Delineating Life-Course Percentile Curves and Normative Values of Multi-Systemic Ageing Metrics in the United Kingdom, the United States, and China

Background

Ageing is a complex and multi-dimensional process that manifests heterogeneities across different organs/systems, individuals and countries. We aimed to delineate the life-course percentile curves and establish the normative values of multi-systemic (e.g., muscle-skeletal, brain, cardiovascular and pulmonary) ageing metrics for people under distinct sociodemographic contexts (i.e., sex, income and education).

Methods

Three national datasets, the UKB (the United Kingdom), the NHANES (the United States) and the CHARLS (China) were utilized for the analyses. We selected 14 ageing metrics (e.g., body mass index, grip strength, fat-free mass index, bone mineral content [BMC], bone mineral density [BMD], diastolic blood pressure, cognitive function and frailty index_Lab) that represent the functions of different organs/systems and plotted their sex-, educational- and income-specific percentile curves utilizing the GMALSS model. We also estimated the age-specific normative values for each ageing metric in distinct sociodemographic contexts.

Results

The functions of all metrics, except for cognitive function, manifested a progressive decline or maintained stability after adulthood (20s), especially after middle age (40s–50s). The cognitive function showed an evident decline in old age (70s–75s) (e.g., in the CHARLS: the median [IQR] cognitive function scores were 11.6 [9.1, 13.8], 10.3 [7.5, 12.9], 8.3 [5.5, 11.0] at the ages of 60, 70 and 80 for males, respectively). In the stratified analyses, males and females manifested disparities in percentile curves of ageing metrics involving the muscle-skeletal and cardiovascular systems. For instance, BMC and BMD manifested an evident decline after middle age in females, whereas they showed a slow decline after adulthood in males. Notably, we observed substantial income and educational disparities in percentile curves of several ageing metrics within Chinese participants: the ‘low-income’ and ‘low-education’ subgroups manifested an evident decline in ageing metrics (e.g., grip strength and frailty index_Lab) representative of multiple systems. By contrast, these income or educational disparities were not observed in the British and American participants.

Conclusions

Our investigation delineated the potential heterogeneities and socioeconomic disparities in percentile curves of multi-systemic ageing metrics and provided their age-specific normative values tailored to different sexes and socioeconomic contexts based on three national datasets. This study may serve as a proof-of-concept for understanding the multi-dimensional signature of systemic ageing and calls for policies to promote health equity across nations when facing dramatic global ageing.