The social determinants of health (SDOH) are widely recognized as important factors in the progression of mild cognitive impairmentment to dementia, but its impact across diverse racial and ethnic groups is not well researched. Dong et al. implemented multiple machine learning techniques to analyze the complex patterns between the SDOH and cognitive health, using a SDOH survey database (n=83,180). Separate analyses were conducted for different racial and ethnic groups, which were split into the following categories: non-Hispanic Whites (n=65,582), non-Hispanic Blacks (n=6207), Hispanics (n=4170), and an Other group including but not limited to Asian, Native American, Pacific Islander, Middle Eastern, and North African identities (n=7221). The socioeconomic information and SDOH survey results were used to collect data regarding incoming, education level, age, gender, cognitive outcomes, neighborhood deprivation index, and more. Among four machine learning techniques used, the XGBoost ensemble model showed strongest cross-validation performance. Dong et al. concluded SDOH drives both MCI and progression but with common and group-specific patterns. Although perceived stress is universally the dominant predictor for MCI across all cohorts, the progression drivers split: daily spiritual experiences are most protective for Black participants (SHAP = 0.34), instrumental social support for Hispanics (SHAP = 0.142), while for Whites perceived stress remains top (SHAP = 0.070). Overall, Dong et al. emphasize that a ‘one-size-fits-all’ approach to addressing the SDOH of health is not appropriate; rather, information of specific racial/ethnic differences in SDOH can allow for more effective screening of relevant factors for early intervention of MCI and dementia.