Trends of use and characterisation of anti-dementia drugs users: a large multinational-network population-based study

BACKGROUND

An updated time-trend analysis of anti-dementia drugs (ADDs) is lacking. The aim of this study is to assess the incident rate (IR) of ADD in individuals with dementia using real-world data.

SETTING

Primary care data (country/database) from the UK/CPRD-GOLD (2007-20), Spain/SIDIAP (2010-20) and the Netherlands/IPCI (2008-20), standardised to a common data model.

METHODS

Cohort study. Participants: dementia patients ≥40 years old with ≥1 year of previous data. Follow-up: until the end of the study period, transfer out of the catchment area, death or incident prescription of rivastigmine, galantamine, donepezil or memantine. Other variables: age/sex, type of dementia, comorbidities. Statistics: overall and yearly age/sex IR, with 95% confidence interval, per 100,000 person-years (IR per 105 PY (95%CI)).

RESULTS

We identified a total of (incident anti-dementia users/dementia patients) 41,024/110,642 in UK/CPRD-GOLD, 51,667/134,927 in Spain/SIDIAP and 2,088/17,559 in the Netherlands/IPCI.In the UK, IR (per 105 PY (95%CI)) of ADD decreased from 2007 (30,829 (28,891-32,862)) to 2010 (17,793 (17,083-18,524)), then increased up to 2019 (31,601 (30,483 to 32,749)) and decrease in 2020 (24,067 (23,021-25,148)). In Spain, IR (per 105 PY (95%CI)) of ADD decreased by 72% from 2010 (51,003 (49,199-52,855)) to 2020 (14,571 (14,109-15,043)). In the Netherlands, IR (per 105 PY (95%CI)) of ADD decreased by 77% from 2009 (21,151 (14,967-29,031)) to 2020 (4763 (4176-5409)). Subjects aged ≥65-79 years and men (in the UK and the Netherlands) initiated more frequently an ADD.

CONCLUSIONS

Treatment of dementia remains highly heterogeneous. Further consensus in the pharmacological management of patients living with dementia is urgently needed.

The impact of the UK COVID-19 lockdown on the screening, diagnostics and incidence of breast, colorectal, lung and prostate cancer in the UK: a population-based cohort study

Introduction

The COVID-19 pandemic had collateral effects on many health systems. Cancer screening and diagnostic tests were postponed, resulting in delays in diagnosis and treatment. This study assessed the impact of the pandemic on screening, diagnostics and incidence of breast, colorectal, lung, and prostate cancer; and whether rates returned to pre-pandemic levels by December, 2021.

Methods

This is a cohort study of electronic health records from the United Kingdom (UK) primary care Clinical Practice Research Datalink (CPRD) GOLD database. The study included individuals registered with CPRD GOLD between January, 2017 and December, 2021, with at least 365 days of clinical history. The study focused on screening, diagnostic tests, referrals and diagnoses of first-ever breast, colorectal, lung, and prostate cancer. Incidence rates (IR) were stratified by age, sex, and region, and incidence rate ratios (IRR) were calculated to compare rates during and after lockdown with rates before lockdown. Forecasted rates were estimated using negative binomial regression models.

Results

Among 5,191,650 eligible participants, the first lockdown resulted in reduced screening and diagnostic tests for all cancers, which remained dramatically reduced across the whole observation period for almost all tests investigated. There were significant IRR reductions in breast (0.69 [95% CI: 0.63-0.74]), colorectal (0.74 [95% CI: 0.67-0.81]), and prostate (0.71 [95% CI: 0.66-0.78]) cancer diagnoses. IRR reductions for lung cancer were non-significant (0.92 [95% CI: 0.84-1.01]). Extrapolating to the entire UK population, an estimated 18,000 breast, 13,000 colorectal, 10,000 lung, and 21,000 prostate cancer diagnoses were missed from March, 2020 to December, 2021.

Discussion

The UK COVID-19 lockdown had a substantial impact on cancer screening, diagnostic tests, referrals, and diagnoses. Incidence rates remained significantly lower than pre-pandemic levels for breast and prostate cancers and associated tests by December, 2021. Delays in diagnosis are likely to have adverse consequences on cancer stage, treatment initiation, mortality rates, and years of life lost. Urgent strategies are needed to identify undiagnosed cases and address the long-term implications of delayed diagnoses.

Tracing Tomorrow: young people's preferences and values related to use of personal sensing to predict mental health, using a digital game methodology

BACKGROUND

Use of personal sensing to predict mental health risk has sparked interest in adolescent psychiatry, offering a potential tool for targeted early intervention.

OBJECTIVES

We investigated the preferences and values of UK adolescents with regard to use of digital sensing information, including social media and internet searching behaviour. We also investigated the impact of risk information on adolescents' self-understanding.

METHODS

Following a Design Bioethics approach, we created and disseminated a purpose-built digital game (www.tracingtomorrow.org) that immersed the player-character in a fictional scenario in which they received a risk assessment for depression Data were collected through game choices across relevant scenarios, with decision-making supported through clickable information points.

FINDINGS

The game was played by 7337 UK adolescents aged 16-18 years. Most participants were willing to personally communicate mental health risk information to their parents or best friend. The acceptability of school involvement in risk predictions based on digital traces was mixed, due mainly to privacy concerns. Most participants indicated that risk information could negatively impact their academic self-understanding. Participants overwhelmingly preferred individual face-to-face over digital options for support.

CONCLUSIONS

The potential of digital phenotyping in supporting early intervention in mental health can only be fulfilled if data are collected, communicated and actioned in ways that are trustworthy, relevant and acceptable to young people.

CLINICAL IMPLICATIONS

To minimise the risk of ethical harms in real-world applications of preventive psychiatric technologies, it is essential to investigate young people's values and preferences as part of design and implementation processes.

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 (β=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, β=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, β=0.06, p=0.0001) and red cell distribution width (RDW, β =-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.

IncidencePrevalence: An R package to calculate population-level incidence rates and prevalence using the OMOP common data model

PURPOSE

Real-world data (RWD) offers a valuable resource for generating population-level disease epidemiology metrics. We aimed to develop a well-tested and user-friendly R package to compute incidence rates and prevalence in data mapped to the observational medical outcomes partnership (OMOP) common data model (CDM).

MATERIALS AND METHODS

We created IncidencePrevalence, an R package to support the analysis of population-level incidence rates and point- and period-prevalence in OMOP-formatted data. On top of unit testing, we assessed the face validity of the package. To do so, we calculated incidence rates of COVID-19 using RWD from Spain (SIDIAP) and the United Kingdom (CPRD Aurum), and replicated two previously published studies using data from the Netherlands (IPCI) and the United Kingdom (CPRD Gold). We compared the obtained results to those previously published, and measured execution times by running a benchmark analysis across databases.

RESULTS

IncidencePrevalence achieved high agreement to previously published data in CPRD Gold and IPCI, and showed good performance across databases. For COVID-19, incidence calculated by the package was similar to public data after the first-wave of the pandemic.

CONCLUSION

For data mapped to the OMOP CDM, the IncidencePrevalence R package can support descriptive epidemiological research. It enables reliable estimation of incidence and prevalence from large real-world data sets. It represents a simple, but extendable, analytical framework to generate estimates in a reproducible and timely manner.

Artificial intelligence for dementia-Applied models and digital health

INTRODUCTION

The use of applied modeling in dementia risk prediction, diagnosis, and prognostics will have substantial public health benefits, particularly as "deep phenotyping" cohorts with multi-omics health data become available.

METHODS

This narrative review synthesizes understanding of applied models and digital health technologies, in terms of dementia risk prediction, diagnostic discrimination, prognosis, and progression. Machine learning approaches show evidence of improved predictive power compared to standard clinical risk scores in predicting dementia, and the potential to decompose large numbers of variables into relatively few critical predictors.

RESULTS

This review focuses on key areas of emerging promise including: emphasis on easier, more transparent data sharing and cohort access; integration of high-throughput biomarker and electronic health record data into modeling; and progressing beyond the primary prediction of dementia to secondary outcomes, for example, treatment response and physical health.

DISCUSSION

Such approaches will benefit also from improvements in remote data measurement, whether cognitive (e.g., online), or naturalistic (e.g., watch-based accelerometry).

Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: A systematic review

INTRODUCTION

Artificial intelligence (AI) and neuroimaging offer new opportunities for diagnosis and prognosis of dementia.

METHODS

We systematically reviewed studies reporting AI for neuroimaging in diagnosis and/or prognosis of cognitive neurodegenerative diseases.

RESULTS

A total of 255 studies were identified. Most studies relied on the Alzheimer's Disease Neuroimaging Initiative dataset. Algorithmic classifiers were the most commonly used AI method (48%) and discriminative models performed best for differentiating Alzheimer's disease from controls. The accuracy of algorithms varied with the patient cohort, imaging modalities, and stratifiers used. Few studies performed validation in an independent cohort.

DISCUSSION

The literature has several methodological limitations including lack of sufficient algorithm development descriptions and standard definitions. We make recommendations to improve model validation including addressing key clinical questions, providing sufficient description of AI methods and validating findings in independent datasets. Collaborative approaches between experts in AI and medicine will help achieve the promising potential of AI tools in practice.

HIGHLIGHTS

There has been a rapid expansion in the use of machine learning for diagnosis and prognosis in neurodegenerative disease Most studies (71%) relied on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset with no other individual dataset used more than five times There has been a recent rise in the use of more complex discriminative models (e.g., neural networks) that performed better than other classifiers for classification of AD vs healthy controls We make recommendations to address methodological considerations, addressing key clinical questions, and validation We also make recommendations for the field more broadly to standardize outcome measures, address gaps in the literature, and monitor sources of bias.

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.

Genetic risk, adherence to healthy lifestyle and acute cardiovascular and thromboembolic complications following SARS-COV-2 infection

Current understanding of determinants for COVID-19-related cardiovascular and thromboembolic (CVE) complications primarily covers clinical aspects with limited knowledge on genetics and lifestyles. Here, we analysed a prospective cohort of 106,005 participants from UK Biobank with confirmed SARS-CoV-2 infection. We show that higher polygenic risk scores, indicating individual's hereditary risk, were linearly associated with increased risks of post-COVID-19 atrial fibrillation (adjusted HR 1.52 [95% CI 1.44 to 1.60] per standard deviation increase), coronary artery disease (1.57 [1.46 to 1.69]), venous thromboembolism (1.33 [1.18 to 1.50]), and ischaemic stroke (1.27 [1.05 to 1.55]). These genetic associations are robust across genders, key clinical subgroups, and during Omicron waves. However, a prior composite healthier lifestyle was consistently associated with a reduction in all outcomes. Our findings highlight that host genetics and lifestyle independently affect the occurrence of CVE complications in the acute infection phrase, which can guide tailored management of COVID-19 patients and inform population lifestyle interventions to offset the elevated cardiovascular burden post-pandemic.

Development and validation of a dementia risk score in the UK Biobank and Whitehall II cohorts

BACKGROUND

Current dementia risk scores have had limited success in consistently identifying at-risk individuals across different ages and geographical locations.

OBJECTIVE

We aimed to develop and validate a novel dementia risk score for a midlife UK population, using two cohorts: the UK Biobank, and UK Whitehall II study.

METHODS

We divided the UK Biobank cohort into a training (n=176 611, 80%) and test sample (n=44 151, 20%) and used the Whitehall II cohort (n=2934) for external validation. We used the Cox LASSO regression to select the strongest predictors of incident dementia from 28 candidate predictors and then developed the risk score using competing risk regression.

FINDINGS

Our risk score, termed the UK Biobank Dementia Risk Score (UKBDRS), consisted of age, education, parental history of dementia, material deprivation, a history of diabetes, stroke, depression, hypertension, high cholesterol, household occupancy, and sex. The score had a strong discrimination accuracy in the UK Biobank test sample (area under the curve (AUC) 0.8, 95% CI 0.78 to 0.82) and in the Whitehall cohort (AUC 0.77, 95% CI 0.72 to 0.81). The UKBDRS also significantly outperformed three other widely used dementia risk scores originally developed in cohorts in Australia (the Australian National University Alzheimer's Disease Risk Index), Finland (the Cardiovascular Risk Factors, Ageing, and Dementia score), and the UK (Dementia Risk Score).

CLINICAL IMPLICATIONS

Our risk score represents an easy-to-use tool to identify individuals at risk for dementia in the UK. Further research is required to determine the validity of this score in other populations.

Interplay of Metabolome and Gut Microbiome in Individuals With Major Depressive Disorder vs Control Individuals

Importance

Metabolomics reflect the net effect of genetic and environmental influences and thus provide a comprehensive approach to evaluating the pathogenesis of complex diseases, such as depression.

Objective

To identify the metabolic signatures of major depressive disorder (MDD), elucidate the direction of associations using mendelian randomization, and evaluate the interplay of the human gut microbiome and metabolome in the development of MDD.

Design, Setting and Participants

This cohort study used data from participants in the UK Biobank cohort (n = 500 000; aged 37 to 73 years; recruited from 2006 to 2010) whose blood was profiled for metabolomics. Replication was sought in the PREDICT and BBMRI-NL studies. Publicly available summary statistics from a 2019 genome-wide association study of depression were used for the mendelian randomization (individuals with MDD = 59 851; control individuals = 113 154). Summary statistics for the metabolites were obtained from OpenGWAS in MRbase (n = 118 000). To evaluate the interplay of the metabolome and the gut microbiome in the pathogenesis of depression, metabolic signatures of the gut microbiome were obtained from a 2019 study performed in Dutch cohorts. Data were analyzed from March to December 2021.

Main Outcomes and Measures

Outcomes were lifetime and recurrent MDD, with 249 metabolites profiled with nuclear magnetic resonance spectroscopy with the Nightingale platform.

Results

The study included 6811 individuals with lifetime MDD compared with 51 446 control individuals and 4370 individuals with recurrent MDD compared with 62 508 control individuals. Individuals with lifetime MDD were younger (median [IQR] age, 56 [49-62] years vs 58 [51-64] years) and more often female (4447 [65%] vs 2364 [35%]) than control individuals. Metabolic signatures of MDD consisted of 124 metabolites spanning the energy and lipid metabolism pathways. Novel findings included 49 metabolites, including those involved in the tricarboxylic acid cycle (ie, citrate and pyruvate). Citrate was significantly decreased (β [SE], -0.07 [0.02]; FDR = 4 × 10-04) and pyruvate was significantly increased (β [SE], 0.04 [0.02]; FDR = 0.02) in individuals with MDD. Changes observed in these metabolites, particularly lipoproteins, were consistent with the differential composition of gut microbiota belonging to the order Clostridiales and the phyla Proteobacteria/Pseudomonadota and Bacteroidetes/Bacteroidota. Mendelian randomization suggested that fatty acids and intermediate and very large density lipoproteins changed in association with the disease process but high-density lipoproteins and the metabolites in the tricarboxylic acid cycle did not.

Conclusions and Relevance

The study findings showed that energy metabolism was disturbed in individuals with MDD and that the interplay of the gut microbiome and blood metabolome may play a role in lipid metabolism in individuals with MDD.

Harnessing the potential of machine learning and artificial intelligence for dementia research

Progress in dementia research has been limited, with substantial gaps in our knowledge of targets for prevention, mechanisms for disease progression, and disease-modifying treatments. The growing availability of multimodal data sets opens possibilities for the application of machine learning and artificial intelligence (AI) to help answer key questions in the field. We provide an overview of the state of the science, highlighting current challenges and opportunities for utilisation of AI approaches to move the field forward in the areas of genetics, experimental medicine, drug discovery and trials optimisation, imaging, and prevention. Machine learning methods can enhance results of genetic studies, help determine biological effects and facilitate the identification of drug targets based on genetic and transcriptomic information. The use of unsupervised learning for understanding disease mechanisms for drug discovery is promising, while analysis of multimodal data sets to characterise and quantify disease severity and subtype are also beginning to contribute to optimisation of clinical trial recruitment. Data-driven experimental medicine is needed to analyse data across modalities and develop novel algorithms to translate insights from animal models to human disease biology. AI methods in neuroimaging outperform traditional approaches for diagnostic classification, and although challenges around validation and translation remain, there is optimism for their meaningful integration to clinical practice in the near future. AI-based models can also clarify our understanding of the causality and commonality of dementia risk factors, informing and improving risk prediction models along with the development of preventative interventions. The complexity and heterogeneity of dementia requires an alternative approach beyond traditional design and analytical approaches. Although not yet widely used in dementia research, machine learning and AI have the potential to unlock current challenges and advance precision dementia medicine.

Impact of time from symptom onset on the diagnostic performance of high-sensitivity cardiac troponin for type 1 myocardial infarction

Background

High-sensitivity cardiac troponin has enabled the rapid rule-out and rule-in of myocardial infarction at presentation. However, increases in cardiac troponin may not be detectable early after symptom onset, and uncertainty remains as to how time of symptom onset influences diagnostic performance.

Purpose

To evaluate the impact of time from symptom onset on the diagnostic performance of high-sensitivity cardiac troponin for type 1 myocardial infarction.

Methods

In a secondary analysis of a prospective multicentre randomised controlled trial of consecutive patients with suspected acute coronary syndrome, we evaluated the diagnostic performance of high-sensitivity cardiac troponin I measurements at presentation stratified by time of symptom onset to blood sampling. Diagnostic performance was evaluated in four groups (≤3 hours, 4–6 hours, 7–12 hours and &gt;12 hours from symptom onset) for recommended thresholds to rule-out (sex-specific 99th centile and optimised threshold [64 ng/L]) type 1 myocardial infarction.

Results

This analysis included 41,104 patients (median 60 [interquartile range 49–74] years, 46% female) of which 12,595 (31%), 10,298 (25%), 7,171 (17%) and 11,040 (27%) presented ≤3 hours, 4–6 hours, 7–12 hours and &gt;12 hours, respectively. Type 1 myocardial infarction was the adjudicated diagnosis in 3,692 (9%) patients. For the rule-out of type 1 myocardial infarction, sensitivity was highest in those tested 7–12 hours from symptom onset and lowest in those tested ≤3 hours. In early presenters, a threshold of &lt;2 ng/L had greater sensitivity and negative predictive value (99.4% [95% CI 98.9 to 99.7%] and 99.7% [95% CI 99.5 to 99.9%]) compared to &lt;5 ng/L (96.7% [95% CI 95.7 to 97.6%] and 99.3% [95% CI 99.1 to 99.5%], respectively). In those tested &gt;3 hrs from symptom onset, the sensitivity and negative predictive value for both thresholds were similar, but a threshold of &lt;5 ng/L correctly ruled out more patients (60% [17,056/28,506] versus 29% [8,316/28,506]). For the rule-in of myocardial infarction, the sensitivity of the 99th centile and 64 ng/L was lowest in patients tested within 3 hours (71.7% [95% CI 69.3 to 74.1%] and 46.5% [95% CI 44.1 to 49.2%], respectively), and increased in those tested later from symptom onset. The specificity and positive predictive value were highest when testing was performed 7–12 hours from symptom onset for the sex-specific 99th centile (92.4% [95% CI 91.8 to 93.0%] and 51.3% [95% CI 48.2–54.5%]) and 64 ng/L (96.2% [95% CI 95.7 to 96.7%] and 61.2% [95% CI 57.3 to 65.2%]).

Conclusions

The diagnostic performance of cardiac troponin for myocardial infarction is strongly influenced by the time from symptom onset to testing. In early presenters the limit of detection may facilitate immediate rule-out of myocardial infarction, but otherwise testing at least 3 hours from symptom onset is needed with the optimal time to rule-in myocardial infarction being 7–12 hours from the onset of symptoms.

Funding Acknowledgement

Type of funding sources: Public grant(s) – National budget only. Main funding source(s): British Heart Foundation (BHF)Medical Research council UK (MRC)

Comparative effect of metformin versus sulfonylureas with dementia and Parkinson's disease risk in US patients over 50 with type 2 diabetes mellitus

INTRODUCTION

Type 2 diabetes is a risk factor for dementia and Parkinson's disease (PD). Drug treatments for diabetes, such as metformin, could be used as novel treatments for these neurological conditions. Using electronic health records from the USA (OPTUM EHR) we aimed to assess the association of metformin with all-cause dementia, dementia subtypes and PD compared with sulfonylureas.

RESEARCH DESIGN AND METHODS

A new user comparator study design was conducted in patients ≥50 years old with diabetes who were new users of metformin or sulfonylureas between 2006 and 2018. Primary outcomes were all-cause dementia and PD. Secondary outcomes were Alzheimer's disease (AD), vascular dementia (VD) and mild cognitive impairment (MCI). Cox proportional hazards models with inverse probability of treatment weighting (IPTW) were used to estimate the HRs. Subanalyses included stratification by age, race, renal function, and glycemic control.

RESULTS

We identified 96 140 and 16 451 new users of metformin and sulfonylureas, respectively. Mean age was 66.4±8.2 years (48% male, 83% Caucasian). Over the 5-year follow-up, 3207 patients developed all-cause dementia (2256 (2.3%) metformin, 951 (5.8%) sulfonylurea users) and 760 patients developed PD (625 (0.7%) metformin, 135 (0.8%) sulfonylurea users). After IPTW, HRs for all-cause dementia and PD were 0.80 (95% CI 0.73 to 0.88) and 1.00 (95% CI 0.79 to 1.28). HRs for AD, VD and MCI were 0.81 (0.70-0.94), 0.79 (0.63-1.00) and 0.91 (0.79-1.04). Stronger associations were observed in patients who were younger (<75 years old), Caucasian, and with moderate renal function.

CONCLUSIONS

Metformin users compared with sulfonylurea users were associated with a lower risk of all-cause dementia, AD and VD but not with PD or MCI. Age and renal function modified risk reduction. Our findings support the hypothesis that metformin provides more neuroprotection for dementia than sulfonylureas but not for PD, but further work is required to assess causality.

Understanding the inter-relationships of type 2 diabetes and hypertension with brain and cognitive health: A UK Biobank study

AIM

To understand the impact of diabetes and co-morbid hypertension on cognitive and brain health.

MATERIALS AND METHODS

We used data from the UK Biobank cohort consisting of ~500 000 individuals aged 40 to 69 years. Our outcomes included brain structural magnetic resonance imaging variables and cognitive function tests in a maximum of 38 918 individuals. We firstly tested associations with all outcomes between those with diabetes (n = 2043) and without (n = 36 875) and, secondly, compared those with co-morbid diabetes/hypertension (n = 1283) with those with only diabetes (n = 760), hypertension (n = 9649) and neither disease (n = 27 226). Our analytical approach comprised linear regression models, with adjustment for a range of demographic and health factors. Standardized betas are reported.

RESULTS

Those with diabetes had worse brain and cognitive health for the majority of neuroimaging and cognitive measures, with the exception of g fractional anisotropy (white matter integrity), amygdala, pairs matching and tower rearranging. Compared with individuals with co-morbid diabetes and hypertension, those with only hypertension had better brain health overall, with the largest difference observed in the pallidum (β = .189, 95% CI = 0.241; 0.137), while those with only diabetes differed in total grey volume (β = .150, 95% CI = 0.122; 0.179). Individuals with only diabetes had better verbal and numeric reasoning (β = .129, 95% CI = 0.077; 0.261), whereas those with only hypertension performed better on the symbol-digit substitution task (β = .117, 95% CI = 0.048; 0.186).

CONCLUSIONS

Individuals with co-morbid diabetes and hypertension have worse brain and cognitive health compared with those with only one of these diseases. These findings potentially suggest that prevention of both diabetes and hypertension may delay changes in brain structure, as well as cognitive decline and dementia diagnosis.

The relationship between isolated hypertension with brain volumes in UK Biobank

BACKGROUND

Hypertension is a well-established risk factor for cognitive impairment, brain atrophy, and dementia. However, the relationship of other types of hypertensions, such as isolated hypertension on brain health and its comparison to systolic-diastolic hypertension (where systolic and diastolic measures are high), is still relatively unknown. Due to its increased prevalence, it is important to investigate the impact of isolated hypertension to help understand its potential impact on cognitive decline and future dementia risk. In this study, we compared a variety of global brain measures between participants with isolated hypertension to those with normal blood pressure (BP) or systolic-diastolic hypertension using the largest cohort of healthy individuals.

METHODS

Using the UK Biobank cohort, we carried out a cross-sectional study using 29,775 participants (mean age 63 years, 53% female) with BP measurements and brain magnetic resonance imaging (MRI) data. We used linear regression models adjusted for multiple confounders to compare a variety of global, subcortical, and white matter brain measures. We compared participants with either isolated systolic or diastolic hypertension with normotensives and then with participants with systolic-diastolic hypertension.

RESULTS

The results showed that participants with isolated systolic or diastolic hypertension taking BP medications had smaller gray matter but larger white matter microstructures and macrostructures compared to normotensives. Isolated systolic hypertensives had larger total gray matter and smaller white matter traits when comparing these regions with participants with systolic-diastolic hypertension.

CONCLUSIONS

These results provide support to investigate possible preventative strategies that target isolated hypertension as well as systolic-diastolic hypertension to maintain brain health and/or reduce dementia risk earlier in life particularly in white matter regions.

Validation of UK Biobank data for mental health outcomes: A pilot study using secondary care electronic health records

UK Biobank (UKB) is widely employed to investigate mental health disorders and related exposures; however, its applicability and relevance in a clinical setting and the assumptions required have not been sufficiently and systematically investigated. Here, we present the first validation study using secondary care mental health data with linkage to UKB from Oxford - Clinical Record Interactive Search (CRIS) focusing on comparison of demographic information, diagnostic outcome, medication record and cognitive test results, with missing data and the implied bias from both resources depicted. We applied a natural language processing model to extract information embedded in unstructured text from clinical notes and attachments. Using a contingency table we compared the demographic information recorded in UKB and CRIS. We calculated the positive predictive value (PPV, proportion of true positives cases detected) for mental health diagnosis and relevant medication. Amongst the cohort of 854 subjects, PPVs for any mental health diagnosis for dementia, depression, bipolar disorder and schizophrenia were 41.6%, and were 59.5%, 12.5%, 50.0% and 52.6%, respectively. Self-reported medication records in UKB had general PPV of 47.0%, with the prevalence of frequently prescribed medicines to each typical mental health disorder considerably different from the information provided by CRIS. UKB is highly multimodal, but with limited follow-up records, whereas CRIS offers a longitudinal high-resolution clinical picture with more than ten years of observations. The linkage of both datasets will reduce the self-report bias and synergistically augment diverse modalities into a unified resource to facilitate more robust research in mental health.

Machine learning to aid in the diagnosis of acute heart failure in the emergency department

Background

B-type natriuretic peptide (BNP) and mid-regional pro-atrial natriuretic peptide (MRproANP) testing are recommended to aid in the diagnosis of acute heart failure. However, the application of these biomarkers for optimal diagnostic performance is uncertain.

Methods

We performed a systematic review and harmonised individual patient-level data to evaluate the diagnostic performance of BNP and MRproANP for the diagnosis of acute heart failure using random-effects meta-analysis. We subsequently developed and externally validated a decision-support tool called CoDE-HF for both BNP and MRproANP that combines the natriuretic peptide concentrations with clinical variables using machine learning to report the probability of acute heart failure for an individual patient.

Results

Fourteen studies from 12 countries provided individual patient-level data in 8,493 patients for BNP and 3,847 patients for MRproANP, in whom, 48.3% (4,105/8,493) and 41.3% (1,611/3899) had an adjudicated diagnosis of acute heart failure, respectively. The negative and positive predictive values of guideline-recommended thresholds for BNP (100 pg/mL) and MR-proANP (120 pg/mL) were 93.6% (95% confidence interval 88.4–96.6%) and 68.8% (62.9–74.2%), and 95.6% (92.2–97.6%) and 64.8% (56.3–72.5%), respectively. However, we observed significant heterogeneity in the diagnostic performance across important patient subgroups (Figure 1). In the external validation cohort, CoDE-HF was well calibrated with excellent discrimination in those without prior acute heart failure for both BNP and MRproANP (area under the curve of 0.946 [0.933–0.958] and 0.943 [0.921–0.964], and Brier scores of 0.105 and 0.073, respectively). CoDE-HF performed consistently across all subgroups for both BNP and MRproANP, and identified 30% and 65.7% at low-probability (negative predictive value of 99.1% [98.8–99.3%] and 99.1% [98.8–99.4%]), and 30% and 17.3% at high-probability (positive predictive value of 91.3% [90.7–91.9%] and 70.0% [68.5–71.4%]) in those without prior heart failure, respectively (Figure 2).

Conclusion

In an international collaborative analysis, we observed that guideline-recommended thresholds for BNP and MRproANP to diagnose acute heart failure varied significantly across patient subgroups. A decision-support tool using machine learning to combine natriuretic peptides as a continuous measure and other clinical variables provides a more accurate and individualised approach.

Funding Acknowledgement

Type of funding sources: Other. Main funding source(s): Medical Research Council and British Heart Foundation Figure 1. NPV of BNP threshold (100 pg/mL)Figure 2. NPV of the CoDE-HF rule-out score

COVID-19 partial school closures and mental health problems: A cross-sectional survey of 11,000 adolescents to determine those most at risk

Background

Understanding adolescents' mental health during lockdown and identifying those most at risk is an urgent public health challenge. This study surveyed school pupils across Southern England during the first COVID‐19 school lockdown to investigate situational factors associated with mental health difficulties and how they relate to pupils' access to in‐school educational provision.

Methods

A total of 11,765 pupils in years 8–13 completed a survey in June–July 2020, including questions on mental health, risk indicators and access to school provision. Pupils at home were compared to those accessing in‐school provision on risk and contextual factors and mental health outcomes. Multilevel logistic regression analyses compared the effect of eight risk and contextual factors, including access to in‐school provision, on depression, anxiety and self‐reported deterioration in mental wellbeing.

Results

Females, pupils who had experienced food poverty and those who had previously accessed mental health support were at greatest risk of depression, anxiety and a deterioration in wellbeing. Pupils whose parents were going out to work and those preparing for national examinations in the subsequent school year were also at increased risk. Pupils accessing in‐school provision had poorer mental health, but this was accounted for by the background risk and contextual factors assessed, in line with the allocation of in‐school places to more vulnerable pupils.

Conclusions

Although the strongest associations with poor mental health during school closures were established risk factors, further contextual factors of particular relevance during lockdown had negative impacts on wellbeing. Identifying those pupils at greatest risk for poor outcomes is critical for ensuring that appropriate educational and social support can be given to pupils either at home or in‐school during subsequent lockdowns.

Risk of depression, suicide and psychosis with hydroxychloroquine treatment for rheumatoid arthritis: a multinational network cohort study

OBJECTIVES

Concern has been raised in the rheumatology community regarding recent regulatory warnings that HCQ used in the coronavirus disease 2019 pandemic could cause acute psychiatric events. We aimed to study whether there is risk of incident depression, suicidal ideation or psychosis associated with HCQ as used for RA.

METHODS

We performed a new-user cohort study using claims and electronic medical records from 10 sources and 3 countries (Germany, UK and USA). RA patients ≥18 years of age and initiating HCQ were compared with those initiating SSZ (active comparator) and followed up in the short (30 days) and long term (on treatment). Study outcomes included depression, suicide/suicidal ideation and hospitalization for psychosis. Propensity score stratification and calibration using negative control outcomes were used to address confounding. Cox models were fitted to estimate database-specific calibrated hazard ratios (HRs), with estimates pooled where I2 <40%.

RESULTS

A total of 918 144 and 290 383 users of HCQ and SSZ, respectively, were included. No consistent risk of psychiatric events was observed with short-term HCQ (compared with SSZ) use, with meta-analytic HRs of 0.96 (95% CI 0.79, 1.16) for depression, 0.94 (95% CI 0.49, 1.77) for suicide/suicidal ideation and 1.03 (95% CI 0.66, 1.60) for psychosis. No consistent long-term risk was seen, with meta-analytic HRs of 0.94 (95% CI 0.71, 1.26) for depression, 0.77 (95% CI 0.56, 1.07) for suicide/suicidal ideation and 0.99 (95% CI 0.72, 1.35) for psychosis.

CONCLUSION

HCQ as used to treat RA does not appear to increase the risk of depression, suicide/suicidal ideation or psychosis compared with SSZ. No effects were seen in the short or long term. Use at a higher dose or for different indications needs further investigation.

TRIAL REGISTRATION

Registered with EU PAS (reference no. EUPAS34497; http://www.encepp.eu/encepp/viewResource.htm? id=34498). The full study protocol and analysis source code can be found at https://github.com/ohdsi-studies/Covid19EstimationHydroxychloroquine2.

Associations Between Brain Volumes and Cognitive Tests with Hypertensive Burden in UK Biobank

BACKGROUND

Mid-life hypertension is an established risk factor for cognitive impairment and dementia and related to greater brain atrophy and poorer cognitive performance. Previous studies often have small sample sizes from older populations that lack utilizing multiple measures to define hypertension such as blood pressure, self-report information, and medication use; furthermore, the impact of the duration of hypertension is less extensively studied.

OBJECTIVE

To investigate the relationship between hypertension defined using multiple measures and length of hypertension with brain measure and cognition.

METHODS

Using participants from the UK Biobank MRI visit with blood pressure measurements (n = 31,513), we examined the cross-sectional relationships between hypertension and duration of hypertension with brain volumes and cognitive tests using generalized linear models adjusted for confounding.

RESULTS

Compared with normotensives, hypertensive participants had smaller brain volumes, larger white matter hyperintensities (WMH), and poorer performance on cognitive tests. For total brain, total grey, and hippocampal volumes, those with greatest duration of hypertension had the smallest brain volumes and the largest WMH, ventricular cerebrospinal fluid volumes. For other subcortical and white matter microstructural regions, there was no clear relationship. There were no significant associations between duration of hypertension and cognitive tests.

CONCLUSION

Our results show hypertension is associated with poorer brain and cognitive health however, the impact of duration since diagnosis warrants further investigation. This work adds further insights by using multiple measures defining hypertension and analysis on duration of hypertension which is a substantial advance on prior analyses-particularly those in UK Biobank which present otherwise similar analyses on smaller subsets.

Stratifying the prognostic capability of cardiovascular magnetic resonance in severe aortic stenosis: a machine learning approach

Background

Cardiovascular magnetic resonance (CMR) demonstrates promise in improving patient risk stratification in aortic stenosis (AS). We explored whether machine learning might provide further insights into the prognostic capability of CMR parameters.

Methods

Severe AS patients (n=440) undergoing AVR were prospectively enrolled across 10 international sites, and CMR performed prior to AVR. A machine learning prediction model using a random survival forest (RSF) was trained with 29 variables, including 13 CMR, 4 echocardiography, and 12 clinical parameters, using post-AVR mortality as an outcome. The impact of the important variables on the outcome (partial dependency) was examined.

Results

The most predictive CMR parameters in the RSF model were the extracellular volume fraction (ECV%), followed by right ventricular ejection fraction (RVEF), late gadolinium enhancement (LGE%), and indexed left ventricular end-diastolic volume (LVEDVi). Regarding the partial effects, the predicted mortality increased strongly once the ECV% exceeded 26.5% (Figure 1A). The LGE% was associated with an increased risk of mortality, which reached a plateau beyond the level of 2% (Figure 1C). There were U-shaped relationships between mortality and both RVEF and LVEDVi, with the lowest mortality seen at RVEF 70% and LVEDVi 68ml/m2 (Figure 1B, D). These trends of predicted outcomes by each variable were verified in the Kaplan-Meier curves and Cox analyses (Table). In both Cox and RSF models, the predictability was substantially increased when these four CMR parameters were added to conventional clinical risk factors. An AS-CMR risk score comprised of these four parameters presented a stepwise increase in mortality with increasing adverse CMR features (p&lt;0.001).

Conclusions

Our machine learning analysis using RSF has identified ECV%, RVEF, LGE%, and LVEDVi as key prognostic markers in severe AS with a nonlinear influence of each parameter on mortality post-AVR.

Figure 1

Funding Acknowledgement

Type of funding source: Public grant(s) – National budget only. Main funding source(s): This study was supported by grants from the Korean Health Technology R & D Project, Ministry of Health, Welfare & Family Affairs, Republic of Korea (HI16C0225 and HI15C0399) and the National Institute for Health Research (NIHR) infrastructure at Leeds.

Thrombus formation on bioprosthetic aortic valves

Background

18F-GP1 is a novel radiotracer with a high affinity for the platelet glycoprotein IIb/IIIa receptor. Subclinical bioprosthetic valve thrombus has been postulated as a trigger for accelerated valve degeneration.

Purpose

To determine the feasibility of 18F-GP1 positron-emission tomography-computed tomography (PET-CT) for the detection of subclinical bioprosthetic aortic valve thrombus.

Methods

(i) Explanted degenerated aortic valve prostheses underwent histology and imaging. (ii) In a prospective observational study, patients with bioprosthetic aortic valve replacement (AVR) underwent echocardiography and 18F-GP1 PET-CT. Valves were assessed for hypoattenuating leaflet thickening (HALT), hypo-attenuation affection leaflet motion (HAM) and GP1 uptake.

Results

(i) GP1 correlated with thrombus on explanted valves (Figure). (ii) The first 6 patients (Table) were asymptomatic and had normally functioning surgical bioprostheses on echocardiography. At a median of 166 (range 122–189) days post-AVR, no patients had HALT or HAM on CT. There was avid focal GP1 uptake on the leaflets of all 6 patients which appeared most prominent along the leaflet edges (Figure). Only one patient had focal uptake in the valve frame, remote from the leaflets. In a separate cohort undergoing 18F-GP1 PET-CT for other conditions, there was no uptake on normal, native aortic valves (n=8).

Conclusion

For the first time, we demonstrate that 18F-GP1 PET-CT is a highly sensitive method of assessing platelet activation on bioprosthetic aortic valves. Despite the absence of CT evidence, early thrombus appeared to be a universal finding on recently implanted valve prostheses. The biological and clinical implications of subclinical bioprosthetic aortic valve thrombus have yet to be established.

GP1 uptake in AVR

Funding Acknowledgement

Type of funding source: Foundation. Main funding source(s): British Heart Foundation

Risk of hydroxychloroquine alone and in combination with azithromycin in the treatment of rheumatoid arthritis: a multinational, retrospective study

Background

Hydroxychloroquine, a drug commonly used in the treatment of rheumatoid arthritis, has received much negative publicity for adverse events associated with its authorisation for emergency use to treat patients with COVID-19 pneumonia. We studied the safety of hydroxychloroquine, alone and in combination with azithromycin, to determine the risk associated with its use in routine care in patients with rheumatoid arthritis.

Methods

In this multinational, retrospective study, new user cohort studies in patients with rheumatoid arthritis aged 18 years or older and initiating hydroxychloroquine were compared with those initiating sulfasalazine and followed up over 30 days, with 16 severe adverse events studied. Self-controlled case series were done to further establish safety in wider populations, and included all users of hydroxychloroquine regardless of rheumatoid arthritis status or indication. Separately, severe adverse events associated with hydroxychloroquine plus azithromycin (compared with hydroxychloroquine plus amoxicillin) were studied. Data comprised 14 sources of claims data or electronic medical records from Germany, Japan, the Netherlands, Spain, the UK, and the USA. Propensity score stratification and calibration using negative control outcomes were used to address confounding. Cox models were fitted to estimate calibrated hazard ratios (HRs) according to drug use. Estimates were pooled where the I ² value was less than 0·4.

Findings

The study included 956 374 users of hydroxychloroquine, 310 350 users of sulfasalazine, 323 122 users of hydroxychloroquine plus azithromycin, and 351 956 users of hydroxychloroquine plus amoxicillin. No excess risk of severe adverse events was identified when 30-day hydroxychloroquine and sulfasalazine use were compared. Self-controlled case series confirmed these findings. However, long-term use of hydroxychloroquine appeared to be associated with increased cardiovascular mortality (calibrated HR 1·65 [95% CI 1·12-2·44]). Addition of azithromycin appeared to be associated with an increased risk of 30-day cardiovascular mortality (calibrated HR 2·19 [95% CI 1·22-3·95]), chest pain or angina (1·15 [1·05-1·26]), and heart failure (1·22 [1·02-1·45]).

Interpretation

Hydroxychloroquine treatment appears to have no increased risk in the short term among patients with rheumatoid arthritis, but in the long term it appears to be associated with excess cardiovascular mortality. The addition of azithromycin increases the risk of heart failure and cardiovascular mortality even in the short term. We call for careful consideration of the benefit-risk trade-off when counselling those on hydroxychloroquine treatment.

Funding

National Institute for Health Research (NIHR) Oxford Biomedical Research Centre, NIHR Senior Research Fellowship programme, US National Institutes of Health, US Department of Veterans Affairs, Janssen Research and Development, IQVIA, Korea Health Industry Development Institute through the Ministry of Health and Welfare Republic of Korea, Versus Arthritis, UK Medical Research Council Doctoral Training Partnership, Foundation Alfonso Martin Escudero, Innovation Fund Denmark, Novo Nordisk Foundation, Singapore Ministry of Health's National Medical Research Council Open Fund Large Collaborative Grant, VINCI, Innovative Medicines Initiative 2 Joint Undertaking, EU's Horizon 2020 research and innovation programme, and European Federation of Pharmaceutical Industries and Associations.

Methotrexate and relative risk of dementia amongst patients with rheumatoid arthritis: a multi-national multi-database case-control study

Background

Inflammatory processes have been shown to play a role in dementia. To understand this role, we selected two anti-inflammatory drugs (methotrexate and sulfasalazine) to study their association with dementia risk.

Methods

A retrospective matched case-control study of patients over 50 with rheumatoid arthritis (486 dementia cases and 641 controls) who were identified from electronic health records in the UK, Spain, Denmark and the Netherlands. Conditional logistic regression models were fitted to estimate the risk of dementia.

Results

Prior methotrexate use was associated with a lower risk of dementia (OR 0.71, 95% CI 0.52–0.98). Furthermore, methotrexate use with therapy longer than 4 years had the lowest risk of dementia (odds ratio 0.37, 95% CI 0.17–0.79). Sulfasalazine use was not associated with dementia (odds ratio 0.88, 95% CI 0.57–1.37).

Conclusions

Further studies are still required to clarify the relationship between prior methotrexate use and duration as well as biological treatments with dementia risk.

3325Incidence, outcomes and microbiology in patients with infective endocarditis

Introduction

Despite recent improvements in management, infective endocarditis remains associated with high morbidity and mortality. Over the last few decades, several factors have impacted on both the incidence and outcomes following infective endocarditis.

Purpose

Using a national linkage approach, we describe the changing age- and sex-stratified incidence and outcomes of infective endocarditis in Scotland over the last 25 years.

Methods

We conducted a consecutive retrospective individual patient linkage study across multiple national databases. Using data extracted from the Scottish hospital discharge dataset held by the Information Services Division of NHS National Services Scotland, we extracted episodes for all patients aged 20 years or older who were admitted with infective endocarditis between January 1, 1990, and December 31, 2014 in Scotland, UK. Patient episodes with infective endocarditis were linked to national prescribing and microbiology databases. The primary outcome was 1-year mortality following the index presentation. Generalised additive models were constructed to estimate the crude and age- and sex-stratified incidence rates (using a poison distribution) as well as trends in mortality (using a binomial distribution) adjusted for age, sex and comorbidity.

Results

Across 12,446 individual patients, there were a total of 12,667 hospitalisations (mean age 68±17 years, 55% females) with infective endocarditis using a 5-year look back period. The estimated crude rate of hospitalisation increased from 7.38 per 100,000 (95% CI 6.58 to 8.28) in 1990 to 15.09 per 100,000 (95% CI 13.90 to 16.39) in 2014 (p<0.001). Over the period of the study, 31% (3,877/12,667) of people admitted to hospital with infective endocarditis died within one year of admission. Case fatality fell markedly in both men and women from 1990 to 2014 (Figure). Microbiology was status was available for 34% of all hospitalisations with staphylococcus cultures associated with worse outcomes.

Conclusions

Despite the crude incidence of infective endocarditis doubling over the last 25 years and case fatality remaining high, the risk of death has markedly fallen over the last two decades. Staphylococcus cultures remain an independent marker of poor prognosis in this cohort.

Acknowledgement/Funding

British Heart Foundation

Plasma Protein Biomarkers for the Prediction of CSF Amyloid and Tau and [18F]-Flutemetamol PET Scan Result

Background: Blood biomarkers may aid in recruitment to clinical trials of Alzheimer's disease (AD) modifying therapeutics by triaging potential trials participants for amyloid positron emission tomography (PET) or cerebrospinal fluid (CSF) Aβ and tau tests. Objective: To discover a plasma proteomic signature associated with CSF and PET measures of AD pathology. Methods: Liquid chromatography-tandem mass spectrometry (LC-MS/MS) based proteomics were performed in plasma from participants with subjective cognitive decline (SCD), mild cognitive impairment (MCI), and AD, recruited to the Amsterdam Dementia Cohort, stratified by CSF Tau/Aβ42 (n = 50). Technical replication and independent validation were performed by immunoassay in plasma from SCD, MCI, and AD participants recruited to the Amsterdam Dementia Cohort with CSF measures (n = 100), MCI participants enrolled in the GE067-005 study with [18F]-Flutemetamol PET amyloid measures (n = 173), and AD, MCI and cognitively healthy participants from the EMIF 500 study with CSF Aβ42 measurements (n = 494). Results: 25 discovery proteins were nominally associated with CSF Tau/Aβ42 (P < 0.05) with associations of ficolin-2 (FCN2), apolipoprotein C-IV and fibrinogen β chain confirmed by immunoassay (P < 0.05). In the GE067-005 cohort, FCN2 was nominally associated with PET amyloid (P < 0.05) replicating the association with CSF Tau/Aβ42. There were nominally significant associations of complement component 3 with PET amyloid, and apolipoprotein(a), apolipoprotein A-I, ceruloplasmin, and PPY with MCI conversion to AD (all P < 0.05). In the EMIF 500 cohort FCN2 was trending toward a significant relationship with CSF Aβ42 (P ≈ 0.05), while both A1AT and clusterin were nominally significantly associated with CSF Aβ42 (both P < 0.05). Conclusion: Associations of plasma proteins with multiple measures of AD pathology and progression are demonstrated. To our knowledge this is the first study to report an association of FCN2 with AD pathology. Further testing of the proteins in larger independent cohorts will be important.

Cross-sectional and longitudinal analyses of outdoor air pollution exposure and cognitive function in UK Biobank

Observational studies have shown consistently increased likelihood of dementia or mild cognitive impairment diagnoses in people with higher air pollution exposure history, but evidence has been less consistent for associations with cognitive test performance. We estimated the association between baseline neighbourhood-level exposure to airborne pollutants (particulate matter and nitrogen oxides) and (1) cognitive test performance at baseline and (2) cognitive score change between baseline and 2.8-year follow-up, in 86,759 middle- to older-aged adults from the UK Biobank general population cohort. Unadjusted regression analyses indicated small but consistent negative associations between air pollutant exposure and baseline cognitive performance. Following adjustment for a range of key confounders, associations were inconsistent in direction and of very small magnitude. The largest of these indicated that 1 interquartile range higher air pollutant exposure was associated on average with 0.35% slower reaction time (95% CI: 0.13, 0.57), a 2.92% higher error rate on a visuospatial memory test (95% CI: 1.24, 4.62), and numeric memory scores that were 0.58 points lower (95% CI: -0.96, -0.19). Follow-up analyses of cognitive change scores did not show evidence of associations. The findings indicate that in this sample, which is five-fold larger than any previous cross-sectional study, the association between air pollution exposure and cognitive performance was weak. Ongoing follow-up of the UK Biobank cohort will allow investigation of longer-term associations into old age, including longitudinal tracking of cognitive performance and incident dementia outcomes.