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Time flies, everyone can not escape the aging, but the aging speed of different people is very different. Acceleration of aging means that the person is older than their peers on a biological level, which leads to an increased burden of chronic diseases and will pose a huge challenge for governments and society in the context of aging.

Past research has shown that social environmental factors from childhood to adulthood contribute to health inequalities between people. Rather than focusing on individual exposures at specific life stages, the life course approach provides a more complete picture of how many factors work together over a long period of time. However, the complexity of the many factors in the life course also presents a challenge for statistical analysis, requiring the modeling of individual factors and their cumulative effects, and addressing the high correlation between childhood and adult factors.

Recently, the team of Professor Liu Zuyun, member of the Basic and Translational Medicine Branch of the Chinese Geriatrics Society and Professor of the School of Public Health of Zhejiang University, collected the life course status, phenotypic and functional aging data of 6224 people from the China Health and Retirement Longitudinal Study (CHARLS), using Shapley method, etc. The effects of more than 70 life course factors, such as socioeconomic status in childhood and adulthood, on accelerated aging were assessed. The study was published in the Lancet subsidiary journal eClinicalMedicine (2021 IF: 17.033).

To assess aging, the researchers constructed two metrics: PD and FI. PD is a phenotypic aging index based on conventional clinical biomarkers, reflecting the disorders of multiple physiological systems. FI is a measure of functional aging that includes multiple dimensions of health (e.g., cognitive function, physical function, etc.) and reflects cumulative health deficits. Both PD and FI performed well in predicting outcome events related to aging. Life course conditions include a comprehensive set of social and environmental factors associated with adverse health outcomes across the life course. The researchers defined six childhood social-environment components (i.e., childhood socioeconomic status, childhood war, childhood health, childhood trauma, childhood relationships, and childhood parental health) and three adult social-environment components (i.e., adult socioeconomic status, adult adversity, and adult social support). The results obtained by the researchers using the Shapley method show that 11 social environmental components contribute 6.3% (SE=-0.0004) to PD and 29.7% (SE=-0.006) to FI, respectively.

Figure: Contribution of 11 social environmental components to PD and FI

Note: The 11 social environmental components included 6 childhood social environmental components, 3 adult social environmental components, 1 behavioral component, and 1 demographic component.

 

The researchers then used hierarchical clustering to identify six subpopulations (see figure below). Further analysis found that compared to the most advantaged subpopulation 1 (less childhood trauma and adult adversity), the most disadvantaged subpopulation 6 had an average increase in PD and FI of 0.14 (one standard deviation, 95% CI=0.06, 0.21; p<0.0001) and 0.10 (one unit, 95% CI=0.09, 0.11; p<0.0001). Vulnerable subpopulations 5 (more wartime births, lower educational attainment) and 6 (more childhood trauma and adult adversity) suggest the need to pay particular attention to certain childhood social circumstances, including war birth, traumatic experiences, poor social relationships and parental health problems, and adult adversity. The researchers interpreted the results by suggesting that adverse childhood experiences may cause biological damage, increase susceptibility to disease, or accelerate the aging process by triggering high levels of stress hormones (such as catecholamines and cortisol), lead to malnutrition, infectious diseases, etc., or lack of access to health care.