Distress and neuroticism as mediators of the effect of childhood and adulthood adversity on cognitive performance in the UK Biobank study.

Childhood adversity and adulthood adversity affect cognition later in life. However, the mechanism through which adversity exerts these effects on cognition remains under-researched. We aimed to investigate if the effect of adversity on cognition was mediated by distress or neuroticism. The UK Biobank is a large, population-based, cohort study designed to investigate risk factors of cognitive health. Here, data were analysed using a cross-sectional design. Structural equation models were fitted to the data with childhood adversity or adulthood adversity as independent variables, distress and neuroticism as mediators and executive function and processing speed as latent dependent variables that were derived from the cognitive scores in the UK Biobank. Complete data were available for 64,051 participants in the childhood adversity model and 63,360 participants in the adulthood adversity model. Childhood adversity did not show a direct effect on processing speed. The effect of childhood adversity on executive function was partially mediated by distress and neuroticism. The effects of adulthood adversity on executive function and processing speed were both partially mediated by distress and neuroticism. In conclusion, distress and neuroticism mediated the deleterious effect of childhood and adulthood adversity on cognition and may provide a mechanism underlying the deleterious consequences of adversity.

Dementias Platform UK: Bringing genetics into life.

INTRODUCTION: The Dementias Platform UK (DPUK) Data Portal is a data repository bringing together a wide range of cohorts. Neurodegenerative dementias are a group of diseases with highly heterogeneous pathology and an overlapping genetic component that is poorly understood. The DPUK collection of independent cohorts can facilitate research in neurodegeneration by combining their genetic and phenotypic data. METHODS: For genetic data processing, pipelines were generated to perform quality control analysis, genetic imputation, and polygenic risk score (PRS) derivation with six genome-wide association studies of neurodegenerative diseases. Pipelines were applied to five cohorts. DISCUSSION: The data processing pipelines, research-ready imputed genetic data, and PRS scores are now available on the DPUK platform and can be accessed upon request though the DPUK application process. Harmonizing genome-wide data for multiple datasets increases scientific opportunity and allows the wider research community to access and process data at scale and pace.

Association between Residential Greenness and Allostatic Load: A Cohort Study.

The association between residential greenness and allostatic load (AL), a marker of composite physiological burden and predictor of chronic disease, remains understudied. This study comprised 212,600 UK Biobank participants recruited over 2007 and 2010 at the baseline. Residential greenness was modeled as the normalized difference vegetation index (NDVI) from high spatial resolution (0.50 m) color infrared imagery and measured within a 0.5 km radial catchment. AL was measured as a composite index from 13 biomarkers comprising three physiological systems (metabolic, cardiovascular, and inflammatory systems) and two organ systems (liver and kidney). Multilevel mixed-effects generalized linear models with a random intercept for UK Biobank assessment centers were employed to examine the association between residential greenness and AL. Each interquartile range (IQR = 0.24) increment in NDVI greenness was associated with lower AL (beta (ß) = -0.28, 95% confidence interval (CI) = -0.55, -0.01). Consistently, relative to the lowest NDVI greenness quintile, participants in the highest quintile had lower AL (ß = -0.64, 95% CI = -1.02, -0.26). The proportion of the association between greenness and AL mediated by the physical activity was 3.2%. In conclusion, residential greenness was protectively associated with AL, a composite marker of wear and tear and general health.

Prospective study design and data analysis in UK Biobank.

Population-based prospective studies, such as UK Biobank, are valuable for generating and testing hypotheses about the potential causes of human disease. We describe how UK Biobank's study design, data access policies, and approaches to statistical analysis can help to minimize error and improve the interpretability of research findings, with implications for other population-based prospective studies being established worldwide.

A psychometric evaluation of the 16-item PHQ-ADS concomitant anxiety and depression scale in the UK biobank using item response theory.

BACKGROUND: The Patient Health Questionnaire Anxiety and Depression Scale (PHQ-ADS) provides a reliable and valid measure of concomitant depression and anxiety. However, research on its psychometric efficiency and optimal scale length using item-response theory (IRT) has not been reported. This study aimed to optimize the length of the PHQ-ADS scale without losing information by discarding items that were a poor fit to the IRT model. METHODS: The UK Biobank is a large cohort study designed to investigate risk factors for a broad range of disease. PHQ-ADS data were available from n = 152,826 participants (age = 55.87 years; SD = 7.73; 56.4 % female), 30.4 % of the entire UK Biobank sample. Psychometric properties of the PHQ-ADS were investigated using a 2-parameter IRT and Mokken analysis. Item statistics included discrimination, difficulty and Loevinger H coefficients of monotonicity. RESULTS: In the entire 16-item scale, item discrimination ranged from 1.40 to 4.22, with the item 'worrying' showing the highest level of discrimination and the item 'sleep disturbance' showing the lowest. Mokken analysis showed that the 16-item PHQ-ADS scale could be reduced to a 7-item scale without loss of test information. The reduced scale comprised mainly items measuring cognitive-affective symptoms of anxiety/depression, whereas items measuring somatic symptoms were discarded. The revised scale showed high discrimination and scalability. LIMITATIONS: Findings are limited by the use of cross-sectional data that only included the baseline online questionnaire, but not other waves. CONCLUSIONS: IRT is a useful technique for scale reductions which serve the clinical and epidemiological need to optimize screening questionnaires to reduce redundancy and maximize information. A reduced-item 7-item PHQ-ADS scale reduces the response burden on participants in epidemiological research settings, without loss of information.

A multi-disciplinary commentary on preclinical research to investigate vascular contributions to dementia.

Although dementia research has been dominated by Alzheimer's disease (AD), most dementia in older people is now recognised to be due to mixed pathologies, usually combining vascular and AD brain pathology. Vascular cognitive impairment (VCI), which encompasses vascular dementia (VaD) is the second most common type of dementia. Models of VCI have been delayed by limited understanding of the underlying aetiology and pathogenesis. This review by a multidisciplinary, diverse (in terms of sex, geography and career stage), cross-institute team provides a perspective on limitations to current VCI models and recommendations for improving translation and reproducibility. We discuss reproducibility, clinical features of VCI and corresponding assessments in models, human pathology, bioinformatics approaches, and data sharing. We offer recommendations for future research, particularly focusing on small vessel disease as a main underpinning disorder.

Predictors and prognosis of population-based subjective cognitive decline: longitudinal evidence from the Caerphilly Prospective Study (CaPS).

OBJECTIVES: To understand associations between the subjective experience of cognitive decline and objective cognition. This subjective experience is often conceptualised as an early step towards neurodegeneration, but this has not been scrutinised at the population level. An alternative explanation is poor meta-cognition, the extreme of which is seen in functional cognitive disorder (FCD). DESIGN: Prospective cohort (Caerphilly Prospective Study). SETTING: Population-based, South Wales, UK. PARTICIPANTS: This men-only study began in 1979; 1225 men participated at an average age of 73 in 2002-2004, including assessments of simple subjective cognitive decline (sSCD, defined as a subjective report of worsening memory or concentration). Dementia outcomes were followed up to 2012-2014. Data on non-completers was additionally obtained from death certificates and local health records. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcome measure was incident dementia over 10 years. Secondary outcome measures included prospective change in objective cognition and cross-sectional cognitive internal inconsistency (the existence of a cognitive ability at some times, and its absence at other times, with no intervening explanatory factors except for focus of attention). RESULTS: sSCD was common (30%) and only weakly associated with prior objective cognitive decline (sensitivity 36% (95% CI 30 to 42) and specificity 72% (95% CI 68 to 75)). Independent predictors of sSCD were older age, poor sleep quality and higher trait anxiety. Those with sSCD did not have excess cognitive internal inconsistency, but results suggested a mild attentional deficit. sSCD did not predict objective cognitive change (linear regression coefficient -0.01 (95% CI -0.13 to 0.15)) nor dementia (odds ratio 1.35 (0.61 to 2.99)) 10 years later. CONCLUSIONS: sSCD is weakly associated with prior objective cognitive decline and does not predict future cognition. Prior sleep difficulties and anxiety were the most robust predictors of sSCD. sSCD in the absence of objective decline appears to be a highly prevalent example of poor meta-cognition (ie, poor self-awareness of cognitive performance), which could be a driver for later FCD.

Associations of Urban Built Environment with Cardiovascular Risks and Mortality: a Systematic Review.

With rapid urbanization, built environment has emerged as a set of modifiable factors of cardiovascular disease (CVD) risks. We conducted a systematic review to synthesize evidence on the associations of attributes of urban built environment (e.g. residential density, land use mix, greenness and walkability) with cardiovascular risk factors (e.g. hypertension and arterial stiffness) and major CVD events including mortality. A total of 63 studies, including 31 of cross-sectional design and 32 of longitudinal design conducted across 21 geographical locations and published between 2012 and 2023 were extracted for review. Overall, we report moderately consistent evidence of protective associations of greenness with cardiovascular risks and major CVD events (cross-sectional studies: 12 of 15 on hypertension/blood pressure (BP) and 2 of 3 on arterial stiffness; and longitudinal studies: 6 of 8 on hypertension/BP, 7 of 8 on CVD mortality, 3 of 3 on ischemic heart disease mortality and 5 of 8 studies on stroke hospitalization or mortality reporting significant inverse associations). Consistently, walkability was associated with lower risks of hypertension, arterial stiffness and major CVD events (cross-sectional studies: 11 of 12 on hypertension/BP and 1 of 1 on arterial stiffness; and longitudinal studies: 3 of 6 on hypertension/BP and 1 of 2 studies on CVD events being protective). Sixty-seven percent of the studies were rated as "probably high" risk of confounding bias because of inability to adjust for underlying comorbidities/family history of diseases in their statistical models. Forty-six percent and 14% of the studies were rated as "probably high" risk of bias for exposure and outcome measurements, respectively. Future studies with robust design will further help elucidate the linkages between urban built environment and cardiovascular health, thereby informing planning policies for creating healthy cities.

Trajectories in chronic disease accrual and mortality across the lifespan in Wales, UK (2005-2019), by area deprivation profile: linked electronic health records cohort study on 965,905 individuals.

Background: Understanding and quantifying the differences in disease development in different socioeconomic groups of people across the lifespan is important for planning healthcare and preventive services. The study aimed to measure chronic disease accrual, and examine the differences in time to individual morbidities, multimorbidity, and mortality between socioeconomic groups in Wales, UK. Methods: Population-wide electronic linked cohort study, following Welsh residents for up to 20 years (2000-2019). Chronic disease diagnoses were obtained from general practice and hospitalisation records using the CALIBER disease phenotype register. Multi-state models were used to examine trajectories of accrual of 132 diseases and mortality, adjusted for sex, age and area-level deprivation. Restricted mean survival time was calculated to measure time spent free of chronic disease(s) or mortality between socioeconomic groups. Findings: In total, 965,905 individuals aged 5-104 were included, from a possible 2.9 m individuals following a 5-year clearance period, with an average follow-up of 13.2 years (12.7 million person-years). Some 673,189 (69.7%) individuals developed at least one chronic disease or died within the study period. From ages 10 years upwards, the individuals living in the most deprived areas consistently experienced reduced time between health states, demonstrating accelerated transitions to first and subsequent morbidities and death compared to their demographic equivalent living in the least deprived areas. The largest difference were observed in 10 and 20 year old males developing multimorbidity (-0.45 years (99% CI: -0.45, -0.44)) and in 70 year old males dying after developing multimorbidity (-1.98 years (99% CI: -2.01, -1.95)). Interpretation: This study adds to the existing literature on health inequalities by demonstrating that individuals living in more deprived areas consistently experience accelerated time to diagnosis of chronic disease and death across all ages, accounting for competing risks. Funding: UK Medical Research Council, Health Data Research UK, and Administrative Data Research Wales.

The pursuit of approaches to federate data to accelerate Alzheimer's disease and related dementia research: GAAIN, DPUK, and ADDI.

There is common consensus that data sharing accelerates science. Data sharing enhances the utility of data and promotes the creation and competition of scientific ideas. Within the Alzheimer's disease and related dementias (ADRD) community, data types and modalities are spread across many organizations, geographies, and governance structures. The ADRD community is not alone in facing these challenges, however, the problem is even more difficult because of the need to share complex biomarker data from centers around the world. Heavy-handed data sharing mandates have, to date, been met with limited success and often outright resistance. Interest in making data Findable, Accessible, Interoperable, and Reusable (FAIR) has often resulted in centralized platforms. However, when data governance and sovereignty structures do not allow the movement of data, other methods, such as federation, must be pursued. Implementation of fully federated data approaches are not without their challenges. The user experience may become more complicated, and federated analysis of unstructured data types remains challenging. Advancement in federated data sharing should be accompanied by improvement in federated learning methodologies so that federated data sharing becomes functionally equivalent to direct access to record level data. In this article, we discuss federated data sharing approaches implemented by three data platforms in the ADRD field: Dementia's Platform UK (DPUK) in 2014, the Global Alzheimer's Association Interactive Network (GAAIN) in 2012, and the Alzheimer's Disease Data Initiative (ADDI) in 2020. We conclude by addressing open questions that the research community needs to solve together.

The associations of socioeconomic status with incident dementia and Alzheimer's disease are modified by leucocyte telomere length: a population-based cohort study.

Socio-economic status (SES) and biological aging are risk factors for dementia, including Alzheimer's disease, however, it is less clear if the associations with SES vary sufficiently across different biological age strata. We used data from 331,066 UK Biobank participants aged 38-73 with mean follow-up of 12 years to examine if associations between SES (assessed by educational attainment, employment status and household income) and dementia and Alzheimer's disease are modified by biological age (assessed by leucocyte telomere length: LTL). Diagnosis of events was ascertained through hospital admissions data. Cox regressions were used to estimate hazard ratios [HRs]. A consistent dose-response relationship was found, with participants in low SES and shorter LTL strata (double-exposed group) reporting 3.28 (95% confidence interval [CI] 2.57-4.20) and 3.44 (95% CI 2.35-5.04) times higher risks of incident dementia and Alzheimer's disease respectively, compared to those of high SES and longer LTL (least-exposed group). Of interest is a synergistic interaction between SES and LTL to increase risk of dementia (RERI 0.57, 95% CI 0.07-1.06) and Alzheimer's disease (RERI 0.79, 95% CI 0.02-1.56). Our findings that SES and biological age (LTL) are synergistic risk factors of dementia and Alzheimer's disease may suggest the need to target interventions among vulnerable sub-groups.

Evaluating the harmonisation potential of diverse cohort datasets.

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.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.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.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.

Neighbourhood residential density, urbanicity and incident dementia and Alzheimer's disease: A 12-year prospective cohort study from the UK Biobank.

INTRODUCTION: An increasing proportion of global population is exposed to urban densification in an aging society. However, little is known of the role of residential density and urbanicity on the risk of developing dementia including Alzheimer's disease. We examined long-term associations between residential density and urbanicity and risks of incident dementia and Alzheimer's disease. METHODS: This prospective cohort study included participants from the UK Biobank who lived at the same residential address, had no self-reported neurological conditions and without dementia at baseline. Residential density was measured as the number of dwelling units within 1-km street neighbourhood of participant's home address. A composite index of urbanicity was developed from neighbourhood-level z-standardized densities of housing, retail, public transport and street centrality. Hazard ratios were derived from Cox proportional hazard models adjusted for known risk factors. RESULTS: The analytic sample included 239,629 participants aged 38-72 years. During a median follow-up of 12.3 years (interquartile range 11.5-13.0 years), 2,176 participants developed dementia and 1,004 Alzheimer's disease. After adjustments for potential risk factors, each 1,000 units/Km2 increment in residential density was associated with higher risks of dementia (hazard ratio [HR]=1.10, 95% confidence interval [CI]: 1.06-1.15) and Alzheimer's disease (HR=1.10, 95% CI: 1.04-1.16). Consistently, categorical models showed that living in neighbourhoods of higher residential density and urbanicity were associated with higher risks of dementia (HR = 1.30, 95% CI: 1.12-1.51 for the highest density quintile compared to the lowest and HR = 1.21, 95% CI: 1.05-1.39 for the highest urbanicity quintile relative to the lowest). The associations were more pronounced in female, age >65 years, and among participants of the low income and those being frail and having shorter leucocyte telomere length (LTL). CONCLUSIONS: Higher residential density and urbanicity was found to be positively associated with elevated risks of dementia and Alzheimer's disease. Optimizing neighbourhood residential density maybe one of the upstream considerations for mitigating against neurodegenerative diseases.

Different effects of cardiometabolic syndrome on brain age in relation to gender and ethnicity.

BACKGROUND: A growing body of evidence shows differences in the prevalence of cardiometabolic syndrome (CMS) and dementia based on gender and ethnicity. However, there is a paucity of information about ethnic- and gender-specific CMS effects on brain age. We investigated the different effects of CMS on brain age by gender in Korean and British cognitively unimpaired (CU) populations. We also determined whether the gender-specific difference in the effects of CMS on brain age changes depending on ethnicity. METHODS: These analyses used de-identified, cross-sectional data on CU populations from Korea and United Kingdom (UK) that underwent brain MRI. After propensity score matching to balance the age and gender between the Korean and UK populations, 5759 Korean individuals (3042 males and 2717 females) and 9903 individuals from the UK (4736 males and 5167 females) were included in this study. Brain age index (BAI), calculated by the difference between the predicted brain age by the algorithm and the chronological age, was considered as main outcome and presence of CMS, including type 2 diabetes mellitus (T2DM), hypertension, obesity, and underweight was considered as a predictor. Gender (males and females) and ethnicity (Korean and UK) were considered as effect modifiers. RESULTS: The presence of T2DM and hypertension was associated with a higher BAI regardless of gender and ethnicity (p < 0.001), except for hypertension in Korean males (p = 0.309). Among Koreans, there were interaction effects of gender and the presence of T2DM (p for T2DM*gender = 0.035) and hypertension (p for hypertension*gender = 0.046) on BAI in Koreans, suggesting that T2DM and hypertension are each associated with a higher BAI in females than in males. In contrast, among individuals from the UK, there were no differences in the effects of T2DM (p for T2DM*gender = 0.098) and hypertension (p for hypertension*gender = 0.203) on BAI between males and females. CONCLUSIONS: Our results highlight gender and ethnic differences as important factors in mediating the effects of CMS on brain age. Furthermore, these results suggest that ethnic- and gender-specific prevention strategies may be needed to protect against accelerated brain aging.

Contribution of clinical information to the predictive performance of plasma ß-amyloid levels for amyloid positron emission tomography positivity.

Background: Early detection of ß-amyloid (Aß) accumulation, a major biomarker for Alzheimer's disease (AD), has become important. As fluid biomarkers, the accuracy of cerebrospinal fluid (CSF) Aß for predicting Aß deposition on positron emission tomography (PET) has been extensively studied, and the development of plasma Aß is beginning to receive increased attention recently. In the present study, we aimed to determine whether APOE genotypes, age, and cognitive status increase the predictive performance of plasma Aß and CSF Aß levels for Aß PET positivity. Methods: We recruited 488 participants who underwent both plasma Aß and Aß PET studies (Cohort 1) and 217 participants who underwent both cerebrospinal fluid (CSF) Aß and Aß PET studies (Cohort 2). Plasma and CSF samples were analyzed using ABtest-MS, an antibody-free liquid chromatography-differential mobility spectrometry-triple quadrupole mass spectrometry method and INNOTEST enzyme-linked immunosorbent assay kits, respectively. To evaluate the predictive performance of plasma Aß and CSF Aß, respectively, logistic regression and receiver operating characteristic analyses were performed. Results: When predicting Aß PET status, both plasma Aß42/40 ratio and CSF Aß42 showed high accuracy (plasma Aß area under the curve (AUC) 0.814; CSF Aß AUC 0.848). In the plasma Aß models, the AUC values were higher than plasma Aß alone model, when the models were combined with either cognitive stage (p < 0.001) or APOE genotype (p = 0.011). On the other hand, there was no difference between the CSF Aß models, when these variables were added. Conclusion: Plasma Aß might be a useful predictor of Aß deposition on PET status as much as CSF Aß, particularly when considered with clinical information such as APOE genotype and cognitive stage.

Research-ready data: the C-Surv data model.

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.

Neurodegenerative disease of the brain: a survey of interdisciplinary approaches.

Neurodegenerative diseases of the brain pose a major and increasing global health challenge, with only limited progress made in developing effective therapies over the last decade. Interdisciplinary research is improving understanding of these diseases and this article reviews such approaches, with particular emphasis on tools and techniques drawn from physics, chemistry, artificial intelligence and psychology.

Parallel randomized controlled feasibility trials of the "Active Brains" digital intervention to protect cognitive health in adults aged 60-85.

Introduction: Multidomain interventions to address modifiable risk factors for dementia are promising, but require more cost-effective, scalable delivery. This study investigated the feasibility of the "Active Brains" digital behavior change intervention and its trial procedures. Materials and methods: Active Brains aims to reduce cognitive decline by promoting physical activity, healthy eating, and online cognitive training. We conducted 12-month parallel-design randomized controlled feasibility trials of "Active Brains" amongst "lower cognitive scoring" (n = 180) and "higher cognitive scoring" (n = 180) adults aged 60-85. Results: We collected 67.2 and 76.1% of our 12-month primary outcome (Baddeley verbal reasoning task) data for the "lower cognitive score" and "higher cognitive score" groups, respectively. Usage of "Active Brains" indicated overall feasibility and satisfactory engagement with the physical activity intervention content (which did not require sustained online engagement), but engagement with online cognitive training was limited. Uptake of the additional brief telephone support appeared to be higher in the "lower cognitive score" trial. Preliminary descriptive trends in the primary outcome data might indicate a protective effect of Active Brains against cognitive decline, but further investigation in fully-powered trials is required to answer this definitively. Discussion: Whilst initial uptake and engagement with the online intervention was modest, it was in line with typical usage of other digital behavior change interventions, and early indications from the descriptive analysis of the primary outcome and behavioral data suggest that further exploration of the potential protective benefits of Active Brains are warranted. The study also identified minor modifications to procedures, particularly to improve online primary-outcome completion. Further investigation of Active Brains will now seek to determine its efficacy in protecting cognitive performance amongst adults aged 60-85 with varied levels of existing cognitive performance.

Decline in Cognition from Mid-Life Improves Specificity of Mini-Mental State Examination: Diagnostic Test Accuracy in Caerphilly Prospective Study (CaPs).

BACKGROUND: The merit of using baseline cognitive assessments in mid-life to help interpret cross-sectional cognitive tests scores in later life is uncertain. OBJECTIVE: Evaluate how accuracy for diagnosing dementia is enhanced by comparing cross-sectional results to a midlife measure. METHODS: Cohort study of 2,512 men with repeated measures of Mini-Mental State Examination (MMSE) over approximately 10 years. Index test MMSE at threshold of 24 indicating normal, as a cross-sectional measure and in combination with decline in MMSE score from mid-life. Reference standard consensus clinical diagnosis of dementia by two clinicians according to Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV). RESULTS: 1,150 men participated at phase 4 of whom 75 had dementia. A cross-sectional MMSE alone produced a sensitivity of 60% (50% to 70%) and specificity 95% (94% to 97%) with a threshold of≥24 points indicating normal. For lower-scoring men in late life, with cross sectional scores of < 22, combining cross-sectional AND a three-point or more decline over time had a sensitivity of 52% (39% to 64%) and specificity 99% (99% to 100%). For higher-scoring men in later life, with cross sectional scores < 26 combining cross-sectional OR decline of at least three points had a sensitivity of 98% (92% to 100%) and specificity 38% (32% to 44%). CONCLUSION: It may be helpful in practice to formally evaluate cognition in mid-life as a baseline to compare with if problems develop in future, as this may enhance diagnostic accuracy and classification of people in later life.

The long arm of childhood socioeconomic deprivation on mid- to later-life cognitive trajectories: A cross-cohort analysis.

Introduction: Earlier 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. Methods: The 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. Results: We 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. Discussion: Our 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.