The goal of this project is to understand female-specific mechanisms of metabolic health and brain integrity in a mid-to-late life. The majority of prior work has focused on universal mechanisms of neurodegeneration, despite evidence that women are diagnosed with neurocognitive diseases, such as Alzheimer’s Disease, at nearly double the rate of their male counterparts. Further, much research over the past decade has pointed at midlife as an important period for investigating vulnerability to later cognitive decline, as many brain pathologies emerge approximately 20 years prior to clinical diagnosis. This time period is particularly important for women as it is marked by major hormonal changes, most notably the loss of estrogen, caused by the menopausal transition. The proposed projects have three specific aims: 1) to explore the complex associations among markers of peripheral metabolic health and brain integrity in men and women separately using network analysis, and test the capacity of network metrics to predict current cognitive function; 2) to examine the role of estradiol in influencing the female network of cardiometabolic health and brain integrity markers in a sample of women straddling the menopausal transition; and 3) to determine if metabolic and inflammatory markers that correspond to the menopausal transition predict plasma tau in an elastic net model. The results of these studies will provide insight into why women might suffer particular vulnerability to neurocognitive disorders and if these mechanisms are visible before onset of clinical symptoms. The use of network analysis and machine learning is novel in this application and provides broader information about relationships between complex biological symptoms. Additionally, information about the role of estrogen in these relationships could aid in understanding how hormones might operate in a neuro- or cardio- protective manner, which could help inform the design of early diagnostic and intervention tools which are sex-specific.