Abstract

Modifiable lifestyle factors, including diet, can impact brain structure and influence dementia risk, but the extent to which diet may impact brain health for an individual is not clear. Clinical trials allow for the modification of a single variable at a time, but these may not generalize to populations due to uncaptured confounding effects. Large scale epidemiological studies can be leveraged to robustly model associations that can be specifically targeted in smaller clinical trials, while modeling confounds. Causal sensitivity analysis can be used to infer causal relationships between diet and brain structure. Here, we use a novel causal modeling approach that is robust to hidden confounding to partially identify sex-specific dose responses of diet treatment on brain structure using data from 42,032 UK Biobank participants. We find that the effects of diet on brain structure are more widespread and also robust to hidden …

Metadata

publication

International Workshop on Machine Learning in Clinical Neuroimaging, 91-101, 2023

year

2023

publication date

2023/10/1

authors

Elizabeth Haddad, Myrl G Marmarelis, Talia M Nir, Aram Galstyan, Greg Ver Steeg, Neda Jahanshad

link

https://link.springer.com/chapter/10.1007/978-3-031-44858-4_9

resource_link

https://www.researchgate.net/profile/Elizabeth-Haddad/publication/374530138_Causal_Sensitivity_Analysis_for_Hidden_Confounding_Modeling_the_Sex-Specific_Role_of_Diet_on_the_Aging_Brain/links/65244362b0df2f20a2219695/Causal-Sensitivity-Analysis-for-Hidden-Confounding-Modeling-the-Sex-Specific-Role-of-Diet-on-the-Aging-Brain.pdf

book

International Workshop on Machine Learning in Clinical Neuroimaging

pages

91-101

publisher

Springer Nature Switzerland