Left ventricular geometry, brain architecture, and cognition: an observational study

Abstract

Background and aims: Cardiovascular (CV) diseases and dementia share common risk factors and often coexist in older adults. Understanding the CV-brain interaction is essential for tackling their interconnected burden. This study investigated the link between CV phenotypes, brain architecture, and cognition.

Methods: Overall, 15 519 UK Biobank participants without neurodegenerative diseases or stroke (median age 64 years, 49% female) were analysed. Confirmatory factor analysis aggregated 18 CV magnetic resonance imaging (MRI) biomarkers into latent variables for left ventricular systolic function (gSyst), diastolic (gDiast) function, and geometry (gGeom); arterial compliance was measured by aortic distensibility (AoD). Multivariable linear regression models evaluated associations with brain MRI phenotypes, including grey matter volume, white matter hyperintensities, MRI diffusion white matter microstructure, and hippocampal volume. Models were adjusted for age, body mass index, height, mean blood pressure, and CV risk factors. Exploratory mediation models evaluated whether hippocampal volume accounted for associations between CV phenotypes and cognition.

Results: gGeom, reflecting greater myocardial mass, wall thickness, and ventricular volumes, showed the strongest association with hippocampal volume [β = 0.082 (0.048-0.117) in females; β = 0.039 (0.006-0.071) in males] and was the only CV phenotype associated with better cognition, including higher fluid intelligence and faster reaction time. Hippocampal volume significantly accounted for the positive relationship between gGeom and cognition across sexes. In contrast, gSyst, gDiast, and AoD demonstrated weaker and less consistent associations with brain structure and were unrelated to cognition.

Conclusions: Ventricular geometry emerged as the CV phenotype most strongly associated with brain architecture and cognition. Hippocampal volume may help explain this association, but further studies are needed to investigate causality.

Keywords: Cardiac magnetic resonance imaging; Heart–brain axis; Ventricular geometry.