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Detectable clonal mosaicism and its relationship to aging and cancer.
In an analysis of 31,717 cancer cases and 26,136 cancer-free controls from 13 genome-wide association studies, we observed large chromosomal abnormalities in a subset of clones in DNA obtained from blood or buccal samples. We observed mosaic abnormalities, either aneuploidy or copy-neutral loss of heterozygosity, of >2 Mb in size in autosomes of 517 individuals (0.89%), with abnormal cell proportions of between 7% and 95%. In cancer-free individuals, frequency increased with age, from 0.23% under 50 years to 1.91% between 75 and 79 years (P = 4.8 × 10(-8)). Mosaic abnormalities were more frequent in individuals with solid tumors (0.97% versus 0.74% in cancer-free individuals; odds ratio (OR) = 1.25; P = 0.016), with stronger association with cases who had DNA collected before diagnosis or treatment (OR = 1.45; P = 0.0005). Detectable mosaicism was also more common in individuals for whom DNA was collected at least 1 year before diagnosis with leukemia compared to cancer-free individuals (OR = 35.4; P = 3.8 × 10(-11)). These findings underscore the time-dependent nature of somatic events in the etiology of cancer and potentially other late-onset diseases.
Association of multimorbidity and disease clusters with neuroimaging and cognitive outcomes in UK Biobank.
BackgroundThe 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.MethodsThis 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.ResultsA 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.ConclusionsMultimorbidity 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.
Association of Daily Steps with Incident Non-Alcoholic Fatty Liver Disease: Evidence from the UK Biobank Cohort.
PurposeLow physical activity has been shown to be associated with higher risk of non-alcoholic fatty liver disease (NAFLD). However, the strength and shape of this association are currently uncertain due to a reliance on self-reported physical activity measures. This report aims to investigate the relationship of median daily step count with NAFLD using accelerometer-derived step count from a large prospective cohort study.MethodsThe wrist-worn accelerometer sub-study of the UK Biobank (N = ~100,000) was used to characterise median daily step count over a seven-day period. NAFLD cases were ascertained via record linkage with hospital inpatient data and death registers or by using a measure of liver fat from imaging. Cox proportional hazards models were employed to assess the association between step count and NAFLD, adjusting for age, sociodemographic, and lifestyle factors. Mediation analyses were conducted.ResultsAmong 91,031 participants (709,440 person-years of follow-up), there were 762 incident NAFLD cases. Higher step count was log-linearly and inversely associated with risk of NAFLD. A 1000-step increase (representing 10 minutes of walking) was associated with a 12% (95% CI: 10%-14%) lower hazard of NAFLD. When using imaging to identify NAFLD, a 1,000-step increase was associated with a 6% (95% CI: 6%-7%) lower risk. There was evidence for mediation by adiposity, accounting for 39% of the observed association.ConclusionsDaily step count, a modifiable risk factor, is log-linearly and inversely associated with NAFLD. This association was only partially explained by adiposity. These findings from a large cohort study may have important implications for strategies to lower NAFLD risk.
Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index.
Obesity is globally prevalent and highly heritable, but its underlying genetic factors remain largely elusive. To identify genetic loci for obesity susceptibility, we examined associations between body mass index and ∼ 2.8 million SNPs in up to 123,865 individuals with targeted follow up of 42 SNPs in up to 125,931 additional individuals. We confirmed 14 known obesity susceptibility loci and identified 18 new loci associated with body mass index (P < 5 × 10⁻⁸), one of which includes a copy number variant near GPRC5B. Some loci (at MC4R, POMC, SH2B1 and BDNF) map near key hypothalamic regulators of energy balance, and one of these loci is near GIPR, an incretin receptor. Furthermore, genes in other newly associated loci may provide new insights into human body weight regulation.
Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis.
By combining genome-wide association data from 8,130 individuals with type 2 diabetes (T2D) and 38,987 controls of European descent and following up previously unidentified meta-analysis signals in a further 34,412 cases and 59,925 controls, we identified 12 new T2D association signals with combined P<5x10(-8). These include a second independent signal at the KCNQ1 locus; the first report, to our knowledge, of an X-chromosomal association (near DUSP9); and a further instance of overlap between loci implicated in monogenic and multifactorial forms of diabetes (at HNF1A). The identified loci affect both beta-cell function and insulin action, and, overall, T2D association signals show evidence of enrichment for genes involved in cell cycle regulation. We also show that a high proportion of T2D susceptibility loci harbor independent association signals influencing apparently unrelated complex traits.
Clinical Implications of Slope of GFR in Clinical Trials of CKD Progression
BackgroundSlope of the GFR is considered a validated surrogate endpoint for CKD trials. However, differing short-term and long-term treatment effects on GFR slope can create ambiguities concerning the appropriate period for evaluating slope, in part because current methods cannot separate the distinct contributions of the acute (before 3 months) and chronic (after 3 months) slopes for treatment effects on clinical endpoints (CEs).MethodsWe estimated treatment effects on the acute and chronic GFR slopes and on the established CE of kidney failure or serum creatinine doubling for 66 randomized treatment comparisons from previous CKD clinical trials. We used a novel Bayesian meta-regression framework to relate treatment effects on the established CE to both the acute and chronic slopes in a single multivariable model to determine the independent contributions of the acute and chronic slopes.ResultsTreatment effects on both the acute and chronic slopes independently predicted the treatment effect on the established CE with a high median R2 (95% credible interval) of 0.95 (0.79 to 1.00). For a fixed treatment effect on the chronic slope, each 1 ml/min per 1.73 m2 greater acute GFR decline for the treatment versus control increased the hazard ratio for the established CE by 11.4% (7.9%-15.0%), against the treatment. The optimal weights for the acute and chronic slopes were consistent with the 3-year total slope defined as the average slope extending from baseline to 3 years.ConclusionsTreatment effects on both the acute and chronic GFR slopes are independent determinants of the effects on the established CE, with variation in acute effects accounting for much of the observed variation in treatment effects on the CE across previous trials. Our results establish that acute effects affect the CE independently of treatment effects on the chronic slope and support the 3-year total slope as the primary slope-based outcome in randomized trials.
Stacking models of brain dynamics to improve prediction of subject traits in fMRI.
Beyond structural and time-averaged functional connectivity brain measures, modelling the way brain activity dynamically unfolds can add important information to our understanding and characterisation of individual cognitive traits. One approach to leveraging this information is to extract features from models of brain network dynamics to predict individual traits. However, these predictions are susceptible to variability due to factors such as variation in model estimation induced by the choice of hyperparameters. We suggest that, rather than merely being statistical noise, this variability may be useful in providing complementary information that can be leveraged to improve prediction accuracy. To leverage this variability, we propose the use of stacking, a prediction-driven approach for model selection. Specifically, we combine predictions developed from multiple hidden Markov models-a probabilistic generative model of network dynamics that identifies recurring patterns of brain activity-to demonstrate that stacking can slightly improve the accuracy and robustness of cognitive trait predictions. By comparing analysis from the Human Connectome Project and UK Biobank datasets, we show that stacking is relatively effective at improving prediction accuracy and robustness when there are enough subjects, and that the effectiveness of combining predictions from static and dynamic functional connectivity approaches depends on the length of scan per subject. We also show that the effectiveness of stacking predictions is driven by the accuracy and diversity in the underlying model estimations.
Pneumococcal Serotype Epidemiology
This book describes the development of the vaccines, their remarkable impact on respiratory infections, and the vaccines wider impact.
Dissecting metabolic dysfunction- and alcohol-associated liver disease (MetALD) using proteomic and metabolomic profiles.
BACKGROUND: & Aim, Metabolic dysfunction associated and alcohol associated liver disease (MetALD) is a poorly understood condition that bridges cardiometabolic and alcohol-related pathological characteristics. We aim to distinguish MetALD patients who share similar molecular signatures with alcohol-related liver disease (ALD) and those share signatures with metabolic dysfunction-associated steatotic liver disease (MASLD), and assess their prognostic risk for complications and mortality. METHODS: Our analysis involved 443,453 European participants from UK Biobank, including 34,147 with MetALD, 11,220 with ALD, and 124,034 with MASLD. We employed Elastic Net Regression to classify ALD and MASLD involving 249 plasma metabolites and/or 2,941 plasma proteins with various sensitivity analyses. We then used the selected concise model in MetALD patients to identify alcohol-predominant group (classified to ALD) and cardiometabolic-predominant group (classified to MASLD). Finally, we explored their 15-year risk of major outcomes (i.e., heart failure, myocardial infarction, stroke, cirrhosis, hepatocellular carcinoma and mortality) using Cox regression. RESULTS: The metabolome alone discriminated ALD from MASLD with an Area under the Curve (AUC) of 0.86, while the proteome alone achieved an AUC of 0.96. Adding age, sex, BMI, liver enzymes, or metabolome information did not enhance the AUC of the proteome model. A ten-protein model differentiated ALD and MASLD with an AUC of 0.93. This model identified that alcohol-predominant MetALD patients had significantly higher risks of mortality, and cirrhosis, along with elevated fibrosis scores and higher fibrosis stages, compared to cardiometabolic-predominant patients. CONCLUSIONS: This study emphasizes the importance of subtyping differentiation using proteome data for personalized treatment and improved prognostic outcomes in MetALD patients.
Neural correlates of cognitive ability and visuo-motor speed: Validation of IDoCT on UK Biobank Data
Automated online and App-based cognitive assessment tasks are becoming increasingly popular in large-scale cohorts and biobanks due to advantages in affordability, scalability, and repeatability. However, the summary scores that such tasks generate typically conflate the cognitive processes that are the intended focus of assessment with basic visuo-motor speeds, testing device latencies, and speed-accuracy tradeoffs. This lack of precision presents a fundamental limitation when studying brain-behaviour associations. Previously, we developed a novel modelling approach that leverages continuous performance recordings from large-cohort studies to achieve an iterative decomposition of cognitive tasks (IDoCT), which outputs data-driven estimates of cognitive abilities, and device and visuo-motor latencies, whilst recalibrating trial-difficulty scales. Here, we further validate the IDoCT approach with UK BioBank imaging data. First, we examine whether IDoCT can improve ability distributions and trial-difficulty scales from an adaptive picture-vocabulary task (PVT). Then, we confirm that the resultant visuo-motor and cognitive estimates associate more robustly with age and education than the original PVT scores. Finally, we conduct a multimodal brain-wide association study with free-text analysis to test whether the brain regions that predict the IDoCT estimates have the expected differential relationships with visuo-motor versus language and memory labels within the broader imaging literature. Our results support the view that the rich performance timecourses recorded during computerised cognitive assessments can be leveraged with modelling frameworks like IDoCT to provide estimates of human cognitive abilities that have superior distributions, re-test reliabilities, and brain-wide associations.
Human immunodeficiency virus (HIV) antibody avidity testing to identify recent infection in newly diagnosed HIV type 1 (HIV-1)-seropositive persons infected with diverse HIV-1 subtypes
A guanidine-based antibody avidity assay for the identification of recently acquired human immunodeficiency virus type 1 (HIV-1) infection was evaluated. The kinetics of maturation of antibody avidity were determined prospectively in 23 persons undergoing acute seroconversion followed for up to 1,075 days. Avidity indices (AI) of ≤0.75 and ≤0.80 reproducibly identified seroconversion within the previous 125 (95% confidence interval [CI], 85 to 164) and 142 (95% CI, 101 to 183) days, respectively. To validate the assay, 432 serum samples from newly diagnosed patients were tested by both the avidity assay and the detuned assay. Results Looijwere highly concordant (kappa value for agreement, 0.85). The avidity assay was subsequently used to screen 134 consecutive newly diagnosed patients, including 55/134 (41%) infected with non-B subtypes (A, C, D, G, CRF01, CRF02, CRF06, CRF13, and CRF16). In this cohort, 25/79 (32%) persons with the B subtype and 7/55 (13%) with non-B subtypes showed an AI of ≤0.75, and there were 16/25 (64%) and 3/7 (43%) persons, respectively, with a documented history of acute seroconversion illness within the predicted seroconversion interval. An AI of ≤0.75 was also observed for four patients (three with the B subtype and one with a non-B subtype) who presented with AIDS-defining conditions. In multivariate analysis, an AI of ≤0.75 was associated with younger age, higher HIV-1 plasma RNA load, and being born in the United Kingdom or Ireland rather than in Africa but not with gender, ethnicity, risk group, HIV-1 subtype, or CD4 counts. In conclusion, HIV antibody avidity testing provides a reliable method for identifying recently acquired HIV-1 infection. Results are affected by advanced disease and should therefore be interpreted in the context of other clinical parameters. Copyright © American Society for Microbiology. All Rights Reserved.
Graph Classification Gaussian Processes via Spectral Features
Graph classification aims to categorise graphs based on their structure and node attributes. In this work, we propose to tackle this task using tools from graph signal processing by deriving spectral features, which we then use to design two variants of Gaussian process models for graph classification. The first variant uses spectral features based on the distribution of energy of a node feature signal over the spectrum of the graph. We show that even such a simple approach, having no learned parameters, can yield competitive performance compared to strong neural network and graph kernel baselines. A second, more sophisticated variant is designed to capture multi-scale and localised patterns in the graph by learning spectral graph wavelet filters, obtaining improved performance on synthetic and real-world data sets. Finally, we show that both models produce well calibrated uncertainty estimates, enabling reliable decision making based on the model predictions.
Sotrovimab versus usual care in patients admitted to hospital with COVID-19: a randomised, controlled, open-label, platform trial (RECOVERY)
Background: Sotrovimab is a neutralising monoclonal antibody targeting the SARS-COV-2 spike protein that was evaluated in the RECOVERY trial, a randomised, controlled, open-label, platform trial testing treatments for COVID-19. Methods: Patients hospitalised with COVID-19 pneumonia from 107 UK hospitals were randomly allocated to either usual care alone or usual care plus a single 1g infusion of sotrovimab, using web-based unstratified randomisation. Participants were retrospectively categorised as ‘high-antigen’ (the prespecified primary analysis population) if baseline serum SARS-CoV-2 nucleocapsid antigen was above the median concentration, and otherwise as ‘low-antigen’. The primary outcome was 28-day mortality assessed by intention to treat. Recruitment closed on 31 March 2024 when funding ended. ISRCTN (50189673) and clinicaltrials.gov (NCT04381936). Findings: From 4 January 2022 to 19 March 2024, 1723 patients were recruited, 828 allocated sotrovimab and 895 allocated usual care. 720 (42%) were classified as high-antigen, 717 (42%) as low-antigen, and 286 (17%) had unknown antigen status. 1389 (81%) patients were vaccinated, 1179/1438 with known serostatus (82%) had anti-spike antibodies at randomisation, and almost all were infected with Omicron variants. Among high-antigen patients, 82/355 (23%) allocated sotrovimab versus 106/365 (29%) allocated usual care died within 28 days (rate ratio 0.75; 95% CI 0.56-0.99; p=0.046). In an analysis of all randomised patients (regardless of antigen status), 177/828 (21%) allocated sotrovimab versus 201/895 (22%) allocated usual care died within 28 days (rate ratio 0.95; 95% CI 0.77-1.16; p=0.60). Interpretation: In patients hospitalised with COVID-19, sotrovimab was associated with reduced mortality in the primary analysis population who had a high serum SARS-CoV-2 antigen concentration at baseline, but not in the overall population. Treatment options for hospitalised patients are limited, and mortality in those receiving current standard care was high. The emergence of high-level resistance to sotrovimab among subsequent SARS-CoV-2 variants limits its current usefulness, but these results indicate that targeted neutralising antibody therapy could potentially still benefit high-risk hospitalised patients in an era of widespread vaccination and Omicron infection.