Neighborhood environment, social cohesion, and epigenetic aging

Martin, Chantel L.; Ward‐Caviness, Cavin; Dhingra, Radhika; Zikry, Tarek M.; Galea, Sandro; Wildman, Derek E.; Uddin, Monica; Aiello, Allison E.

doi:10.1101/2020.10.19.345777

Cited by 1 publication

(1 citation statement)

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“…To enhance this feature, a second generation of epigenetic clocks was developed; these incorporated health biomarkers and phenotypes alongside chronological age in the model. Increasing numbers of studies are now considering how social determinants of health might impact epigenetic ageing, with evidence indicating positive associations between accelerated epigenetic aging and social adversity measured in relation to: socioeconomic position (George et al, 2021), education (Fiorito et al, 2019), neighbourhood characteristics (Martin et al, 2021, Lawrence et al, 2020, Simons et al, 2021, and racial discrimination (Simons et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

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

Watkins¹,

Testa²,

Chen³

et al. 2022

Preprint

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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 was used to construct 13 commonly used epigenetic clocks. We found that although some of the epigenetic clocks were created utilising data provided by individuals from different 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 no socioeconomic data is provided by any of the clock papers. 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 other researchers can make informed judgements about the appropriateness of the model for their study population.

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