An exploratory causal analysis of the relationships between the brain age gap and cardiovascular risk factors

Pauline Mouches; Matthias Wilms; Jordan J Bannister; Agampreet Aulakh; Sönke Langner; Nils D Forkert

doi:10.3389/fnagi.2022.941864

An exploratory causal analysis of the relationships between the brain age gap and cardiovascular risk factors

Front Aging Neurosci. 2022 Aug 22:14:941864. doi: 10.3389/fnagi.2022.941864. eCollection 2022.

Authors

Pauline Mouches^{1

2

3}, Matthias Wilms^{2

3

4}, Jordan J Bannister^{1

2

3}, Agampreet Aulakh⁵, Sönke Langner⁶, Nils D Forkert^{2

3

4}

Affiliations

¹ Biomedical Engineering Program, University of Calgary, Calgary, AB, Canada.
² Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.
³ Department of Radiology, University of Calgary, Calgary, AB, Canada.
⁴ Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada.
⁵ Schulich School of Engineering, University of Calgary, Calgary, AB, Canada.
⁶ Institute for Diagnostic Radiology and Neuroradiology, Rostock University Medical Center, Rostock, Germany.

Abstract

The brain age gap (BAG) has been shown to capture accelerated brain aging patterns and might serve as a biomarker for several neurological diseases. Moreover, it was also shown that it captures other biological information related to modifiable cardiovascular risk factors. Previous studies have explored statistical relationships between the BAG and cardiovascular risk factors. However, none of those studies explored causal relationships between the BAG and cardiovascular risk factors. In this work, we employ causal structure discovery techniques and define a Bayesian network to model the assumed causal relationships between the BAG, estimated using morphometric T1-weighted magnetic resonance imaging brain features from 2025 adults, and several cardiovascular risk factors. This setup allows us to not only assess observed conditional probability distributions of the BAG given cardiovascular risk factors, but also to isolate the causal effect of each cardiovascular risk factor on BAG using causal inference. Results demonstrate the feasibility of the proposed causal analysis approach by illustrating intuitive causal relationships between variables. For example, body-mass-index, waist-to-hip ratio, smoking, and alcohol consumption were found to impact the BAG, with the greatest impact for obesity markers resulting in higher chances of developing accelerated brain aging. Moreover, the findings show that causal effects differ from correlational effects, demonstrating the importance of accounting for variable relationships and confounders when evaluating the information captured by a biomarker. Our work demonstrates the feasibility and advantages of using causal analyses instead of purely correlation-based and univariate statistical analyses in the context of brain aging and related problems.

Keywords: Bayesian network; brain age gap; brain aging (normal); cardiovascular risk factors; causal analyses.