Ancestry, ethnicity, and race: explaining inequalities in cardiometabolic disease

Population differences in cardiometabolic disease remain unexplained. Misleading assumptions over genetic explanations are partly due to terminology used to distinguish populations, specifically ancestry, race, and ethnicity. These terms differentially implicate environmental and biological causal pathways, which should inform their use. Genetic variation alone accounts for a limited fraction of population differences in cardiometabolic disease. Research effort should focus on societally driven, lifelong environmental determinants of population differences in disease. Rather than pursuing population stratifiers to personalize medicine, we advocate removing socioeconomic barriers to receipt of and adherence to healthcare interventions, which will have markedly greater impact on improving cardiometabolic outcomes. This requires multidisciplinary collaboration and public and policymaker engagement to address inequalities driven by society rather than biology per se.

An epigenome-wide analysis of DNA methylation, racialized and economic inequities, and air pollution

Importance

DNA methylation (DNAm) provides a plausible mechanism by which adverse exposures become embodied and contribute to health inequities, due to its role in genome regulation and responsiveness to social and biophysical exposures tied to societal context. However, scant epigenome-wide association studies (EWAS) have included structural and lifecourse measures of exposure, especially in relation to structural discrimination.

Objective

Our study tests the hypothesis that DNAm is a mechanism by which racial discrimination, economic adversity, and air pollution become biologically embodied.

Design

A series of cross-sectional EWAS, conducted in My Body My Story (MBMS, biological specimens collected 2008-2010, DNAm assayed in 2021); and the Multi Ethnic Study of Atherosclerosis (MESA; biological specimens collected 2010-2012, DNAm assayed in 2012-2013); using new georeferenced social exposure data for both studies (generated in 2022).

Setting

MBMS was recruited from four community health centers in Boston; MESA was recruited from four field sites in: Baltimore, MD; Forsyth County, NC; New York City, NY; and St. Paul, MN.

Participants

Two population-based samples of US-born Black non-Hispanic (Black NH), white non-Hispanic (white NH), and Hispanic individuals (MBMS; n=224 Black NH and 69 white NH) and (MESA; n=229 Black NH, n=555 white NH and n=191 Hispanic).

Exposures

Eight social exposures encompassing racial discrimination, economic adversity, and air pollution.

Main outcome

Genome-wide changes in DNAm, as measured using the Illumina EPIC BeadChip (MBMS; using frozen blood spots) and Illumina 450k BeadChip (MESA; using purified monocytes). Our hypothesis was formulated after data collection.

Results

We observed the strongest associations with traffic-related air pollution (measured via black carbon and nitrogen oxides exposure), with evidence from both studies suggesting that air pollution exposure may induce epigenetic changes related to inflammatory processes. We also found suggestive associations of DNAm variation with measures of structural racial discrimination (e.g., for Black NH participants, born in a Jim Crow state; adult exposure to racialized economic residential segregation) situated in genes with plausible links to effects on health.

Conclusions and Relevance

Overall, this work suggests that DNAm is a biological mechanism through which structural racism and air pollution become embodied and may lead to health inequities.

Epigenetic clocks and research implications of the lack of data on whom they have been developed: a review of reported and missing sociodemographic characteristics

Epigenetic clocks are increasingly being used as a tool to assess the impact of a wide variety of phenotypes and exposures on healthy ageing, with a recent focus on social determinants of health. However, little attention has been paid to the sociodemographic characteristics of participants on whom these clocks have been based. Participant characteristics are important because sociodemographic and socioeconomic factors are known to be associated with both DNA methylation variation and healthy ageing. It is also well known that machine learning algorithms have the potential to exacerbate health inequities through the use of unrepresentative samples - prediction models may underperform in social groups that were poorly represented in the training data used to construct the model. To address this gap in the literature, we conducted a review of the sociodemographic characteristics of the participants whose data were used to construct 13 commonly used epigenetic clocks. We found that although some of the epigenetic clocks were created utilizing data provided by individuals from different ages, sexes/genders, and racialized groups, sociodemographic characteristics are generally poorly reported. Reported information is limited by inadequate conceptualization of the social dimensions and exposure implications of gender and racialized inequality, and socioeconomic data are infrequently reported. It is important for future work to ensure clear reporting of tangible data on the sociodemographic and socioeconomic characteristics of all the participants in the study to ensure that other researchers can make informed judgements about the appropriateness of the model for their study population.

The impact of low input DNA on the reliability of DNA methylation as measured by the Illumina Infinium MethylationEPIC BeadChip

DNA methylation (DNAm) is commonly assayed using the Illumina Infinium MethylationEPIC BeadChip, but there is currently little published evidence to define the lower limits of the amount of DNA that can be used whilst preserving data quality. Such evidence is valuable for analyses utilizing precious or limited DNA sources. We used a single pooled sample of DNA in quadruplicate at three dilutions to define replicability and noise, and an independent population dataset of 328 individuals (from a community-based study including US-born non-Hispanic Black and white persons) to assess the impact of total DNA input on the quality of data generated using the Illumina Infinium MethylationEPIC BeadChip. We found that data are less reliable and more noisy as DNA input decreases to 40ng, with clear reductions in data quality; and that low DNA input is associated with a reduction in power to detect EWAS associations, requiring larger sample sizes. We conclude that DNA input as low as 40ng can be used with the Illumina Infinium MethylationEPIC BeadChip, provided quality checks and sensitivity analyses are undertaken.

Individual-, family- and school-based interventions to prevent multiple risk behaviours relating to alcohol, tobacco and drug use in young people aged 8-25 years: a systematic review and meta-analysis

BACKGROUND

Engagement in multiple substance use risk behaviours such as tobacco smoking, alcohol and drug use during adolescence can result in adverse health and social outcomes. The impact of interventions that address multiple substance use risk behaviours, and the differential impact of universal versus targeted approaches, is unclear given findings from systematic reviews have been mixed. Our objective was to assess effects of interventions targeting multiple substance use behaviours in adolescents.

METHODS

Eight databases were searched to October 2019. Individual and cluster randomised controlled trials were included if they addressed two or more substance use behaviours in individuals aged 8-25 years. Data were pooled in random-effects meta-analyses, reported by intervention and setting. Quality of evidence was assessed using GRADE. Heterogeneity was assessed using between-study variance, τ2 and Ι2, and the p-value of between-study heterogeneity statistic Q. Sensitivity analyses were undertaken using the highest and lowest intra-cluster correlation coefficient (ICC).

RESULTS

Of 66 included studies, most were universal (n=52) and school-based (n=41). We found moderate quality evidence that universal school-based interventions are likely to have little or no short-term benefit (up to 12 months) in relation to alcohol use (OR 0.94, 95% CI: 0.84, 1.04), tobacco use (OR 0.98, 95% CI: 0.83, 1.15), cannabis use (OR 1.06, 95% CI: 0.86, 1.31) and other illicit drug use (OR 1.09, 95% CI: 0.85, 1.39). For targeted school-level interventions, there was low quality evidence of no or a small short-term benefit: alcohol use (OR 0.90, 95% CI: 0.74-1.09), tobacco use (OR 0.86, 95% CI: 0.66, 1.11), cannabis use (OR 0.84, 95% CI: 0.66-1.07) and other illicit drug use (OR 0.79, 95% CI 0.62-1.02). There were too few family-level (n=4), individual-level (n=2) and combination level (n=5) studies to draw confident conclusions. Sensitivity analyses of ICC did not change results.

CONCLUSIONS

There is low to moderate quality evidence that universal and targeted school-level interventions have no or a small beneficial effect for preventing substance use multiple risk behaviours in adolescents. Higher quality trials and study reporting would allow better evidence syntheses, which is needed given small benefit of universal interventions can have high public health benefit.

TRIAL REGISTRATION

Cochrane Database of Systematic Reviews 2014, Issue 11. Art. No.: CD011374. DOI: 10.1002/14651858.CD011374.