Special Report: The Biology of Inequalities in Health: The Lifepath Consortium

Abstract

Funded by the European Commission Horizon 2020 programme, the Lifepath research consortium aimed to investigate the effects of socioeconomic inequalities on the biology of healthy aging. The main research questions included the impact of inequalities on health, the role of behavioral and other risk factors, the underlying biological mechanisms, the efficacy of selected policies, and the general implications of our findings for theories and policies. The project adopted a life-course and comparative approach, considering lifetime effects from childhood and adulthood, and pooled data on up to 1.7 million participants of longitudinal cohort studies from Europe, USA, and Australia. These data showed that socioeconomic circumstances predicted mortality and functional decline as strongly as established risk factors currently targeted by global prevention programmes. Analyses also looked at socioeconomically patterned biological markers, allostatic load, and DNA methylation using richly phenotyped cohorts, unraveling their association with aging processes across the life-course. Lifepath studies suggest that socioeconomic circumstances are embedded in our biology from the outset-i.e., disadvantage influences biological systems from molecules to organs. Our findings have important implications for policy, suggesting that (a) intervening on unfavorable socioeconomic conditions is complementary and as important as targeting well-known risk factors, such as tobacco and alcohol consumption, low fruit and vegetable intake, obesity and a sedentary lifestyle, and that (b) effects of preventive interventions in early life integrate interventions in adulthood. The report has an executive summary that refers to the different sections of the main paper.

Keywords: biology; healthy aging; life-course; omics; social inequalities; socioeconomic position.

Figure 1

Geographic location of the cohorts used in Lifepath.

Figure 2

Risk factors for cardiometabolic disease by age and cumulative neighborhood socioeconomic disadvantage. The x-axis is age (years) and the y-axis is the prevalence of risk factors for cardiometabolic diseases at different ages from Kivimaki et al. (10).

Figure 3

Health trajectories in the life-course. The x-axis is age and the y-axis is a theoretical measure of function in different organs.

Figure 4

Galton's “Eugenics in Britain”.

Figure 5

The cost of living: Life-course model of healthy aging (Kelly-Irving, unpublished).

Figure 6

Trends in all-cause mortality by educational attainment, absolute and relative index of inequality, ca. 1980-ca. 2014 (29). Dashed lines: Eastern Europe. Dotted line: Western European countries most severely hit by the economic crisis. Measures of mortality on the y-axis are rate differences, rate ratios or ASMR = age-standardized mortality rate.

Figure 7

Path Analyses investigation of the relative contribution of different SEP factors to the individual AL (6).

Figure 8

Distribution of the Biological Health Score (BHS) in Understanding Society for Men (A) and Women (B) by age class (46). 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.

Figure 9

Meta-analysis of the association between CRP and SEP as measured by father's occupational position (A), own education (B), and own occupation (C) in the full study population (orange), men (blue), and women (green). Several models are considered and are gradually adjusted for exposures and behaviors (36).

Figure 10

Results from the methylome-wide association study relating cord blood DNA methylation profiles and maternal and paternal education or occupation. (A) Maternal education, (B) Paternal education, (C) Maternal occupation, (D) Paternal occupation. Results are derived from the ALSPAC cohort (40). In the “volcano plot” the x axis is related to a measure of the strength of the signal (i.e., association with SEP in this case), and the y axis is related to the statistical significance of the signal. CpGs above the red line are statistically significant after correction for multiple comparisons.

Figure 11

Cox proportional hazard ratio using 14 blood derived biomarkers, or the full AL as predictors of all-cause mortality. Results are presented unadjusted or adjusted for early life, childhood, young adulthood, and adulthood confounders. Results are represented as sex adjusted Hazard ratios (84).

Figure 12

Distal and proximal influences on adverse childhood experiences.