Associations of carotid arterial compliance and white matter diffusion metrics during midlife: modulation by sex

Associations of Arterial Thickness, Stiffness, and Blood Pressure With Brain Morphology in Early Adolescence: A Prospective Population-Based Study

BACKGROUND

Arterial wall thickness and stiffness, and high blood pressure have been repeatedly associated with poorer brain health. However, previous studies largely focused on mid- or late-life stages. It is unknown whether any arterial health-related brain changes may be observable already in adolescence.

METHODS

We examined whether (1) carotid intima-media thickness, (2) carotid distensibility, and (3) systolic blood pressure and diastolic blood pressure, measured at the age of 10 years, were associated with brain volumes and white matter microstructure (ie, fractional anisotropy and mean diffusivity) at the age of 14 years. In addition to cross-sectional analyses, we explored associations with longitudinal change in each brain outcome from 10 to 14 years. Analyses were based on 5341 children from the Generation R Study.

RESULTS

Higher diastolic blood pressure was associated with lower total brain volume (β, -0.04 [95% CI, -0.07 to -0.01]) and gray matter volume (β, -0.04 [95% CI, -0.07 to -0.01]) at the age of 14 years, with stronger associations in higher diastolic blood pressure ranges. Similar associations emerged between systolic blood pressure and brain volumes, but these were no longer significant after adjusting for birth weight. No associations were observed between blood pressure and white matter microstructure or between carotid intima-media thickness or distensibility and brain morphology.

CONCLUSIONS

Arterial blood pressure, but not intima-media thickness and distensibility, is associated with structural neuroimaging markers in early adolescence. Volumetric measures may be more sensitive to these early arterial health differences compared with microstructural properties of the white matter, but further studies are needed to confirm these results and assess potential causal mechanisms.

A comparison between diffusion tensor imaging and generalized q-sampling imaging in the age prediction of healthy adults via machine learning approaches

OBJECTIVE

Brain age, which is predicted using neuroimaging data, has become an important biomarker in aging research. This study applied diffusion tensor imaging (DTI) and generalized q-sampling imaging (GQI) model to predict age respectively, with the purpose of evaluating which diffusion model is more accurate in estimating age and revealing age-related changes in the brain.

APPROACH

Diffusion MRI data of 125 subjects from two sites were collected. Fractional anisotropy (FA) and quantitative anisotropy (QA) from the two diffusion models were calculated and were used as features of machine learning models. Sequential backward elimination algorithm was used for feature selection. Six machine learning approaches including linear regression, ridge regression, support vector regression (SVR) with linear kernel, quadratic kernel and radial basis function (RBF) kernel and feedforward neural network were used to predict age using FA and QA features respectively.

MAIN RESULTS

Age predictions using FA features were more accurate than predictions using QA features for all the 6 machine learning algorithms. Post-hoc analysis revealed that FA was more sensitive to age-related white matter alterations in the brain. In addition, SVR with RBF kernel based on FA features achieved better performances than the competing algorithms with MAE ranging from 7.74 to 10.54, MSE ranging from 87.79 to 150.86, and nMSE ranging from 0.05 to 0.14 Significance: FA from DTI model was more suitable than QA from GQI model in age prediction. FA metric was more sensitive to age-related white matter changes in the brain and FA of several brain regions could be used as white matter biomarkers in aging.

Network Modeling Sex Differences in Brain Integrity and Metabolic Health

Hypothesis-driven studies have demonstrated that sex moderates many of the relationships between brain health and cardiometabolic disease, which impacts risk for later-life cognitive decline. In the present study, we sought to further our understanding of the associations between multiple markers of brain integrity and cardiovascular risk in a midlife sample of 266 individuals by using network analysis, a technique specifically designed to examine complex associations among multiple systems at once. Separate network models were constructed for male and female participants to investigate sex differences in the biomarkers of interest, selected based on evidence linking them with risk for late-life cognitive decline: all components of metabolic syndrome (obesity, hypertension, dyslipidemia, and hyperglycemia); neuroimaging-derived brain-predicted age minus chronological age; ratio of white matter hyperintensities to whole brain volume; seed-based resting state functional connectivity in the Default Mode Network, and ratios of N-acetyl aspartate, glutamate and myo-inositol to creatine, measured through proton magnetic resonance spectroscopy. Males had a sparse network (87.2% edges = 0) relative to females (69.2% edges = 0), indicating fewer relationships between measures of cardiometabolic risk and brain integrity. The edges in the female network provide meaningful information about potential mechanisms between brain integrity and cardiometabolic health. Additionally, Apolipoprotein ϵ4 (ApoE ϵ4) status and waist circumference emerged as central nodes in the female model. Our study demonstrates that network analysis is a promising technique for examining relationships between risk factors for cognitive decline in a midlife population and that investigating sex differences may help optimize risk prediction and tailor individualized treatments in the future.

Cognition, Brain Structure, and Brain Function in Individuals with Obesity and Related Disorders

PURPOSE OF REVIEW

Obesity is one of the most serious public health concerns. Excess adipose tissue, particularly with a centralized distribution, is associated with cognitive decline. Indeed, obesity has been associated with a number of adverse changes in brain function and structure that can be detected by neuroimaging techniques. These obesity-associated changes in the brain are associated with cognitive dysfunction.

RECENT FINDINGS

While the pathways by which excess adipose tissue affects brain function are not fully understood, available evidence points towards insulin resistance, inflammation, and vascular dysfunction, as possible mechanisms responsible for the observed relations between obesity and cognitive impairment. It appears that weight loss is related to better brain and cognitive outcomes and that cognitive impairment due to obesity may be reversible.

Obesity and the Brain: Another Brain-Body Versus Body-Brain Conundrum

Midlife obesity has been associated with poor cognitive functioning in older age, but the bidirectional pathways linking the brain and excessive adipose tissue require further research. In this issue of Psychosomatic Medicine, two investigations address the brain responses to food-related cues and psychological stressors relevant to obesity. Moazzami and colleagues document the relationship between abdominal obesity and brain responses to stress among patients with coronary artery disease and find that stress-related brain activity plays a potentially important role in the link between psychological distress, food cravings, and eating patterns relevant to obesity. Donofry and colleagues compare food cue-evoked functional connectivity in adults with obesity and report that brain areas involved in impaired self-regulation and reward processing may increase the risk of obesity by influencing decisions regarding diet and exercise. In this editorial, these findings are discussed in the context of brain-obesity interactions and the need for personalized multidisciplinary interventions for obesity. It is possible that functional magnetic resonance imaging and other indices of brain functioning will be useful in tailoring interventions that target weight reduction and/or cognitive functioning and monitoring treatment progress.