Causal relationships involving brain imaging-derived phenotypes based on UKB imaging cohort: a review of Mendelian randomization studies.

The UK Biobank (UKB) has the largest adult brain imaging dataset, which encompasses over 40,000 participants. A significant number of Mendelian randomization (MR) studies based on UKB neuroimaging data have been published to validate potential causal relationships identified in observational studies. Relevant articles published before December 2023 were identified following the PRISMA protocol. Included studies (n = 34) revealed that there were causal relationships between various lifestyles, diseases, biomarkers, and brain image-derived phenotypes (BIDPs). In terms of lifestyle habits and environmental factors, there were causal relationships between alcohol consumption, tea intake, coffee consumption, smoking, educational attainment, and certain BIDPs. Additionally, some BIDPs could serve as mediators between leisure/physical inactivity and major depressive disorder. Regarding diseases, BIDPs have been found to have causal relationships not only with Alzheimer's disease, stroke, psychiatric disorders, and migraine, but also with cardiovascular diseases, diabetes, poor oral health, osteoporosis, and ankle sprain. In addition, there were causal relationships between certain biological markers and BIDPs, such as blood pressure, LDL-C, IL-6, telomere length, and more.

Associations between frailty, depression and risk of hospitalisation for infection: A large prospective cohort study.

BACKGROUND: There is a considerable lack of epidemiological evidence on whether frailty, and frailty comorbid depression could increase the risk of infections in older adults. This study aimed to examine the prospective association between frailty, depression, and risk of infections. METHODS: A total of 308,892 eligible participants were included. Linked hospital admission records (HES) were used to identify a primary or secondary diagnosis of depression, and infection. Frailty was assessed by Fried frailty phenotype indicators. Cox proportional hazard model was conducted to examine the associated risk between frailty, depression, comorbid frailty and depression and risk of incident infections. Results were stratified by age and gender. RESULTS: During the follow-up, 74,749 (24.19 %) incident any infection cases were identified, the incidence density of any infection was 17.29/1000 person years. Frailty alone (HR = 1.38, 95 % CI: 1.33-1.43), depression alone (HR = 1.90, 95 % CI: 1.86-1.94), and comorbid frailty and depression (HR = 1.91, 95 % CI: 1.82-1.99) were associated with greater risks of any infections relative to participants with neither frailty nor depression. The associations between frailty alone, depression alone, comorbid frailty and depression, and any infections/most infection subtypes were significant for all age strata in both male and female. LIMITATIONS: Frailty phenotype was assessed through the adapted Fried criteria, based on a mix of self-reported and objective measurements. CONCLUSION: Frailty, depression, and comorbid frailty and depression were significantly associated with increased risk of incident infections.

The predictive validity of a Brain Care Score for dementia and stroke: data from the UK Biobank cohort.

Introduction: The 21-point Brain Care Score (BCS) was developed through a modified Delphi process in partnership with practitioners and patients to promote behavior changes and lifestyle choices in order to sustainably reduce the risk of dementia and stroke. We aimed to assess the associations of the BCS with risk of incident dementia and stroke. Methods: The BCS was derived from the United Kingdom Biobank (UKB) baseline evaluation for participants aged 40-69 years, recruited between 2006-2010. Associations of BCS and risk of subsequent incident dementia and stroke were estimated using Cox proportional hazard regressions, adjusted for sex assigned at birth and stratified by age groups at baseline. Results: The BCS (median: 12; IQR:11-14) was derived for 398,990 UKB participants (mean age: 57; females: 54%). There were 5,354 incident cases of dementia and 7,259 incident cases of stroke recorded during a median follow-up of 12.5 years. A five-point higher BCS at baseline was associated with a 59% (95%CI: 40-72%) lower risk of dementia among participants aged <50. Among those aged 50-59, the figure was 32% (95%CI: 20-42%) and 8% (95%CI: 2-14%) for those aged >59 years. A five-point higher BCS was associated with a 48% (95%CI: 39-56%) lower risk of stroke among participants aged <50, 52% (95%CI, 47-56%) among those aged 50-59, and 33% (95%CI, 29-37%) among those aged >59. Discussion: The BCS has clinically relevant and statistically significant associations with risk of dementia and stroke in approximately 0.4 million UK people. Future research includes investigating the feasibility, adaptability and implementation of the BCS for patients and providers worldwide.

Revealing polygenic pleiotropy using genetic risk scores for asthma.

In this study we examined how genetic risk for asthma associates with different features of the disease and with other medical conditions and traits. Using summary statistics from two multi-ancestry genome-wide association studies of asthma, we modeled polygenic risk scores (PRSs) and validated their predictive performance in the UK Biobank. We then performed phenome-wide association studies of the asthma PRSs with 371 heritable traits in the UK Biobank. We identified 228 total significant associations across a variety of organ systems, including associations that varied by PRS model, sex, age of asthma onset, ancestry, and human leukocyte antigen region alleles. Our results highlight pervasive pleiotropy between asthma and numerous other traits and conditions and elucidate pathways that contribute to asthma and its comorbidities.

Identifiability in Functional Connectivity May Unintentionally Inflate Prediction Results.

Functional magnetic resonance (fMRI) is an invaluable tool in studying cognitive processes in vivo. Many recent studies use functional connectivity (FC), partial correlation connectivity (PC), or fMRI-derived brain networks to predict phenotypes with results that sometimes cannot be replicated. At the same time, FC can be used to identify the same subject from different scans with great accuracy. In this paper, we show a method by which one can unknowingly inflate classification results from 61% accuracy to 86% accuracy by treating longitudinal or contemporaneous scans of the same subject as independent data points. Using the UK Biobank dataset, we find one can achieve the same level of variance explained with 50 training subjects by exploiting identifiability as with 10,000 training subjects without double-dipping. We replicate this effect in four different datasets: the UK Biobank (UKB), the Philadelphia Neurodevelopmental Cohort (PNC), the Bipolar and Schizophrenia Network for Intermediate Phenotypes (BSNIP), and an OpenNeuro Fibromyalgia dataset (Fibro). The unintentional improvement ranges between 7% and 25% in the four datasets. Additionally, we find that by using dynamic functional connectivity (dFC), one can apply this method even when one is limited to a single scan per subject. One major problem is that features such as ROIs or connectivities that are reported alongside inflated results may confuse future work. This article hopes to shed light on how even minor pipeline anomalies may lead to unexpectedly superb results.

Pancreatitis and Pancreatic Cancer Risk.

Purpose: The present retrospective study aimed to explore the relationship between pancreatitis and pancreatic cancer in the population cohort of the UK Biobank (UKB) (https://www.ukbiobank.ac.uk). Methods: From the 500 thousand population cohort of UKB, according to the age and gender of patients with pancreatic cancer 1:10, matching the control without pancreatic cancer, the binary Logistic regression model was used to analyze the relationship between pancreatitis and pancreatic cancer, and subgroup analyses were used to identify potential effect modifiers. Results: A total of 1538 patients with pancreatic cancer were compared with 15 380 controls. In the fully adjusted model, patients with pancreatitis had a significantly increased risk of pancreatic cancer compared with no pancreatitis. The risk of pancreatitis and pancreatic cancer increased with the age of pancreatitis, and the risk of pancreatic cancer was highest in the 61 to 70 age group. In addition, in the first 3 years of acute pancreatitis, the risk of pancreatic cancer increased significantly with the increase in the duration of the disease (odds ratio [OR] 29.13, 95% confidence interval [CI]: 16.34-51.93), after 3 years, the trend of increase decreased. After more than 10 years, there was no significant correlation between the risk of acute pancreatitis and pancreatic cancer. However, patients with chronic pancreatitis were significantly associated with an increased risk of pancreatic cancer only in the first 3 years (OR 28.14, 95% CI: 14.86-53.31). Conclusion: Pancreatitis may associate with an increased risk of pancreatic cancer. The older the age of pancreatitis, the higher the risk of pancreatic cancer. The risk of pancreatic cancer increases significantly in the first 3 years of the course of pancreatitis. This may provide an alternative strategy for the early identification of individuals at high risk of pancreatic cancer.

High producer variant of lipoprotein lipase may protect from hepatocellular carcinoma in alcohol-associated cirrhosis.

Background & Aims: Progression of alcohol-associated liver disease (ALD) is driven by genetic predisposition. The rs13702 variant in the lipoprotein lipase (LPL) gene is linked to non-alcoholic fatty liver disease. We aimed at clarifying its role in ALD. Methods: Patients with alcohol-associated cirrhosis, with (n = 385) and without hepatocellular carcinoma (HCC) (n = 656), with HCC attributable to viral hepatitis C (n = 280), controls with alcohol abuse without liver damage (n = 366), and healthy controls (n = 277) were genotyped regarding the LPL rs13702 polymorphism. Furthermore, the UK Biobank cohort was analysed. LPL expression was investigated in human liver specimens and in liver cell lines. Results: Frequency of the LPL rs13702 CC genotype was lower in ALD with HCC in comparison to ALD without HCC both in the initial (3.9% vs. 9.3%) and the validation cohort (4.7% vs. 9.5%; p <0.05 each) and compared with patients with viral HCC (11.4%), alcohol misuse without cirrhosis (8.7%), or healthy controls (9.0%). This protective effect (odds ratio [OR] = 0.5) was confirmed in multivariate analysis including age (OR = 1.1/year), male sex (OR = 3.0), diabetes (OR = 1.8), and carriage of the PNPLA3 I148M risk variant (OR = 2.0). In the UK Biobank cohort, the LPL rs13702 C allele was replicated as a risk factor for HCC. Liver expression of LPL mRNA was dependent on LPL rs13702 genotype and significantly higher in patients with ALD cirrhosis compared with controls and alcohol-associated HCC. Although hepatocyte cell lines showed negligible LPL protein expression, hepatic stellate cells and liver sinusoidal endothelial cells expressed LPL. Conclusions: LPL is upregulated in the liver of patients with alcohol-associated cirrhosis. The LPL rs13702 high producer variant confers protection against HCC in ALD, which might help to stratify people for HCC risk. Impact and implications: Hepatocellular carcinoma is a severe complication of liver cirrhosis influenced by genetic predisposition. We found that a genetic variant in the gene encoding lipoprotein lipase reduces the risk for hepatocellular carcinoma in alcohol-associated cirrhosis. This genetic variation may directly affect the liver, because, unlike in healthy adult liver, lipoprotein lipase is produced from liver cells in alcohol-associated cirrhosis.

Accelerated ageing is associated with increased COVID-19 severity and differences across ethnic groups may exist.

Background: While increased age is an established risk factor for COVID-19, there is great heterogeneity in outcomes within age groups. This is because chronological age does not reflect health, unlike biological age. We intend to investigate the association between accelerated ageing and COVID-19 outcomes through the lens of three measures, namely phenotypic age acceleration (PhenoAgeAccel), telomere length (Adjusted T/S Ratio) and facial ageing, and to examine whether there are differences across ethnic groups. Methods: Taking participants from the UK Biobank, we associated accelerated ageing with severe COVID-19 outcomes, defined as COVID-related hospitalisation or death. Separate logistic regressions models were created for age and the three accelerated ageing-related variables, adjusting for a variety of covariates in each model. Multivariable logistic regression models were also created within White, Black, Asian and Other ethnic groups to assess for potential differing associations. Forward likelihood ratio logistic regression models were created to evaluate importance of the variables and to assess for patterns of association across the total population and ethnic groups. Results: After adjusting for all covariates, the odds ratio (OR) and 95% confidence interval (95% CI) of COVID-19 severe outcomes for age was 1.080 (1.074-1.086). After further adjusting age for the accelerated ageing variables, the ORs were 1.029 (1.020-1.039) for PhenoAgeAccel and 0.847 (0.772-0.929) for Facial Ageing's "Younger Than You Are" while Adjusted T/S ratio and "Older Than You Are" were statistically insignificant. The OR for age remained similar across ethnic groups. Both PhenoAgeAccel and younger facial ages in the White population and PhenoAgeAccel in the Black population had ORs of 1.031 (1.021-1.042), 0.853 (0.774-0.939), and 1.049 (1.001-1.100), respectively. Both Adjusted T/S Ratio and older facial ages showed statistical insignificance in all ethnicities. In forward logistic regression, age and PhenoAgeAccel were the age-related variables selected most frequently in all models. Interpretation: Accelerated ageing is associated with increased COVID-19 severity. The mechanisms at work here are likely immunosenescence and inflamaging. This association indicates that anti-ageing treatment may improve COVID-19 outcome. The results within ethnic groups and that of telomere length were inconclusive, but point to a need for future, more focused research on the topic.

Development of a novel dementia risk prediction model in the general population: A large, longitudinal, population-based machine-learning study.

Background: The existing dementia risk models are limited to known risk factors and traditional statistical methods. We aimed to employ machine learning (ML) to develop a novel dementia prediction model by leveraging a rich-phenotypic variable space of 366 features covering multiple domains of health-related data. Methods: In this longitudinal population-based cohort of the UK Biobank (UKB), 425,159 non-demented participants were enrolled from 22 recruitment centres across the UK between March 1, 2006 and October 31, 2010. We implemented a data-driven strategy to identify predictors from 366 candidate variables covering a comprehensive range of genetic and environmental factors and developed the ML model to predict incident dementia and Alzheimer's Disease (AD) within five, ten, and much longer years (median 11.9 [Interquartile range 11.2-12.5] years). Findings: During a follow-up of 5,023,337 person-years, 5287 and 2416 participants developed dementia and AD, respectively. A novel UKB dementia risk prediction (UKB-DRP) model comprising ten predictors including age, ApoE ε4, pairs matching time, leg fat percentage, number of medications taken, reaction time, peak expiratory flow, mother's age at death, long-standing illness, and mean corpuscular volume was established. Our prediction model was internally evaluated based on five-fold cross-validation on discrimination and calibration, and it was further compared with existing prediction scales. The UKB-DRP model can achieve high discriminative accuracy in dementia (AUC 0.848 ± 0.007) and even better in AD (AUC 0.862 ± 0.015). The model was well-calibrated (Hosmer-Lemeshow goodness-of-fit p-value = 0.92), and the predictive power was solid in different incidence time groups. More importantly, our model presented an apparent superiority over existing models like Cardiovascular Risk Factors, Aging, and Incidence of Dementia Risk Score (AUC 0.705 ± 0.008), the Dementia Risk Score (AUC 0.752 ± 0.007), and the Australian National University Alzheimer's Disease Risk Index (AUC 0.584 ± 0.017). The model was internally validated in the general population of European ancestry and White ethnicity; thus, further validation with independent datasets is necessary to confirm these findings. Interpretation: Our ML-based UKB-DRP model incorporated ten easily accessible predictors with solid predictive power for incident dementia and AD within five, ten, and much longer years, which can be used to identify individuals at high risk of dementia and AD in the general population. Funding: This study was funded by grants from the Science and Technology Innovation 2030 Major Projects (2022ZD0211600), National Key R&D Program of China (2018YFC1312904, 2019YFA070950), National Natural Science Foundation of China (282071201, 81971032, 82071997), Shanghai Municipal Science and Technology Major Project (2018SHZDZX01), Research Start-up Fund of Huashan Hospital (2022QD002), Excellence 2025 Talent Cultivation Program at Fudan University (3030277001), Shanghai Rising-Star Program (21QA1408700), Medical Engineering Fund of Fudan University (yg2021-013), and the 111 Project (No. B18015).

Association of ambient air pollution exposure with low birth weight.

Increasing evidence has shown that exposure to air pollution is linked to adverse birth outcomes, but the results are not consistent. This study was performed on a subset of participants from the UK Biobank between 2006 and 2010. The land use regression (LUR) model was constructed to calculate the concentrations of particulate matter (PM2.5, PM2.5-10 and PM10), nitrogen oxides (NOx), and nitrogen dioxide (NO2). Binary logistic/multivariate linear regression models were applied to explore the potential linear relationships between air pollution exposure and newborn low birth weight (LBW) or BW. The Cochran-Armitage trend test was used to explore the possible association between the air pollution level and LBW. A restricted cubic spline (RCS) transformation of exposure variables was applied to visualize the relation of air pollutants to BW. Exposure to air pollutants, especially PM2.5 and PM10, was positively associated with LBW, and the odds ratios (ORs) and 95% confidence intervals (CIs) for each 10-µg/m3 increase in PM2.5 and PM10 were 1.25 ([1.03, 1.51], P = 0.025) and 1.12 ([1.02, 1.24], P = 0.021), respectively. A negative correlation was observed between the BW and PM2.5 (-0.05 [-0.08, -0.02], P = 0.001), PM10 (-0.03 [-0.05, -0.02], P < 0.001), PM2.5-10 (-0.04 [-0.07, -0.01], P < 0.001) and NOx (0.00 [0.00, 0.00], P = 0.021). Additionally, the BW changed dramatically up to a specific point (PM2.5 for 10.74 µg/m3, Pnonlinearity = 0.004; PM10 for 16.06 µg/m3, Pnonlinearity = 0.004; NO2 for 25.58 µg/m3, Pnonlinearity <0.001; and NOx for 39.88 µg/m3, Pnonlinearity <0.001), subsequently becoming relatively stable. PM2.5 and PM10 exposure were positively associated with LBW, and a negative correlation was observed between PM2.5, PM2.5-10, PM10 and NOx and BW.

The Risk of Hearing Impairment From Ambient Air Pollution and the Moderating Effect of a Healthy Diet: Findings From the United Kingdom Biobank.

The link between hearing impairment and air pollution has not been established, and the moderating effect of a healthy diet has never been investigated before. The purpose of this study was to investigate the association between air pollution and hearing impairment in British adults aged 37-73 years, and whether the association was modified by a healthy diet. We performed a cross-sectional population-based study with 158,811 participants who provided data from United Kingdom Biobank. A multivariate logistic regression model was used to investigate the link between air pollution and hearing impairment. Subgroup and effect modification analyses were carried out according to healthy diet scores, gender, and age. In the fully adjusted model, we found that exposure to PM10, NOX, and NO2 was associated with hearing impairment [PM10: odds ratio (OR) = 1.15, 95% confidence interval (95% CI) 1.02-1.30, P = 0.023; NOX: OR = 1.02, 95% CI 1.00-1.03, P = 0.040; NO2: OR = 1.03, 95% CI 1.01-1.06, P = 0.044], while PM2.5 and PM2.5 absorbance did not show similar associations. We discovered an interactive effect of age and air pollution on hearing impairment, but a healthy diet did not. The findings suggested that exposure to PM10, NOX and NO2 was linked to hearing impairment in British adults, whereas PM2.5 and PM2.5 absorbance did not show similar associations. These may help researchers focus more on the impact of air pollution on hearing impairment and provide a basis for developing effective prevention strategies.

Adapting the UK Biobank Brain Imaging Protocol and Analysis Pipeline for the C-MORE Multi-Organ Study of COVID-19 Survivors.

SARS-CoV-2 infection has been shown to damage multiple organs, including the brain. Multiorgan MRI can provide further insight on the repercussions of COVID-19 on organ health but requires a balance between richness and quality of data acquisition and total scan duration. We adapted the UK Biobank brain MRI protocol to produce high-quality images while being suitable as part of a post-COVID-19 multiorgan MRI exam. The analysis pipeline, also adapted from UK Biobank, includes new imaging-derived phenotypes (IDPs) designed to assess the possible effects of COVID-19. A first application of the protocol and pipeline was performed in 51 COVID-19 patients post-hospital discharge and 25 controls participating in the Oxford C-MORE study. The protocol acquires high resolution T1, T2-FLAIR, diffusion weighted images, susceptibility weighted images, and arterial spin labelling data in 17 min. The automated imaging pipeline derives 1,575 IDPs, assessing brain anatomy (including olfactory bulb volume and intensity) and tissue perfusion, hyperintensities, diffusivity, and susceptibility. In the C-MORE data, IDPs related to atrophy, small vessel disease and olfactory bulbs were consistent with clinical radiology reports. Our exploratory analysis tentatively revealed some group differences between recovered COVID-19 patients and controls, across severity groups, but not across anosmia groups. Follow-up imaging in the C-MORE study is currently ongoing, and this protocol is now being used in other large-scale studies. The protocol, pipeline code and data are openly available and will further contribute to the understanding of the medium to long-term effects of COVID-19.

Higher maternal adiposity reduces offspring birthweight if associated with a metabolically favourable profile.

AIMS/HYPOTHESIS: Higher maternal BMI during pregnancy is associated with higher offspring birthweight, but it is not known whether this is solely the result of adverse metabolic consequences of higher maternal adiposity, such as maternal insulin resistance and fetal exposure to higher glucose levels, or whether there is any effect of raised adiposity through non-metabolic (e.g. mechanical) factors. We aimed to use genetic variants known to predispose to higher adiposity, coupled with a favourable metabolic profile, in a Mendelian randomisation (MR) study comparing the effect of maternal 'metabolically favourable adiposity' on offspring birthweight with the effect of maternal general adiposity (as indexed by BMI). METHODS: To test the causal effects of maternal metabolically favourable adiposity or general adiposity on offspring birthweight, we performed two-sample MR. We used variants identified in large, published genetic-association studies as being associated with either higher adiposity and a favourable metabolic profile, or higher BMI (n = 442,278 and n = 322,154 for metabolically favourable adiposity and BMI, respectively). We then extracted data on the metabolically favourable adiposity and BMI variants from a large, published genetic-association study of maternal genotype and offspring birthweight controlling for fetal genetic effects (n = 406,063 with maternal and/or fetal genotype effect estimates). We used several sensitivity analyses to test the reliability of the results. As secondary analyses, we used data from four cohorts (total n = 9323 mother-child pairs) to test the effects of maternal metabolically favourable adiposity or BMI on maternal gestational glucose, anthropometric components of birthweight and cord-blood biomarkers. RESULTS: Higher maternal adiposity with a favourable metabolic profile was associated with lower offspring birthweight (-94 [95% CI -150, -38] g per 1 SD [6.5%] higher maternal metabolically favourable adiposity, p = 0.001). By contrast, higher maternal BMI was associated with higher offspring birthweight (35 [95% CI 16, 53] g per 1 SD [4 kg/m2] higher maternal BMI, p = 0.0002). Sensitivity analyses were broadly consistent with the main results. There was evidence of outlier SNPs for both exposures; their removal slightly strengthened the metabolically favourable adiposity estimate and made no difference to the BMI estimate. Our secondary analyses found evidence to suggest that a higher maternal metabolically favourable adiposity decreases pregnancy fasting glucose levels while a higher maternal BMI increases them. The effects on neonatal anthropometric traits were consistent with the overall effect on birthweight but the smaller sample sizes for these analyses meant that the effects were imprecisely estimated. We also found evidence to suggest that higher maternal metabolically favourable adiposity decreases cord-blood leptin while higher maternal BMI increases it. CONCLUSIONS/INTERPRETATION: Our results show that higher adiposity in mothers does not necessarily lead to higher offspring birthweight. Higher maternal adiposity can lead to lower offspring birthweight if accompanied by a favourable metabolic profile. DATA AVAILABILITY: The data for the genome-wide association studies (GWAS) of BMI are available at https://portals.broadinstitute.org/collaboration/giant/index.php/GIANT_consortium_data_files . The data for the GWAS of body fat percentage are available at https://walker05.u.hpc.mssm.edu .