Project Summary/Abstract Socioeconomic inequalities in risk for both dementia and cardiometabolic diseases are significant public health problems. A barrier to addressing these inequalities lies in accurately identifying the specific types (i.e. objective or subjective) and dimensions of socioeconomic status (SES; i.e. income or education) that may be driving these inequalities, as well as discerning which age groups exhibit the most vulnerability. For instance, some evidence suggests that accelerated brain aging, when machine learning predicted brain age exceeds chronological age, tracks a socioeconomic gradient. Accelerated brain aging has been linked to dementia risk and cardiometabolic health, but the limited available literature on SES and brain age is mixed and inconclusive. One reason for this empirical variability is that studies tend to use only a few incomplete SES indicators (i.e. years of education and/or income) that while important, do not accurately capture the multifaceted nature of SES. Use of select and potentially biased measures may also vary in their ability to index SES across ages. Moreover, studies on social inequalities in brain age do not examine whether results vary by chronological age, which limits our ability to identify whether SES inequalities in brain aging varies across the lifespan as observed with other outcomes. Lastly, studies on inequalities in brain age tend to omit indicators of cardiometabolic health or they rely on self-report measures of cardiometabolic health that could confer risk for accelerated brain aging and dementia. The present study is thus designed to address these issues by being the first to integrate comprehensive socioeconomic data, laboratory-based biomarkers of cardiometabolic risk, and neuroimaging data to test whether objective and subjective SES relate to brain age and cardiometabolic health among mid and late life adults. We leverage cross-sectional data from 3 NIH studies on midlife adults (N =1,122) and 1 NIH study on older adults (N = 648). We have 3 Aims: Aim 1. Determine whether multidimensional SES indicators associate with brain age and cardiometabolic health in midlife adults Aim 2. Examine the potential mediating effect of cardiometabolic health on the association between SES and brain age among midlife adults. Aim 3. Determine whether the findings from Aim 1 and 2 replicate in older adults and in an independent sample of midlife adults. This fellowship will provide training in data science and harmonization, brain age quantification, structural equation modeling, and manuscript writing that are necessary to help the applicant complete this project and achieve his long-term goal of becoming an independent clinical scientist in the area of neurocognitive aging and dementia. Training is facilitated by individual and group meetings with an experienced mentoring team, as well as guided readings and didactics. Results of the proposed study will improve our understanding of socioe...