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Evidence indicates most people were resilient to the impact of the COVID-19 pandemic on mental health. However, evidence also suggests the pandemic effect on mental health may be heterogeneous. Therefore, we aimed to identify groups of trajectories of common mental disorders' (CMD) symptoms assessed before (2017-19) and during the COVID-19 pandemic (2020-2021), and to investigate predictors of trajectories.We assessed 2,705 participants of the ELSA-Brasil COVID-19 Mental Health Cohort study who reported Clinical Interview Scheduled-Revised (CIS-R) data in 2017-19 and Depression Anxiety Stress Scale-21 (DASS-21) data in May-July 2020, July-September 2020, October-December 2020, and April-June 2021. We used an equi-percentile approach to link the CIS-R total score in 2017-19 with the DASS-21 total score. Group-based trajectory modeling was used to identify CMD trajectories and adjusted multinomial logistic regression was used to investigate predictors of trajectories.Six groups of CMD symptoms trajectories were identified: low symptoms (17.6%), low-decreasing symptoms (13.7%), low-increasing symptoms (23.9%), moderate-decreasing symptoms (16.8%), low-increasing symptoms (23.3%), severe-decreasing symptoms (4.7%). The severe-decreasing trajectory was characterized by age 60 years, female sex, low family income, sedentary behavior, previous mental disorders, and the experience of adverse events in life.Pre-pandemic characteristics were associated with lack of response to assessments. Our occupational cohort sample is not representative.More than half of the sample presented low levels of CMD symptoms. Predictors of trajectories could be used to detect individuals at-risk for presenting CMD symptoms in the context of global adverse events.

Background Data discovery, the ability to find datasets relevant to an analysis, increases scientific opportunity, improves rigour and accelerates activity. Rapid growth in the depth, breadth, quantity and availability of data provides unprecedented opportunities and challenges for data discovery. A potential tool for increasing the efficiency of data discovery, particularly across multiple datasets is data harmonisation. Methods A set of 124 variables, identified as being of broad interest to neurodegeneration, were harmonised using the C-Surv data model. Harmonisation strategies used were simple calibration, algorithmic transformation and standardisation to the Z-distribution. Widely used data conventions, optimised for inclusiveness rather than aetiological precision, were used as harmonisation rules. The harmonisation scheme was applied to data from four diverse population cohorts. Results Of the 120 variables that were found in the datasets, correspondence between the harmonised data schema and cohort-specific data models was complete or close for 111 (93%). For the remainder, harmonisation was possible with a marginal a loss of granularity. Conclusions Although harmonisation is not an exact science, sufficient comparability across datasets was achieved to enable data discovery with relatively little loss of informativeness. This provides a basis for further work extending harmonisation to a larger variable list, applying the harmonisation to further datasets, and incentivising the development of data discovery tools.

BackgroundAlzheimer’s disease, type 2 diabetes mellitus, and depression are significant challenges facing public health. Research has demonstrated common comorbidities amongst these three conditions, typically focusing on two of them at a time.ObjectivesThe goal of this study, however, was to assess the interrelationships between the three conditions, focusing on mid-life risk before the emergence of dementia caused by Alzheimer’s Disease.MethodsIn the current study, we used data from 665 participants from the prospective cohort study, PREVENT.FindingsUsing structural equation modelling, we showed that (i) insulin resistance predicts executive dysfunction in older but not younger adults in midlife, that (ii) insulin resistance predicts self-reported depression in both older and younger middle-aged adults, and that (iii) depression predicts deficits in visuospatial memory in older but not younger adults in midlife.ConclusionsTogether, we demonstrate the interrelations between three common non-communicable diseases in middle-aged adults.Clinical ImplicationsWe emphasise the need for combined interventions and the utilisation of resources to help adults in midlife to modify risk factors for cognitive impairment, such as depression and diabetes.FundingThe PREVENT study was funded by the Alzheimer’s Society (grant numbers 178 and 264), the Alzheimer’s Association (grant number TriBEKa-17-519007) and philanthropic donations. GR acknowledges funding for this work for his research programme funded by the Medical Research council (Dementias Platform UK) and Five Lives Ltd. IK declares funding for this project through Medical Research Council (Dementias Platform UK), NIHR Oxford Health Biomedical Research Centre and NIHR personal awards. SG acknowledges funding for salary from the Medical Research Council Nutrition Research Partnership Collaboration Award (MR/T001852/1).Key MessagesWhat is already known on this topicMood disorders and metabolic diseases are known to be frequently comorbid. Furthermore, both conditions are known to be associated with cognitive impairment and cognitive decline. There has been some evidence that the risk of cognitive impairment associated with diabetes and depression is most notable in midlife. However, studies focusing on this period of life have been sparse and most research has modelled bivariate correlations amongst cognitive impairment, depression, and diabetes. As such, this study was conducted in order to model the interrelations between the three conditions in a large cohort, whilst focusing on midlife as depression and diabetes in this period are thought to carry higher risk for cognitive impairment.What this study addsWhilst insulin resistance, as a core feature of diabetes, was related to depression across all stages of midlife, the relationship with cognitive functioning was more complex. In the current study, we found that the stage of midlife in which middle-aged adults find themselves moderates the relationship between insulin resistance and cognition and depression and cognition. That is, only in older middle aged adults does insulin resistance predict impaired cognition (i.e., executive function) and does depression predict impaired cognition (i.e., visuospatial memory).How this study might affect research, practice or policyClinicians should be mindful of the impact of comorbidities between cognitive impairment, metabolic diseases, such as diabetes, and mood disorders, such as depression in midlife. Given the risk of intractable dementia in individuals with cognitive impairment, available resources for intervening in modifiable risk factors, such as depression and diabetes, should be considered for adults in the middle period of life.

Background There is mixed evidence on increasing rates of psychiatric disorders and symptoms during the coronavirus disease 2019 (COVID-19) pandemic in 2020. We evaluated pandemic-related psychopathology and psychiatry diagnoses and their determinants in the Brazilian Longitudinal Study of Health (ELSA-Brasil) São Paulo Research Center. Methods Between pre-pandemic ELSA-Brasil assessments in 2008–2010 (wave-1), 2012–2014 (wave-2), 2016–2018 (wave-3) and three pandemic assessments in 2020 (COVID-19 waves in May–July, July–September, and October–December), rates of common psychiatric symptoms, and depressive, anxiety, and common mental disorders (CMDs) were compared using the Clinical Interview Scheduled-Revised (CIS-R) and the Depression Anxiety Stress Scale-21 (DASS-21). Multivariable generalized linear models, adjusted by age, gender, educational level, and ethnicity identified variables associated with an elevated risk for mental disorders. Results In 2117 participants (mean age 62.3 years, 58.2% females), rates of CMDs and depressive disorders did not significantly change over time, oscillating from 23.5% to 21.1%, and 3.3% to 2.8%, respectively; whereas rate of anxiety disorders significantly decreased (2008–2010: 13.8%; 2016–2018: 9.8%; 2020: 8%). There was a decrease along three wave-COVID assessments for depression [β = −0.37, 99.5% confidence interval (CI) −0.50 to −0.23], anxiety (β = −0.37, 99.5% CI −0.48 to −0.26), and stress (β = −0.48, 99.5% CI −0.64 to −0.33) symptoms (all ps < 0.001). Younger age, female sex, lower educational level, non-white ethnicity, and previous psychiatric disorders were associated with increased odds for psychiatric disorders, whereas self-evaluated good health and good quality of relationships with decreased risk. Conclusion No consistent evidence of pandemic-related worsening psychopathology in our cohort was found. Indeed, psychiatric symptoms slightly decreased along 2020. Risk factors representing socioeconomic disadvantages were associated with increased odds of psychiatric disorders.

BackgroundRates of depression have increased worldwide during the COVID-19 pandemic. One known protective factor for depression is social support, but more work is needed to quantify the extent to which social support could reduce depression risk during a global crisis, and specifically to identify which types of support are most helpful, and who might benefit most.MethodsData were obtained from participants in the All of Us Research Program who responded to the COVID-19 Participant Experience (COPE) survey administered monthly from May 2020 to July 2020 (N=69,066, 66% female). Social support was assessed using 10 items measuring emotional/informational support (e.g., someone to confide in or talk to about yourself or your problems), positive social interaction support (e.g., someone to do things with to help you get your mind off things), and tangible support (e.g., someone to help with daily chores if sick). Elevated depression symptoms were defined based on having a moderate-to-severe (≥10) score on the Patient Health Questionnaire (PHQ-9). Mixed-effects logistic regression models were used to test associations across time between overall social support and its subtypes with depression, adjusting for age, sex, race, ethnicity, and socioeconomic factors. We then assessed interactions between social support and potential effect modifiers: age, sex, pre-pandemic mood disorder, and pandemic-related stressors (e.g., financial insecurity).ResultsApproximately 16% of the sample experienced elevated depressive symptoms. Overall social support was associated with significantly reduced odds of depression (adjusted odds ratio, aOR [95% CI]=0.44 [0.42-0.45]). Among subtypes, emotional/informational support (aOR=0.42 [0.41-0.43]) and positive social interactions (aOR=0.43 [0.41-0.44]) showed the largest protective associations with depression, followed by tangible support (aOR=0.63 [0.61-0.65]). Sex, age, and pandemic-related financial stressors were statistically significant modifiers of the association between social support and depression.ConclusionsIndividuals reporting higher levels of social support were at reduced risk of depression during the early COVID-19 pandemic. The perceived availability of emotional support and positive social interactions, more so than tangible support, was key. Individuals more vulnerable to depression (e.g., women, younger individuals, and those experiencing financial stressors) may particularly benefit from enhanced social support, supporting a precision prevention approach.

Background: Data discovery, the ability to find datasets relevant to an analysis, accelerates science, increases scientific opportunity, and improves scientific rigour. Rapid growth in the depth, breadth, quantity and availability of data provides unprecedented opportunities and challenges for data discovery. A potential tool for increasing the efficiency of data discovery, particularly across multiple datasets is data harmonisation.Methods: A core set of 124 variables, identified as being of broad interest to neurodegeneration, were harmonised using the C-Surv data model for the purpose of data discovery and conducting feasibility analyses. Widely used data conventions, optimised for inclusiveness rather than aetiological precision, were used as harmonisation rules. The harmonisation scheme was applied to data from four diverse population cohorts.Results: Core variables covered 15 of 18 data themes used by the C-Surv data model. Correspondence between the harmonised data schema and cohort-specific data models was close for 61 variables that were directly mapped, 48 variables that were transformed to a binary (yes /no) format and for 6 variables than were standardised to the Z distribution. Harmonisation was more challenging for the remaining 9 socioeconomic and lifestyle variables; these requiring value judgements prioritising inclusivity over informativeness to be made.Conclusions: Although harmonisation is not an exact science, sufficient comparability across datasets was achieved to enable data discovery with relatively little loss of informativeness. This provides a basis for further work extending harmonisation to increase the core variable list, applying the harmonisation to further datasets, and incentivising the development of data discovery tools.

Background Adversity experiences (AEs) are major risk factors for psychiatric illness, and ample evidence suggests that adversity‐related changes in brain structure enhance this vulnerability. To achieve greater understanding of the underlying biological pathways, increased convergence among findings is needed. Suggested future directions may benefit from the use of large population samples which may contribute to achieving this goal. We addressed mechanistic pathways by investigating the associations between multiple brain phenotypes and retrospectively reported AEs in early life (child adversity) and adulthood (partner abuse) in a large population sample, using a cross‐sectional approach. Methods The UK Biobank resource was used to access imaging‐derived phenotypes (IDPs) from 6,751 participants (aged: M = 62.1, SD = 7.2, range = 45‐80), together with selected reports of childhood AEs and adult partner abuse. Principal component analysis was used to reduce the dimensionality of the data prior to multivariate tests. Results The data showed that participants who reported experiences of childhood emotional abuse (‘felt hated by family member as a child’) had smaller cerebellar and ventral striatum volumes. This result was also depicted in a random subset of participants; however, we note small effect sizes (urn:x-wiley:00219630:media:jcpp13298:jcpp13298-math-0001 < .01), suggestive of modest biological changes. Conclusions Using a large population cohort, this study demonstrates the value of big datasets in the study of adversity and using automatically preprocessed neuroimaging phenotypes. While retrospective and cross‐sectional characteristics limit interpretation, this study demonstrates that self‐perceived adversity reports, however nonspecific, may still expose neural consequences, identifiable with increased statistical power.

IntroductionThe ROADMAP project aimed to provide an integrated overview of European real‐world data on Alzheimer's disease (AD) across the disease spectrum.MethodsMetadata were identified from data sources in catalogs of European AD projects. Priority outcomes for different stakeholders were identified through systematic literature review, patient and public consultations, and stakeholder surveys.ResultsInformation about 66 data sources and 13 outcome domains were integrated into a Data Cube. Gap analysis identified cognitive ability, functional ability/independence, behavioral/neuropsychiatric symptoms, treatment, comorbidities, and mortality as the outcomes collected most. Data were most lacking in caregiver‐related outcomes. In general, electronic health records covered a broader, less detailed data spectrum than research cohorts.DiscussionThis integrated real‐world AD data overview provides an intuitive visual model that facilitates initial assessment and identification of gaps in relevant outcomes data to inform future prospective data collection and matching of data sources and outcomes against research protocols.

The association between adversity and cognition varies according to the specific adversity, when the adversity was experienced, and the cognitive domains investigated. Disentangling the effect of one type of adversity and the underlying mechanistic pathway is therefore difficult.The association between one type of adversity accumulated over the life course (i.e., maltreatment) and executive function (i.e., cognitive flexibility), as well as two mechanisms that may underlie this association were investigated.A path analysis was performed on the baseline population of the UK Biobank study (N = 73,489, Mdnage = 56, SDage = 7.63, 55.74% of women).Higher cumulative adverse experiences were associated with lower performance in cognitive flexibility and this effect was partly mediated by the level of depression.The association between cumulative life course adversity, depression, and performance in cognitive flexibility has been highlighted as well as the specificity of these findings to the cumulative effect of life course adversity.

ObjectiveThe COVID-19 pandemic has coincided with an increase in depressive symptoms as well as a growing awareness of health inequities and structural racism in the United States. Here, we examine the mental health impact of everyday discrimination during the pandemic in a large and diverse cohort of theAll of UsResearch Program.MethodsUsing repeated assessments of 62,651 participants in May to July of 2020, we fitted mixed-effects models to assess the effect of everyday discrimination on moderate to severe depression (Patient Health Questionnaire (PHQ)-9 ≥ 10) and suicidal ideation (PHQ-9 item 9 > 0), and applied inverse probability weights to account for non-random probabilities of completing the voluntary survey.ResultsEveryday discrimination was associated with increased odds of depression (adjusted odds ratio (aOR) [95% CI]: 1.21 [1.20 -1.22]) and suicidal ideation (1.17 [1.16-1.18]). For depression, the effects were larger in earlier phases of the pandemic (interaction p=8.2×10−5), which varied by main reason for discrimination and self-reported race and ethnicity. Among those who identified race or ancestry/national origin(s) as a primary reason for discrimination, Asian and Black or African American participants had 24% and 17% increase in the odds of depression in May of 2020 (1.24 [1.17-1.31] and 1.17 [1.12-1.22]), respectively, versus a 3% and 7% increase in July (1.03 [0.96-1.10] and 1.07 [1.02-1.12]).ConclusionIn this large and diverse sample, increased levels of everyday discrimination were associated with higher odds of depression, particularly during the early phase of the pandemic among participants self-identifying as Asian or Black.

Research-ready data (data curated to a defined standard) increase scientific opportunity and rigour by integrating the data environment. The development of research platforms has highlighted the value of research-ready data, particularly for multi-cohort analyses. Following stakeholder consultation, a standard data model (C-Surv) optimised for data discovery, was developed using data from 5 population and clinical cohort studies. The model uses a four-tier nested structure based on 18 data themes selected according to user behaviour or technology. Standard variable naming conventions are applied to uniquely identify variables within the context of longitudinal studies. The data model was used to develop a harmonised dataset for 11 cohorts. This dataset populated the Cohort Explorer data discovery tool for assessing the feasibility of an analysis prior to making a data access request. Data preparation times were compared between cohort specific data models and C-Surv.It was concluded that adopting a common data model as a data standard for the discovery and analysis of research cohort data offers multiple benefits.

Research-ready data (that curated to a defined standard) increases scientific opportunity and rigour by integrating the data environment. The development of research platforms has highlighted the value of research-ready data, particularly for multi-cohort analyses. Following user consultation, a standard data model (C-Surv), optimised for data discovery, was developed using data from 12 Dementias Platform UK (DPUK) population and clinical cohort studies. The model uses a four-tier nested structure based on 18 data themes selected according to user behaviour or technology. Standard variable naming conventions are applied to uniquely identify variables within the context of longitudinal studies. The data model was used to develop a harmonised dataset for 11 cohorts. This dataset populated the Cohort Explorer data discovery tool for assessing the feasibility of an analysis prior to making a data access request. It was concluded that developing and applying a standard data model (C-Surv) for research cohort data is feasible and useful.

IntroductionEarlier studies of the effects of childhood socioeconomic status (SES) on later-life cognitive function consistently report a social gradient in later-life cognitive function. Evidence for their effects on cognitive decline is, however, less clear.MethodsThe sample consists of 5324 participants in the Whitehall II study, 8572 in the Health and Retirement Study (HRS), and 1413 in the Kame Project, who completed self-report questionnaires on their early life experiences and underwent repeated cognitive assessments. We characterized cognitive trajectories using latent class mixed models, and explored associations between childhood SES and latent class membership using logistic regressions.ResultsWe identified distinct trajectories classes for all cognitive measures examined. Childhood socioeconomic deprivation was associated with an increased likelihood of being in a lower trajectory class.DiscussionOur findings support the notions that cognitive aging is a heterogeneous process and early life circumstances may have lasting effects on cognition across the life-course.

The COVID-19 pandemic has coincided with an increase in depressive symptoms as well as a growing awareness of health inequities and structural racism in the United States.To examine the association of mental health with everyday discrimination during the pandemic in a large and diverse cohort of the All of Us Research Program.Using repeated assessments in the early months of the pandemic, mixed-effects models were fitted to assess the associations of discrimination with depressive symptoms and suicidal ideation, and inverse probability weights were applied to account for nonrandom probabilities of completing the voluntary survey.The exposure and outcome measures were ascertained using the Everyday Discrimination Scale and the 9-item Patient Health Questionnaire (PHQ-9), respectively. Scores for PHQ-9 that were greater than or equal to 10 were classified as moderate to severe depressive symptoms, and any positive response to the ninth item of the PHQ-9 scale was considered as presenting suicidal ideation.A total of 62 651 individuals (mean [SD] age, 59.3 [15.9] years; female sex at birth, 41 084 [65.6%]) completed at least 1 assessment between May and July 2020. An association with significantly increased likelihood of moderate to severe depressive symptoms and suicidal ideation was observed as the levels of discrimination increased. There was a dose-response association, with 17.68-fold (95% CI, 13.49-23.17; P .001) and 10.76-fold (95% CI, 7.82-14.80; P .001) increases in the odds of moderate to severe depressive symptoms and suicidal ideation, respectively, on experiencing discrimination more than once a week. In addition, the association with depressive symptoms was greater when the main reason for discrimination was race, ancestry, or national origins among Hispanic or Latino participants at all 3 time points and among non-Hispanic Asian participants in May and June 2020. Furthermore, high levels of discrimination were as strongly associated with moderate to severe depressive symptoms as was history of prepandemic mood disorder diagnosis.In this large and diverse sample, increased levels of discrimination were associated with higher odds of experiencing moderate to severe depressive symptoms. This association was particularly evident when the main reason for discrimination was race, ancestry, or national origins among Hispanic or Latino participants and, early in the pandemic, among non-Hispanic Asian participants.

INTRODUCTIONEarlier studies of the effects of childhood socioeconomic status (SES) on later life cognitive function consistently report a social gradient in later life cognitive function. Evidence for their effects on cognitive decline is, however, less clear.METHODSThe sample consists of 5,324 participants in the Whitehall II Study, 8,572 in the Health and Retirement Study, and 1,413 in the Kame Project, who completed self-report questionnaires on their early-life experiences and underwent repeated cognitive assessments. We characterised cognitive trajectories using latent class mixed models, and explored associations between childhood SES and latent class membership using logistic regressions.RESULTSWe identified distinct trajectories classes for all cognitive measures examined. Childhood socioeconomic deprivation was associated with an increased likelihood of being in a lower trajectory class.DISCUSSIONOur findings support the notions that cognitive ageing is a heterogeneous process and early-life circumstances may have lasting effects on cognition across the life-course.Research in contextSystematic review: We reviewed the literature on childhood socioeconomic status (SES) as a predictor for cognitive decline in mid- to later-life using PubMed. Studies generally reported lower childhood SES is associated with poorer baseline cognition, but not a faster rate of decline. These studies generally focused on the mean rate of decline in the population; no study to date has explored associations between childhood SES and different cognitive trajectories. Relevant studies have been appropriately cited.Interpretation: Our findings suggest that cognitive trajectories differ between individuals and across cognitive domains. Individuals of lower childhood SES were more likely to be in a lower cognitive trajectory class, which may or may not involve more rapid decline.Future directions: Future studies should include more cognitive outcomes and longer follow-ups, as well as investigate the impact of social mobility to further improve our understanding on how early-life circumstances influence cognitive decline.

Background Increasing the price of alcohol reduces alcohol consumption and harm. The role of food complementarity, transaction costs and inflation on alcohol demand are determined and discussed in relation to alcohol price policies. Methods UK Biobank (N = 502,628) was linked by region to retail price quotes for the years 2007 to 2010. The log residual food and alcohol prices, and alcohol availability were regressed onto log daily alcohol consumption. Model standard errors were adjusted for clustering by region. Results Associations with alcohol consumption were found for alcohol price (β = −0.56, 95% CI, −0.92 to −0.20) and availability (β = 0.06, 95% CI, 0.04 to 0.07). Introducing, food price reduced the alcohol price consumption association (β = −0.26, 95% CI, −0.50 to −0.03). Alcohol (B = 0.001, 95% CI, 0.0004 to 0.001) and food (B = 0.001, 95% CI, 0.0005 to 0.0006) price increased with time and were associated (ρ = 0.57, P Conclusion Alcohol and food are complements, and the price elasticity of alcohol reduces when the effect of food price is accounted for. Transaction costs did not affect the alcohol price consumption relationship. Fixed alcohol price policies are susceptible to inflation.

Background The SCCAI was designed to facilitate assessment of disease activity in ulcerative colitis (UC). We aimed to interrogate the metric properties of individual items of the SCCAI using item response theory (IRT) analysis, to simplify and improve its performance. Methods The original 9-item SCCAI was collected through TrueColours, a real-time software platform which allows remote entry and monitoring of patients with UC. Data were securely uploaded onto Dementias Platform UK Data Portal, where they were analysed in Stata 16.1 SE. A 2-parameter (2-PL) logistic IRT model was estimated to evaluate each item of the SCCAI for its informativeness (discrimination). A revised scale was generated and re-assessed following systematic removal of items. Results SCCAI data for 516 UC patients (41 years, SD = 15) treated in Oxford were examined. After initial item deletion (Erythema nodosum, Pyoderma gangrenosum), a 7-item scale was estimated. Discrimination values (information) ranged from 0.41 to 2.52 indicating selected item inefficiency with three items ) or non-endorsement of the trait (disease activity). Conclusion Reduction of the SCCAI from the original 9-item scale to a 4-item scale provides optimum trait information that will minimise response burden. This new 4-item scale needs validation against other measures of disease activity such as faecal calprotectin, endoscopy and histopathology.

The increasing prevalence of dementia in older adults warrants attention to the identification of practices that can delay or reduce likelihood of progression to early forms of cognitive impairment, in particular, to mild cognitive impairment (MCI) which is often considered a transitional stage between healthy aging and dementia. In this study, we investigated the effect of hearing impairment and hearing aid usage on cognitive decline and progression to MCI in cognitively healthy individuals. We used data from a large referral-based cohort obtained from the National Alzheimer’s Coordinating Center. The baseline sample included 5721 cognitively normal subjects aged ≥ 40. We found that hearing impairment was associated with increased risk of progression to MCI (hazard ratio [HR] = 1.40, 95%CI, 1.16-1.68, false discovery rate [FDR] P < 0.001) and an accelerated rate of cognitive decline (P < 0.001). Among hearing-impaired participants, hearing aid users were less likely to develop MCI (HR, 0.33; 95% CI, 0.23-0.47; FDR P < 0.001) and experienced slower cognitive decline (P = 0.004) when compared to those not using hearing aids. We found no statistically significant differences in risk of conversion to MCI between individuals with normal hearing and hearing-impaired adults using hearing aids (HR, 1.23; 95% CI, 0.99-1.50; FDR P = 0.08). Our findings highlight the need for a randomized clinical trial that will allow us to investigate whether there is a causal relationship between hearing loss, hearing aid use, and conversion to MCI. Such knowledge could provide new and novel insights into prevention of cognitive impairment and dementia.

BackgroundThe SCCAI was designed to facilitate assessment of disease activity in ulcerative colitis (UC). We aimed to interrogate the metric properties of individual items of the SCCAI using item response theory (IRT) analysis, to simplify and improve its performance. MethodsThe original 9-item SCCAI was collected through TrueColours, a real-time software platform which allows remote entry and monitoring of patients with UC. Data were securely uploaded onto Dementias Platform UK Data Portal, where they were analysed in Stata 16.1 SE. A 2-parameter (2-PL) logistic IRT model was estimated to evaluate each item of the SCCAI for its informativeness (discrimination). A revised scale was generated and re-assessed following systematic removal of items. ResultsSCCAI data for 516 UC patients (41 years, SD=15) treated in Oxford were examined. After initial item deletion (Erythema nodosum, Pyoderma gangrenosum), a 7-item scale was estimated. Discrimination values (information) ranged from 0.41 to 2.52 indicating selected item inefficiency with three items ) or non-endorsement of the trait (disease activity). Conclusion Reduction of the SCCAI from the original 9-item scale to a 4-item scale provides optimum trait information that will minimise response burden. This new 4-item scale needs validation against other measures of disease activity such as faecal calprotectin, endoscopy and histopathology.

BackgroundConceptualising comorbidity is complex and the term is used variously. Here, it is the coexistence of two or more diagnoses which might be defined as ‘chronic’ and, although they may be pathologically related, they may also act independently. Of interest here is the comorbidity of common psychiatric disorders and impaired cognition.ObjectivesTo examine whether anxiety and/or depression are/is important longitudinal predictors of cognitive change.MethodsUK Biobank participants used at three time points (n=502 664): baseline, first follow-up (n=20 257) and first imaging study (n=40 199). Participants with no missing data were 1175 participants aged 40–70 years, 41% women. Machine learning was applied and the main outcome measure of reaction time intraindividual variability (cognition) was used.FindingsUsing the area under the receiver operating characteristic curve, the anxiety model achieves the best performance with an area under the curve (AUC) of 0.68, followed by the depression model with an AUC of 0.63. The cardiovascular and diabetes model, and the covariates model have weaker performance in predicting cognition, with an AUC of 0.60 and 0.56, respectively.ConclusionsOutcomes suggest that psychiatric disorders are more important comorbidities of long-term cognitive change than diabetes and cardiovascular disease, and demographic factors. Findings suggest that psychiatric disorders (anxiety and depression) may have a deleterious effect on long-term cognition and should be considered as an important comorbid disorder of cognitive decline.Clinical implicationsImportant predictive effects of poor mental health on longitudinal cognitive decline should be considered in secondary and also primary care.

The Dementias Platform UK (DPUK) Data Portal is a data repository facilitating access to data for 3 370 929 individuals in 42 cohorts. The Data Portal is an end-to-end data management solution providing a secure, fully auditable, remote access environment for the analysis of cohort data. All projects utilising the data are by default collaborations with the cohort research teams generating the data.The Data Portal uses UK Secure eResearch Platform (UKSeRP) infrastructure to provide three core utilities: data discovery, access, and analysis. These are delivered using a 7 layered architecture comprising: data ingestion, data curation, platform interoperability, data discovery, access brokerage, data analysis and knowledge preservation. Automated, streamlined, and standardised procedures reduce the administrative burden for all stakeholders, particularly for requests involving multiple independent datasets, where a single request may be forwarded to multiple data controllers. Researchers are provided with their own secure ‘lab’ using VMware which is accessed using two factor authentication.Over the last 2 years, 160 project proposals involving 579 individual cohort data access requests were received. These were received from 268 applicants spanning 72 institutions (56 academic, 13 commercial, 3 government) in 16 countries with 84 requests involving multiple cohorts. Project are varied including multi-modal, machine learning, and Mendelian randomisation analyses. Data access is usually free at point of use although a small number of cohorts require a data access fee.

Cohort studies have displayed mixed findings on changes in mental symptoms severity in 2020, when the COVID- 19 pandemic outbreak started. Network approaches can provide additional insights by analyzing the connectivity of such symptoms. We assessed the network structure of mental symptoms in the Brazilian Longitudinal Study of Health (ELSA-Brasil) in 3 waves: 2008–2010, 2017–2019, and 2020, and hypothesized that the 2020 network would present connectivity changes. We used the Clinical Interview Scheduled-Revised (CIS-R) questionnaire to evaluates the severity of 14 common mental symptoms. Networks were graphed using unregularized Gaussian models and compared using centrality and connectivity measures. The predictive power of centrality measures and individual symptoms were also estimated. Among 2011 participants (mean age: 62.1 years, 58% females), the pandemic symptom 2020 network displayed higher overall connectivity, especially among symptoms that were related to general worries, with increased local connectivity between general worries and worries about health, as well as between anxiety and phobia symptoms. There was no difference between 2008 and 2010 and 2017–2019 networks. According to the network theory of mental disorders, external factors could explain why the network structure became more densely connected in 2020 compared to previous observations. We speculate that the COVID-19 pandemic and its innumerous social, economical, and political consequences were prominent external factors driving such changes; although further assessments are warranted.

Summary Background The role of environmental exposures in chronic obstructive pulmonary disease (COPD) remains inconclusive. We examined the association between environmental exposures (PM2·5, greenness, and urbanicity) and COPD prevalence using the UK Biobank cohort data to identify key built environment correlates of COPD. Methods In this cross-sectional, observational study we used baseline data for UK Biobank participants. Included participants were aged 39 years and older, white, had available spirometry data, and had complete data for phenotypes and exposures. COPD was defined by spirometry with the 2017 Global Initiative for Chronic Obstructive Lung Disease criteria. Environmental exposures were PM2·5 derived from monitoring data and interpolated using land-use regression at the participants' geocoded residential addresses. Built environment metrics of residential greenness were modelled in terms of normalised difference vegetation index from remotely sensed colour infrared data within a 500 m residential catchment, and an urbanicity index derived from spatial analyses and measured with a 1 km buffer around each participant's residential address. Logistic regression models examined the associations between environmental exposures and COPD prevalence adjusting for a range of confounders. Subgroup analyses by urbanicity and effect modification by white blood cell count as an inflammatory marker were also done. Findings We assessed 96 779 participants recruited between April 4, 2006, and Oct 1, 2010, of which 5391 participants had COPD with a prevalence of 5·6%. Each 10 μg/m3 increment in ambient PM2·5 exposure at a participant's residential location was associated with higher odds of COPD (odds ratio 1·55, 95% CI 1·14–2·10). Among the built environment metrics, urbanicity was associated with higher odds of COPD (1·05, 1·01–1·08 per interquartile increment), whereas residential greenness was protective, being associated with lower odds of COPD (0·89, 0·84–0·93 for each interquartile increment in greenness). The results remained consistent in models of COPD defined as per lower limit of normal criteria. The highest quartile of white blood cell count was associated with lower lung function and higher COPD risk with a significant interaction between PM2·5 and white blood cell count only in the model of lung function (p=0·0003). Interpretation In this study of the built environment and COPD, to our knowledge the largest done in the UK, we found that exposure to ambient PM2·5 and urbanicity were associated with a higher risk of COPD. Residing in greener areas, as measured by normalised difference vegetation index, was associated with lower odds of COPD, suggesting the potential value of urban planning and design in minimising or offsetting environmental risks for the prevention and management of COPD. Funding University of Hong Kong, UK Biobank, and UK Economic & Social Research Council.

BackgroundConceptualising comorbidity is complex and the term is used variously. Here, it is the coexistence of two or more diagnoses which might be defined as ‘chronic’ and, although they may be pathologically related, they may also act independently1. Of interest here is the comorbidity of common psychiatric disorders and impaired cognition.ObjectivesTo examine whether anxiety and/or depression are important longitudinal predictors of cognitive change.MethodsUK Biobank participants used at three time points (n= 502,664): baseline, 1st follow-up (n= 20,257) and 1st imaging study (n=40,199). Participants with no missing data were 1,175 participants aged 40 to 70 years, 41% female. Machine learning (ML) was applied and the main outcome measure of reaction time intraindividual variability (cognition) was used.FindingsUsing the area under the Receiver Operating Characteristic (ROC) curve, the anxiety model achieves the best performance with an Area Under the Curve (AUC) of 0.68, followed by the depression model with an AUC of 0.63. The cardiovascular and diabetes model, and the covariates model have weaker performance in predicting cognition, with an AUC of 0.60 and 0.56, respectively.ConclusionsOutcomes suggest psychiatric disorders are more important comorbidities of long-term cognitive change than diabetes and cardiovascular disease, and demographic factors. Findings suggest that psychiatric disorders (anxiety and depression) may have a deleterious effect on long-term cognition and should be considered as an important comorbid disorder of cognitive decline.Clinical implicationsImportant predictive effects of poor mental health on longitudinal cognitive decline should be considered in secondary and also primary care.Summary BoxWhat is already known about this subject? 3-4 bullet pointsPoor mental health is associated with cognitive deficits.One in four older adults experience a decline in affective state with increasing age.ML approaches have certain advantages in identifying patterns of information useful for the prediction of an outcome.What are the new findings? 3-4 bullet pointsPsychiatric disorders are important comorbid disorders of long-term cognitive change.Machine-learning methods such as sequence learning based methods are able to offer non-parametric joint modelling, allow for multiplicity of factors and provide prediction models that are more robust and accurate for longitudinal dataThe outcome of the RNN analysis found that anxiety and depression were stronger predictors of change IIV over time than either cardiovascular disease and diabetes or the covariate variables.How might it impact on clinical practice in the foreseeable future?The important predictive effect of mental health on longitudinal cognition should be noted and, its comorbidity relationship with other conditions such as cardiovascular disease likewise to be considered in primary care and other clinical settings

ObjectiveTo explore sex differences in the associations between arterial stiffness index, carotid intima-media thickness, white matter hyperintensities, depression and cognition.MethodsUK Biobank is a population-based cohort study of 502,664 healthy community dwelling adults aged 37-73 years. A select number of participants were recalled to participate in an online reassessment and imaging study, both of which included repeat cognitive assessments. A total of 7,394 volunteers aged 45-73 years (55% female) participated in the imaging visit and completed the self-report mental health questionnaire in the online follow-up were included in the analyses reported here. The main outcome measure of depression was measured using the PHQ-9 and cognition was assessed through measures of reaction time, verbal-numeric reasoning and visual memory. Pulse wave velocity (PWV) was assessed non-invasively using finger photoplethysmography, carotid intima-media thickness (CIMT) with automated ultrasound, and white matter hyperintensity volume with combined T1 and T2-weighted fluid-attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI).ResultsCross-sectionally, greater arterial stiffness was associated with greater depression in men but with better cognition in women. When white matter hyperintensities burden was added to the model, it mediated the relationships of carotid intima-media thickness with depression and cognition only in men.ConclusionsWe report sex differences in brain microvascular changes, depression and cognition in ageing, and suggest that they may be partly explained by sex-specific effects of vascular ageing.Summary boxesSection 1: What is already known on this topicArterial stiffness and carotid intima-media thickness are two non-invasive vascular ageing markers that have been shown to be associated with depression, cognitive impairment and dementia.Some studies report sex differences in arterial stiffness and carotid intima-media thickness.There is, however, a paucity of research on sex differences in the associations between these vascular ageing markers, white matter hyperintensities, depression and cognition.Section 2: What this study addsCross-sectionally, greater arterial stiffness was associated with greater depression in men but with better cognition in women. When white matter hyperintensities burden was added to the model, it mediated the relationships of carotid intima-media thickness with depression and cognition only in men.Our findings add to the existing evidence base of sex differences in brain microvascular changes, depression and cognition in ageing, and suggest that they may be partly explained by sex-specific effects of vascular ageing.

BackgroundExperiences of chronic stress and trauma are major risk factors for psychiatric illness. Evidence suggests that adversity-related changes in brain structure and function accelerate this vulnerability. It is yet to be determined whether neuroendocrine effects on the brain are a result of the interference with neural development during sensitive periods or a consequence of cumulative lifetime adversity. To address this question, the present study investigated the associations between brain structure and self-reported data of childhood and adult adversity using machine learning techniques and structural equation models (SEM).MethodsThe UK Biobank resource was used to access Imaging Derived Phenotypes (IDPs) of grey matter and white matter tract integrity of 7003 participants, together with selected childhood and adult adversity data. Latent measures of adversity and imaging phenotypes were estimated to evaluate their associations using SEM.ResultsWe demonstrated that increased incidence of childhood adversity events may be associated with smaller grey matter in frontal, insular, subcallosal and cerebellar regions of the brain. There were no significant associations between brain phenotypes and negative experiences during adulthood.ConclusionsUsing a large population cohort dataset, this study contributes to the suggestion that childhood adversity may determine grey matter reductions in brain regions, which are putatively sensitive to the neurotoxic effects of chronic stress. Furthermore, it provides novel evidence to support the “sensitive periods” model though which adversity affects the brain.

Background Neuroticism has been described as a broad and pervasive personality dimension or ‘heterogeneous’ trait measuring components of mood instability such as worry; anxiety; irritability; moodiness; self-consciousness; sadness and irritabililty. Consistent with depression and anxiety-related disorders, increased neuroticism places an individual vulnerable for other unipolar and bipolar mood disorders. However, the measurement of neuroticism remains a challenge. Our aim was to identify psychometrically efficient items and inform the inclusion of redundant items across the 12-item EPQ-R Neuroticism scale using Item Response Theory (IRT). Methods The 12-item binary EPQ-R Neuroticism scale was evaluated by estimating a two-parameter (2-PL) IRT model on data from 502,591 UK Biobank participants aged 37 to 73 years (M = 56.53 years; SD = 8.05), 54% female. Models were run listwise (n= 401,648) and post-estimation mathematical assumptions were computed. All analyses were conducted in STATA 16 SE on the Dementias Platform UK (DPUK) Data Portal. Results A plot of θ values (Item Information functions) showed that most items clustered around the mid-range where discrimination values ranged from 1.34 to 2.28. Difficulty values for individual item θ scores ranged from -0.13 to 1.41. A Mokken analysis suggested a weak to medium level of monotonicity between the items, no items reach strong scalability (H=0.35-0.47). Systematic item deletions and rescaling found that an 7-item scale is more efficient and with information (discrimination) ranging from 1.56 to 2.57 and stronger range of scalability (H=0.47-0.52). A 3-item scale is highly discriminatory but offers a narrow range of person ability (difficulty). A logistic regression differential item function (DIF) analysis exposed significant gender item bias functioning uniformly across all versions of the scale. Conclusions Across 401,648 UK Biobank participants, the 12-item EPQ-R neuroticism scale exhibited psychometric inefficiency with poor discrimination at the extremes of the scale-range. High and low scores are relatively poorly represented and uninformative suggesting that high neuroticism scores derived from the EPQ-R are a function of cumulative mid-range values. The scale also shows evidence of gender item bias and future scale development should consider the former along with item deletions.

Background: Neuroticism has been described as a broad and pervasive personality dimension or heterogeneous trait measuring components of mood instability; worry; anxiety; irritability; moodiness; self-consciousness; sadness and irritabililty. Consistent with depression and anxiety-related disorders, increased neuroticism places an individual vulnerable for other unipolar and bipolar mood disorders. However, the measurement of neuroticism through a self-report scale remains a challenge. Our aim was to identify psychometrically efficient items and inform the inclusion of redundant items across the 12-item EPQ-R Neuroticism scale (S. B. Eysenck, Eysenck, & Barrett, 1985) using Item Response Theory (IRT). Methods: The 12-item binary EPQ-R Neuroticism scale was evaluated by estimating a two-parameter (2-PL) IRT model on data from 384,183 UK Biobank participants aged 39 to 73 years. Post-estimation mathematical assumptions were computed and all analyses were processed in STATA SE 15.1 (StataCorp, 2018) on the Dementias Platform UK (DPUK) Data Portal (Bauermeister et al., Preprint). Results: A plot of {theta} values (Item Information functions) showed that most items clustered around the mid-range where discrimination values ranged from 1.34 to 2.27. Difficulty values for individual item {theta} scores ranged from -0.14 to 1.25. A Mokken analysis suggested a weak to medium level of monotonicity between the items, no items reach strong scalability (H=0.35-0.47). Systematic item deletions and rescaling found that an 8-item scale is more efficient and reliable with information ranging from 1.43 to 2.36 and strong scalability (H=0.43-0.53). A 3-item scale is highly discriminatory but offers a narrow range of person ability (difficulty). A logistic regression differential item function (DIF) analysis exposed significant gender item bias functioning uniformly across both all versions of the scale. Conclusions: Across 384,183 UK Biobank participants the 12-item EPQ-R neuroticism scale exhibited psychometric inefficiency with poor discrimination at the extremes of the scale-range. High and low scores are relatively poorly represented and uninformative suggesting that high neuroticism scores derived from the EPQ-R are a function of cumulative mid-range values. The scale also shows evidence of gender item bias and future scale development should consider the former and, selective item deletions and validation of new items to increase scale informativeness and reliability. Keywords: Item Response Theory; IRT; neuroticism; psychometric; EPQ-R; UK Biobank; epidemiology

Intraindividual variability in the present context refers to the moment-to-moment variation in attentional or executive engagement over a given time period. Typically, it is measured using the response latencies collected across the trials of a behavioral neurocognitive task. In aging research, the measure has received a lot of recent interest as it may provide important insights into age-related cognitive decline and neuropathology as well as having potential as a neurocognitive assessment tool in healthcare settings. In the present chapter, we begin by reviewing the key empirical findings relating to age and intraindividual variability. Here, research shows that intraindividual variability increases with age and predicts a range of age-related outcomes including gait impairment, falls and errors more broadly, mild cognitive impairment, dementia, and mortality. Brain imaging research suggests that greater variability is associated with age-related or neuropathological changes to a frontal–cingulate–parietal network and that white matter compromise and dopamine depletion may be key underlying mechanisms. We then consider the cognitive and neurobiological theoretical underpinnings of the construct before providing a description of the various methods and metrics that have been used to compute measures of variability – reaction time cut-offs, raw and residualized intraindividual standard deviations, coefficient of variation, ex-Gaussian curve and fast Fourier transformation. A further section considers the range of neurocognitive tasks that have been used to assess intraindividual variability. Broadly, these tasks can be classified on a continuum of cognitive demands as psychomotor, executive control or higher-order cognitive tasks (e.g., episodic memory). Finally, we provide some pointers concerning the pressing issues that future research needs to address in the area. We conclude that the existing body of theoretical and empirical work underlines the potential of intraindividual reaction time variability measures as additions to the neuropsychological test batteries that are used in the early detection of a range of age-related neurocognitive disorders in healthcare settings.

DPUK relaunched the Data Portal in November 2017 to present openly available information on the data availability and technical capability of the Data Portal, which supports multi-modal research studies with various objectives from disease model validation to observation investigation.DPUK not only brings clinical data together from cohorts, but is now supporting multi-modal studies in genetics and imaging, as well as linkage opportunities to routine data using world-leading technical solutions to data sharing.The capacity, adaptability and sophistication of the UK Secure eResearch Platform which the Portal is housed on, allows for unprecedented levels of centralised access to rich cohort and routine data, which is consequentially leading to international collaboration and development ambition within epidemiology, bioinformatics, research methodology and technical research solutions.As of March 2018, DPUK is supporting 50 cohorts, 41 from the UK and 9 from across the rest of the world, alongside furthering links and access to routine data held in the UK and across the world. 20 research studies are underway, and the DPUK mission to enhance data science within dementia research is leading the conversation for developing a community of excellence in this field and across other research genres.

IntroductionModern team science requires effective sharing of data and skills. The DPUK Data Portal is a collection of tools, datasets and networks that allows for epidemiologists and specialist researchers alike to access, analyse and investigate cohort and different modalities of routine data across UK and international sources. Objectives and ApproachThe Portal is housed on an instance of UKSeRP (UK Secure eResearch Platform), that allows customisable infrastructure to be used for multi-modal research (thus far live in genetics, imaging and clinical data) for researchers across the world using remote access technology whilst allowing governance to remain with the data provider. A central team at Swansea University is responsible for data curation and processing, and runs an access procedure for researchers to apply to use data from multiple sources to be analysed in a central analysis environment. Other modalities are similarly hosted, with input from partner sites in Cardiff and Oxford. ResultsDPUK facilitates data access and research on 49 cohorts, 40 UK-based and 9 international. The centralised repository model including remote access and ability to store and make available different modalities of data, from phenotypic data, to genetic and imaging data, has allowed DPUK to begin to support research of varying topics, from those studying cognitive decline and Dementia as a disease, to those maturing analytical models. By providing access to data platforms specialising in genetics, imaging and routine clinical data, as well as to specialists in disease and biology to aid with its understanding, DPUK has realised a large-scale research exercise combining major data modalities on a central platform, and allow access to such rich data across the world under an umbrella of robust governance. Conclusion/ImplicationsGlobally, cohorts are pooling data, expertise and desire to enrich their own aims in partnership with a federated research community to enable in-depth scrutiny of the biological origins of dementia and the development and evaluation of novel approach to disease prevention and cure.