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.

Prevalence and risk factors of psychiatric symptoms and diagnoses before and during the COVID-19 pandemic: findings from the ELSA-Brasil COVID-19 mental health cohort.

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 [ß = -0.37, 99.5% confidence interval (CI) -0.50 to -0.23], anxiety (ß = -0.37, 99.5% CI -0.48 to -0.26), and stress (ß = -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.

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.

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.

Artificial intelligence for dementia prevention.

INTRODUCTION: A wide range of modifiable risk factors for dementia have been identified. Considerable debate remains about these risk factors, possible interactions between them or with genetic risk, and causality, and how they can help in clinical trial recruitment and drug development. Artificial intelligence (AI) and machine learning (ML) may refine understanding. METHODS: ML approaches are being developed in dementia prevention. We discuss exemplar uses and evaluate the current applications and limitations in the dementia prevention field. RESULTS: Risk-profiling tools may help identify high-risk populations for clinical trials; however, their performance needs improvement. New risk-profiling and trial-recruitment tools underpinned by ML models may be effective in reducing costs and improving future trials. ML can inform drug-repurposing efforts and prioritization of disease-modifying therapeutics. DISCUSSION: ML is not yet widely used but has considerable potential to enhance precision in dementia prevention. HIGHLIGHTS: Artificial intelligence (AI) is not widely used in the dementia prevention field. Risk-profiling tools are not used in clinical practice. Causal insights are needed to understand risk factors over the lifespan. AI will help personalize risk-management tools for dementia prevention. AI could target specific patient groups that will benefit most for clinical trials.

Depression and memory function - evidence from cross-lagged panel models with unit fixed effects in ELSA and HRS.

BACKGROUND: Individuals with depression are often found to perform worse on cognitive tests and to have an increased risk of dementia. The causes and the direction of these associations are however not well understood. We looked at two specific hypotheses, the aetiological risk factor hypothesis and the reverse causality hypothesis. METHOD: We analysed observational data from two cohorts, English Longitudinal Study of Ageing (ELSA) and Health and Retirement Study (HRS), using cross-lagged panel models with unit fixed effects. Each model was run once with depression and repeated with cognition as the dependent variable and the other variable as the main explanatory variable. All models were estimated separately for contemporaneous effects and lagged effects up to 8 years in the past. We contrasted the results with models making the random effects assumption. RESULTS: Evidence from the fixed effects models is mixed. We find no evidence for the reverse causality hypothesis in ELSA and HRS. While there is no evidence for the aetiological risk factors hypothesis in ELSA, results from HRS indicate some effects. CONCLUSION: Our findings suggest that current levels of cognitive function do not influence future levels of depression. Results in HRS provide some evidence that current levels of depressive symptoms influence future cognition.

Alcohol affordability: implications for alcohol price policies. A cross-sectional analysis in middle and older adults from UK Biobank.

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 (ß = -0.56, 95% CI, -0.92 to -0.20) and availability (ß = 0.06, 95% CI, 0.04 to 0.07). Introducing, food price reduced the alcohol price consumption association (ß = -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 < 0.001). 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.

Trajectories of common mental disorders symptoms before and during the COVID-19 pandemic: findings from the ELSA-Brasil COVID-19 Mental Health Cohort.

AIM: 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. METHODS: 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. RESULTS: 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. LIMITATIONS: Pre-pandemic characteristics were associated with lack of response to assessments. Our occupational cohort sample is not representative. CONCLUSION: 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.

Associations of perceived adverse lifetime experiences with brain structure in UK Biobank participants.

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 ( ηp2  < .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.

Using item response theory (IRT) to improve the efficiency of the Simple Clinical Colitis Activity Index (SCCAI) for patients with ulcerative colitis.

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 < 1.70 which is a suggested discriminatory value for optimal efficiency. Systematic item deletions found that a 4-item scale (bowel frequency day; bowel frequency nocturnal; urgency to defaecation; rectal bleeding) was more informative and discriminatory of trait severity (discrimination values of 1.50 to 2.78). The 4-item scale possesses higher scalability and unidimensionality, suggesting that the responses to items are either direct endorsement (patient selection by symptom) 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 Dementias Platform UK (DPUK) Data Portal.

The Dementias Platform UK 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 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. Projects 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.

Real-world evidence in Alzheimer's disease: The ROADMAP Data Cube.

INTRODUCTION: The ROADMAP project aimed to provide an integrated overview of European real-world data on Alzheimer's disease (AD) across the disease spectrum. METHODS: Metadata 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. RESULTS: Information 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. DISCUSSION: This 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.

Associations with Corneal Hysteresis in a Population Cohort: Results from 96 010 UK Biobank Participants.

PURPOSE: To describe the distribution of corneal hysteresis (CH) in a large cohort and explore its associated factors and possible clinical applications. DESIGN: Cross-sectional study within the UK Biobank, a large cohort study in the United Kingdom. PARTICIPANTS: We analyzed CH data from 93 345 eligible participants in the UK Biobank cohort, aged 40 to 69 years. METHODS: All analyses were performed using left eye data. Linear regression models were used to evaluate associations between CH and demographic, lifestyle, ocular, and systemic variables. Piecewise logistic regression models were used to explore the relationship between self-reported glaucoma and CH. MAIN OUTCOME MEASURES: Corneal hysteresis (mmHg). RESULTS: The mean CH was 10.6 mmHg (10.4 mmHg in male and 10.8 mmHg in female participants). After adjusting for covariables, CH was significantly negatively associated with male sex, age, black ethnicity, self-reported glaucoma, diastolic blood pressure, and height. Corneal hysteresis was significantly positively associated with smoking, hyperopia, diabetes, systemic lupus erythematosus (SLE), greater deprivation (Townsend index), and Goldmann-correlated intraocular pressure (IOPg). Self-reported glaucoma and CH were significantly associated when CH was less than 10.1 mmHg (odds ratio, 0.86; 95% confidence interval, 0.79-0.94 per mmHg CH increase) after adjusting for covariables. When CH exceeded 10.1 mmHg, there was no significant association between CH and self-reported glaucoma. CONCLUSIONS: In our analyses, CH was significantly associated with factors including age, sex, and ethnicity, which should be taken into account when interpreting CH values. In our cohort, lower CH was significantly associated with a higher prevalence of self-reported glaucoma when CH was less than 10.1 mmHg. Corneal hysteresis may serve as a biomarker aiding glaucoma case detection.

A Systematic Review of Longitudinal Associations Between Reaction Time Intraindividual Variability and Age-Related Cognitive Decline or Impairment, Dementia, and Mortality.

OBJECTIVES: Intraindividual variability (IIV) in reaction time refers to the trial-to-trial fluctuations in responding across a given cognitive task. Cross-sectional research suggests that IIV increases with normal and neuropathological ageing and it may serve as a marker of neurobiological integrity. This raises the possibility that IIV may also predict future cognitive decline and, indeed, neuropathology. Therefore, we conducted a systematic review to address these issues. METHODS: A search of electronic databases Embase, Medline, PsycINFO, and Web of Science was completed on May 17, 2016 that identified longitudinal investigations of IIV in middle-aged or older adults. RESULTS: A total of 688 studies were initially identified of which 22 met the inclusion criteria. Nine included longitudinal IIV measures and 17 predicted subsequent outcome (cognitive decline or impairment, dementia, mortality) from baseline IIV. The results suggested IIV increased over time, particularly in participants aged over 75 years. Greater baseline IIV was consistently associated with increased risk of adverse outcomes including cognitive decline or impairment, and mortality. CONCLUSIONS: Increased IIV over time is associated with normal ageing. However, further increases in IIV over and above those found in normal ageing may be a risk factor for future cognitive impairment or mortality. Measures of IIV may, therefore, have considerable potential as a supplement to existing clinical assessment to aid identification of individuals at risk of adverse outcomes such as dementia or death. (JINS, 2017, 23, 431-445).

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.

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.

Lifetime influences on imaging markers of adverse brain health and vascular disease.

Cerebral small vessel disease (cSVD) is highly prevalent in the general population, increases with age and vascular risk factor exposure, and is a common cause of stroke and dementia. There is great variation in cSVD burden experienced in older age, and maintaining brain health across the life course requires looking beyond an individual's current clinical status and traditional vascular risk factors. Of particular importance are social determinants of health which can be more important than healthcare or lifestyle choices in influencing later life health outcomes, including brain health. In this paper we discuss the social determinants of cerebrovascular disease, focusing on the impact of socioeconomic status on markers of cSVD. We outline the potential mechanisms behind these associations, including early life exposures, health behaviours and brain reserve and maintenance, and we highlight the importance of public health interventions to address the key determinants and risk factors for cSVD from early life stages.

Semantic Harmonization of Alzheimer's Disease Datasets Using AD-Mapper.

Background: Despite numerous past endeavors for the semantic harmonization of Alzheimer's disease (AD) cohort studies, an automatic tool has yet to be developed. Objective: As cohort studies form the basis of data-driven analysis, harmonizing them is crucial for cross-cohort analysis. We aimed to accelerate this task by constructing an automatic harmonization tool. Methods: We created a common data model (CDM) through cross-mapping data from 20 cohorts, three CDMs, and ontology terms, which was then used to fine-tune a BioBERT model. Finally, we evaluated the model using three previously unseen cohorts and compared its performance to a string-matching baseline model. Results: Here, we present our AD-Mapper interface for automatic harmonization of AD cohort studies, which outperformed a string-matching baseline on previously unseen cohort studies. We showcase our CDM comprising 1218 unique variables. Conclusion: AD-Mapper leverages semantic similarities in naming conventions across cohorts to improve mapping performance.

Anemia, hemoglobin concentration and cognitive function in the Longitudinal Ageing Study in India-Harmonized Diagnostic Assessment of Dementia (LASI-DAD) and the Health and Retirement Study.

Background: In India, anemia is widely researched in children and women of reproductive age, however, studies in older populations are lacking. Given the adverse effect of anemia on cognitive function and dementia this older population group warrants further study. The Longitudinal Ageing Study in India - Harmonized Diagnostic Assessment of Dementia (LASI-DAD) dataset contains detailed measures to allow a better understanding of anaemia as a potential risk factor for dementia. Method: 2,758 respondents from the LASI-DAD cohort, aged 60 or older, had a complete blood count measured from venous blood as well as cognitive function tests including episodic memory, executive function and verbal fluency. Linear regression was used to test the associations between blood measures (including anemia and hemoglobin concentration (g/dL)) with 11 cognitive domains. All models were adjusted for age and gender with the full model containing adjustments for rural location, years of education, smoking, region, BMI and population weights.Results from LASI-DAD were validated using the USA-based Health and Retirement Study (HRS) cohort (n=5720) to replicate associations between blood cell measures and global cognition. Results: In LASI-DAD, we showed an association between anemia and poor memory (p=0.0054). We found a positive association between hemoglobin concentration and ten cognitive domains tested (ß=0.041-0.071, p<0.05). The strongest association with hemoglobin was identified for memory-based tests (immediate episodic, delayed episodic and broad domain memory, ß=0.061-0.071, p<0.005). Positive associations were also shown between the general cognitive score and the other red blood count tests including mean corpuscular hemoglobin concentration (MCHC, ß=0.06, p=0.0001) and red cell distribution width (RDW, ß =-0.11, p<0.0001). In the HRS cohort, positive associations were replicated between general cognitive score and other blood count tests (Red Blood Cell, MCHC and RDW, p<0.05). Conclusion: We have established in a large South Asian population that low hemoglobin and anaemia are associated with low cognitive function, therefore indicating that anaemia could be an important modifiable risk factor. We have validated this result in an external cohort demonstrating both the variability of this risk factor cross-nationally and its generalizable association with cognitive outcomes.

Identifying Dynamic Patterns of Polypharmacy for Patients with Dementia from Primary Care Electronic Health Records: A Machine Learning Driven Longitudinal Study.

It is unclear how medication use evolved before diagnosis of dementia (DoD). This study aims to identify varied patterns of polypharmacy before DoD, their prevalence and possible complications. We collected primary care e-health records for 33,451 dementia patients in Wales from 1990 to 2015. The medication uses in every 5-year period along with 20-years prior to dementia diagnosis were considered. Exploratory factor analysis was used to identify clusters of medicines for every 5-year period. The prevalence of patients taking three or more medications was 82.16%, 69.7%, 41.1% and 5.5% in the Period 1 (0-5 years before DoD) ~ Period 4 (16-20 years before DoD) respectively. The Period 1 showed 3 clusters of polypharmacy - medicines for respiratory/urinary infections, arthropathies and rheumatism, and cardio-vascular disease (CVD) (66.55%); medicines for infections, arthropathies and rheumatism (AR), cardio-metabolic disease (CMD) and depression (22.02%); and medicines for arthropathies, rheumatism and osteoarthritis (2.6%). The Period 2 showed 4 clusters of polypharmacy - medicines for infections, arthropathies, and CVD (69.7%); medicines for CVD and depression (3%); medicines for CMD and arthropathies (0.3%); and medicines for AR, and CVD (2,5%). The Period 3 showed 6 clusters of polypharmacy - medicines for infections, arthropathies, and CVD (41.1%); medicines for CVD, acute-respiratory-infection (ARI), and arthropathies (1.25%); medicines for AR (1.16%); medicines for depression, anxiety (0.06%); medicines for CMD (1.4%); and medicines for dermatologic disorders (0.9%). The Period 4 showed 3 main clusters of polypharmacy - medicines for infections, arthropathy, and CVD (5.5%); medicines for anxiety, ARI (2.4%); and medicines for ARI and CVD (2.1%). As the development towards dementia progressed, the associative diseases tended to cluster with a larger prevalence in each cluster. Farther away before DoD, the clusters of polypharmacy tended to be clearly distinct between each other, resulting in an increasing number of patterns, but in a smaller prevalence.