Relative associations of behavioral and physiological risks for cardiometabolic disease with cognition in bipolar disorder during mid and later-life: findings from the UK biobank

BACKGROUND

Cardiometabolic disease risk factors are disproportionately prevalent in bipolar disorder (BD) and are associated with cognitive impairment. It is, however, unknown which health risk factors for cardiometabolic disease are relevant to cognition in BD. This study aimed to identify the cardiometabolic disease risk factors that are the most important correlates of cognitive impairment in BD; and to examine whether the nature of the relationships vary between mid and later life.

METHODS

Data from the UK Biobank were available for 966 participants with BD, aged between 40 and 69 years. Individual cardiometabolic disease risk factors were initially regressed onto a global cognition score in separate models for the following risk factor domains; (1) health risk behaviors (physical activity, sedentary behavior, smoking, and sleep) and (2) physiological risk factors, stratified into (2a) anthropometric and clinical risk (handgrip strength, body composition, and blood pressure), and (2b) cardiometabolic disease risk biomarkers (CRP, lipid profile, and HbA1c). A final combined multivariate regression model for global cognition was then fitted, including only the predictor variables that were significantly associated with cognition in the previous models.

RESULTS

In the final combined model, lower mentally active and higher passive sedentary behavior, higher levels of physical activity, inadequate sleep duration, higher systolic and lower diastolic blood pressure, and lower handgrip strength were associated with worse global cognition.

CONCLUSIONS

Health risk behaviors, as well as blood pressure and muscular strength, are associated with cognitive function in BD, whereas other traditional physiological cardiometabolic disease risk factors are not.

Two neurostructural subtypes: results of machine learning on brain images from 4,291 individuals with schizophrenia

Machine learning can be used to define subtypes of psychiatric conditions based on shared clinical and biological foundations, presenting a crucial step toward establishing biologically based subtypes of mental disorders. With the goal of identifying subtypes of disease progression in schizophrenia, here we analyzed cross-sectional brain structural magnetic resonance imaging (MRI) data from 4,291 individuals with schizophrenia (1,709 females, age=32.5 years±11.9) and 7,078 healthy controls (3,461 females, age=33.0 years±12.7) pooled across 41 international cohorts from the ENIGMA Schizophrenia Working Group, non-ENIGMA cohorts and public datasets. Using a machine learning approach known as Subtype and Stage Inference (SuStaIn), we implemented a brain imaging-driven classification that identifies two distinct neurostructural subgroups by mapping the spatial and temporal trajectory of gray matter (GM) loss in schizophrenia. Subgroup 1 (n=2,622) was characterized by an early cortical-predominant loss (ECL) with enlarged striatum, whereas subgroup 2 (n=1,600) displayed an early subcortical-predominant loss (ESL) in the hippocampus, amygdala, thalamus, brain stem and striatum. These reconstructed trajectories suggest that the GM volume reduction originates in the Broca's area/adjacent fronto-insular cortex for ECL and in the hippocampus/adjacent medial temporal structures for ESL. With longer disease duration, the ECL subtype exhibited a gradual worsening of negative symptoms and depression/anxiety, and less of a decline in positive symptoms. We confirmed the reproducibility of these imaging-based subtypes across various sample sites, independent of macroeconomic and ethnic factors that differed across these geographic locations, which include Europe, North America and East Asia. These findings underscore the presence of distinct pathobiological foundations underlying schizophrenia. This new imaging-based taxonomy holds the potential to identify a more homogeneous sub-population of individuals with shared neurobiological attributes, thereby suggesting the viability of redefining existing disorder constructs based on biological factors.

Increased cortical surface area but not altered cortical thickness or gyrification in bipolar disorder following stabilisation from a first episode of mania

BACKGROUND

Despite reports of altered brain morphology in established bipolar disorder (BD), there is limited understanding of when these morphological abnormalities emerge. Assessment of patients during the early course of illness can help to address this gap, but few studies have examined surface-based brain morphology in patients at this illness stage.

METHODS

We completed a secondary analysis of baseline data from a randomised control trial of BD individuals stabilised after their first episode of mania (FEM). The magnetic resonance imaging scans of n = 35 FEM patients and n = 29 age-matched healthy controls were analysed. Group differences in cortical thickness, surface area and gyrification were assessed at each vertex of the cortical surface using general linear models. Significant results were identified at p < 0.05 using cluster-wise correction.

RESULTS

The FEM group did not differ from healthy controls with regards to cortical thickness or gyrification. However, there were two clusters of increased surface area in the left hemisphere of FEM patients, with peak coordinates falling within the lateral occipital cortex and pars triangularis.

CONCLUSIONS

Cortical thickness and gyrification appear to be intact in the aftermath of a first manic episode, whilst cortical surface area in the inferior/middle prefrontal and occipitoparietal cortex is increased compared to age-matched controls. It is possible that increased surface area in the FEM group is the outcome of abnormalities in a premorbidly occurring process. In contrast, the findings raise the hypothesis that cortical thickness reductions seen in past studies of individuals with more established BD may be more attributable to post-onset factors.

Psychological-health correlates of physical activity and sedentary behaviour during the COVID pandemic

BACKGROUND

While physical inactivity is associated with adverse psychological outcomes, less is known about the psychological outcomes associated with sedentary behaviour, and specifically, its mentally active and passive forms. The COVID-19 pandemic represents a unique opportunity to study associations between these variables in light of widespread stay-at-home mandates and restrictions on outdoor exercise/social activities. Using a cross-sectional dataset acquired during the COVID-19 pandemic in Australia, we examined whether physical activity and sedentary behaviour were associated with subjective quality of life (sQoL) and subjective cognitive dysfunction, and whether these associations were mediated by depressive symptoms.

METHODS

658 participants (males = 169, females = 489) self-reported data on physical activity and sedentary behaviour in an online survey during May 2020-May 2021. Data on physical activity and sedentary behaviour (both mentally active and passive types) was compared according to whether it was collected during or out of a lockdown period. Regression models were used to test associations of physical activity and sedentary behaviour with sQoL and subjective cognitive dysfunction, and whether these associations were mediated by depression severity.

RESULTS

Physical activity was beneficially associated with sQoL, whereas sedentary behaviour (both total hours and the reduction of mentally active/increase in mentally passive behaviour) was detrimentally associated with sQoL. These associations were mediated by depression severity. Physical activity and sedentary behaviour were also indirectly associated with subjective cognitive dysfunction by virtue of their associations with depression severity.

CONCLUSIONS

There are important differences in the psychological correlates of mentally passive and active sedentary behaviours. Our findings suggest that health promotion strategies should focus on not only increasing physical activity but also reducing passive sedentary behaviours as a means of maintaining good psychological health.

The impact of smoking status on cognition and brain morphology in schizophrenia spectrum disorders

BACKGROUND

Cigarette smoking is associated with worse cognition and decreased cortical volume and thickness in healthy cohorts. Chronic cigarette smoking is prevalent in schizophrenia spectrum disorders (SSD), but the effects of smoking status on the brain and cognition in SSD are not clear. This study aimed to understand whether cognitive performance and brain morphology differed between smoking and non-smoking individuals with SSD compared to healthy controls.

METHODS

Data were obtained from the Australian Schizophrenia Research Bank. Cognitive functioning was measured in 299 controls and 455 SSD patients. Cortical volume, thickness and surface area data were analysed from T1-weighted structural scans obtained in a subset of the sample (n = 82 controls, n = 201 SSD). Associations between smoking status (cigarette smoker/non-smoker), cognition and brain morphology were tested using analyses of covariance, including diagnosis as a moderator.

RESULTS

No smoking by diagnosis interactions were evident, and no significant differences were revealed between smokers and non-smokers across any of the variables measured, with the exception of a significantly thinner left posterior cingulate in smokers compared to non-smokers. Several main effects of smoking in the cognitive, volume and thickness analyses were initially significant but did not survive false discovery rate (FDR) correction.

CONCLUSIONS

Despite the general absence of significant FDR-corrected findings, trend-level effects suggest the possibility that subtle smoking-related effects exist but were not uncovered due to low statistical power. An investigation of this topic is encouraged to confirm and expand on our findings.

General and specific graphic health warning labels reduce willingness to consume sugar-sweetened beverages

Sugar-sweetened beverage (SSB) consumption is associated with obesity and other severe negative health consequences. The present study examined the effectiveness of two types of health warning labels (HWLs) in modulating dietary choices for SSBs: specific HWLs, presenting health consequences associated with consuming SSBs, and general HWLs, presenting health consequences of an unhealthy diet and obesity. While electroencephalography (EEG) was recorded, 63 participants completed a computer-based task in which they were first randomly allocated to view either SBB-specific, general, or non-warning control HWLs. They then viewed images of a range of SSB products, varying on perceived healthiness and tastiness, and rated their willingness to consume (WTC) each one. Linear mixed-effect model analyses revealed that general and specific HWLs both decreased WTC SSBs perceived as tasty, compared to the control condition. For general HWLs, this effect was reduced for SSBs perceived to be healthy, suggesting that specific HWLs may be more effective at reducing SSB consumption. The EEG data showed that SSBs considered unhealthy elicited greater N1 amplitudes, and tasty SSBs elicited greater late positive potential (LPP) amplitudes, possibly reflecting attentional allocation and craving responses, respectively. However, no strong differences between HWL types were found. Taken together, the results suggest that graphic HWLs, both general and specific, have the potential to reduce SSB consumption, but they do not strongly modulate craving-related neural responses to SSBs.