Global population aging is accelerating, with projections indicating over 4 billion people will be aged 60 or above by 2050. However, gains in healthy life expectancy have not kept pace with this demographic transition. Age-related diseases-particularly cardiovascular and cerebrovascular disorders-are expected to impose growing public health and economic burdens. These challenges underscore the urgent need for accurate models to quantify biological aging using large-scale population cohort data. Advances in multi-omics technologies now provide unprecedented opportunities to precisely measure biological age and assess the pace of aging.
Although numerous aging clocks have been developed using various omics data types-such as physiological parameters, methylomics, transcriptomics, proteomics, lipid metabolomics, and radiomics-systematic comparisons of predictive models that integrate multiple omics layers remain limited. This study will leverage data from the Shanghai Brain Aging Study (SBAS), a community-based prospective cohort in China. We will quantify proteomic and targeted lipid metabolomic profiles using mass spectrometry, methylomic data via the MethylationEPICv2 beadchip, and structural neuroimaging metrics through sMRI. Multiple statistical approaches, including multiple linear regression and elastic net regression, will be employed to construct aging prediction models, identify cross-omics biomarkers, and evaluate their associations with health outcomes.
Data from the UK Biobank will be used to validate the identified biomarkers and explore factors influencing aging rates. By integrating data from SBAS and the UK Biobank, this research aims to develop and validate multi-omics aging models incorporating methylomics, proteomics, metabolomics, and radiomics data, compare their predictive performance, and elucidate similarities and differences in aging mechanisms across populations with varying genetic and lifestyle backgrounds.