Principal Investigator: Astrid Suchy-Dicey


When life stresses are especially intense, chronic, or overwhelming, deleterious health effects can occur,

including inflammation, cardiovascular disease, disability, depression, low quality of life, and dementia. In this

context, resilience can be defined as the ability to maintain a healthy aging trajectory despite adverse conditions of stress. American Indians (AI) have a unique history and ongoing experience of trauma and disparities in environmental and socioeconomic conditions, which amplify daily stresses and contribute to health risks. Despite these adverse circumstances, remarkable resilience has been described in AI populations. Recent work by our group suggests that social support and alignment with Native culture correlate with lower levels of stress, negativity, anger, hostility, depression, mortality, and cardiovascular disease. However, our findings on cultural alignment are limited, and none has yet explored associations of resilience and social support. It remains an open question whether neurodegenerative conditions such as Alzheimer’s disease and related dementias (ADRD) can result from chronic stress, or whether individual psychosocial characteristics such as resilience can mediate such risk. We propose to address these knowledge gaps by efficiently leveraging an existing effort funded by the NHLBI in the Strong Heart Study, a longitudinal cohort of AI adults from 13 tribal communities across the US. The existing contract covers recruitment, consenting, and basic clinical examination of 3,000 eligible participants in 2022-2024; we propose to augment the limited protocol by administering additional psychosocial and neuropsychological instruments on resilience, social support, cultural identity and alignment, and cognition. Our Specific Aims are to: describe associations of individual resilience among AI adults with identity and self-regard, social support, and cultural alignment, by age and sex; evaluate resilience, social support, and cultural features in relation to ADRD; and use machine learning to develop explanatory models of resilience and dementia. Our study has the potential to advance epidemiologic knowledge of modifiable psychosocial conditions in a vulnerable, underserved population, and consequently to offer a clearer picture of the relative contributions of psychosocial, behavioral, interpersonal, and socioeconomic factors related to ADRD.