health-inflammation relationship, offering insights into the timing for effective interventions