The Impact of Social Isolation, Loneliness, and Support on Vascular Brain Injury: From Subclinical Small Vessel Disease to Stroke and Post-Stroke Outcomes.

About This Grant

Project Abstract Social determinants such as lack of social support, social isolation, and loneliness are increasingly recognized as important and highly prevalent modifiable contributors to cardiovascular and brain health. These factors have been linked to increased risk of stroke and Alzheimer’s disease and related dementias (ADRD), yet their specific roles in subclinical vascular brain injury, stroke characteristics, and post-stroke outcomes—including recovery, post-stroke dementia, and mortality—are not well understood. Establishing a causal relationship would position social factors as critical and actionable targets for stroke prevention and recovery. The overall objective of this proposal is to elucidate how unfavorable social factors affect brain vascular injury and stroke-related outcomes, and to incorporate these findings into risk prediction tools. We will use two large, prospective, population-based cohorts: the Framingham Heart Study (FHS), with over 75 years of lifespan data and detailed post-stroke follow-up, and the UK Biobank (UKB), with questionnaire-based measures of social exposures and linked health outcomes. Aim 1: Determine the associations between poor social relationships (low support, isolation, and loneliness) and subclinical brain injury, clinical stroke, and post-stroke outcomes. We hypothesize that unfavorable social exposures across early, mid, and late life will be associated with greater CSVD burden on MRI, increased stroke risk, and poorer post-stroke recovery and survival. Aim 2: Investigate the pathways linking social factors to brain outcomes via vascular risk. Using mediation analyses and Mendelian randomization, we will assess whether key vascular risk factors (e.g., hypertension, diabetes, smoking) mediate or confound the relationship between social exposures and stroke-related outcomes, and the causal role of these social exposures. Aim 3: Develop and validate clinical risk prediction tools integrating social and vascular risk factors. We will evaluate performance using traditional regression and machine learning methods (e.g., random forests, neural networks), and assess clinical utility using Decision Curve Analysis. This work will advance the field by identifying modifiable social factors as potential intervention targets for stroke prevention and recovery, enhancing risk stratification, and informing public health strategies aimed at reducing the burden of stroke and ADRD.