Publications

Introduction Poor housing quality associates with risk for depression. However, previous research often lacks consideration of socioeconomic status (SES) baseline depressive symptoms and biological processes, leading to concerns of confounding and reverse causation.

Methods In a sample of up to 9669 adults, we investigated cross-sectional and longitudinal associations between housing quality (assessed at age 28, 1-year and 2 year follow-ups) and depressive symptoms (at four intervals between enrolment and 18-year follow-up). In subsamples (n=871, n=731), we investigated indirect effects via DNA methylation.

Results Poor housing quality associated with depressive symptoms cross-sectionally (beta range: 0.02–0.06) after controlling for SES and other factors. Longitudinally, this association persisted at the ~2 year, but not the ~18-year follow-up period. Indirect effects (β=0.002–0.012) linked to genes related to ageing, obesity and brain health.

Conclusion These results highlight poor housing quality as a risk factor for depression and the potential role of DNA methylation in this association.

Numerous studies have detected associations between poor housing quality and increased risk for mental illness. However, it currently remains unclear in associations between poor housing quality and increased risk for women’s mental illness which housing quality indicators drive this association and hence which specific indicators should be prioritised in housing quality assessments or improvements.

In a sample of up to 9,669 pregnant women, we used a network analysis to investigate cross-sectional associations between poor housing quality indicators (e.g., house size, facilities, leaks or condensation/mould, decorations, and feelings towards the home) and depressive symptoms (assessed at age 28).

All 36 edges showed non-zero associations, whereby when considering all poor housing quality indicators ‘feelings-towards-the-home’ had the strongest association with depressive symptoms, and ‘feelings-towards-the-home’, in turn, was most strongly associated with house problems, size, and facilities.

Our findings highlight the importance of using multiple (or composite) person-centred measures of housing quality in the context of maternal mental health.

Recent research suggests biological age, based on epigenetic or neuroimaging measures, may predict health traits in adulthood more accurately than chronological age. However, it is unclear if these findings apply earlier in life.

We aimed to characterise the performance and interdependence between measures of biological age in young people, leveraging a longitudinal subsample from the population-based ALSPAC cohort (n = 386). We derived four epigenetic age measures from blood samples in young people (17–19 years) and a measure of brain age derived from structural neuroimaging data (18–24 years). We examined associations between measures of biological age, and relationships with five measures of physical, cognitive and mental health (8–18 years). 

We found little evidence for an association between brain age and epigenetic age measures, after accounting for age, sex, cell type, array and study (beta range: -0.59 to 0.59, all p > 0.05). Increased smoking _DNAm was associated with advanced epigenetic age (PACE and Zhang clock), and increased BMI _sds with advanced EpiAge _{Horvath(diff)} (all p < 0.05), but not brain age. Depressive symptoms and cognitive ability were unrelated to all measures of biological age.

Our findings highlight the variability of epigenetic- and brain-based age measures in young people, emphasizing the importance of tracking ageing in younger populations.