Early-life inequalities and biological ageing: a multisystem Biological Health Score approach in UnderstandingSociety

Abstract

Social position is known to play a role in the quality of ageing, notably through the stimulation/dysregulation of key physiological systems in response to external stresses. Using data from one wave of Understanding Society including 9088 participants, we defined, as an extension of the allostatic load, a synthetic Biological Health Score (BHS) capturing the wear-and-tear of four physiological systems (endocrine, inflammatory, cardiovascular and metabolic systems) and two organs (liver and kidney). We used 16 established blood-derived biomarkers of these systems to calculate the BHS and explored the relative contribution of socioeconomic position to the BHS and its main components across age groups. We identified a systematic decreasing education-related gradient of the BHS (p<0.001) leading to lower biological risk in participants with longer education. Education-related differences in the BHS were detected early in life, and were not attributable to lifestyle and behavioural factors. We found a consistent contribution of the inflammatory and metabolic systems to the overall score throughout from early adulthood onwards, while the contribution of the other four systems seems to vary across age groups and gender. Our findings highlight the social-to-biological processes ultimately leading to health inequalities, and suggest that such disparities can already be detected in the 20-40 years old age group and cannot be fully explained by lifestyle and behavioural factors. This may define early adulthood social condition as a precursor to accelerated biological ageing and as an important target for public health policies.

Keywords: biological ageing; biomarkers; social epidemiology.

Figure 1

BHS distributions by age groups and educational levels. For each category the point estimate of the mean BHS is represented by a bullet, and the vertical line represents the 2.5%–97.5% CI of the score in that category. Low, intermediate and high education are represented in red, green and blue, respectively, and results are presented for men (A) and women (B) separately. For both genders and within each age class, potential differences in mean BHS by SEP (Socio Economic Position) category are tested using a Student’s t-test, setting the mean BHS in the high education category as a reference. We report the corresponding p value above the boxplots for low and intermediate education groups. Trends across the three education categories for both genders and within each age class were tested for using a Kruskal-Wallis test, and the corresponding p value is reported in the upper part of the plots. For readability, p values were coded as * for p values in (0.05, 0.01), ** for p values in (0.01, 0.001) and *** for p values <0.001. BHS, Biological Health Score.

Figure 2

Sensitivity analysis investigating changes in p value induced by the exclusion of each system separately from the Biological Health Score (BHS). We report on the x axis the different scores considered (ie, removing one system at a time or both kidney and liver functions to mimic the allostatic load). The p value reported on the y axis measures, in each age group and gender, the significance of the difference between the mean score in the low (red) and intermediate (green) educational level group compared with the mean score in the reference group (higher education). As a reference, we report results from the full BHS (horizontal coloured dashed line). Results are presented for each age class separately in men (A) and women (B), and the black horizontal dotted lines represent the 0.05 significance level.

Figure 3

Relative contribution of each system-specific subscore to the Biological Health Score (BHS). Results are presented for men (A) and women (B) separately, and for each group of educational level: low (left), intermediate (middle) and high (right). To account for the differential number of biomarkers assayed in each system, the contribution is calculated based on a normalised system-specific score.