The health of the body and the brain are intrinsically connected, with the structure and function of the ageing human brain being vulnerable to the effects of poor cardiovascular health. Cardio- vascular and -metabolic risk factors such as smoking, dyslipidemia, high blood pressure, obesity, and markers of inflammation are associated with an increased risk of neurocognitive conditions such as dementia and stroke, in addition to a range of mental disorders and age-related cognitive decline.
While poor cardiometabolic health may negatively impact brain health, strategies that promote cardiometabolic health may conversely halt the onset of age-related pathological changes in the brain. Increasing knowledge about the mechanisms of cardiometabolic risk factors (CMRs) and their association with brain structure and integrity is necessary for the development of treatment strategies that delay ageing-related neurodegeneration.
In the current thesis, we used a combination of cross-sectional and longitudinal datasets to investigate brain and cardiometabolic health. Utilising brain age prediction based on neuroimaging data (Franke, Ziegler, Kloppel, Gaser, & Alzheimer’s Disease Neuroimaging, 2010), we first tested the age prediction accuracy of brain age models based on diffusion magnetic resonance imaging (MRI) metrics, followed by investigating brain age gap (BAG, the difference between the brain-predicted age and chronological age) associations with CMRs, including clinical measures, blood test measures, and measures of adipose tissue distribution from body MRI.
More specifically, in paper I, we used linear mixed effect models and machine learning based brain age prediction to investigate the age prediction accuracy of six different diffusion MRI models (dMRI), including diffusion tensor (DTI) and kurtosis imaging (DKI), neurite orientation dispersion and density imaging (NODDI), restriction spectrum imaging (RSI), spherical mean technique multi-compartment (SMT-mc), and white matter tract integrity (WMTI). The results validated brain age prediction as a reliable biomarker for brain ageing and indicated that while advanced dMRI models (DKI, NODDI, RSI, SMT mc, WMTI) provide sensitive measures of age-related microstructural changes of white matter (WM) in the brain, conventional DTI showed comparable age prediction accuracy to the highest performing advanced dMRI models.
In paper II, we extended on our brain age work and trained brain age models utilising T1-weighted imaging in addition to DTI. Moreover, we assessed deviations from expected age trajectories using BAG. The tissue-specific BAGs were then used to investigate associations with CMRs and other health indicators including anthropometric measures, lifestyle factors, and blood biomarkers. Briefly, the results indicated that people with higher cardiometabolic risk (low-grade inflammation, smoking, higher pulse and blood pressure) had older-appearing brains. Longitudinal evidence supported interactions between both T1 and DTI-based BAG and waist-to-hip ratio (WHR), and between DTI-based BAG and smoking and systolic blood pressure, indicating accelerated ageing in people with higher cardiometabolic risk (WHR, smoking, and higher blood pressure). The results demonstrated that cardiometabolic risk factors are associated with brain ageing.
Building on the results and methodology of paper II, in paper III we also utilised tissue-specific brain age prediction, this time investigating the associations with adipose tissue distribution and intra-abdominal fat from (cross-sectional) body MRI data. Utilising a similar Bayesian statistical framework to paper II, we investigated associations between DTI and T1-based BAGs and conventional anthropometric adipose measures (WHR, body-mass index (BMI)), and state-of-the-art adipose tissue distribution measures from body MRI (liver fat, fat ratio, weight-to-muscle ratio, total adipose volume, muscle fat infiltration, and visceral fat index). The results indicated that specific measures of fat distribution are associated with brain ageing and that different adiposity subtypes may be differentially linked with brain health.
In summary, brain-predicted age is a reliable biomarker of individual brain health and ageing, with brain age gap being a viable method to assess deviations from expected age trajectories. CMRs and other health indicators represent risk factors for ageing-related cognitive impairment and dementia, and the current results support that poor cardiometabolic health is associated with older-appearing brains and accelerated brain ageing in a healthy sample.
The connection between cardiometabolic and brain health represents a window of opportunity for interventions targeting cardiometabolic health. However, the myriad of risk factors that impact brain ageing and health suggests that the body-brain connection is very complex. Further research is required to verify our findings, including randomised controlled trials utilising highly powered and genetically informed longitudinal designs including data on biological markers and detailed assessments of dietary routines, alcohol intake, and physical exercise, to name a few.