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Prominent health problems, socioeconomic deprivation, and higher brain age in lonely and isolated individuals: A population-based study.
Loneliness is linked to increased risk for Alzheimer's disease, but little is known about factors potentially contributing to adverse brain health in lonely individuals. In this study, we used data from 24,867 UK Biobank participants to investigate risk factors related to loneliness and estimated brain age based on neuroimaging data. The results showed that on average, individuals who self-reported loneliness on a single yes/no item scored higher on neuroticism, depression, social isolation, and socioeconomic deprivation, performed less physical activity, and had higher BMI compared to individuals who did not report loneliness. In line with studies pointing to a genetic overlap of loneliness with neuroticism and depression, permutation feature importance ranked these factors as the most important for classifying lonely vs. not lonely individuals (ROC AUC = 0.83). While strongly linked to loneliness, neuroticism and depression were not associated with brain age estimates. Conversely, objective social isolation showed a main effect on brain age, and individuals reporting both loneliness and social isolation showed higher brain age relative to controls - as part of a prominent risk profile with elevated scores on socioeconomic deprivation and unhealthy lifestyle behaviours, in addition to neuroticism and depression. While longitudinal studies are required to determine causality, this finding may indicate that the combination of social isolation and a genetic predisposition for loneliness involves a risk for adverse brain health. Importantly, the results underline the complexity in associations between loneliness and adverse health outcomes, where observed risks likely depend on a combination of interlinked variables including genetic as well as social, behavioural, physical, and socioeconomic factors.
No Association Between Loneliness, Episodic Memory and Hippocampal Volume Change in Young and Healthy Older Adults: A Longitudinal European Multicenter Study.
BACKGROUND: Loneliness is most prevalent during adolescence and late life and has been associated with mental health disorders as well as with cognitive decline during aging. Associations between longitudinal measures of loneliness and verbal episodic memory and brain structure should thus be investigated. METHODS: We sought to determine associations between loneliness and verbal episodic memory as well as loneliness and hippocampal volume trajectories across three longitudinal cohorts within the Lifebrain Consortium, including children, adolescents (N = 69, age range 10-15 at baseline examination) and older adults (N = 1468 over 60). We also explored putative loneliness correlates of cortical thinning across the entire cortical mantle. RESULTS: Loneliness was associated with worsening of verbal episodic memory in one cohort of older adults. Specifically, reporting medium to high levels of loneliness over time was related to significantly increased memory loss at follow-up examinations. The significance of the loneliness-memory change association was lost when eight participants were excluded after having developed dementia in any of the subsequent follow-up assessments. No significant structural brain correlates of loneliness were found, neither hippocampal volume change nor cortical thinning. CONCLUSION: In the present longitudinal European multicenter study, the association between loneliness and episodic memory was mainly driven by individuals exhibiting progressive cognitive decline, which reinforces previous findings associating loneliness with cognitive impairment and dementia.
People's interest in brain health testing: Findings from an international, online cross-sectional survey.
UNLABELLED: Brain health entails mental wellbeing and cognitive health in the absence of brain disorders. The past decade has seen an explosion of tests, cognitive and biological, to predict various brain conditions, such as Alzheimer's Disease. In line with these current developments, we investigated people's willingness and reasons to-or not to-take a hypothetical brain health test to learn about risk of developing a brain disease, in a cross-sectional multilanguage online survey. The survey was part of the Global Brain Health Survey, open to the public from 4th June 2019 to 31st August 2020. Respondents were largely recruited via European brain councils and research organizations. 27,590 people responded aged 18 years or older and were predominantly women (71%), middle-aged or older (>40 years; 83%), and highly educated (69%). Responses were analyzed to explore the relationship between demographic variables and responses. RESULTS: We found high public interest in brain health testing: over 91% would definitely or probably take a brain health test and 86% would do so even if it gave information about a disease that cannot be treated or prevented. The main reason for taking a test was the ability to respond if one was found to be at risk of brain disease, such as changing lifestyle, seeking counseling or starting treatment. Higher interest in brain health testing was found in men, respondents with lower education levels and those with poor self-reported cognitive health. CONCLUSION: High public interest in brain health and brain health testing in certain segments of society, coupled with an increase of commercial tests entering the market, is likely to put pressure on public health systems to inform the public about brain health testing in years to come.
Sleep duration over 28 years, cognition, gray matter volume, and white matter microstructure: a prospective cohort study.
STUDY OBJECTIVES: To examine the association between sleep duration trajectories over 28 years and measures of cognition, gray matter volume, and white matter microstructure. We hypothesize that consistently meeting sleep guidelines that recommend at least 7 hours of sleep per night will be associated with better cognition, greater gray matter volumes, higher fractional anisotropy, and lower radial diffusivity values. METHODS: We studied 613 participants (age 42.3 ± 5.03 years at baseline) who self-reported sleep duration at five time points between 1985 and 2013, and who had cognitive testing and magnetic resonance imaging administered at a single timepoint between 2012 and 2016. We applied latent class growth analysis to estimate membership into trajectory groups based on self-reported sleep duration over time. Analysis of gray matter volumes was carried out using FSL Voxel-Based-Morphometry and white matter microstructure using Tract Based Spatial Statistics. We assessed group differences in cognitive and MRI outcomes using nonparametric permutation testing. RESULTS: Latent class growth analysis identified four trajectory groups, with an average sleep duration of 5.4 ± 0.2 hours (5%, N = 29), 6.2 ± 0.3 hours (37%, N = 228), 7.0 ± 0.2 hours (45%, N = 278), and 7.9 ± 0.3 hours (13%, N = 78). No differences in cognition, gray matter, and white matter measures were detected between groups. CONCLUSIONS: Our null findings suggest that current sleep guidelines that recommend at least 7 hours of sleep per night may not be supported in relation to an association between sleep patterns and cognitive function or brain structure.
Self-reported sleep relates to hippocampal atrophy across the adult lifespan: results from the Lifebrain consortium.
OBJECTIVES: Poor sleep is associated with multiple age-related neurodegenerative and neuropsychiatric conditions. The hippocampus plays a special role in sleep and sleep-dependent cognition, and accelerated hippocampal atrophy is typically seen with higher age. Hence, it is critical to establish how the relationship between sleep and hippocampal volume loss unfolds across the adult lifespan. METHODS: Self-reported sleep measures and MRI-derived hippocampal volumes were obtained from 3105 cognitively normal participants (18-90 years) from major European brain studies in the Lifebrain consortium. Hippocampal volume change was estimated from 5116 MRIs from 1299 participants for whom longitudinal MRIs were available, followed up to 11 years with a mean interval of 3.3 years. Cross-sectional analyses were repeated in a sample of 21,390 participants from the UK Biobank. RESULTS: No cross-sectional sleep-hippocampal volume relationships were found. However, worse sleep quality, efficiency, problems, and daytime tiredness were related to greater hippocampal volume loss over time, with high scorers showing 0.22% greater annual loss than low scorers. The relationship between sleep and hippocampal atrophy did not vary across age. Simulations showed that the observed longitudinal effects were too small to be detected as age-interactions in the cross-sectional analyses. CONCLUSIONS: Worse self-reported sleep is associated with higher rates of hippocampal volume decline across the adult lifespan. This suggests that sleep is relevant to understand individual differences in hippocampal atrophy, but limited effect sizes call for cautious interpretation.
Leisure Activities and Their Relationship With MRI Measures of Brain Structure, Functional Connectivity, and Cognition in the UK Biobank Cohort.
Introduction: This study aimed to evaluate whether engagement in leisure activities is linked to measures of brain structure, functional connectivity, and cognition in early old age. Methods: We examined data collected from 7,152 participants of the United Kingdom Biobank (UK Biobank) study. Weekly participation in six leisure activities was assessed twice and a cognitive battery and 3T MRI brain scan were administered at the second visit. Based on responses collected at two time points, individuals were split into one of four trajectory groups: (1) stable low engagement, (2) stable weekly engagement, (3) low to weekly engagement, and (4) weekly to low engagement. Results: Consistent weekly attendance at a sports club or gym was associated with connectivity of the sensorimotor functional network with the lateral visual (β = 0.12, 95%CI = [0.07, 0.18], FDR q = 2.48 × 10-3) and cerebellar (β = 0.12, 95%CI = [0.07, 0.18], FDR q = 1.23 × 10-4) networks. Visiting friends and family across the two timepoints was also associated with larger volumes of the occipital lobe (β = 0.15, 95%CI = [0.08, 0.21], FDR q = 0.03). Additionally, stable and weekly computer use was associated with global cognition (β = 0.62, 95%CI = [0.35, 0.89], FDR q = 1.16 × 10-4). No other associations were significant (FDR q > 0.05). Discussion: This study demonstrates that not all leisure activities contribute to cognitive health equally, nor is there one unifying neural signature across diverse leisure activities.
Development and validation of a dementia risk score in the UK Biobank and Whitehall II cohorts.
BACKGROUND: Current dementia risk scores have had limited success in consistently identifying at-risk individuals across different ages and geographical locations. OBJECTIVE: We aimed to develop and validate a novel dementia risk score for a midlife UK population, using two cohorts: the UK Biobank, and UK Whitehall II study. METHODS: We divided the UK Biobank cohort into a training (n=176 611, 80%) and test sample (n=44 151, 20%) and used the Whitehall II cohort (n=2934) for external validation. We used the Cox LASSO regression to select the strongest predictors of incident dementia from 28 candidate predictors and then developed the risk score using competing risk regression. FINDINGS: Our risk score, termed the UK Biobank Dementia Risk Score (UKBDRS), consisted of age, education, parental history of dementia, material deprivation, a history of diabetes, stroke, depression, hypertension, high cholesterol, household occupancy, and sex. The score had a strong discrimination accuracy in the UK Biobank test sample (area under the curve (AUC) 0.8, 95% CI 0.78 to 0.82) and in the Whitehall cohort (AUC 0.77, 95% CI 0.72 to 0.81). The UKBDRS also significantly outperformed three other widely used dementia risk scores originally developed in cohorts in Australia (the Australian National University Alzheimer's Disease Risk Index), Finland (the Cardiovascular Risk Factors, Ageing, and Dementia score), and the UK (Dementia Risk Score). CLINICAL IMPLICATIONS: Our risk score represents an easy-to-use tool to identify individuals at risk for dementia in the UK. Further research is required to determine the validity of this score in other populations.
Association of cerebral small vessel disease burden with brain structure and cognitive and vascular risk trajectories in mid-to-late life.
We characterize the associations of total cerebral small vessel disease (SVD) burden with brain structure, trajectories of vascular risk factors, and cognitive functions in mid-to-late life. Participants were 623 community-dwelling adults from the Whitehall II Imaging Sub-study with multi-modal MRI (mean age 69.96, SD = 5.18, 79% men). We used linear mixed-effects models to investigate associations of SVD burden with up to 25-year retrospective trajectories of vascular risk and cognitive performance. General linear modelling was used to investigate concurrent associations with grey matter (GM) density and white matter (WM) microstructure, and whether these associations were modified by cognitive status (Montreal Cognitive Asessment [MoCA] scores of < 26 vs. ≥ 26). Severe SVD burden in older age was associated with higher mean arterial pressure throughout midlife (β = 3.36, 95% CI [0.42-6.30]), and faster cognitive decline in letter fluency (β = -0.07, 95% CI [-0.13--0.01]), and verbal reasoning (β = -0.05, 95% CI [-0.11--0.001]). Moreover, SVD burden was related to lower GM volumes in 9.7% of total GM, and widespread WM microstructural decline (FWE-corrected p
Multi-centre diagnostic classification of individual structural neuroimaging scans from patients with major depressive disorder.
Quantitative abnormalities of brain structure in patients with major depressive disorder have been reported at a group level for decades. However, these structural differences appear subtle in comparison with conventional radiologically defined abnormalities, with considerable inter-subject variability. Consequently, it has not been possible to readily identify scans from patients with major depressive disorder at an individual level. Recently, machine learning techniques such as relevance vector machines and support vector machines have been applied to predictive classification of individual scans with variable success. Here we describe a novel hybrid method, which combines machine learning with feature selection and characterization, with the latter aimed at maximizing the accuracy of machine learning prediction. The method was tested using a multi-centre dataset of T(1)-weighted 'structural' scans. A total of 62 patients with major depressive disorder and matched controls were recruited from referred secondary care clinical populations in Aberdeen and Edinburgh, UK. The generalization ability and predictive accuracy of the classifiers was tested using data left out of the training process. High prediction accuracy was achieved (~90%). While feature selection was important for maximizing high predictive accuracy with machine learning, feature characterization contributed only a modest improvement to relevance vector machine-based prediction (~5%). Notably, while the only information provided for training the classifiers was T(1)-weighted scans plus a categorical label (major depressive disorder versus controls), both relevance vector machine and support vector machine 'weighting factors' (used for making predictions) correlated strongly with subjective ratings of illness severity. These results indicate that machine learning techniques have the potential to inform clinical practice and research, as they can make accurate predictions about brain scan data from individual subjects. Furthermore, machine learning weighting factors may reflect an objective biomarker of major depressive disorder illness severity, based on abnormalities of brain structure.
Diagnosing and managing vascular dementia.
Vascular dementia (VaD) is common. Pure vascular disease may account for 5-20% of all cases of dementia, while mixed dementia, Alzheimer's disease (AD) with VaD, occurs at least as frequently. There is no specific treatment or cure for VaD, but its proximity to other conditions may make it amenable to interventions at various stages. The causes of VaD are multifactorial and involve neuronal networks needed for memory and cognition, executive function and behaviour. Hypertensive angiopathy is the major known causative factor for VaD. Recent research suggests that VaD and AD occupy ends of the same spectrum and share common risk factors. As VaD is closely related to cardiovascular disease, modifying cardiovascular risk factors may assist in its prevention. Hypertension in midlife increases the risk of all-cause dementia. Regular screening of high-risk individuals could help to detect dementia early on enabling appropriate preventive intervention. Medication for hypertension, diabetes, and hypercholesterolaemia is recommended. Behavioural treatments include enhancing and encouraging cognitive and physical activity, social engagement, smoking cessation and healthy diet, including alcohol reduction. Comorbid depression is common in older people with dementia and treating this can improve cognition. Typically, patients are in their late sixties or early seventies, and may present after a cerebrovascular event. The onset is usually more acute than that of AD. Typical signs and symptoms are gait disturbance, unsteadiness and falls, urinary symptoms not explained by urological disease, pseudobulbar palsy and personality and mood changes. Insight is preserved until late in the disease and seizures or other manifestations of cerebral ischaemic accidents are not infrequent. VaD is characterised by stepwise deterioration with periods of partial recovery that can last months between periods of deterioration and cognitive decline.
Vascular risk status as a predictor of later-life depressive symptoms: a cohort study.
BACKGROUND: Common etiology of vascular diseases and later-life depression may provide important synergies for prevention. We examined whether standard clinical risk profiles developed for vascular diseases also predict depressive symptoms in older adults. METHODS: Data were drawn from the Whitehall II study with baseline examination in 1991; follow-up screenings in 1997, 2003, and 2008; and additional disease ascertainment from hospital data and registry linkage on 5318 participants (mean age 54.8 years, 31% women) without depressive symptoms at baseline. Vascular risk was assessed with the Framingham Cardiovascular, Coronary Heart Disease, and Stroke Risk Scores. New depressive symptoms at each follow-up screening were identified by General Health Questionnaire caseness, a Center for Epidemiologic Studies Depression Scale score ≥16, and use of antidepressant medication. RESULTS: Diagnosed vascular disease (that is, coronary heart disease or stroke) was associated with an increased risk for depressive symptoms, age- and sex-adjusted odds ratios from 1.5 (95% confidence interval 1.0-2.2) to 2.0 (1.4-3.0), depending on the indicator of depressive symptoms. Among participants without manifest vascular disease, the Stroke Risk Score was associated with Center for Epidemiologic Studies Depression Scale depressive symptoms before age 65 (age- and sex-adjusted odds ratio per 10% absolute change in the score = 3.1 [1.5-6.5]), but none of the risk scores predicted new-onset depressive symptoms in those aged ≥65 (odds ratios from .8 to 1.2). CONCLUSIONS: These data suggest that public health measures to improve vascular risk status will influence the incidence of later-life depressive symptoms via reduced rates of manifest vascular disease.
Diagnosing and managing psychosis in primary care.
Psychosis is broadly defined as the presence of delusions and hallucinations. It can be organic or functional. The former is secondary to an underlying medical condition, such as delirium or dementia, the latter to a psychiatric disorder, such as schizophrenia or bipolar disorder. The identification and treatment of psychosis is vital as it is associated with a 10% lifetime risk of suicide and significant social exclusion. Psychosis can be recognised by taking a thorough history, examining the patient's mental state and obtaining a collateral history. The history usually enables a distinction to be made between bipolar disorder, schizophrenia and other causes. Early symptoms often include low mood, declining educational or occupational functioning, poor motivation, changes in sleep, perceptual changes, suspiciousness and mistrust. The patient's appearance, e.g. unkempt or inappropriately attired, may reflect their predominant mental state. There may be signs of agitation, hostility or distractibility. Speech may be disorganised and difficult to follow or there may be evidence of decreased speech. Mood may be depressed or elated or change rapidly. Patients may describe abnormal thoughts and enquiry into thoughts of suicide should be routine. Disturbances of thought such as insertion or withdrawal may be present along with perceptual abnormalities i.e. illusions, hallucinations. Insight varies during the course of a psychotic illness but should be explored as it has implications for management. All patients presenting with first episode psychosis for which no organic cause can be found should be referred to the local early intervention service. In patients with a known diagnosis consider referral if there is: poor response or nonadherence to treatment; intolerable side effects; comorbid substance misuse; risk to self or others.