Social and economic factors and accelerated biological aging in Black and White older adults
In a cross-sectional 2016 Health and Retirement Study analysis, Black adults aged 60+ more often showed accelerated biological aging than White adults; social and economic factors largely explained this difference. [1]
Automatic summary · not editorially reviewed
The key findings
Among 2086 adults aged 60+, 57.1% of Black and 41.8% of White participants had accelerated biological aging measured by GrimAge. [1]
Age and education explained 21.1% of the difference; adding wealth raised the explained share to 51.8%. [1]
After adjusting for all domains, the difference between Black and White participants was no longer significant. [1]
The source at a glance
- Study type / format
- Cross-sectional analysis of survey data
- Population / context
- 2086 community-dwelling adults aged 60 or older in the 2016 Health and Retirement Study (1757 non-Hispanic White, 329 non-Hispanic Black).
- Summary based on
- Study abstract
- Original publication
- July 1, 2026
GrimAge-based accelerated aging in 2086 older adults
The analysis used 2016 data from 2086 community-dwelling adults aged 60 or older: 1757 non-Hispanic White and 329 non-Hispanic Black participants. Biological age was measured with GrimAge, a DNA methylation-based measure. Accelerated aging meant a biological age older than expected for one's chronological age. Black participants had a higher share with accelerated aging than White participants (57.1% vs 41.8%).
Supporting source [1]
Wealth, social frailty and health explained most of the gap
A staged decomposition added groups of factors in turn. Age and education explained 21.1% of the difference. The explained share rose to 51.8% with wealth, 77.6% with social frailty (a composite index of social connections, financial autonomy, neighborhood environment, volunteering and employment engagement), 80.3% with health behaviors, 90.5% with medical conditions and 91.6% with physical disability. After adjusting for all domains in logistic regression, the difference between the groups was no longer significant.
Supporting source [1]
Cross-sectional design limits causal conclusions
This was a cross-sectional analysis, so it describes associations at one time point and cannot show that social or economic factors caused the aging differences. The authors describe the findings as identifying policy-relevant targets to improve healthy aging, which is a proposal, not a tested intervention. Only the abstract was reviewed; the full text was not reviewed.
Supporting source [1]
How to interpret this
Abstract only; the full text was not reviewed. The design is cross-sectional, so associations are described, not causes. The policy targets are proposed, not tested.
Based on the study abstract; the full paper, tables and supplementary data were not reviewed.
Source & supporting passages
Abugroun A, Neilands TB, Livaudais-Toman J, Johnson JK, Karliner L, Fang MC. · europepmc.org · July 1, 2026