Quantifying bias in psychological and physical health in the UK Biobank imaging sub-sample

Mediation Analyses Link Cardiometabolic Factors and Liver Fat With White Matter Hyperintensities and Cognitive Performance: A UK Biobank Study

Background

Liver fat is associated with cardiometabolic disease, cerebrovascular disease, and dementia. Cerebrovascular disease, most often cerebral small vessel disease, identified by magnetic resonance imaging as white matter hyperintensities (WMHs) often contributes to dementia. However, liver fat's role in the relationship between cardiometabolic risk, WMHs, and cognitive performance is unclear.

Methods

In the UK Biobank cohort (N = 32,461, 52.6% female; mean age 64.2 ± 7.7 years; n = 23,354 in the cognitive performance subsample), we used linear regression to investigate associations between cardiometabolic factors measured at baseline and liver fat, WMHs, and cognitive performance measured at follow-up, which was 9.3 ± 2.0 years later on average. We used structural equation modeling to investigate whether liver fat mediated associations between cardiometabolic factors and WMHs and whether WMHs mediated associations between liver fat and cognitive performance.

Results

Nearly all cardiometabolic factors were significantly associated with liver fat (|r| range = 0.03-0.41, p = 3.4 × 10-8 to 0) and WMHs (|r| = 0.04-0.15, p = 5.8 × 10-13 to 7.0 × 10-159) in regression models. Liver fat was associated with WMHs (r = 0.11, p = 4.3 × 10-82) and cognitive performance (r = -0.03, p = 1.6 × 10-7). Liver fat mediated the associations between cardiometabolic factors and WMHs (|βmediation| = 0.003-0.027, p mediation = 1.9 × 10-8 to 0), and WMHs mediated the associations between liver fat and cognitive performance (βmediation = -0.01, p mediation = 0).

Conclusions

Our findings indicate that liver fat mediates associations between cardiometabolic factors and WMHs and that WMHs mediate the association between liver fat and cognitive performance. This suggests that liver fat may be important for understanding the effects of cardiometabolic factors on cerebrovascular disease and cognitive function. Experimental studies are warranted to determine relevant targets for preventing vascular-driven cognitive impairment.

Association of multimorbidity and disease clusters with neuroimaging and cognitive outcomes in UK Biobank

BACKGROUND

The relationship between multimorbidity, particularly disease clusters, with neuroimaging and cognitive outcomes that typically manifest prior to clinical diagnosis of dementia, remains understudied. This study investigated whether multimorbidity is associated with dementia-related neuroimaging and cognitive outcomes in the UK Biobank cohort.

METHODS

This cross-sectional study used data from UK Biobank participants who attended imaging assessments between 2014-2023, and were free from neurological conditions, including dementia. Multimorbidity was defined as the coexistence of two or more long-term conditions, selected from a standardised criteria of 39 conditions. Latent class analyses were used to identify disease clusters. Neuroimaging outcomes were measured using magnetic resonance imaging, and cognition was assessed by seven tests measuring different cognitive domains. Multivariable linear regression was used to assess the association between multimorbidity and disease clusters with neuroimaging and cognitive outcomes.

RESULTS

A total of 43,160 participants were included (mean [standard deviation] age, 64.2 [7.7] years, 53.1 % female). Multimorbidity was present among 14,339 (33.2 %) participants, and was associated with reduced grey matter volume, total brain volume, left hippocampal volume, increased cerebrovascular pathology as well as reduced domain-specific cognitive function. A strong dose-response relationship was observed with the increasing number of multimorbid conditions across these outcomes. A disease cluster driven by cardiometabolic conditions was consistently associated with poorer brain health across all outcomes. Disease clusters driven by respiratory, mental health and other conditions showed less consistent associations.

CONCLUSIONS

Multimorbidity was strongly associated with poorer brain health, particularly within the cardiometabolic disease cluster. Given that UK Biobank participants are, on average, healthier than the general population, future studies in more diverse and representative cohorts would be valuable.

Sex-specific genetics underlie increased chronic pain risk in women: genome-wide association studies from the UK Biobank

BACKGROUND

Chronic pain disproportionately affects women, but the reasons for this disparity are unclear.

METHODS

We investigated this from a genetic perspective using data from the UK Biobank, focusing on multi-site chronic pain, which is highly heritable and manifests a sex bias.

RESULTS

Genome-wide association studies (GWAS) revealed that women have approximately 4500 sex-specific causal loci for overlapping pains-four times more than men-accounting for their higher heritability. Heritability partitioning indicated that pain-related loci are primarily enriched in brain regions, but only in women. Additionally, 200 imaging-derived brain phenotypes were significantly associated with pain in women, compared with only six in men. GWAS of these brain phenotypes showed stronger genetic correlations with pain in women, particularly regarding cortical thickness and striatal volume. When disentangling pleiotropy from causation in genetically correlated pairs of brain- and pain-related traits, we found that the genetics of brain phenotypes are more often causally implicated with the presence of chronic pain in women.

CONCLUSIONS

Our findings suggest that genetics play a crucial role in the increased risk of chronic pain observed in women.

Combining cross-sectional and longitudinal genomic approaches to identify determinants of cognitive and physical decline

Large-scale genomic studies focusing on the genetic contribution to human aging have mostly relied on cross-sectional data. With the release of longitudinally curated aging phenotypes by the UK Biobank (UKBB), it is now possible to study aging over time at genome-wide scale. In this work, we evaluated the suitability of competing models of change in realistic simulation settings, performed genome-wide association scans on simulation-validated measures of age-related deweekcline, and followed up with LD-score regression and Mendelian Randomization (MR) analyses. Focusing on global cognitive and physical function, we observed marked differences between baseline function (θ) and accelerated decline (Δ). Both outcomes showed distinct heritability levels (e.g., 31.38%h θ 2 versus 3.15%h Δ 2 for physical function) and different associated loci (e.g., DUSP6 specific to physical Δ). Further, we found little commonalities across the two dimensions of aging-while cognitive decline was largely driven by Alzheimer's disease liability (standardized MR-effect, γ = 0.17), physical decline was mostly impacted by telomere length (γ = -0.05) and bone mineral density (γ = -0.05). Our work highlights the utility of longitudinal genomic efforts to scrutinize age-dependent genetic and environmental effects on physical and cognitive outcomes. Careful modelling and attention to participation characteristics are, however, crucial for valid inference.

Depression and dementia: interrogating the causality of the relationship

BACKGROUND

Depression is often cited as a major modifiable risk factor for dementia, though the relative contributions of a true causal relationship, reverse causality and confounding factors remain unclear. This study applied a subset of the Bradford Hill criteria for causation to depression and dementia including strength of effect, specificity, temporality, biological gradient and coherence.

METHODS

A total of 491 557 participants in UK Biobank aged between 40 and 69 at enrolment and followed up for a mean duration of 12.4 years were studied. Diagnoses of depression and dementia were ascertained from linked health records, self-reports and death certificate registration. Depressive symptoms were measured at enrolment using a combination of questions based on the Patient Health Questionnaire-9 depression screening questionnaire. Regional grey matter volumes were measured using T1-weighted MRI in 41 929 participants.

RESULTS

Depression was a strong risk factor for incident dementia with an OR of 1.76 (95% CI 1.63 to 1.90), a relationship which was found to be specific to depression rather than commonly proposed confounders. Depressive symptoms increased rapidly in the 10 years prior to dementia diagnosis. The severity of depressive symptoms showed a dose-response relationship with dementia risk. Depression at older ages correlated with reduced grey matter volume in an Alzheimer's pattern whereas younger onset depression was associated with reduced grey matter volume in the frontal lobes and cerebellum.

CONCLUSIONS

This study provides evidence that the link between depression and dementia is due to reverse causation with a smaller component of causation with clear evidence of both mechanisms driving the association.

Lifetime depression, sleep disruption and brain structure in the UK Biobank cohort

Whether depression and poor sleep interact or have statistically independent associations with brain structure and its change over time is not known. Within a subset of UK Biobank participants with neuroimaging and subjective and/or objective sleep data (n = 28,351), we examined associations between lifetime depression and sleep disruption, and their interaction with structural neuroimaging measures, both cross-sectionally and longitudinally. Sleep variables were: self-reported insomnia and difficulty getting up; actigraphy-derived short sleep (<7 h); sustained inactivity bouts during daytime (SIBD); and sleep efficiency. Imaging measures were white matter microstructure, subcortical volumes, cortical thickness and surface area of 24 cortical regions of interest. Individuals with lifetime depression (self-reported, mental health questionnaire or health records) were contrasted with healthy controls. Interactions between depression and difficulty getting up for i) right nucleus accumbens volume and ii) mean diffusivity of forceps minor, reflected a larger negative association of poor sleep in the presence vs. absence of depression. Depression was associated with widespread reductions in white matter integrity. Depression, higher SIBD and difficulty getting up were individually associated with smaller cortical volumes and surface area, particularly in the frontal and parietal lobes. Many regions showed age-related decline, but this was not exacerbated by either depression or sleep disturbance. Overall, we identified widespread cross-sectional associations of both lifetime depression and sleep measures with brain structure. Findings were more consistent with additive rather than synergistic effects - although in some regions we observed greater magnitude of deleterious associations from poor sleep phenotypes in the presence of depression.

Snoring and risk of dementia: a prospective cohort and Mendelian randomization study

STUDY OBJECTIVES

The association between snoring, a very common condition that increases with age, and dementia risk is controversial. We aimed to investigate the observational and causal relationship between snoring and dementia, and to elucidate the role of body mass index (BMI).

METHODS

Using data from 451 250 participants who were dementia-free at baseline, we examined the association between self-reported snoring and incident dementia using Cox proportional-hazards models. Causal relationship between snoring and Alzheimer's disease (AD) was examined using bidirectional two-sample Mendelian randomization (MR) analysis.

RESULTS

During a median follow-up of 13.6 years, 8325 individuals developed dementia. Snoring was associated with a lower risk of all-cause dementia (hazard ratio [HR] 0.93; 95% confidence interval [CI] 0.89 to 0.98) and AD (HR 0.91; 95% CI 0.84 to 0.97). The association was slightly attenuated after adjusting for BMI, and was stronger in older individuals, APOE ε4 allele carriers, and during shorter follow-up periods. MR analyses suggested no causal effect of snoring on AD; however, genetic liability to AD was associated with a lower risk of snoring. Multivariable MR indicated that the effect of AD on snoring was primarily driven by BMI.

CONCLUSIONS

The phenotypic association between snoring and lower dementia risk likely stems from reverse causation, with genetic predisposition to AD associated with reduced snoring. This may be driven by weight loss in prodromal AD. Increased attention should be paid to reduced snoring and weight loss in older adults as potential early indicators of dementia risk.

Pain can't be carved at the joints: defining function-based pain profiles and their relevance to chronic disease management in healthcare delivery design

BACKGROUND

Pain is a complex problem that is triaged, diagnosed, treated, and billed based on which body part is painful, almost without exception. While the "body part framework" guides the organization and treatment of individual patients' pain conditions, it remains unclear how to best conceptualize, study, and treat pain conditions at the population level. Here, we investigate (1) how the body part framework agrees with population-level, biologically derived pain profiles; (2) how do data-derived pain profiles interface with other symptom domains from a whole-body perspective; and (3) whether biologically derived pain profiles capture clinically salient differences in medical history.

METHODS

To understand how pain conditions might be best organized, we applied a carefully designed a multi-variate pattern-learning approach to a subset of the UK Biobank (n = 34,337), the largest publicly available set of real-world pain experience data to define common population-level profiles. We performed a series of post hoc analyses to validate that each pain profile reflects real-world, clinically relevant differences in patient function by probing associations of each profile across 137 medication categories, 1425 clinician-assigned ICD codes, and 757 expert-curated phenotypes.

RESULTS

We report four unique, biologically based pain profiles that cut across medical specialties: pain interference, depression, medical pain, and anxiety, each representing different facets of functional impairment. Importantly, these profiles do not specifically align with variables believed to be important to the standard pain evaluation, namely painful body part, pain intensity, sex, or BMI. Correlations with individual-level clinical histories reveal that our pain profiles are largely associated with clinical variables and treatments of modifiable, chronic diseases, rather than with specific body parts. Across profiles, notable differences include opioids being associated only with the pain interference profile, while antidepressants linked to the three complimentary profiles. We further provide evidence that our pain profiles offer valuable, additional insights into patients' wellbeing that are not captured by the body-part framework and make recommendations for how our pain profiles might sculpt the future design of healthcare delivery systems.

CONCLUSION

Overall, we provide evidence for a shift in pain medicine delivery systems from the conventional, body-part-based approach to one anchored in the pain experience and holistic profiles of patient function. This transition facilitates a more comprehensive management of chronic diseases, wherein pain treatment is integrated into broader health strategies. By focusing on holistic patient profiles, our approach not only addresses pain symptoms but also supports the management of underlying chronic conditions, thereby enhancing patient outcomes and improving quality of life. This model advocates for a seamless integration of pain management within the continuum of care for chronic diseases, emphasizing the importance of understanding and treating the interdependencies between chronic conditions and pain.

Clinical Phenotypes Associated With Cerebral Small Vessel Disease: A Study of 45,013 UK Biobank Participants

BACKGROUND AND OBJECTIVES

Cerebral small vessel disease (cSVD) is the most common pathology underlying vascular cognitive impairment. Although other clinical features of cSVD are increasingly recognized, it is likely that certain symptoms are being overlooked. A comprehensive description of cSVD associations with clinical phenotypes at scale is lacking. The objective of this study was to conduct a large-scale, hypothesis-free study of associations between cSVD and clinical phenotypes in UK Biobank (UKB).

METHODS

We included participants from the UKB imaging study who had available information on total volume of white matter hyperintensities (WMHs), the most common cSVD neuroimaging feature. We included various UKB variables describing clinical phenotypes, defined as observable signs and symptoms of individuals with concurrent neuroimaging evidence of cSVD. We conducted a phenome scan using the open-source PHESANT software package. Total volume of WMHs was introduced as the independent variable and clinical phenotypes as the dependent variables in the regression model. The association of each phenotype with total volume of WMHs was tested using one of several regression analyses (all age at recruitment and sex-adjusted). All associations were corrected for multiple comparisons using the false discovery rate (FDR) correction method.

RESULTS

We included 45,013 participants in the analysis (mean age = 54.97 years, SD = 7.55). We confirm previously reported associations with depression (odds ratio [OR] = 1.07 [95% CI 1.05-1.10]), apathy (OR = 1.11 [95% CI 1.08-1.14]), falls (OR = 1.11 [95% CI 1.09-1.13]), respiratory problems (OR = 1.14 [95% CI 1.04-1.25]), and sleep disturbance (OR = 1.07 [95% CI 1.04-1.09], all FDR-adjusted p < 0.001). We further identified associations with all-cause dental issues (OR = 0.94 [95% CI 0.96-0.92]), hearing problems (OR = 1.06 [95% CI 1.03-1.08]), and eye problems (OR = 0.93 [95% CI 0.91-0.95], all FDR-adjusted p < 0.001).

DISCUSSION

Our findings suggest that presence of cSVD associates with concurrent clinical phenotypes across several body systems. We have corroborated established associations of cSVD and present novel ones. While our results do not provide causality or direction of association because of the cross-sectional nature of our study, they support the need for a more holistic view of cSVD in research, practice, and policy.

Deep Learning-Based Detection of Carotid Plaques Informs Cardiovascular Risk Prediction and Reveals Genetic Drivers of Atherosclerosis

Atherosclerotic cardiovascular disease, the leading cause of global mortality, is driven by lipid accumulation and plaque formation within arterial walls. Carotid plaques, detectable via ultrasound, are a well-established marker of subclinical atherosclerosis. In this study, we trained a deep learning model to detect plaques in 177,757 carotid ultrasound images from 19,499 UK Biobank (UKB) participants (aged 47-83 years) to assess the prevalence, risk factors, prognostic significance, and genetic architecture of carotid atherosclerosis in a large population-based cohort. The model demonstrated high performance metrics with accuracy, sensitivity, specificity, and positive predictive value of 89.3%, 89.5%, 89.2%, and 82.9%, respectively, identifying carotid plaques in 45% of the population. Plaque presence and count were significantly associated with future cardiovascular events over a median follow-up period of up to 7 years, leading to improved risk reclassification beyond established clinical prediction models. A genome-wide association study (GWAS) meta-analysis of carotid plaques (29,790 cases, 36,847 controls) uncovered two novel genomic loci (p < 5×10-8) with downstream analyses implicating lipoprotein(a) and interleukin-6 signaling, both targets of investigational drugs in advanced clinical development. Observational and Mendelian randomization analyses showed associations between smoking, low-density-lipoprotein (LDL) cholesterol, and high blood pressure and the odds of carotid plaque presence. Our study underscores the potential of carotid plaque assessment for improving cardiovascular risk prediction, provides novel insights into the genetic basis of subclinical atherosclerosis, and offers a valuable resource for advancing atherosclerosis research at the population scale.

A history of traumatic brain injury is associated with poorer cognition and imaging evidence of altered white matter tract integrity in UK Biobank (n = 50 376)

Traumatic brain injury (TBI) is a risk factor for neurodegenerative disease. We currently have no means to identify patients most at risk of neurodegenerative disease following injury and, resultantly, no means to target risk mitigation interventions. To address this, we explored the association between history of traumatic brain injury with cognitive performance and imaging measures of white matter integrity. From the UK Biobank imaging sub-study (n = 50 376), participants were identified with either self-reported (n = 177) or health record coded broad- (injury codes; n = 1096) or narrow-band (TBI specific codes; n = 274) TBI, or as controls with no such documented history (n = 49 280). Cognitive scores and imaging measures of corpus callosum white matter integrity were compared between injury participants (versus no injury), corrected for age, sex, socioeconomic status and medications. TBI was associated with poorer cognitive and imaging phenotypes. The strongest deleterious associations were for narrow-band injury (β difference 0.2-0.3; P < 0.01). All cognitive and imaging phenotypes were strongly inter-correlated (P < 0.001). This study provides insight into possible early biomarkers predating neurodegenerative disease following brain injury. Measures of cognition and white matter following injury may provide means to identify individuals most at risk of neurodegenerative disease, to which mitigation strategies might be targeted.

Development and external validation of a head and neck cancer risk prediction model

BACKGROUND

Head and neck cancer (HNC) incidence is on the rise, often diagnosed at late stage and associated with poor prognoses. Risk prediction tools have a potential role in prevention and early detection.

METHODS

The IARC-ARCAGE European case-control study was used as the model development dataset. A clinical HNC risk prediction model using behavioral and demographic predictors was developed via multivariable logistic regression analyses. The model was then externally validated in the UK Biobank cohort. Model performance was tested using discrimination and calibration metrics.

RESULTS

1926 HNC cases and 2043 controls were used for the development of the model. The development dataset model including sociodemographic, smoking, and alcohol variables had moderate discrimination, with an area under curve (AUC) value of 0.75 (95% CI, 0.74-0.77); the calibration slope (0.75) and tests were suggestive of good calibration. 384 616 UK Biobank participants (with 1177 HNC cases) were available for external validation of the model. Upon external validation, the model had an AUC of 0.62 (95% CI, 0.61-0.64).

CONCLUSION

We developed and externally validated a HNC risk prediction model using the ARCAGE and UK Biobank studies, respectively. This model had moderate performance in the development population and acceptable performance in the validation dataset. Demographics and risk behaviors are strong predictors of HNC, and this model may be a helpful tool in primary dental care settings to promote prevention and determine recall intervals for dental examination. Future addition of HPV serology or genetic factors could further enhance individual risk prediction.

Association of Metabolic Syndrome With Neuroimaging and Cognitive Outcomes in the UK Biobank

OBJECTIVE

Metabolic syndrome (MetS) has been linked to dementia. In this study, we examined the association of MetS with neuroimaging and cognition in dementia-free adults, offering insight into the impact of MetS on brain health prior to dementia onset.

RESEARCH DESIGN AND METHODS

We included 37,395 dementia-free adults from the UK Biobank database. MetS was defined as having at least three of the following components: larger waist circumference; elevated levels of triglycerides, blood pressure, HbA1c; or reduced HDL cholesterol levels. Multivariable-adjusted linear regression was used to assess associations of MetS with structural neuroimaging and cognitive domains.

RESULTS

MetS was associated with lower total brain (standardized β: -0.06; 95% CI -0.08, -0.04), gray matter (β: -0.10; 95% CI -0.12, -0.08) and hippocampal (for left side, β: -0.03, 95% CI -0.05, -0.01; for right side, β: -0.04, 95% CI -0.07, -0.02) volumes, and greater white matter hyperintensity (WMH) volume (β: 0.08; 95% CI 0.06, 0.11). Study participants with MetS performed poorer on cognitive tests of working memory (β: -0.10; 95% CI -0.13, -0.07), verbal declarative memory (β: -0.08; 95% CI -0.11, -0.05), processing speed (β: -0.06; 95% CI -0.09, -0.04), verbal and numerical reasoning (β: -0.07; 95% CI -0.09, -0.04), nonverbal reasoning (β: -0.03; 95% CI -0.05, -0.01), and on tests of executive function, where higher scores indicated poorer performance (β: 0.05; 95% CI 0.03, 0.08). More MetS components were also associated with less brain volume, greater WMH, and poorer cognition across all domains.

CONCLUSIONS

MetS was associated poorer brain health in dementia-free adults, characterized by less brain volume, greater vascular pathology, and poorer cognition. Further research is necessary to understand whether reversal or improvement of MetS can improve brain health.

Association of neuroticism with incident dementia, neuroimaging outcomes, and cognitive function

INTRODUCTION

Higher neuroticism might be associated with dementia risk. Here we investigated modification by genetic predisposition to dementia, mediation by mental health and vascular conditions, neuroimaging outcomes, and cognitive function.

METHODS

Cox proportional-hazards models were used to assess the association between neuroticism score and incident dementia over up to 15 years in 1,74,164 participants. Cross-sectional analyses on dementia-related neuroimaging outcomes and cognitive function were conducted in 39,459 dementia-free participants.

RESULTS

Higher neuroticism was associated with an 11% higher risk of incident dementia, especially vascular dementia (15% higher risk), regardless of genetic predisposition to dementia. Mental and vascular conditions mediated the association of neuroticism with all-cause dementia and vascular dementia. Neuroticism was associated with higher cerebrovascular pathology, lower gray matter volume, and worse function across multiple cognitive domains.

DISCUSSION

Neuroticism could represent a risk factor for dementia, and vascular and mental health might drive these associations.

HIGHLIGHTS

Neuroticism was associated with an increased risk of incident all-cause dementia, particularly vascular dementia. Associations were not modified by genetic predisposition to dementia. Associations were largely mediated by mental and vascular conditions. Neuroticism was associated with increased cerebrovascular pathology and lower gray matter volume. Neuroticism was associated with worse function across multiple cognitive domains.

Head motion in the UK Biobank imaging subsample: longitudinal stability, associations with psychological and physical health, and risk of incomplete data

Participant motion in brain magnetic resonance imaging is associated with processing problems including potentially non-useable/incomplete data. This has implications for representativeness in research. Few large studies have investigated predictors of increased motion in the first instance. We exploratively tested for association between multiple psychological and physical health traits with concurrent motion during T1 structural, diffusion, average resting-state and task functional magnetic resonance imaging in N = 52 951 UK Biobank imaging subsample participants. These traits included history of cardiometabolic, inflammatory, neurological and psychiatric conditions, as well as concurrent cognitive test scores and anthropometric traits. We tested for stability in motion in participants with longitudinal imaging data (n = 5305, average 2.64 years later). All functional and T1 structural motion variables were significantly intercorrelated (Pearson r range 0.3-0.8, all P < 0.001). Diffusion motion variables showed weaker correlations around r = 0.1. Most physical and psychological phenotypes showed significant association with at least one measure of increased motion including specifically in participants with complete useable data (highest β = 0.66 for diabetes versus resting-state functional magnetic resonance imaging motion). Poorer values in most health traits predicted lower odds of complete imaging data, with the largest association for history of traumatic brain injury (odds ratio = 0.720, 95% confidence interval = 0.562 to 0.923, P = 0.009). Worse psychological and physical health are consistent predictors of increased average functional and structural motion during brain imaging and associated with lower odds of complete data. Average motion levels were largely consistent across modalities and longitudinally in participants with repeat data. Together, these findings have implications for representativeness and bias in imaging studies of generally healthy population samples.

Distinct Longitudinal Brain White Matter Microstructure Changes and Associated Polygenic Risk of Common Psychiatric Disorders and Alzheimer's Disease in the UK Biobank

Background

During the course of adulthood and aging, white matter (WM) structure and organization are characterized by slow degradation processes such as demyelination and shrinkage. An acceleration of such aging processes has been linked to the development of a range of diseases. Thus, an accurate description of healthy brain maturation, particularly in terms of WM features, is fundamental to the understanding of aging.

Methods

We used longitudinal diffusion magnetic resonance imaging to provide an overview of WM changes at different spatial and temporal scales in the UK Biobank (UKB) (n = 2678; agescan 1 = 62.38 ± 7.23 years; agescan 2 = 64.81 ± 7.1 years). To examine the genetic overlap between WM structure and common clinical conditions, we tested the associations between WM structure and polygenic risk scores for the most common neurodegenerative disorder, Alzheimer's disease, and common psychiatric disorders (unipolar and bipolar depression, anxiety, obsessive-compulsive disorder, autism, schizophrenia, attention-deficit/hyperactivity disorder) in longitudinal (n = 2329) and cross-sectional (n = 31,056) UKB validation data.

Results

Our findings indicate spatially distributed WM changes across the brain, as well as distributed associations of polygenic risk scores with WM. Importantly, brain longitudinal changes reflected genetic risk for disorder development better than the utilized cross-sectional measures, with regional differences giving more specific insights into gene-brain change associations than global averages.

Conclusions

We extend recent findings by providing a detailed overview of WM microstructure degeneration on different spatial levels, helping to understand fundamental brain aging processes. Further longitudinal research is warranted to examine aging-related gene-brain associations.

Characterizing the Causal Pathway From Childhood Adiposity to Right Heart Physiology and Pulmonary Circulation Using Lifecourse Mendelian Randomization

BACKGROUND

Observational epidemiological studies have reported an association between childhood adiposity and altered cardiac morphology and function in later life. However, whether this is due to a direct consequence of being overweight during childhood has been difficult to establish, particularly as accounting for other measures of body composition throughout the lifecourse can be exceptionally challenging.

METHODS AND RESULTS

In this study, we used human genetics to investigate this using a causal inference technique known as lifecourse Mendelian randomization. This approach allowed us to evaluate the effect of childhood body size on 11 measures of right heart and pulmonary circulation independent of other anthropometric traits at various stages in the lifecourse. We found strong evidence that childhood body size has a direct effect on an enlarged right heart structure in later life (eg, right ventricular end-diastolic volume: β=0.24 [95% CI, 0.15-0.33]; P=3×10-7) independent of adulthood body size. In contrast, childhood body size effects on maximum ascending aorta diameter attenuated upon accounting for body size in adulthood, suggesting that this effect is likely attributed to individuals remaining overweight into later life. Effects of childhood body size on pulmonary artery traits and measures of right atrial function became weaker upon accounting for adulthood fat-free mass and childhood height, respectively.

CONCLUSIONS

Our findings suggest that, although childhood body size has a long-term influence on an enlarged heart structure in adulthood, associations with the other structural components of the cardiovascular system and their function may be largely attributed to body composition at other stages in the lifecourse.

Clonal haematopoiesis of indeterminate potential predicts incident cardiac arrhythmias

BACKGROUND AND AIMS

Clonal haematopoiesis of indeterminate potential (CHIP), the age-related expansion of blood cells with preleukemic mutations, is associated with atherosclerotic cardiovascular disease and heart failure. This study aimed to test the association of CHIP with new-onset arrhythmias.

METHODS

UK Biobank participants without prevalent arrhythmias were included. Co-primary study outcomes were supraventricular arrhythmias, bradyarrhythmias, and ventricular arrhythmias. Secondary outcomes were cardiac arrest, atrial fibrillation, and any arrhythmia. Associations of any CHIP [variant allele fraction (VAF) ≥ 2%], large CHIP (VAF ≥10%), and gene-specific CHIP subtypes with incident arrhythmias were evaluated using multivariable-adjusted Cox regression. Associations of CHIP with myocardial interstitial fibrosis [T1 measured using cardiac magnetic resonance (CMR)] were also tested.

RESULTS

This study included 410 702 participants [CHIP: n = 13 892 (3.4%); large CHIP: n = 9191 (2.2%)]. Any and large CHIP were associated with multi-variable-adjusted hazard ratios of 1.11 [95% confidence interval (CI) 1.04-1.18; P = .001] and 1.13 (95% CI 1.05-1.22; P = .001) for supraventricular arrhythmias, 1.09 (95% CI 1.01-1.19; P = .031) and 1.13 (95% CI 1.03-1.25; P = .011) for bradyarrhythmias, and 1.16 (95% CI, 1.00-1.34; P = .049) and 1.22 (95% CI 1.03-1.45; P = .021) for ventricular arrhythmias, respectively. Associations were independent of coronary artery disease and heart failure. Associations were also heterogeneous across arrhythmia subtypes and strongest for cardiac arrest. Gene-specific analyses revealed an increased risk of arrhythmias across driver genes other than DNMT3A. Large CHIP was associated with 1.31-fold odds (95% CI 1.07-1.59; P = .009) of being in the top quintile of myocardial fibrosis by CMR.

CONCLUSIONS

CHIP may represent a novel risk factor for incident arrhythmias, indicating a potential target for modulation towards arrhythmia prevention and treatment.

Linking sarcopenia, brain structure and cognitive performance: a large-scale UK Biobank study

Sarcopenia refers to age-related loss of muscle mass and function and is related to impaired somatic and brain health, including cognitive decline and Alzheimer's disease. However, the relationships between sarcopenia, brain structure and cognition are poorly understood. Here, we investigate the associations between sarcopenic traits, brain structure and cognitive performance. We included 33 709 UK Biobank participants (54.2% female; age range 44-82 years) with structural and diffusion magnetic resonance imaging, thigh muscle fat infiltration (n = 30 561) from whole-body magnetic resonance imaging (muscle quality indicator) and general cognitive performance as indicated by the first principal component of a principal component analysis across multiple cognitive tests (n = 22 530). Of these, 1703 participants qualified for probable sarcopenia based on low handgrip strength, and we assigned the remaining 32 006 participants to the non-sarcopenia group. We used multiple linear regression to test how sarcopenic traits (probable sarcopenia versus non-sarcopenia and percentage of thigh muscle fat infiltration) relate to cognitive performance and brain structure (cortical thickness and area, white matter fractional anisotropy and deep and lower brain volumes). Next, we used structural equation modelling to test whether brain structure mediated the association between sarcopenic and cognitive traits. We adjusted all statistical analyses for confounders. We show that sarcopenic traits (probable sarcopenia versus non-sarcopenia and muscle fat infiltration) are significantly associated with lower cognitive performance and various brain magnetic resonance imaging measures. In probable sarcopenia, for the included brain regions, we observed widespread significant lower white matter fractional anisotropy (77.1% of tracts), predominantly lower regional brain volumes (61.3% of volumes) and thinner cortical thickness (37.9% of parcellations), with |r| effect sizes in (0.02, 0.06) and P-values in (0.0002, 4.2e-29). In contrast, we observed significant associations between higher muscle fat infiltration and widespread thinner cortical thickness (76.5% of parcellations), lower white matter fractional anisotropy (62.5% of tracts) and predominantly lower brain volumes (35.5% of volumes), with |r| effect sizes in (0.02, 0.07) and P-values in (0.0002, 1.9e-31). The regions showing the most significant effect sizes across the cortex, white matter and volumes were of the sensorimotor system. Structural equation modelling analysis revealed that sensorimotor brain regions mediate the link between sarcopenic and cognitive traits [probable sarcopenia: P-values in (0.0001, 1.0e-11); muscle fat infiltration: P-values in (7.7e-05, 1.7e-12)]. Our findings show significant associations between sarcopenic traits, brain structure and cognitive performance in a middle-aged and older adult population. Mediation analyses suggest that regional brain structure mediates the association between sarcopenic and cognitive traits, with potential implications for dementia development and prevention.

Common infections and neuroimaging markers of dementia in three UK cohort studies

INTRODUCTION

We aimed to investigate associations between common infections and neuroimaging markers of dementia risk (brain volume, hippocampal volume, white matter lesions) across three population-based studies.

METHODS

We tested associations between serology measures (pathogen serostatus, cumulative burden, continuous antibody responses) and outcomes using linear regression, including adjustments for total intracranial volume and scanner/clinic information (basic model), age, sex, ethnicity, education, socioeconomic position, alcohol, body mass index, and smoking (fully adjusted model). Interactions between serology measures and apolipoprotein E (APOE) genotype were tested. Findings were meta-analyzed across cohorts (Nmain  = 2632; NAPOE-interaction  = 1810).

RESULTS

Seropositivity to John Cunningham virus associated with smaller brain volumes in basic models (β = -3.89 mL [-5.81, -1.97], Padjusted  < 0.05); these were largely attenuated in fully adjusted models (β = -1.59 mL [-3.55, 0.36], P = 0.11). No other relationships were robust to multiple testing corrections and sensitivity analyses, but several suggestive associations were observed.

DISCUSSION

We did not find clear evidence for relationships between common infections and markers of dementia risk. Some suggestive findings warrant testing for replication.

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.

Peripheral vertigo and subsequent risk of depression and anxiety disorders: a prospective cohort study using the UK Biobank

BACKGROUND

Peripheral vertigo is often comorbid with psychiatric disorders. However, no longitudinal study has quantified the association between peripheral vertigo and risk of psychiatric disorders. Furthermore, it remains unknown how the white matter integrity of frontal-limbic network relates to the putative peripheral vertigo-psychiatric disorder link.

METHODS

We conducted a cohort study including 452,053 participants of the UK Biobank with a follow-up from 2006 through 2021. We assessed the risks of depression and anxiety disorders in relation to a hospitalization episode involving peripheral vertigo using Cox proportional hazards models. We also examined the associations of peripheral vertigo, depression, and anxiety with MRI fractional anisotropy (FA) in a subsample with brain MRI data (N = 36,087), using multivariable linear regression.

RESULTS

Individuals with an inpatient diagnosis of peripheral vertigo had elevated risks of incident depression (hazard ratio (HR) 2.18; 95% confidence interval (CI) 1.79-2.67) and anxiety (HR 2.11; 95% CI 1.71-2.61), compared to others, particularly within 2 years after hospitalization (HR for depression 2.91; 95% CI 2.04-4.15; HR for anxiety 4.92; 95% CI 3.62-6.69). Depression was associated with lower FA in most studied white matter regions, whereas anxiety and peripheral vertigo did not show statistically significant associations with FA.

CONCLUSIONS

Individuals with an inpatient diagnosis of peripheral vertigo have increased subsequent risks of depression and anxiety disorders, especially within 2 years after hospitalization. Our findings further indicate a link between depression and lower microstructural connectivity as well as integrity beyond the frontal-limbic network.

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.

Precision MRI phenotyping of muscle volume and quality at a population scale

Introduction: Magnetic resonance imaging (MRI) enables direct measurements of muscle volume and quality, allowing for an in-depth understanding of their associations with anthropometric traits, and health conditions. However, it is unclear which muscle volume measurements: total muscle volume, regional measurements, measurements of muscle quality: intermuscular adipose tissue (IMAT) or proton density fat fraction (PDFF), are most informative and associate with relevant health conditions such as dynapenia and frailty. Methods: We have measured image-derived phenotypes (IDPs) including total and regional muscle volumes and measures of muscle quality, derived from the neck-to-knee Dixon images in 44,520 UK Biobank participants. We further segmented paraspinal muscle from 2D quantitative MRI to quantify muscle PDFF and iron concentration. We defined dynapenia based on grip strength below sex-specific cut-off points and frailty based on five criteria (weight loss, exhaustion, grip strength, low physical activity and slow walking pace). We used logistic regression to investigate the association between muscle volume and quality measurements and dynapenia and frailty. Results: Muscle volumes were significantly higher in male compared with female participants, even after correcting for height while, IMAT (corrected for muscle volume) and paraspinal muscle PDFF were significantly higher in female compared with male participants. From the overall cohort, 7.6% (N = 3,261) were identified with dynapenia, and 1.1% (N = 455) with frailty. Dynapenia and frailty were positively associated with age and negatively associated with physical activity levels. Additionally, reduced muscle volume and quality measurements were associated with both dynapenia and frailty. In dynapenia, muscle volume IDPs were most informative, particularly total muscle exhibiting odds ratios (OR) of 0.392, while for frailty, muscle quality was found to be most informative, in particular thigh IMAT volume indexed to height squared (OR = 1.396), both with p-values below the Bonferroni-corrected threshold (p < 8.8 × 10 - 5 ). Conclusion: Our fully automated method enables the quantification of muscle volumes and quality suitable for large population-based studies. For dynapenia, muscle volumes particularly those including greater body coverage such as total muscle are the most informative, whilst, for frailty, markers of muscle quality were the most informative IDPs. These results suggest that different measurements may have varying diagnostic values for different health conditions.

Brain asymmetries from mid- to late life and hemispheric brain age

The human brain demonstrates structural and functional asymmetries which have implications for ageing and mental and neurological disease development. We used a set of magnetic resonance imaging (MRI) metrics derived from structural and diffusion MRI data in N=48,040 UK Biobank participants to evaluate age-related differences in brain asymmetry. Most regional grey and white matter metrics presented asymmetry, which were higher later in life. Informed by these results, we conducted hemispheric brain age (HBA) predictions from left/right multimodal MRI metrics. HBA was concordant to conventional brain age predictions, using metrics from both hemispheres, but offers a supplemental general marker of brain asymmetry when setting left/right HBA into relationship with each other. In contrast to WM brain asymmetries, left/right discrepancies in HBA are lower at higher ages. Our findings outline various sex-specific differences, particularly important for brain age estimates, and the value of further investigating the role of brain asymmetries in brain ageing and disease development.

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.

HLA-C*07:01 and HLA-DQB1*02:01 protect against white matter hyperintensities and deterioration of cognitive function: A population-based cohort study

BACKGROUND

Neuroinflammation and aberrant immune regulation are increasingly implicated in the pathophysiology of white matter hyperintensities (WMH), an imaging marker of cerebrovascular pathologies and predictor of cognitive impairment. The role of human leukocyte antigen (HLA) genes, critical in immunoregulation and associated with susceptibility to neurodegenerative diseases, in WMH pathophysiology remains unexplored.

METHODS

We performed association analyses between classical HLA alleles and WMH volume, derived from MRI scans of 38 302 participants in the UK Biobank. To identify independent functional alleles driving these associations, we conducted conditional forward stepwise regression and lasso regression. We further investigated whether these functional alleles showed consistent associations with WMH across subgroups characterized by varying levels of clinical determinants. Additionally, we validated the clinical relevance of the identified alleles by examining their association with cognitive function (n = 147 549) and dementia (n = 460 029) in a larger cohort.

FINDINGS

Four HLA alleles (DQB1*02:01, DRB1*03:01, C*07:01, and B*08:01) showed an association with reduced WMH volume after Bonferroni correction for multiple comparisons. Among these alleles, DQB1*02:01 exhibited the most significant association (β = -0.041, 95 % CI: -0.060 to -0.023, p = 1.04 × 10-5). Forward selection and lasso regression analyses indicated that DQB1*02:01 and C*07:01 primarily drove this association. The protective effect against WMH conferred by DQB1*02:01 and C*07:01 persisted in clinically relevant subgroups, with a stronger effect observed in older participants. Carrying DQB1*02:01 and C*07:01 was associated with higher cognitive function, but no association with dementia was found.

INTERPRETATION

Our population-based findings support the involvement of immune-associated mechanisms, particularly both HLA class I and class II genes, in the pathogenesis of WMH and subsequent consequence of cognitive functions.

Kidney shape statistical analysis: associations with disease and anthropometric factors

BACKGROUND

Organ measurements derived from magnetic resonance imaging (MRI) have the potential to enhance our understanding of the precise phenotypic variations underlying many clinical conditions.

METHODS

We applied morphometric methods to study the kidneys by constructing surface meshes from kidney segmentations from abdominal MRI data in 38,868 participants in the UK Biobank. Using mesh-based analysis techniques based on statistical parametric maps (SPMs), we were able to detect variations in specific regions of the kidney and associate those with anthropometric traits as well as disease states including chronic kidney disease (CKD), type-2 diabetes (T2D), and hypertension. Statistical shape analysis (SSA) based on principal component analysis was also used within the disease population and the principal component scores were used to assess the risk of disease events.

RESULTS

We show that CKD, T2D and hypertension were associated with kidney shape. Age was associated with kidney shape consistently across disease groups. Body mass index (BMI) and waist-to-hip ratio (WHR) were also associated with kidney shape for the participants with T2D. Using SSA, we were able to capture kidney shape variations, relative to size, angle, straightness, width, length, and thickness of the kidneys, within disease populations. We identified significant associations between both left and right kidney length and width and incidence of CKD (hazard ratio (HR): 0.74, 95% CI: 0.61-0.90, p < 0.05, in the left kidney; HR: 0.76, 95% CI: 0.63-0.92, p < 0.05, in the right kidney) and hypertension (HR: 1.16, 95% CI: 1.03-1.29, p < 0.05, in the left kidney; HR: 0.87, 95% CI: 0.79-0.96, p < 0.05, in the right kidney).

CONCLUSIONS

The results suggest that shape-based analysis of the kidneys can augment studies aiming at the better categorisation of pathologies associated with chronic kidney conditions.

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).

Severe and common mental disorders and risk of emergency hospital admissions for ambulatory care sensitive conditions among the UK Biobank cohort

BACKGROUND

People with mental disorders have worse physical health compared with the general population, which could be attributable to receiving poorer quality healthcare.

AIMS

To examine the relationship between severe and common mental disorders and risk of emergency hospital admissions for ambulatory care sensitive conditions (ACSCs), and factors associated with increased risk.

METHOD

Baseline data for England (N = 445 814) were taken from UK Biobank, which recruited participants aged 37-73 years during 2006-2010, and linked to hospital admission records up to 31 December 2019. Participants were grouped into those with a history of either schizophrenia, bipolar disorder, depression or anxiety, or no mental disorder. Survival analysis was used to assess the risk of hospital admission for ACSCs among those with mental disorders compared with those without, adjusting for factors in different domains (sociodemographic, socioeconomic, health and biomarkers, health-related behaviours, social isolation and psychological).

RESULTS

People with schizophrenia had the highest (unadjusted) risk of hospital admission for ACSCs compared with those with no mental disorder (hazard ratio 4.40, 95% CI 4.04-4.80). People with bipolar disorder (hazard ratio 2.48, 95% CI 2.28-2.69) and depression or anxiety (hazard ratio 1.76, 95% CI 1.73-1.80) also had higher risk. Associations were more conservative when including all admissions, as opposed to first admissions only. The observed associations persisted after adjusting for a range of factors.

CONCLUSIONS

People with severe mental disorders have the highest risk of preventable hospital admissions. Ensuring people with mental disorders receive adequate ambulatory care is essential to reduce the large health inequalities they experience.

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 [β] = -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 β = -0.026, P < .001), whole brain volume (β = -0.072, P = .0113) and a general factor of frontal lobe GM (β = -0.022, P < .001) in partially and fully adjusted models. HbA1c-PRS were significantly associated with GM volume in the fully adjusted model (β = -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.

Estimating the effect of physical activity on cognitive function within the UK Biobank cohort

BACKGROUND

Physical activity (PA) has been associated with benefits for cognitive function (CF), but previous estimates of the strength of this relationship may have been biased due to limitations in statistical modelling practices that are common among observational studies. We aimed to address this by using a rigorously constructed conceptual causal model to guide an empirical analysis estimating the effect of PA on CF in the UK Biobank cohort of middle-aged and older adults.

METHODS

This study analysed a subsample of 334 227 adults from the UK Biobank prospective cohort study. PA was measured subjectively by self-report and by device using accelerometry, and CF was measured using objective cognitive tests. Composite CF measures were derived to represent general and domain-specific performance. Effect coefficients were estimated using regression models, adjusting for a wide range of confounders specified by the assumed causal model, including genetic risk factors, and relevant health, sociodemographic and behavioural variables from across the lifespan.

RESULTS

Results indicated very small effect sizes (standardized mean difference estimates all <0.01) of inconsistent direction, for both cross-sectional and longitudinal analyses.

CONCLUSIONS

The expected protective effect of PA on CF was not observed. This may reflect selection bias within UK Biobank, or the relatively young age of the sample at follow-up.

A prognostic risk score for development and spread of chronic pain

Chronic pain is a complex condition influenced by a combination of biological, psychological and social factors. Using data from the UK Biobank (n = 493,211), we showed that pain spreads from proximal to distal sites and developed a biopsychosocial model that predicted the number of coexisting pain sites. This data-driven model was used to identify a risk score that classified various chronic pain conditions (area under the curve (AUC) 0.70-0.88) and pain-related medical conditions (AUC 0.67-0.86). In longitudinal analyses, the risk score predicted the development of widespread chronic pain, the spreading of chronic pain across body sites and high-impact pain about 9 years later (AUC 0.68-0.78). Key risk factors included sleeplessness, feeling 'fed-up', tiredness, stressful life events and a body mass index >30. A simplified version of this score, named the risk of pain spreading, obtained similar predictive performance based on six simple questions with binarized answers. The risk of pain spreading was then validated in the Northern Finland Birth Cohort (n = 5,525) and the PREVENT-AD cohort (n = 178), obtaining comparable predictive performance. Our findings show that chronic pain conditions can be predicted from a common set of biopsychosocial factors, which can aid in tailoring research protocols, optimizing patient randomization in clinical trials and improving pain management.

Brain-wide associations between white matter and age highlight the role of fornix microstructure in brain ageing

Unveiling the details of white matter (WM) maturation throughout ageing is a fundamental question for understanding the ageing brain. In an extensive comparison of brain age predictions and age-associations of WM features from different diffusion approaches, we analyzed UK Biobank diffusion magnetic resonance imaging (dMRI) data across midlife and older age (N = 35,749, 44.6-82.8 years of age). Conventional and advanced dMRI approaches were consistent in predicting brain age. WM-age associations indicate a steady microstructure degeneration with increasing age from midlife to older ages. Brain age was estimated best when combining diffusion approaches, showing different aspects of WM contributing to brain age. Fornix was found as the central region for brain age predictions across diffusion approaches in complement to forceps minor as another important region. These regions exhibited a general pattern of positive associations with age for intra axonal water fractions, axial, radial diffusivities, and negative relationships with age for mean diffusivities, fractional anisotropy, kurtosis. We encourage the application of multiple dMRI approaches for detailed insights into WM, and the further investigation of fornix and forceps as potential biomarkers of brain age and ageing.

Anticholinergic burden in middle and older age is associated with lower cognitive function, but not with brain atrophy

AIMS

The aim of this study is to estimate the association between anticholinergic burden, general cognitive ability and various measures of brain structural MRI in relatively healthy middle-aged and older individuals.

METHODS

In the UK Biobank participants with linked health-care records (n = 163,043, aged 40-71 at baseline), of whom about 17 000 had MRI data available, we calculated the total anticholinergic drug burden according to 15 different anticholinergic scales and due to different classes of drugs. We then used linear regression to explore the associations between anticholinergic burden and various measures of cognition and structural MRI, including general cognitive ability, 9 separate cognitive domains, brain atrophy, volumes of 68 cortical and 14 subcortical areas and fractional anisotropy and median diffusivity of 25 white-matter tracts.

RESULTS

Anticholinergic burden was modestly associated with poorer cognition across most anticholinergic scales and cognitive tests (7/9 FDR-adjusted significant associations, standardised betas (β) range: -0.039, -0.003). When using the anticholinergic scale exhibiting the strongest association with cognitive functions, anticholinergic burden due to only some classes of drugs exhibited negative associations with cognitive function, with β-lactam antibiotics (β = -0.035, PFDR < 0.001) and opioids (β = -0.026, PFDR < 0.001) exhibiting the strongest effects. Anticholinergic burden was not associated with any measure of brain macrostructure or microstructure (PFDR > 0.08).

CONCLUSIONS

Anticholinergic burden is weakly associated with poorer cognition, but there is little evidence for associations with brain structure. Future studies might focus more broadly on polypharmacy or more narrowly on distinct drug classes, instead of using purported anticholinergic action to study the effects of drugs on cognitive ability.

Shared genetic architecture between mental health and the brain functional connectome in the UK Biobank

Psychiatric disorders are complex clinical conditions with large heterogeneity and overlap in symptoms, genetic liability and brain imaging abnormalities. Building on a dimensional conceptualization of mental health, previous studies have reported genetic overlap between psychiatric disorders and population-level mental health, and between psychiatric disorders and brain functional connectivity. Here, in 30,701 participants aged 45-82 from the UK Biobank we map the genetic associations between self-reported mental health and resting-state fMRI-based measures of brain network function. Multivariate Omnibus Statistical Test revealed 10 genetic loci associated with population-level mental symptoms. Next, conjunctional FDR identified 23 shared genetic variants between these symptom profiles and fMRI-based brain network measures. Functional annotation implicated genes involved in brain structure and function, in particular related to synaptic processes such as axonal growth (e.g. NGFR and RHOA). These findings provide further genetic evidence of an association between brain function and mental health traits in the population.

Structural brain correlates of childhood trauma with replication across two large, independent community-based samples

INTRODUCTION

Childhood trauma and adversity are common across societies and have strong associations with physical and psychiatric morbidity throughout the life-course. One possible mechanism through which childhood trauma may predispose individuals to poor psychiatric outcomes is via associations with brain structure. This study aimed to elucidate the associations between childhood trauma and brain structure across two large, independent community cohorts.

METHODS

The two samples comprised (i) a subsample of Generation Scotland (n=1,024); and (ii) individuals from UK Biobank (n=27,202). This comprised n=28,226 for mega-analysis. MRI scans were processed using Free Surfer, providing cortical, subcortical, and global brain metrics. Regression models were used to determine associations between childhood trauma measures and brain metrics and psychiatric phenotypes.

RESULTS

Childhood trauma associated with lifetime depression across cohorts (OR 1.06 GS, 1.23 UKB), and related to early onset and recurrent course within both samples. There was evidence for associations between childhood trauma and structural brain metrics. This included reduced global brain volume, and reduced cortical surface area with highest effects in the frontal (β=-0.0385, SE=0.0048, p(FDR)=5.43x10-15) and parietal lobes (β=-0.0387, SE=0.005, p(FDR)=1.56x10-14). At a regional level the ventral diencephalon (VDc) displayed significant associations with childhood trauma measures across both cohorts and at mega-analysis (β=-0.0232, SE=0.0039, p(FDR)=2.91x10-8). There were also associations with reduced hippocampus, thalamus, and nucleus accumbens volumes.

DISCUSSION

Associations between childhood trauma and reduced global and regional brain volumes were found, across two independent UK cohorts, and at mega-analysis. This provides robust evidence for a lasting effect of childhood adversity on brain structure.

Brain volumes, thicknesses, and surface areas as mediators of genetic factors and childhood adversity on intelligence

Although genetic and environmental factors influence general intelligence (g-factor), few studies examined the neuroanatomical measures mediating environmental and genetic effects on intelligence. Here, we investigate the brain volumes, cortical mean thicknesses, and cortical surface areas mediating the effects of the g-factor polygenic score (gPGS) and childhood adversity on the g-factor in the UK Biobank. We first examined the global and regional brain measures that contribute to the g-factor. Most regions contributed to the g-factor through global brain size. Parieto-frontal integration theory (P-FIT) regions were not more associated with the g-factor than non-PFIT regions. After adjusting for global brain size and regional associations, only a few regions predicted intelligence and were included in the mediation analyses. We conducted mediation analyses on global measures, regional volumes, mean thicknesses, and surface areas, separately. Total brain volume mediated 7.04% of the gPGS' effect on the g-factor and 2.50% of childhood adversity's effect on the g-factor. In comparison, the fraction of the gPGS and childhood adversity's effects mediated by individual regional volumes, surfaces, and mean thicknesses was 10-15 times smaller. Therefore, genetic and environmental effects on intelligence may be mediated to a larger extent by other brain properties.

Neurodegenerative disease risk among former international rugby union players

BACKGROUND

Autopsy studies of former contact sports athletes, including soccer and rugby players, frequently report chronic traumatic encephalopathy, a neurodegenerative pathology associated with traumatic brain injury. Nevertheless, little is known about the risk of neurodegenerative disease in these populations. We hypothesised that neurodegenerative disease risk would be higher among former elite rugby union players than the general population.

METHODS

We conducted a retrospective cohort study accessing national electronic records on death certification, hospital admissions and dispensed prescriptions for a cohort of 412 male Scottish former international rugby union players and 1236 members of the general population, matched to former players by age, sex and area socioeconomic status. Mortality and incident neurodegenerative disease diagnoses among former rugby players were then compared with the matched comparison group.

RESULTS

Over a median 32 years follow-up from study entry at age 30 years, 121 (29.4%) former rugby players and 381 (30.8%) of the matched comparison group died. All-cause mortality was lower among former rugby players until 70 years of age with no difference thereafter. During follow-up, 47 (11.4%) former rugby players and 67 (5.4%) of the comparison group were diagnosed with incident neurodegenerative disease (HR 2.67, 95% CI 1.67 to 4.27, p<0.001).

CONCLUSIONS

This study adds to our understanding of the association between contact sports participation and the risk of neurodegenerative disease. While further research exploring this interaction is required, in the meantime strategies to reduce exposure to head impacts and head injuries in sport should be promoted.

Causal inference on neuroimaging data with Mendelian randomisation

While population-scale neuroimaging studies offer the promise of discovery and characterisation of subtle risk factors, massive sample sizes increase the power for both meaningful associations and those attributable to confounds. This motivates the need for causal modelling of observational data that goes beyond statements of association and towards deeper understanding of complex relationships between individual traits and phenotypes, clinical biomarkers, genetic variation, and brain-related measures of health. Mendelian randomisation (MR) presents a way to obtain causal inference on the basis of genetic data and explicit assumptions about the relationship between genetic variables, exposure and outcome. In this work, we provide an introduction to and overview of causal inference methods based on Mendelian randomisation, with examples involving imaging-derived phenotypes from UK Biobank to make these methods accessible to neuroimaging researchers. We motivate the use of MR techniques, lay out the underlying assumptions, introduce common MR methods and focus on several scenarios in which modelling assumptions are potentially violated, resulting in biased effect estimates. Importantly, we give a detailed account of necessary steps to increase the reliability of MR results with rigorous sensitivity analyses.

A lifecourse mendelian randomization study highlights the long-term influence of childhood body size on later life heart structure

Children with obesity typically have larger left ventricular heart dimensions during adulthood. However, whether this is due to a persistent effect of adiposity extending into adulthood is challenging to disentangle due to confounding factors throughout the lifecourse. We conducted a multivariable mendelian randomization (MR) study to separate the independent effects of childhood and adult body size on 4 magnetic resonance imaging (MRI) measures of heart structure and function in the UK Biobank (UKB) study. Strong evidence of a genetically predicted effect of childhood body size on all measures of adulthood heart structure was identified, which remained robust upon accounting for adult body size using a multivariable MR framework (e.g., left ventricular end-diastolic volume (LVEDV), Beta = 0.33, 95% confidence interval (CI) = 0.23 to 0.43, P = 4.6 × 10-10). Sensitivity analyses did not suggest that other lifecourse measures of body composition were responsible for these effects. Conversely, evidence of a genetically predicted effect of childhood body size on various other MRI-based measures, such as fat percentage in the liver (Beta = 0.14, 95% CI = 0.05 to 0.23, P = 0.002) and pancreas (Beta = 0.21, 95% CI = 0.10 to 0.33, P = 3.9 × 10-4), attenuated upon accounting for adult body size. Our findings suggest that childhood body size has a long-term (and potentially immutable) influence on heart structure in later life. In contrast, effects of childhood body size on other measures of adulthood organ size and fat percentage evaluated in this study are likely explained by the long-term consequence of remaining overweight throughout the lifecourse.