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Socioeconomic status (SES) anchors individuals in their social network layers. Our embedding in the societal fabric resonates with habitus, world view, opportunity, and health disparity. It remains obscure how distinct facets of SES are reflected in the architecture of the central nervous system. Here, we capitalized on multivariate multi-output learning algorithms to explore possible imprints of SES in gray and white matter structure in the wider population (n  ≈ 10,000 UK Biobank participants). Individuals with higher SES, compared with those with lower SES, showed a pattern of increased region volumes in the left brain and decreased region volumes in the right brain. The analogous lateralization pattern emerged for the fiber structure of anatomical white matter tracts. Our multimodal findings suggest hemispheric asymmetry as an SES-related brain signature, which was consistent across six different indicators of SES: degree, education, income, job, neighborhood and vehicle count. Hence, hemispheric specialization may have evolved in human primates in a way that reveals crucial links to SES. [ABSTRACT FROM AUTHOR]

Human variation in brain morphology and behavior are related and highly heritable. Yet, it is largely unknown to what extent specific features of brain morphology and behavior are genetically related. Here, we introduce a computationally efficient approach for multivariate genomic-relatedness-based restricted maximum likelihood (MGREML) to estimate the genetic correlation between a large number of phenotypes simultaneously. Using individual-level data (N = 20,190) from the UK Biobank, we provide estimates of the heritability of gray-matter volume in 74 regions of interest (ROIs) in the brain and we map genetic correlations between these ROIs and health-relevant behavioral outcomes, including intelligence. We find four genetically distinct clusters in the brain that are aligned with standard anatomical subdivision in neuroscience. Behavioral traits have distinct genetic correlations with brain morphology which suggests trait-specific relevance of ROIs. These empirical results illustrate how MGREML can be used to estimate internally consistent and high-dimensional genetic correlation matrices in large datasets. Ronald de Vlaming and Eric Slob et al. present MGREML, a multivariate tool to estimate pairwise genetic correlations between multiple traits. They apply MGREML to UK Biobank data for 74 brain imaging phenotypes and 8 behavioral traits, demonstrating that these phenotypes have distinct genetic correlations with brain morphology. [ABSTRACT FROM AUTHOR]

Previous studies link obesity and components of metabolic health, such as hypertension or inflammation, to increased hospitalizations and mortality of patients with COVID-19. Here, in two overlapping samples of over 1,000 individuals from the UK Biobank we investigate whether metabolic health as measured by waist circumference, dyslipidemia, hypertension, type 2 diabetes, and systemic inflammation is related to increased COVID-19 infection and mortality rate. Using logistic regression and controlling for confounding variables such as socioeconomic status, age, sex or ethnicity, we find that individuals with worse metabolic health (measured on average eleven years prior to 2020) have an increased risk for COVID-19-related death (adjusted odds ratio: 1.75). We also find that specific factors contributing to increased mortality are increased serum glucose levels, systolic blood pressure and waist circumference. [ABSTRACT FROM AUTHOR]

Humans survive and thrive through social exchange. Yet, social dependency also comes at a cost. Perceived social isolation, or loneliness, affects physical and mental health, cognitive performance, overall life expectancy, and increases vulnerability to Alzheimer's disease-related dementias. Despite severe consequences on behavior and health, the neural basis of loneliness remains elusive. Using the UK Biobank population imaging-genetics cohort (n = ~40,000, aged 40–69 years when recruited, mean age = 54.9), we test for signatures of loneliness in grey matter morphology, intrinsic functional coupling, and fiber tract microstructure. The loneliness-linked neurobiological profiles converge on a collection of brain regions known as the 'default network'. This higher associative network shows more consistent loneliness associations in grey matter volume than other cortical brain networks. Lonely individuals display stronger functional communication in the default network, and greater microstructural integrity of its fornix pathway. The findings fit with the possibility that the up-regulation of these neural circuits supports mentalizing, reminiscence and imagination to fill the social void. Here, using pattern-learning analyses of structural, functional, and diffusion brain scans in ~40,000 UK Biobank participants, the authors provide population-scale evidence that the default network is associated with perceived social isolation. [ABSTRACT FROM AUTHOR]

Motivation theories of obesity suggest that one of the brain mechanisms underlying pathological eating and weight gain is the dysregulation of dopaminergic circuits. Although these dysregulations likely occur at the microscopic level, studies on grey matter volume report macroscopic differences associated with obesity. One region suggested to play a key role in the pathophysiology of obesity is the nucleus accumbens (NAcc). We performed a meta‐analysis of findings regarding NAcc volume and overweight/obesity. We additionally examined whether grey matter volume in the NAcc and other mesolimbic areas depends on the longitudinal trajectory of obesity, using the UK Biobank dataset. To this end, we analysed the data using a latent growth model, which identifies whether a certain variable of interest (eg, NAcc volume) is related to another variable's (body mass index [BMI]) initial values or longitudinal trajectories. Our meta‐analysis showed that, overall, NAcc volume is positively related to BMI. However, further analyses revealed that the relationship between NAcc volume and BMI is dependent on age. For younger individuals, such a relationship is positive, whereas, for older adults, it is negative. This was corroborated by our analysis in the UK Biobank dataset, which includes older adults, where we found that a higher BMI was associated with a lower NAcc and thalamus volume. Overall, the present study suggests that increased NAcc volume at a young age might be a vulnerability factor for obesity, whereas, at an older age, decreased NAcc volume with increased BMI might be an effect of prolonged influences of neuroinflammation on the brain. [ABSTRACT FROM AUTHOR]

Dysregulated neural mechanisms in reward and somatosensory circuits result in an increased appetitive drive for and reduced inhibitory control of eating, which in turn causes obesity. Despite many studies investigating the brain mechanisms of obesity, the role of macroscale whole‐brain functional connectivity remains poorly understood. Here, we identified a neuroimaging‐based functional connectivity pattern associated with obesity phenotypes by using functional connectivity analysis combined with machine learning in a large‐scale (n ~ 2,400) dataset spanning four independent cohorts. We found that brain regions containing the reward circuit positively associated with obesity phenotypes, while brain regions for sensory processing showed negative associations. Our study introduces a novel perspective for understanding how the whole‐brain functional connectivity correlates with obesity phenotypes. Furthermore, we demonstrated the generalizability of our findings by correlating the functional connectivity pattern with obesity phenotypes in three independent datasets containing subjects of multiple ages and ethnicities. Our findings suggest that obesity phenotypes can be understood in terms of macroscale whole‐brain functional connectivity and have important implications for the obesity neuroimaging community. [ABSTRACT FROM AUTHOR]

Sleep disturbances are increasingly recognized as adversely affecting brain health in aging. Our aim was to investigate interrelations between subjective sleep-related symptoms, obesity, cardiometabolic disorders, brain structure and cognitive decline in a population-based aging sample. Data were extracted from the UK Biobank for anthropometric and demographic information, self-reported sleep behaviours, cardiometabolic measures, structural brain magnetic resonance imaging and cognitive test scores. "Sleep-related symptoms" (SRS) were measured using four questionnaire items: loud snoring, daytime sleepiness, likelihood to nap and difficulty getting up in the morning. Associations were tested using a structural equation model (SEM), adjusted for confounders. Further, multiple regression analysis was used to test for direct relationships between SRS and specific cognitive domains. Among 36,468 participants with an average age of 63.6 (SD 7.5) years and 46.7% male, we found that SRS were associated with obesity and several pre-existing cardiometabolic disturbances. In turn, cardiometabolic disorders were associated with increased white matter hyperintensities and cortical thinning, which were related to cognitive dysfunction. SRS were also directly related to several structural brain changes and to cognitive dysfunction. Regression analyses showed that SRS were directly associated with slower reaction times, and lower scores in fluid intelligence, working memory and executive function. Self-reported sleep-related symptoms were associated with cognitive dysfunction directly and through pre-existing cardiometabolic disorders and brain structural alterations. These findings provide evidence that symptoms of sleep disturbances, here defined primarily by hypersomnolence and snoring, are important risk factors or markers for cognitive dysfunction in an aging population. • How sleep-related symptoms lead to cognitive dysfunction is not well understood. • Obesity, cardiometabolic disturbances and structural brain changes may be involved. • These variables had complex interrelationships in a large population sample. • Sleep-related symptoms were associated with poorer cognition directly. • Cardiometabolic and brain structural changes were also important pathways. [ABSTRACT FROM AUTHOR]