The biology of inequalities in health: The LIFEPATH project

Socioeconomic differences in health have been consistently observed worldwide. Physical health deteriorates more rapidly with age among men and women with lower socioeconomic status (SES) than among those with higher SES. The biological processes underlying these differences are best understood by adopting a life course approach. In this paper we introduce the pan- European LIFEPATH project which uses multiple cohorts - including biomarker data - to investigate ageing as a phenomenon with two broad stages across life: build-up and decline. The ‘build-up’ stage, from conception and early intra-uterine life to late adolescence or early twenties, is characterised by rapid successions of developmentally and socially sensitive periods. The second stage, starting in early adulthood, is a period of ‘decline’ from maximum attained health to loss of function, overt disease and death. LIFEPATH adopts a study design that integrates social science and public health approaches with biology (including molecular epidemiology), using well-characterised population cohorts and omics measurements (particularly epigenomics). LIFEPATH includes information and biological samples from 17 cohorts, including several with extensive phenotyping and repeat biological samples, and a very large cohort (1 million individuals) without biological samples (WHIP, from Italy). The countries that are covered by the cohorts are France, Italy, Portugal, Ireland, UK, Finland, Switzerland and Australia. These cohorts are only a small proportion of all cohorts available in Europe, but we have chosen them for the combination of good measures of socioeconomic status, risk factors for non-communicable diseases (NCDs) and biomarkers already measured (or availability of blood samples for further testing). The majority of cohorts include ‘hard’ outcomes (diabetes, cancer, Cardiovascular Disease (CVD), total mortality), and the extensively phenotyped cohorts also include several measurements of the functional components of healthy ageing, including frailty, impaired vision, cognitive function, renal and brain function, osteoporosis, sleep disturbances and mental health. All age groups are represented with two birth cohorts, one cohort of adolescents and several cohorts encompassing young adults (age 18 and above). Furthermore, there is a strong representation of elderly subjects in seven cohorts. The specific objectives of the project are: (a) to show that healthy ageing is an achievable goal for society; (b) to improve the understanding of the mechanisms through which healthy ageing pathways diverge by SES, by investigating life course biological pathways using omic technologies; (c) to examine the consequences of the current economic recession on health and the biology of ageing (and the consequent increase in social inequalities); (d) to provide updated, relevant and innovative evidence for healthy ageing policies (particularly ‘health in all policies’) using both observational studies and an experimental approach based on a reanalysis of data from a ‘conditional cash transfer’ randomised experiment in New York and new data collected as part of an earned income tax credit randomised experiment in Atlanta and New York. To achieve these objectives, data are used from three categories of studies: 1. national census-based followup data to obtain mortality by socioeconomic status; 2. cohorts with intense phenotyping and repeat biological samples; 3. large cohorts with biological samples. With these objectives and methodologies, LIFEPATH seeks to provide updated, relevant and innovative evidence to underpin future policies and strategies for the promotion of healthy ageing, targeted disease prevention and clinical interventions that address the issue of social disparities in ageing and the social determinants of health. The present paper describes the design and some initial results of LIFEPATH as an example of the integration of social and biological sciences to provide evidence for public health policies

Allostatic load and subsequent all-cause mortality: which biological markers drive the relationship? Findings from a UK birth cohort

The concept of allostatic load (AL) refers to the idea of a global physiological ‘wear and tear’ resulting from the adaptation to the environment through the stress response systems over the life span. The link between socioeconomic position (SEP) and mortality has now been established, and there is evidence that AL may capture the link between SEP and mortality. In order to quantitatively assess the role of AL on mortality, we use data from the 1958 British birth cohort including eleven year mortality in 8,113 adults. Specifically, we interrogate the hypothesis of a cumulative biological risk (allostatic load) reflecting 4 physiological systems potentially predicting future risk of death (N = 132). AL was defined using 14 biomarkers assayed in blood from a biosample collected at 44 years of age. Cox proportional hazard regression analysis revealed that higher allostatic load at 44 years old was a significant predictor of mortality 11 years later [HR = 3.56 (2.3 to 5.53)]. We found that this relationship was not solely related to early-life SEP, adverse childhood experiences and young adulthood health status, behaviours and SEP [HR = 2.57 (1.59 to 4.15)] . Regarding the ability of each physiological system and biomarkers to predict future death, our results suggest that the cumulative measure was advantageous compared to evaluating each physiological system sub-score and biomarker separately. Our findings add some evidence of a biological embodiment in response to stress which ultimately affects mortality

Evidence of causal effect of major depression on alcohol dependence: findings from the psychiatric genomics consortium

BACKGROUND Despite established clinical associations among major depression (MD), alcohol dependence (AD), and alcohol consumption (AC), the nature of the causal relationship between them is not completely understood. We leveraged genome-wide data from the Psychiatric Genomics Consortium (PGC) and UK Biobank to test for the presence of shared genetic mechanisms and causal relationships among MD, AD, and AC. METHODS Linkage disequilibrium score regression and Mendelian randomization (MR) were performed using genome-wide data from the PGC (MD: 135 458 cases and 344 901 controls; AD: 10 206 cases and 28 480 controls) and UK Biobank (AC-frequency: 438 308 individuals; AC-quantity: 307 098 individuals). RESULTS Positive genetic correlation was observed between MD and AD (rgMD−AD = + 0.47, P = 6.6 × 10−10). AC-quantity showed positive genetic correlation with both AD (rgAD−AC quantity = + 0.75, P = 1.8 × 10−14) and MD (rgMD−AC quantity = + 0.14, P = 2.9 × 10−7), while there was negative correlation of AC-frequency with MD (rgMD−AC frequency = −0.17, P = 1.5 × 10−10) and a non-significant result with AD. MR analyses confirmed the presence of pleiotropy among these four traits. However, the MD-AD results reflect a mediated-pleiotropy mechanism (i.e. causal relationship) with an effect of MD on AD (beta = 0.28, P = 1.29 × 10−6). There was no evidence for reverse causation. CONCLUSION This study supports a causal role for genetic liability of MD on AD based on genetic datasets including thousands of individuals. Understanding mechanisms underlying MD-AD comorbidity addresses important public health concerns and has the potential to facilitate prevention and intervention efforts

Integrated analysis of environmental and genetic influences on cord blood DNA methylation in new-borns

Epigenetic processes, including DNA methylation (DNAm), are among the mechanisms allowing integration of genetic and environmental factors to shape cellular function. While many studies have investigated either environmental or genetic contributions to DNAm, few have assessed their integrated effects. Here we examine the relative contributions of prenatal environmental factors and genotype on DNA methylation in neonatal blood at variably methylated regions (VMRs) in 4 independent cohorts (overall n = 2365). We use Akaike’s information criterion to test which factors best explain variability of methylation in the cohort-specific VMRs: several prenatal environmental factors (E), genotypes in cis (G), or their additive (G + E) or interaction (GxE) effects. Genetic and environmental factors in combination best explain DNAm at the majority of VMRs. The CpGs best explained by either G, G + E or GxE are functionally distinct. The enrichment of genetic variants from GxE models in GWAS for complex disorders supports their importance for disease ris

A common biological basis of obesity and nicotine addiction

J. Kaprio ja J. Tuomilehto työryhmien jäseniä (yht. 281).Peer reviewe