Background Depression is one of the leading causes of disability worldwide and is a major contributor to the overall global burden of disease among older adults. Using polygenic score (PGS) analyses, which measure an individual genetic propensity to a trait by combining the effects of many common genetic variants, confirmed that depressive symptoms are highly polygenic in nature. However, it is unknown if polygenetic predisposition to individual differences in depressive symptoms also influences rate of change in depressive symptoms over time in older adults, and if this effect is further moderated by socio-economic markers (education and financial resources).
Methods Utilising data from the English Longitudinal Study of Ageing, which is an ongoing multidisciplinary study of the English population aged ≥50 years, polygenetic scores for depressive symptoms were calculated using summary statistics for 1) depressive symptoms (PGS-DSsingle), and 2) multiple genetically correlated traits (PGS-DSmulti-trait) encompassing depressive symptoms, subjective well-being, neuroticism, loneliness, and self-rated health using the multi-trait analysis of genome-wide association summary statistics. The self-reported depressive symptoms over the past week were measured using the eight-item Centre for Epidemiologic Studies Depression Scale. To assess the relationships of PGSs with depressive symptoms at baseline and the rate of change in depressive symptoms during the 14-year follow-up, we employed linear mixed effect models with maximum likelihood estimation.
Results The sample included a total number of 6202 participants with an average age of 65.4 years old (standard deviation (SD)=9.8). One standard deviation increase in each PGS was associated with a higher baseline score in depressive symptoms (effect sizes ranged 0.12–0.35). Each additional year of completed schooling was associated with a lower baseline score in depression symptoms (β=-0.06, 95%CI=-0.07 to -0.05, p<0.001). Similarly, intermediate and low accumulated wealth were shown to associate with a higher baseline score in depressive symptoms by an average 0.30–0.31 and 0.76–0.77 points, respectively. However, there were no significant association between PGS-DSs, socio-economic factors, and rate of change in the depressive symptoms during the 14-year follow-up period.
Conclusion Although common genetic variants associated with depressive symptoms additively are associated with a higher score in depressive symptoms in older adults, a polygenic predisposition to depressive symptoms was not associated with the rate of change in depressive symptoms during a 14-year follow-up. Lower socio-economic status is also an important factor influencing individual levels of depressive symptoms, independently from polygenic predisposition to depressive symptoms and in interaction with PGS, in older adults.
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