Validation of the brain health index in the European Prevention of Alzheimer's Dementia cohort.

Background: Brain Health Index (BHI) assimilates various MRI sequences, giving a quantitative measure of brain health. To date, BHI validation has been cross-sectional and limited to selected populations. Further large-scale validation and assessment of temporal change is required to understand its clinical utility. Aim: Assess 1) relationships between variables associated with cognitive decline and BHI 2) associations between BHI and measures of cognition and 3) longitudinal changes in BHI and relationship with cognitive function. Methods: BHI computation involved Gaussian mixture-model cluster analysis of T1, T2, T2*, and T2 FLAIR MRI data from participants within the European Prevention of Alzheimer's Dementia (EPAD) cohort. Group differences (gender- and health-based) were evaluated using independent samples Welch's t-tests. Relationships between BHI, age and cognitive tests used linear regression. Longitudinal analysis (12/24 months) utilised mixed linear regression models to examine BHI changes, and paired BHI/cognition associations. Results: Data from N = 1496 predominantly Caucasian participants (50-88 years old, 43.32% male) were used. BHI scores were lower in those with diabetes (p < 0.001, d = 0.419), hypertension (p < 0.001, d = 0.375), hypercholesterolemia (p < 0.001, d = 0.193) and stroke (p < 0.05, d = 0.512). APOE was not significantly related to BHI scores. After correction for age, cross-sectional BHI scores were significantly associated with all measures of cognitive function in males, but only the Four Mountains Test (4MT) in females. Longitudinal change in BHI and cognition were not consistently related. Conclusions: BHI is a valid marker of cognitive decline and relatively stable over 1-2 year follow-up periods. Further work should assess temporal changes over a longer duration and determine relationships between BHI and cognition in more diverse populations.

Causal Associations of Modifiable Risk Factors With Migraine: Evidence From Mendelian Randomization Analysis.

Background and objectives The exact etiology of migraine is unknown; however, it is likely a mixture of genetic and non-genetic factors including lifestyle variables like smoking and diet. This study aims to assess the causal effect of modifiable risk factors on the risk of migraine using two-sample Mendelian randomization. Materials and methods The study used publicly available genome-wide significant single nucleotide polymorphisms (SNPs). The study evaluated a diverse smoking exposure, encompassing age at smoking initiation, smoking intensity, and maternal smoking, alongside other pertinent risk factors, namely key dietary aspects, coffee consumption, BMI, and physical activity. Self-reported migraine was the outcome of the study. The genetic data for migraine were obtained from the FinnGen (Finland) and the UK Biobank (United Kingdom) cohorts. Results With sample sizes ranging from 64,949 to 632,802 for each risk factor collected from several consorts, the study included a total of 282 SNPs for all risk factors. The findings demonstrated that in the FinnGen consortium, genetically estimated dietary factors as well as BMI, were significantly associated with the risk of migraine (OR 0.765 per single unit of BMI, p = 0.011; OR 0.468 per one SD higher cheese intake, p = 0.012; OR 0.286 per one SD higher salad intake, p = 0.004, and 0.625 per one SD higher coffee consumption, p = 0.003, respectively). The results also showed that in the UK Biobank specifically, a genetically estimated history of maternal smoking was significantly associated with an elevated risk of migraine (OR=1.02, p=0.004). Conclusions The latest study implies a connection between maternal smoking and a heightened risk of migraines, whereas cheese intake, salad intake, coffee consumption, BMI, and physical activity are associated with a lower risk of migraine development.

Helicobacter pylori, persistent infection burden and structural brain imaging markers.

Persistent infections, whether viral, bacterial or parasitic, including Helicobacter pylori infection, have been implicated in non-communicable diseases, including dementia and other neurodegenerative diseases. In this cross-sectional study, data on 635 cognitively normal participants from the UK Biobank study (2006-21, age range: 40-70 years) were used to examine whether H. pylori seropositivity (e.g. presence of antibodies), serointensities of five H. pylori antigens and a measure of total persistent infection burden were associated with selected brain volumetric structural MRI (total, white, grey matter, frontal grey matter (left/right), white matter hyperintensity as percent intracranial volume and bi-lateral sub-cortical volumes) and diffusion-weighted MRI measures (global and tract-specific bi-lateral fractional anisotropy and mean diffusivity), after an average 9-10 years of lag time. Persistent infection burden was calculated as a cumulative score of seropositivity for over 20 different pathogens. Multivariable-adjusted linear regression analyses were conducted, whereby selected potential confounders (all measures) and intracranial volume (sub-cortical volumes) were adjusted, with stratification by Alzheimer's disease polygenic risk score tertile when exposures were H. pylori antigen serointensities. Type I error was adjusted to 0.007. We report little evidence of an association between H. pylori seropositivity and persistent infection burden with various volumetric outcomes (P > 0.007, from multivariable regression models), unlike previously reported in past research. However, H. pylori antigen serointensities, particularly immunoglobulin G against the vacuolating cytotoxin A, GroEL and outer membrane protein antigens, were associated with poorer tract-specific white matter integrity (P < 0.007), with outer membrane protein serointensity linked to worse outcomes in cognition-related tracts such as the external capsule, the anterior limb of the internal capsule and the cingulum, specifically at low Alzheimer's disease polygenic risk. Vacuolating cytotoxin A serointensity was associated with greater white matter hyperintensity volume among individuals with mid-level Alzheimer's disease polygenic risk, while among individuals with the highest Alzheimer's disease polygenic risk, the urease serointensity was consistently associated with reduced bi-lateral caudate volumes and the vacuolating cytotoxin A serointensity was linked to reduced right putamen volume (P < 0.007). Outer membrane protein and urease were associated with larger sub-cortical volumes (e.g. left putamen and right nucleus accumbens) at middle Alzheimer's disease polygenic risk levels (P < 0.007). Our results shed light on the relationship between H. pylori seropositivity, H. pylori antigen levels and persistent infection burden with brain volumetric structural measures. These data are important given the links between infectious agents and neurodegenerative diseases, including Alzheimer's disease, and can be used for the development of drugs and preventive interventions that would reduce the burden of those diseases.

Clinical Phenotypes Associated With Cerebral Small Vessel Disease: An Overview of Systematic Reviews.

BACKGROUND AND OBJECTIVES: Cerebral small vessel disease (cSVD) causes lacunar and hemorrhagic stroke and is an important contributor to vascular cognitive impairment. Other potential physical and psychological consequences of cSVD have been described across various body systems. Descriptions of cSVD are available in journals specific to those individual body systems, but a comprehensive assessment of clinical manifestations across this disparate literature is lacking. We conducted an overview of systematic reviews describing clinical cSVD phenotypes. METHODS: We searched multidisciplinary databases from inception to December 2023. We included reviews describing concurrent clinical phenotypes in individuals with neuroimaging evidence of cSVD, defined using the STandards for ReportIng Vascular changes on nEuroimaging criteria. We broadly classified phenotypes into cognitive, mood and neuropsychiatric, respiratory, cardiovascular, renal-urinary, peripheral nervous system, locomotor, and gastrointestinal. We included both studies assessing multiple cSVD features and studies examining individual cSVD markers. We extracted risk factor-adjusted effect estimates, where possible, and assessed methodologic quality using the Assessment of Multiple Systematic Reviews-2 tool. RESULTS: After screening 6,156 publications, we included 24 systematic reviews reporting on 685 original studies and 1,135,943 participants. Cognitive and neuropsychiatric phenotypes were examined most often, particularly in relation to white matter hyperintensities (range of risk ratios [RRs] for cognitive phenotypes 1.21-1.49, range of 95% CI 1.01-1.84; for neuropsychiatric, RR 1.02-5.71, 95% CI 0.96-19.69). Two reviews focused solely on perivascular spaces. No reviews assessed lacunes or small subcortical infarcts separately from other cSVD features. Reviews on peripheral nervous system, urinary, or gastrointestinal phenotypes were lacking. Fourteen reviews had high methodologic quality, 5 had moderate quality, and 5 had low quality. Heterogeneity in cSVD definitions and phenotypic assessments was substantial. DISCUSSION: Neuroimaging markers of cSVD are associated with various clinical manifestations, suggesting a multisystem phenotype. However, features classically associated with cSVD, for example, gait, had limited supporting evidence, and for many body systems, there were no available reviews. Similarly, while white matter hyperintensities were relatively well studied, there were limited data on phenotypes associated with other cSVD features. Future studies should characterize the full clinical spectrum of cSVD and explore clinical associations beyond neurocognitive and neuropsychiatric presentations.

Comprehensive assessment of sleep duration, insomnia, and brain structure within the UK Biobank cohort.

STUDY OBJECTIVES: To assess for associations between sleeping more than or less than recommended by the National Sleep Foundation (NSF), and self-reported insomnia, with brain structure. METHODS: Data from the UK Biobank cohort were analyzed (N between 9K and 32K, dependent on availability, aged 44 to 82 years). Sleep measures included self-reported adherence to NSF guidelines on sleep duration (sleeping between 7 and 9 hours per night), and self-reported difficulty falling or staying asleep (insomnia). Brain structural measures included global and regional cortical or subcortical morphometry (thickness, surface area, volume), global and tract-related white matter microstructure, brain age gap (difference between chronological age and age estimated from brain scan), and total volume of white matter lesions. RESULTS: Longer-than-recommended sleep duration was associated with lower overall grey and white matter volumes, lower global and regional cortical thickness and volume measures, higher brain age gap, higher volume of white matter lesions, higher mean diffusivity globally and in thalamic and association fibers, and lower volume of the hippocampus. Shorter-than-recommended sleep duration was related to higher global and cerebellar white matter volumes, lower global and regional cortical surface areas, and lower fractional anisotropy in projection fibers. Self-reported insomnia was associated with higher global gray and white matter volumes, and with higher volumes of the amygdala, hippocampus, and putamen. CONCLUSIONS: Sleeping longer than recommended by the NSF is associated with a wide range of differences in brain structure, potentially indicative of poorer brain health. Sleeping less than recommended is distinctly associated with lower cortical surface areas. Future studies should assess the potential mechanisms of these differences and investigate long sleep duration as a putative marker of brain health.

Consistent effects of the genetics of happiness across the lifespan and ancestries in multiple cohorts.

Happiness is a fundamental human affective trait, but its biological basis is not well understood. Using a novel approach, we construct LDpred-inf polygenic scores of a general happiness measure in 2 cohorts: the Adolescent Brain Cognitive Development (ABCD) cohort (N = 15,924, age range 9.23-11.8 years), the Add Health cohort (N = 9129, age range 24.5-34.7) to determine associations with several well-being and happiness measures. Additionally, we investigated associations between genetic scores for happiness and brain structure in ABCD (N = 9626, age range (8.9-11) and UK Biobank (N = 16,957, age range 45-83). We detected significant (p.FDR < 0.05) associations between higher genetic scores vs. several well-being measures (best r2 = 0.019) in children of multiple ancestries in ABCD and small yet significant correlations with a happiness measure in European participants in Add Health (r2 = 0.004). Additionally, we show significant associations between lower genetic scores for happiness with smaller structural brain phenotypes in a white British subsample of UK Biobank and a white sub-sample group of ABCD. We demonstrate that the genetic basis for general happiness level appears to have a consistent effect on happiness and wellbeing measures throughout the lifespan, across multiple ancestral backgrounds, and multiple brain structures.

Pathways explaining racial/ethnic and socio-economic disparities in dementia incidence: the UK Biobank study.

BACKGROUND: Pathways explaining racial/ethnic disparities in dementia risk are under-evaluated. METHODS: We examine those disparities and their related pathways among UK Biobank study respondents (50-74 y, N = 323,483; 3.6% non-White minorities) using a series of Cox proportional hazards and generalized structural equations models (GSEM). RESULTS: After ≤15 years, 5,491 all-cause dementia cases were diagnosed. Racial minority status (RACE_ETHN, Non-White vs. White) increased dementia risk by 24% (HR = 1.24, 95% CI: 1.07-1.45, P = 0.005), an association attenuated by socio-economic status (SES), (HR = 1.12, 95% CI: 0.96-1.31). Total race-dementia effect was mediated through both SES and Life's Essential 8 lifestyle sub-score (LE8LIFESTYLE), combining diet, smoking, physical activity, and sleep factors. SES was inversely related to dementia risk (HR = 0.69, 95% CI: 0.67, 0.72, P < 0.001). Pathways explaining excess dementia risk among racial minorities included 'RACE_ETHN(-) → SES(-) → DEMENTIA', 'RACE_ETHN(-) → SES(-) → Poor cognitive performance, COGN(+) → DEMENTIA' and 'RACE_ETHN(-) → SES(+) → LE8LIFESTYLE(-) → DEMENTIA'. CONCLUSIONS: Pending future interventions, lifestyle factors including diet, smoking, physical activity, and sleep are crucial for reducing racial and socio-economic disparities in dementia.

Polygenic risk of major depressive disorder as a risk factor for venous thromboembolism.

Major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SCZ) are associated with an increased risk of cardiovascular diseases, including venous thromboembolism (VTE). The reasons for this are complex and include obesity, smoking, and use of hormones and psychotropic medications. Genetic studies have increasingly provided evidence of the shared genetic risk of psychiatric and cardiometabolic illnesses. This study aimed to determine whether a genetic predisposition to MDD, BD, or SCZ is associated with an increased risk of VTE. Genetic correlations using the largest genome-wide genetic meta-analyses summary statistics for MDD, BD, and SCZ (Psychiatric Genetics Consortium) and a recent genome-wide genetic meta-analysis of VTE (INVENT Consortium) demonstrated a positive association between VTE and MDD but not BD or SCZ. The same summary statistics were used to construct polygenic risk scores for MDD, BD, and SCZ in UK Biobank participants of self-reported White British ancestry. These were assessed for impact on self-reported VTE risk (10 786 cases, 285 124 controls), using logistic regression, in sex-specific and sex-combined analyses. We identified significant positive associations between polygenic risk for MDD and the risk of VTE in men, women, and sex-combined analyses, independent of the known risk factors. Secondary analyses demonstrated that this association was not driven by those with lifetime experience of mental illness. Meta-analyses of individual data from 6 additional independent cohorts replicated the sex-combined association. This report provides evidence for shared biological mechanisms leading to MDD and VTE and suggests that, in the absence of genetic data, a family history of MDD might be considered when assessing the risk of VTE.

Testing for associations between HbA1c levels, polygenic risk and brain health in UK Biobank (N = 39 283).

AIM: To investigate whether continuous HbA1c levels and HbA1c-polygenic risk scores (HbA1c-PRS) are significantly associated with worse brain health independent of type 2 diabetes (T2D) diagnosis (vs. not), by examining brain structure and cognitive test score phenotypes. METHODS: Using UK Biobank data (n = 39 283), we tested whether HbA1c levels and/or HbA1c-PRS were associated with cognitive test scores and brain imaging phenotypes. We adjusted for confounders of age, sex, Townsend deprivation score, level of education, genotyping chip, eight genetic principal components, smoking, alcohol intake frequency, cholesterol medication, body mass index, T2D and apolipoprotein (APOE) e4 dosage. RESULTS: We found an association between higher HbA1c levels and poorer performance on symbol digit substitution scores (standardized beta [ß] = -0.022, P = .001) in the fully adjusted model. We also found an association between higher HbA1c levels and worse brain MRI phenotypes of grey matter (GM; fully-adjusted ß = -0.026, P < .001), whole brain volume (ß = -0.072, P = .0113) and a general factor of frontal lobe GM (ß = -0.022, P < .001) in partially and fully adjusted models. HbA1c-PRS were significantly associated with GM volume in the fully adjusted model (ß = -0.010, P = .0113); however, when adjusted for HbA1c levels, the association was not significant. CONCLUSIONS: Our findings suggest that measured HbA1c is associated with poorer cognitive health, and that HbA1c-PRS do not add significant information to this.

Genetic architecture of DCC and influence on psychological, psychiatric and cardiometabolic traits in multiple ancestry groups in UK Biobank.

BACKGROUND: People with severe mental illness have a higher risk of cardiometabolic disease than the general population. Traditionally attributed to sociodemographic, behavioural factors and medication effects, recent genetic studies have provided evidence of shared biological mechanisms underlying mental illness and cardiometabolic disease. We aimed to determine whether signals in the DCC locus, implicated in psychiatric and cardiometabolic traits, were shared or distinct. METHODS: In UK Biobank, we systematically assessed genetic variation in the DCC locus for association with metabolic, cardiovascular and psychiatric-related traits in unrelated "white British" participants (N = 402,837). Logistic or linear regression were applied assuming an additive genetic model and adjusting for age, sex, genotyping chip and population structure. Bonferroni correction for the number of independent variants was applied. Conditional analyses (including lead variants as covariates) and trans-ancestry analyses were used to investigate linkage disequilibrium between signals. RESULTS: Significant associations were observed between DCC variants and smoking, anhedonia, body mass index (BMI), neuroticism and mood instability. Conditional analyses and linkage disequilibrium structure suggested signals for smoking and BMI were distinct from each other and the mood traits, whilst individual mood traits were inter-related in a complex manner. LIMITATIONS: Restricting analyses in non-"white British" individuals to the phenotypes significant in the "white British" sample is not ideal, but the smaller samples sizes restricted the phenotypes possible to analyse. CONCLUSIONS: Genetic variation in the DCC locus had distinct effects on BMI, smoking and mood traits, and therefore is unlikely to contribute to shared mechanisms underpinning mental and cardiometabolic traits.

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).

HEalth And Dementia outcomes following Traumatic Brain Injury (HEAD-TBI): protocol for a retrospective cohort study.

BACKGROUND: It is estimated that by 2050 the global incidence of dementia will have exceeded 152 million. At present, there are no effective therapies for dementia, with a focus in research now turning to strategies for disease prevention. Traumatic brain injury (TBI) is recognised as a major risk factor for dementia; estimated to be responsible for at least 3% of cases in the community. However, adverse health outcomes after TBI are not restricted to dementia. A wide range of conditions are documented among TBI survivors, many of which also increase dementia risk. 'HEalth And Dementia outcomes following Traumatic Brain Injury' is a study aiming to explore the hypothesis that increased dementia risk following TBI reflects both the direct effect of the injury on the brain and the indirect effects of wider, adverse health outcomes associated with TBI which, in turn, increase dementia risk. METHODS AND ANALYSIS: Comprehensive electronic medical and death certification records will be analysed for individuals with a documented history of TBI, compared with those of a matched general population control cohort with no documented TBI exposure. Cox proportional hazard regression models will be run to compare outcomes. Furthermore, existing diagnostic imaging and radiological reports for the cohort will be analysed to identify evidence of specific white matter abnormalities in TBI exposed individuals and their controls, and establish their potential diagnostic utility. ETHICS AND DISSEMINATION: Approvals for the study have been obtained from the University of Glasgow College of Medical, Veterinary, and Life Sciences Research Ethics Committee (project number 200220038) and from National Health Service Scotland's Public Benefits and Privacy Panel (application 2122-0224). As results emerge, these will be presented at appropriate multidisciplinary research conferences and made available through open access platforms where possible.

Subjective and objective sleep and circadian parameters as predictors of depression-related outcomes: A machine learning approach in UK Biobank.

BACKGROUND: Sleep and circadian disruption are associated with depression onset and severity, but it is unclear which features (e.g., sleep duration, chronotype) are important and whether they can identify individuals showing poorer outcomes. METHODS: Within a subset of the UK Biobank with actigraphy and mental health data (n = 64,353), penalised regression identified the most useful of 51 sleep/rest-activity predictors of depression-related outcomes; including case-control (Major Depression (MD) vs. controls; postnatal depression vs. controls) and within-case comparisons (severe vs. moderate MD; early vs. later onset, atypical vs. typical symptoms; comorbid anxiety; suicidality). Best models (of lasso, ridge, and elastic net) were selected based on Area Under the Curve (AUC). RESULTS: For MD vs. controls (n(MD) = 24,229; n(control) = 40,124), lasso AUC was 0.68, 95 % confidence interval (CI) 0.67-0.69. Discrimination was reasonable for atypical vs. typical symptoms (n(atypical) = 958; n(typical) = 18,722; ridge: AUC 0.74, 95 % CI 0.71-0.77) but poor for remaining models (AUCs 0.59-0.67). Key predictors across most models included: difficulty getting up, insomnia symptoms, snoring, actigraphy-measured daytime inactivity and lower morning activity (~8 am). In a distinct subset (n = 310,718), the number of these factors shown was associated with all depression outcomes. LIMITATIONS: Analyses were cross-sectional and in middle-/older aged adults: comparison with longitudinal investigations and younger cohorts is necessary. DISCUSSION: Sleep and circadian measures alone provided poor to moderate discrimination of depression outcomes, but several characteristics were identified that may be clinically useful. Future work should assess these features alongside broader sociodemographic, lifestyle and genetic features.

Psychosocial predictors of COVID-19 infection in UK biobank (N = 104 201).

BACKGROUND: Since the outbreak of COVID-19, data on its psychosocial predictors are limited. We therefore aimed to explore psychosocial predictors of COVID-19 infection at the UK Biobank (UKB). METHODS: This was a prospective cohort study conducted among UKB participants. RESULTS: The sample size was N = 104 201, out of which 14 852 (14.3%) had a positive COVID-19 test. The whole sample analysis showed significant interactions between sex and several predictor variables. Among females, absence of college/university degree [odds ratio (OR) 1.55, 95% confidence interval (CI) 1.45-1.66] and socioeconomic deprivation (OR 1.16 95% CI 1.11-1.21) were associated with higher odds of COVID-19 infection, while history of psychiatric consultation (OR 0.85 95% CI 0.77-0.94) with lower odds. Among males, absence of college/university degree (OR 1.56, 95% CI 1.45-1.68) and socioeconomic deprivation (OR 1.12, 95% CI 1.07-1.16) were associated with higher odds, while loneliness (OR 0.87, 95% CI 0.78-0.97), irritability (OR 0.91, 95% CI 0.83-0.99) and history of psychiatric consultation (OR 0.85, 95% CI 0.75-0.97) were associated with lower odds. CONCLUSION: Sociodemographic factors predicted the odds of COVID-19 infection equally among male and female participants, while psychological factors had differential impacts.

Cognitive Function in People With Familial Risk of Depression.

Importance: Cognitive impairment in depression is poorly understood. Family history of depression is a potentially useful risk marker for cognitive impairment, facilitating early identification and targeted intervention in those at highest risk, even if they do not themselves have depression. Several research cohorts have emerged recently that enable findings to be compared according to varying depths of family history phenotyping, in some cases also with genetic data, across the life span. Objective: To investigate associations between familial risk of depression and cognitive performance in 4 independent cohorts with varied depth of assessment, using both family history and genetic risk measures. Design, Setting, and Participants: This study used data from the Three Generations at High and Low Risk of Depression Followed Longitudinally (TGS) family study (data collected from 1982 to 2015) and 3 large population cohorts, including the Adolescent Brain Cognitive Development (ABCD) study (data collected from 2016 to 2021), National Longitudinal Study of Adolescent to Adult Health (Add Health; data collected from 1994 to 2018), and UK Biobank (data collected from 2006 to 2022). Children and adults with or without familial risk of depression were included. Cross-sectional analyses were conducted from March to June 2022. Exposures: Family history (across 1 or 2 prior generations) and polygenic risk of depression. Main Outcomes and Measures: Neurocognitive tests at follow-up. Regression models were adjusted for confounders and corrected for multiple comparisons. Results: A total of 57 308 participants were studied, including 87 from TGS (42 [48%] female; mean [SD] age, 19.7 [6.6] years), 10 258 from ABCD (4899 [48%] female; mean [SD] age, 12.0 [0.7] years), 1064 from Add Health (584 [49%] female; mean [SD] age, 37.8 [1.9] years), and 45 899 from UK Biobank (23 605 [51%] female; mean [SD] age, 64.0 [7.7] years). In the younger cohorts (TGS, ABCD, and Add Health), family history of depression was primarily associated with lower performance in the memory domain, and there were indications that this may be partly associated with educational and socioeconomic factors. In the older UK Biobank cohort, there were associations with processing speed, attention, and executive function, with little evidence of education or socioeconomic influences. These associations were evident even in participants who had never been depressed themselves. Effect sizes between familial risk of depression and neurocognitive test performance were largest in TGS; the largest standardized mean differences in primary analyses were -0.55 (95% CI, -1.49 to 0.38) in TGS, -0.09 (95% CI, -0.15 to -0.03) in ABCD, -0.16 (95% CI, -0.31 to -0.01) in Add Health, and -0.10 (95% CI, -0.13 to -0.06) in UK Biobank. Results were generally similar in the polygenic risk score analyses. In UK Biobank, several tasks showed statistically significant associations in the polygenic risk score analysis that were not evident in the family history models. Conclusions and Relevance: In this study, whether assessed by family history or genetic data, depression in prior generations was associated with lower cognitive performance in offspring. There are opportunities to generate hypotheses about how this arises through genetic and environmental determinants, moderators of brain development and brain aging, and potentially modifiable social and lifestyle factors across the life span.

Association of Dementia Risk With Focal Epilepsy and Modifiable Cardiovascular Risk Factors.

Importance: Epilepsy has been associated with cognitive impairment and potentially dementia in older individuals. However, the extent to which epilepsy may increase dementia risk, how this compares with other neurological conditions, and how modifiable cardiovascular risk factors may affect this risk remain unclear. Objective: To compare the differential risks of subsequent dementia for focal epilepsy compared with stroke and migraine as well as healthy controls, stratified by cardiovascular risk. Design, Setting, and Participants: This cross-sectional study is based on data from the UK Biobank, a population-based cohort of more than 500 000 participants aged 38 to 72 years who underwent physiological measurements and cognitive testing and provided biological samples at 1 of 22 centers across the United Kingdom. Participants were eligible for this study if they were without dementia at baseline and had clinical data pertaining to a history of focal epilepsy, stroke, or migraine. The baseline assessment was performed from 2006 to 2010, and participants were followed up until 2021. Exposures: Mutually exclusive groups of participants with epilepsy, stroke, and migraine at baseline assessment and controls (who had none of these conditions). Individuals were divided into low, moderate, or high cardiovascular risk groups based on factors that included waist to hip ratio, history of hypertension, hypercholesterolemia, diabetes, and smoking pack-years. Main Outcomes and Measures: Incident all-cause dementia; measures of executive function; and brain total hippocampal, gray matter, and white matter hyperintensity volumes. Results: Of 495 149 participants (225 481 [45.5%] men; mean [SD] age, 57.5 [8.1] years), 3864 had a diagnosis of focal epilepsy only, 6397 had a history of stroke only, and 14 518 had migraine only. Executive function was comparable between participants with epilepsy and stroke and worse than the control and migraine group. Focal epilepsy was associated with a higher risk of developing dementia (hazard ratio [HR], 4.02; 95% CI, 3.45 to 4.68; P < .001), compared with stroke (HR, 2.56; 95% CI, 2.28 to 2.87; P < .001), or migraine (HR, 1.02; 95% CI, 0.85 to 1.21; P = .94). Participants with focal epilepsy and high cardiovascular risk were more than 13 times more likely to develop dementia (HR, 13.66; 95% CI, 10.61 to 17.60; P < .001) compared with controls with low cardiovascular risk. The imaging subsample included 42 353 participants. Focal epilepsy was associated with lower hippocampal volume (mean difference, -0.17; 95% CI, -0.02 to -0.32; t = -2.18; P = .03) and lower total gray matter volume (mean difference, -0.33; 95% CI, -0.18 to -0.48; t = -4.29; P < .001) compared with controls. There was no significant difference in white matter hyperintensity volume (mean difference, 0.10; 95% CI, -0.07 to 0.26; t = 1.14; P = .26). Conclusions and Relevance: In this study, focal epilepsy was associated with a significant risk of developing dementia, to a greater extent than stroke, which was magnified substantially in individuals with high cardiovascular risk. Further findings suggest that targeting modifiable cardiovascular risk factors may be an effective intervention to reduce dementia risk in individuals with epilepsy.

Investigating the potential impact of PCSK9-inhibitors on mood disorders using eQTL-based Mendelian randomization.

Prescription of PCSK9-inhibitors has increased in recent years but not much is known about its off-target effects. PCSK9-expression is evident in non-hepatic tissues, notably the brain, and genetic variation in the PCSK9 locus has recently been shown to be associated with mood disorder-related traits. We investigated whether PCSK9 inhibition, proxied by a genetic reduction in expression of PCSK9 mRNA, might have a causal adverse effect on mood disorder-related traits. We used genetic variants in the PCSK9 locus associated with reduced PCSK9 expression (eQTLs) in the European population from GTEx v8 and examined the effect on PCSK9 protein levels and three mood disorder-related traits (major depressive disorder, mood instability, and neuroticism), using summary statistics from the largest European ancestry genome-wide association studies. We conducted summary-based Mendelian randomization analyses to estimate the causal effects, and attempted replication using data from eQTLGen, Brain-eMETA, and the CAGE consortium. We found that genetically reduced PCSK9 gene-expression levels were significantly associated with reduced PCSK9 protein levels but not with increased risk of mood disorder-related traits. Further investigation of nearby genes demonstrated that reduced USP24 gene-expression levels was significantly associated with increased risk of mood instability (p-value range = 5.2x10-5-0.03), and neuroticism score (p-value range = 2.9x10-5-0.02), but not with PCSK9 protein levels. Our results suggest that genetic variation in this region acts on mood disorders through a PCSK9-independent pathway, and therefore PCSK9-inhibitors are unlikely to have an adverse impact on mood disorder-related traits.

Associations of rheumatoid arthritis and rheumatoid factor with mental health, sleep and cognition characteristics in the UK Biobank.

While previous rheumatoid arthritis (RA) studies have focussed on cardiometabolic and lifestyle factors, less research has focussed on psychological variables including mood and cognitive health, and sleep. Cross-sectional analyses tested for associations between RA and RF+ (positive rheumatoid factor) vs. mental health (depression, anxiety, neuroticism), sleep variables and cognition scores in UK Biobank (total n = 484,064). Those RF+ were more likely to report longer sleep duration (ß = 0.01, SE = 0.004, p < 0.01) and less likely to get up in the morning easily (OR 0.95, 95% CI 0.92-0.99, p = 0.01). Those reporting RA were more likely to score higher for neuroticism (ß = 0.05, SE = 0.01, p < 0.001), to nap during the day (OR 1.10, 95% CI 1.06-1.14, p < 0.001), have insomnia (OR 1.28, 95% CI 1.22-1.35, p < 0.001), have slower reaction times (ß = 0.02, SE = 0.008, p < 0.005) and score less for fluid intelligence (ß = - 0.03, SE = 0.01, p < 0.05) and less likely to get up easily (OR 0.61, 95% CI 0.58-0.64, p < 0.001). The current study suggests that prevalent RA, and RF+ status are associated with differences in mental health, sleep, and cognition, highlighting the importance of addressing these aspects in clinical settings and future research.