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Depression is linked to dementia in older adults.
Depression and dementia are both common conditions in older people, and they frequently occur together. Late life depression affects about 3.0-4.5% of adults aged 65 and older. Depression occurs in up to 20% of patients with Alzheimer’s disease and up to 45% of patients with vascular dementia. Rather than a risk factor, depression with onset in later life is more likely to be either prodromal to dementia or a condition that unmasks pre-existing cognitive impairment by compromising cognitive reserve. Depression can be a psychological response to receiving a diagnosis of dementia. The distinction between depression and early dementia may be particularly difficult. Detailed histories obtained from patients and their relatives as well as longitudinal follow-up are important. Cognitive testing can be very helpful. It is preferable to use a neuropsychological test that is sensitive to subtle cognitive changes and assesses all cognitive domains, such as the Montreal Cognitive Assessment. Older people with depression are at raised risk of dementia and this risk is increased if they have had symptoms for a long time, if their symptoms are severe, where there are multiple (vascular) comorbidities, and where there are structural brain changes including hippocampal atrophy and white matter abnormalities.
The level of cognitive function and recognition of emotions in older adults.
BACKGROUND: The association between cognitive decline and the ability to recognise emotions in interpersonal communication is not well understood. We aimed to investigate the association between cognitive function and the ability to recognise emotions in other people's facial expressions across the full continuum of cognitive capacity. METHODS: Cross-sectional analysis of 4039 participants (3016 men, 1023 women aged 59 to 82 years) in the Whitehall II study. Cognitive function was assessed using a 30-item Mini-Mental State Examination (MMSE), further classified into 8 groups: 30, 29, 28, 27, 26, 25, 24, and <24 (possible dementia) MMSE points. The Facial Expression Recognition Task (FERT) was used to examine recognition of anger, fear, disgust, sadness, and happiness. RESULTS: The multivariable adjusted difference in the percentage of accurate recognition between the highest and lowest MMSE group was 14.9 (95%CI, 11.1-18.7) for anger, 15.5 (11.9-19.2) for fear, 18.5 (15.2-21.8) for disgust, 11.6 (7.3-16.0) for sadness, and 6.3 (3.1-9.4) for happiness. However, recognition of several emotions was reduced already after 1 to 2-point reduction in MMSE and with further points down in MMSE, the recognition worsened at an accelerated rate. CONCLUSIONS: The ability to recognize emotion in facial expressions is affected at an early stage of cognitive impairment and might decline at an accelerated rate with the deterioration of cognitive function. Accurate recognition of happiness seems to be less affected by a severe decline in cognitive performance than recognition of negatively valued emotions.
Multimodal brain-age prediction and cardiovascular risk: The Whitehall II MRI sub-study.
Brain age is becoming a widely applied imaging-based biomarker of neural aging and potential proxy for brain integrity and health. We estimated multimodal and modality-specific brain age in the Whitehall II (WHII) MRI cohort using machine learning and imaging-derived measures of gray matter (GM) morphology, white matter microstructure (WM), and resting state functional connectivity (FC). The results showed that the prediction accuracy improved when multiple imaging modalities were included in the model (R2 = 0.30, 95% CI [0.24, 0.36]). The modality-specific GM and WM models showed similar performance (R2 = 0.22 [0.16, 0.27] and R2 = 0.24 [0.18, 0.30], respectively), while the FC model showed the lowest prediction accuracy (R2 = 0.002 [-0.005, 0.008]), indicating that the FC features were less related to chronological age compared to structural measures. Follow-up analyses showed that FC predictions were similarly low in a matched sub-sample from UK Biobank, and although FC predictions were consistently lower than GM predictions, the accuracy improved with increasing sample size and age range. Cardiovascular risk factors, including high blood pressure, alcohol intake, and stroke risk score, were each associated with brain aging in the WHII cohort. Blood pressure showed a stronger association with white matter compared to gray matter, while no differences in the associations of alcohol intake and stroke risk with these modalities were observed. In conclusion, machine-learning based brain age prediction can reduce the dimensionality of neuroimaging data to provide meaningful biomarkers of individual brain aging. However, model performance depends on study-specific characteristics including sample size and age range, which may cause discrepancies in findings across studies.
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.
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.
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.
Specialty choice in UK junior doctors: is psychiatry the least popular specialty for UK and international medical graduates?
BACKGROUND: In the UK and many other countries, many specialties have had longstanding problems with recruitment and have increasingly relied on international medical graduates to fill junior and senior posts. We aimed to determine what specialties were the most popular and desirable among candidates for training posts, and whether this differed by country of undergraduate training. METHODS: We conducted a database analysis of applications to Modernising Medical Careers for all training posts in England in 2008. Total number of applications (as an index of popularity) and applications per vacancy (as an index of desirability) were analysed for ten different specialties. We tested whether mean consultant incomes correlated with specialty choice. RESULTS: In, 2008, there were 80,949 applications for specialty training in England, of which 31,434 were UK graduates (39%). Among UK medical graduates, psychiatry was the sixth most popular specialty (999 applicants) out of 10 specialty groups, while it was fourth for international graduates (5,953 applicants). Among UK graduates, surgery (9.4 applicants per vacancy) and radiology (8.0) had the highest number of applicants per vacancy and paediatrics (1.2) and psychiatry (1.1) the lowest. Among international medical graduates, psychiatry had the fourth highest number of applicants per place (6.3). Specialty popularity for UK graduates was correlated with predicted income (p = 0.006). CONCLUSION: Based on the number of applicants per place, there was some consistency in the most popular specialties for both UK and international medical graduates, but there were differences in the popularity of psychiatry. With anticipated decreases in the number of new international medical graduates training in the UK, university departments and professional associations may need to review strategies to attract more UK medical graduates into certain specialties, particularly psychiatry and paediatrics.