Publications
198 results found
Vermeulen R, Bodinier B, Dagnino S, et al., 2024, A prospective study of smoking‐related white blood cell DNA methylation markers and risk of bladder cancer, European Journal of Epidemiology, ISSN: 0393-2990
Wada R, Peng F-J, Lin C-A, et al., 2024, Hair-derived exposome exploration of cardiometabolic health: piloting a Bayesian multi-trait variable selection approach, Environmental Science and Technology (Washington), Vol: 58, Pages: 5383-5393, ISSN: 0013-936X
Cardiometabolic health is complex and characterized by an ensemble of correlated and/or co-occurring conditions including obesity, dyslipidemia, hypertension, and diabetes mellitus. It is affected by social, lifestyle, and environmental factors, which in-turn exhibit complex correlation patterns. To account for the complexity of (i) exposure profiles and (ii) health outcomes, we propose to use a multitrait Bayesian variable selection approach and identify a sparse set of exposures jointly explanatory of the complex cardiometabolic health status. Using data from a subset (N = 941 participants) of the nutrition, environment, and cardiovascular health (NESCAV) study, we evaluated the link between measurements of the cumulative exposure to (N = 33) pollutants derived from hair and cardiometabolic health as proxied by up to nine measured traits. Our multitrait analysis showed increased statistical power, compared to single-trait analyses, to detect subtle contributions of exposures to a set of clinical phenotypes, while providing parsimonious results with improved interpretability. We identified six exposures that were jointly explanatory of cardiometabolic health as modeled by six complementary traits, of which, we identified strong associations between hexachlorobenzene and trifluralin exposure and adverse cardiometabolic health, including traits of obesity, dyslipidemia, and hypertension. This supports the use of this type of approach for the joint modeling, in an exposome context, of correlated exposures in relation to complex and multifaceted outcomes.
Hampshire A, Azor A, Atchison C, et al., 2024, Cognition and memory after Covid-19 in a large community sample, New England Journal of Medicine, Vol: 390, Pages: 806-818, ISSN: 0028-4793
BACKGROUND: Cognitive symptoms after coronavirus disease 2019 (Covid-19), the disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), are well-recognized. Whether objectively measurable cognitive deficits exist and how long they persist are unclear. METHODS: We invited 800,000 adults in a study in England to complete an online assessment of cognitive function. We estimated a global cognitive score across eight tasks. We hypothesized that participants with persistent symptoms (lasting ≥12 weeks) after infection onset would have objectively measurable global cognitive deficits and that impairments in executive functioning and memory would be observed in such participants, especially in those who reported recent poor memory or difficulty thinking or concentrating ("brain fog"). RESULTS: Of the 141,583 participants who started the online cognitive assessment, 112,964 completed it. In a multiple regression analysis, participants who had recovered from Covid-19 in whom symptoms had resolved in less than 4 weeks or at least 12 weeks had similar small deficits in global cognition as compared with those in the no-Covid-19 group, who had not been infected with SARS-CoV-2 or had unconfirmed infection (-0.23 SD [95% confidence interval {CI}, -0.33 to -0.13] and -0.24 SD [95% CI, -0.36 to -0.12], respectively); larger deficits as compared with the no-Covid-19 group were seen in participants with unresolved persistent symptoms (-0.42 SD; 95% CI, -0.53 to -0.31). Larger deficits were seen in participants who had SARS-CoV-2 infection during periods in which the original virus or the B.1.1.7 variant was predominant than in those infected with later variants (e.g., -0.17 SD for the B.1.1.7 variant vs. the B.1.1.529 variant; 95% CI, -0.20 to -0.13) and in participants who had been hospitalized than in those who had not been hospitalized (e.g., intensive care unit admission, -0.35 SD; 95% CI, -0.49 to -0.20). Results of the analyses were similar to
Neufcourt L, Castagné R, Wilsgaard T, et al., 2024, Educational patterning in biological health seven years apart: findings from the Tromsø Study, Psychoneuroendocrinology, Vol: 160, ISSN: 0306-4530
BACKGROUND: Social-to-biological processes is one set of mechanisms underlying the relationship between social position and health. However, very few studies have focused on the relationship between social factors and biology at multiple time points. This work investigates the relationship between education and the dynamic changes in a composite Biological Health Score (BHS) using two time points seven years apart in a Norwegian adult population. METHODS: We used data from individuals aged 30 years and above who participated in Tromsø6 (2007-2008) and Tromsø7 (2015-2016) (n = 8117). BHS was defined using ten biomarkers measured from blood samples and representing three physiological systems (cardiovascular, metabolic, inflammatory). The higher the BHS, the poorer the health status. FINDINGS: Linear regression models carried out on BHS revealed a strong educational gradient at two distinct time points but also over time. People with lower educational attainment were at higher risk of poor biological health at a given time point (βlow education Tromsø6=0.30 [95 %-CI=0.18-0.43] and βlow education Tromsø7=0.30 [95 %-CI=0.17-0.42]). They also presented higher longitudinal BHS compared to people with higher education (βlow education = 0.89 [95 %-CI=0.56-1.23]). Certain biomarkers related to the cardiovascular system and the metabolic system were strongly socially distributed, even after adjustment for sex, age, health behaviours and body mass index. CONCLUSION: This longitudinal analysis highlights that participants with lower education had their biological health deteriorated to a greater extent over time compared to people with higher education. Our findings provide added evidence of the biological embodiment of social position, particularly with respect to dynamic aspects for which little evidence exists.
Borges MC, Clayton GL, Freathy RM, et al., 2024, Integrating multiple lines of evidence to assess the effects of maternal BMI on pregnancy and perinatal outcomes., BMC Med, Vol: 22
BACKGROUND: Higher maternal pre-pregnancy body mass index (BMI) is associated with adverse pregnancy and perinatal outcomes. However, whether these associations are causal remains unclear. METHODS: We explored the relation of maternal pre-/early-pregnancy BMI with 20 pregnancy and perinatal outcomes by integrating evidence from three different approaches (i.e. multivariable regression, Mendelian randomisation, and paternal negative control analyses), including data from over 400,000 women. RESULTS: All three analytical approaches supported associations of higher maternal BMI with lower odds of maternal anaemia, delivering a small-for-gestational-age baby and initiating breastfeeding, but higher odds of hypertensive disorders of pregnancy, gestational hypertension, preeclampsia, gestational diabetes, pre-labour membrane rupture, induction of labour, caesarean section, large-for-gestational age, high birthweight, low Apgar score at 1 min, and neonatal intensive care unit admission. For example, higher maternal BMI was associated with higher risk of gestational hypertension in multivariable regression (OR = 1.67; 95% CI = 1.63, 1.70 per standard unit in BMI) and Mendelian randomisation (OR = 1.59; 95% CI = 1.38, 1.83), which was not seen for paternal BMI (OR = 1.01; 95% CI = 0.98, 1.04). Findings did not support a relation between maternal BMI and perinatal depression. For other outcomes, evidence was inconclusive due to inconsistencies across the applied approaches or substantial imprecision in effect estimates from Mendelian randomisation. CONCLUSIONS: Our findings support a causal role for maternal pre-/early-pregnancy BMI on 14 out of 20 adverse pregnancy and perinatal outcomes. Pre-conception interventions to support women maintaining a healthy BMI may reduce the burden of obstetric and neonatal complications. FUNDING: Medical Research Council, British Heart Foundation, Europe
Peng F-J, Lin C-A, Wada R, et al., 2024, Association of hair polychlorinated biphenyls and multiclass pesticides with obesity, diabetes, hypertension and dyslipidemia in NESCAV study., J Hazard Mater, Vol: 461
Obesity, diabetes, hypertension and dyslipidemia are well-established risk factors for cardiovascular diseases (CVDs), and have been associated with exposure to persistent organic pollutants. However, studies have been lacking as regards effects of non-persistent pesticides on CVD risk factors. Here, we investigated whether background chronic exposure to polychlorinated biphenyls (PCBs) and multiclass pesticides were associated with the prevalence of these CVD risk factors in 502 Belgian and 487 Luxembourgish adults aged 18-69 years from the Nutrition, environment and cardiovascular health (NESCAV) study 2007-2013. We used hair analysis to evaluate the chronic internal exposure to three PCBs, seven organochlorine pesticides (OCs) and 18 non-persistent pesticides. We found positive associations of obesity with hexachlorobenzene (HCB), β-hexachlorocyclohexane (β-HCH) and chlorpyrifos, diabetes with pentachlorophenol (PCP), fipronil and fipronil sulfone, hypertension with PCB180 and chlorpyrifos, and dyslipidemia with diflufenican and oxadiazon, among others. However, we also found some inverse associations, such as obesity with PCP, diabetes with γ-HCH, hypertension with diflufenican, and dyslipidemia with chlorpyrifos. These results add to the existing evidence that OC exposure may contribute to the development of CVDs. Additionally, the present study revealed associations between CVD risk factors and chronic environmental exposure to currently used pesticides such as organophosphorus and pyrethroid pesticides.
Asamoah K, Chung K, Bodinier B, et al., 2024, Proteomic signatures of eosinophilic and neutrophilic asthma from serum and sputum, EBioMedicine, Vol: 99, ISSN: 2352-3964
BackgroundEosinophilic and neutrophilic asthma defined by high levels of blood and sputum eosinophils and neutrophils exemplifies the inflammatory heterogeneity of asthma, particularly severe asthma. We analysed the serum and sputum proteome to identify biomarkers jointly associated with these different phenotypes.MethodsProteomic profiles (N = 1129 proteins) were assayed in sputum (n = 182) and serum (n = 574) from two cohorts (U-BIOPRED and ADEPT) of mild-moderate and severe asthma by SOMAscan. Using least absolute shrinkage and selection operator (LASSO)-penalised logistic regression in a stability selection framework, we sought sparse sets of proteins associated with either eosinophilic or neutrophilic asthma with and without adjustment for established clinical factors including oral corticosteroid use and forced expiratory volume.FindingsWe identified 13 serum proteins associated with eosinophilic asthma, including 7 (PAPP-A, TARC/CCL17, ALT/GPT, IgE, CCL28, CO8A1, and IL5-Rα) that were stably selected while adjusting for clinical factors yielding an AUC of 0.84 (95% CI: 0.83–0.84) compared to 0.62 (95% CI: 0.61–0.63) for clinical factors only. Sputum protein analysis selected only PAPP-A (AUC = 0.81 [95% CI: 0.80–0.81]). 12 serum proteins were associated with neutrophilic asthma, of which 5 (MMP-9, EDAR, GIIE/PLA2G2E, IL-1-R4/IL1RL1, and Elafin) complemented clinical factors increasing the AUC from 0.63 (95% CI: 0.58–0.67) for the model with clinical factors only to 0.89 (95% CI: 0.89–0.90). Our model did not select any sputum proteins associated with neutrophilic status.InterpretationTargeted serum proteomic profiles are a non-invasive and scalable approach for subtyping of neutrophilic and eosinophilic asthma and for future functional understanding of these phenotypes.FundingU-BIOPRED has received funding from the Innovative Medicines Initiative (IMI) Joint Undertaking under grant agreement no. 115010, resources of which a
Chung MK, House JS, Akhtari FS, et al., 2024, Decoding the exposome: data science methodologies and implications in exposome-wide association studies (ExWASs)., Exposome, Vol: 4
This paper explores the exposome concept and its role in elucidating the interplay between environmental exposures and human health. We introduce two key concepts critical for exposomics research. Firstly, we discuss the joint impact of genetics and environment on phenotypes, emphasizing the variance attributable to shared and nonshared environmental factors, underscoring the complexity of quantifying the exposome's influence on health outcomes. Secondly, we introduce the importance of advanced data-driven methods in large cohort studies for exposomic measurements. Here, we introduce the exposome-wide association study (ExWAS), an approach designed for systematic discovery of relationships between phenotypes and various exposures, identifying significant associations while controlling for multiple comparisons. We advocate for the standardized use of the term "exposome-wide association study, ExWAS," to facilitate clear communication and literature retrieval in this field. The paper aims to guide future health researchers in understanding and evaluating exposomic studies. Our discussion extends to emerging topics, such as FAIR Data Principles, biobanked healthcare datasets, and the functional exposome, outlining the future directions in exposomic research. This abstract provides a succinct overview of our comprehensive approach to understanding the complex dynamics of the exposome and its significant implications for human health.
Hedges M, Priestman M, Chadeau-Hyam M, et al., 2023, Characterising a mobile reference station (MoRS) to quantify personal exposure to air quality, Atmospheric Environment, Vol: 315, ISSN: 1352-2310
There is increasing clinical, epidemiological, and toxicological evidence linking exposure to air pollution with multiple health outcomes that lead to increased mortality and morbidity. Traditionally, fixed air quality monitors have been used to provide ambient air pollution measurements, but they have spatial and temporal limitations. Rapid advances in instrument miniaturisation have made novel sensing technologies more accessible but these are prone to high sensitivity and inaccuracies. To bridge the gap between fixed monitors and small sensors we have developed a Mobile Reference Station (MoRS) – a portable platform delivering high quality measurements of air pollutants using smaller, low power reference grade instruments at high time resolutions. MoRS enables the simultaneous measurement of a broad aerosol size distribution (10 nm–35 μm), gaseous pollutant concentrations (nitrogen dioxide (NO2) and ozone (O3)), environmental parameters (noise, relatively humidity (RH) and temperature) as well as collecting filter samples for laboratory analysis. The MoRS instrumentation is described and the major challenges in ensuring that high data quality standards are maintained are discussed. Laboratory and field tests were used to derive scaling factors for all the MoRSinstrumentation. Field testing of MoRS showed excellent intercomparability against reference instrumentation (R2 > 0.98) and good agreement with reference instruments in the ultrafine aerosol range, although there was an overestimation of fine particle aerosols. Measurements taken during example mainline train and London Underground (LU) journeys are displayed showing the value of the high-quality data derived from MoRS and how this can help to disentangle multiple confounding environmental pollutants and enrich epidemiological studies.
Alcolea J, Donat-Vargas C, Chrysovalantou Chatziioannou A, et al., 2023, Metabolomic signatures of exposure to nitrate and trihalomethanes in drinking water and colorectal cancer risk in a Spanish multicentric study (MCC-Spain), Environmental Science and Technology (Washington), Vol: 57, Pages: 19316-19329, ISSN: 0013-936X
We investigated the metabolomic profile associated with exposure to trihalomethanes (THMs)and nitrate in drinking water, and with colorectal cancer risk in 296 cases and 295 controlsfrom the Multi Case-Control Spain project. Untargeted metabolomic analysis was conductedin blood samples using ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry. A variety of univariate and multivariate association analyses wereconducted after data quality control, normalization and imputation. Linear regression andpartial least square analyses were conducted for chloroform, brominated THMs, total THMs,and nitrate among controls, and for case-control status, together with a N-integration modeldiscriminating colorectal cancer cases from controls through interrogation of correlationsbetween the exposure variables and the metabolomic features. Results revealed a total of 568metabolomic features associated with at least one water contaminant or colorectal cancer.Annotated metabolites and pathway analysis suggest a number of pathways as potentiallyinvolved in the link between exposure to these water contaminants and colorectal cancer,including nicotinamide, cytochrome P-450, and tyrosine metabolism. These findings provideinsights into the underlying biological mechanisms and potential biomarkers associated withwater contaminants exposure and colorectal cancer risk. Further research in this area isneeded to better understand the causal relationship and public health implications.
Bodinier B, Filippi S, Haugdahl Nost T, et al., 2023, Automated calibration for stability selection in penalised regression and graphical models, Journal of the Royal Statistical Society Series C: Applied Statistics, Vol: 72, Pages: 1375-1393, ISSN: 0035-9254
Stability selection represents an attractive approach to identify sparse sets of features jointly associated with an outcome in high-dimensional contexts. We introduce an automated calibration procedure via maximisation of an in-house stability score and accommodating a priori-known block structure (e.g. multi-OMIC) data. It applies to [Least Absolute Shrinkage Selection Operator (LASSO)] penalised regression and graphical models. Simulations show our approach outperforms non-stability-based and stability selection approaches using the original calibration. Application to multi-block graphical LASSO on real (epigenetic and transcriptomic) data from the Norwegian Women and Cancer study reveals a central/credible and novel cross-OMIC role of LRRN3 in the biological response to smoking. Proposed approaches were implemented in the R package sharp.
Atehortúa A, Gkontra P, Camacho M, et al., 2023, Cardiometabolic risk estimation using exposome data and machine learning., Int J Med Inform, Vol: 179
BACKGROUND: The human exposome encompasses all exposures that individuals encounter throughout their lifetime. It is now widely acknowledged that health outcomes are influenced not only by genetic factors but also by the interactions between these factors and various exposures. Consequently, the exposome has emerged as a significant contributor to the overall risk of developing major diseases, such as cardiovascular disease (CVD) and diabetes. Therefore, personalized early risk assessment based on exposome attributes might be a promising tool for identifying high-risk individuals and improving disease prevention. OBJECTIVE: Develop and evaluate a novel and fair machine learning (ML) model for CVD and type 2 diabetes (T2D) risk prediction based on a set of readily available exposome factors. We evaluated our model using internal and external validation groups from a multi-center cohort. To be considered fair, the model was required to demonstrate consistent performance across different sub-groups of the cohort. METHODS: From the UK Biobank, we identified 5,348 and 1,534 participants who within 13 years from the baseline visit were diagnosed with CVD and T2D, respectively. An equal number of participants who did not develop these pathologies were randomly selected as the control group. 109 readily available exposure variables from six different categories (physical measures, environmental, lifestyle, mental health events, sociodemographics, and early-life factors) from the participant's baseline visit were considered. We adopted the XGBoost ensemble model to predict individuals at risk of developing the diseases. The model's performance was compared to that of an integrative ML model which is based on a set of biological, clinical, physical, and sociodemographic variables, and, additionally for CVD, to the Framingham risk score. Moreover, we assessed the proposed model for potential bias related to sex, ethnicity, and age. Lastly, we interpreted the model's results us
Bodinier B, Vuckovic D, Rodrigues S, et al., 2023, Automated calibration of consensus weighted distance-based clustering approaches using sharp, Bioinformatics, Vol: 39, ISSN: 1367-4803
Motivation:In consensus clustering, a clustering algorithm is used in combination with a subsampling procedure to detect stable clusters. Previous studies on both simulated and real data suggest that consensus clustering outperforms native algorithms.Results:We extend here consensus clustering to allow for attribute weighting in the calculation of pairwise distances using existing regularised approaches. We propose a procedure for the calibration of the number of clusters (and regularisation parameter) by maximising the sharp score, a novel stability score calculated directly from consensus clustering outputs, making it extremely computationally competitive. Our simulation study shows better clustering performances of (i) approaches calibrated by maximising the sharp score compared to existing calibration scores, and (ii) weighted compared to unweighted approaches in the presence of features that do not contribute to cluster definition. Application on real gene expression data measured in lung tissue reveals clear clusters corresponding to different lung cancer subtypes.Availability and implementation:The R package sharp (version ≥ 1.4.3) is available on CRAN at https://CRAN.R-project.org/package=sharp.
Bortz J, Guariglia A, Klaric L, et al., 2023, Biological age estimation using circulating blood biomarkers, Communications Biology, Vol: 6, ISSN: 2399-3642
Biological age captures physiological deterioration better than chronological age and is amenable to interventions. Blood-based biomarkers have been identified as suitable candidates for biological age estimation. This study aims to improve biological age estimation using machine learning models and a feature-set of 60 circulating biomarkers available from the UK Biobank (n = 306,116). We implement an Elastic-Net derived Cox model with 25 selected biomarkers to predict mortality risk (C-Index = 0.778; 95% CI [0.767–0.788]), which outperforms the well-known blood-biomarker based PhenoAge model (C-Index = 0.750; 95% CI [0.739–0.761]), providing a C-Index lift of 0.028 representing an 11% relative increase in predictive value. Importantly, we then show that using common clinical assay panels, with few biomarkers, alongside imputation and the model derived on the full set of biomarkers, does not substantially degrade predictive accuracy from the theoretical maximum achievable for the available biomarkers. Biological age is estimated as the equivalent age within the same-sex population which corresponds to an individual’s mortality risk. Values ranged between 20-years younger and 20-years older than individuals’ chronological age, exposing the magnitude of ageing signals contained in blood markers. Thus, we demonstrate a practical and cost-efficient method of estimating an improved measure of Biological Age, available to the general population.
Atchison C, Davies B, Cooper E, et al., 2023, Long-term impact of COVID-19 among 242,712 adults in England, Nature Communications, Vol: 14, ISSN: 2041-1723
The COVID-19 pandemic is having a lasting impact on health and well-being. We compare current self-reported health, quality of life and symptom profiles for people with ongoing symptoms following COVID-19 to those who have never tested positive for SARS-CoV-2 infection and those who have recovered from COVID-19. Overall, 276,840/800,000 (34·6%) of invited participants took part. Mental health and health-related quality of life were worse among participants with ongoing persistent symptoms post-COVID compared with those who had never had COVID-19 or had recovered. In this study, median duration of COVID-related symptoms (N = 130,251) was 1·3 weeks (inter-quartile range 6 days to 2 weeks), with 7·5% and 5·2% reporting ongoing symptoms ≥12 weeks and ≥52 weeks respectively. Female sex, ≥1 comorbidity and being infected when Wild-type variant was dominant were associated with higher probability of symptoms lasting ≥12 weeks and longer recovery time in those with persistent symptoms. Although COVID-19 is usually of short duration, some adults experience persistent and burdensome illness.
Ward H, Atchison C, Whitaker M, et al., 2023, Design and implementation of a national program to monitor the prevalence of SARS-CoV-2 IgG antibodies in England using self-testing: the REACT-2 study, American Journal of Public Health, Pages: e1-e9, ISSN: 0090-0036
Data System. The UK Department of Health and Social Care funded the REal-time Assessment of Community Transmission-2 (REACT-2) study to estimate community prevalence of SARS-CoV-2 IgG (immunoglobulin G) antibodies in England. Data Collection/Processing. We obtained random cross-sectional samples of adults from the National Health Service (NHS) patient list (near-universal coverage). We sent participants a lateral flow immunoassay (LFIA) self-test, and they reported the result online. Overall, 905 991 tests were performed (28.9% response) over 6 rounds of data collection (June 2020-May 2021). Data Analysis/Dissemination. We produced weighted estimates of LFIA test positivity (validated against neutralizing antibodies), adjusted for test performance, at local, regional, and national levels and by age, sex, and ethnic group and area-level deprivation score. In each round, fieldwork occurred over 2 weeks, with results reported to policymakers the following week. We disseminated results as preprints and peer-reviewed journal publications. Public Health Implications. REACT-2 estimated the scale and variation in antibody prevalence over time. Community self-testing and -reporting produced rapid insights into the changing course of the pandemic and the impact of vaccine rollout, with implications for future surveillance. (Am J Public Health. Published online ahead of print September 21, 2023:e1-e9. https://doi.org/10.2105/AJPH.2023.307381).
Oosterwegel MJ, Ibi D, Portengen L, et al., 2023, Variability of the human serum metabolome over 3 months in the EXPOsOMICS Personal Exposure Monitoring study, Environmental Science and Technology (Washington), Vol: 57, Pages: 12752-12759, ISSN: 0013-936X
Liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) and untargeted metabolomics are increasingly used in exposome studies to study the interactions between nongenetic factors and the blood metabolome. To reliably and efficiently link detected compounds to exposures and health phenotypes in such studies, it is important to understand the variability in metabolome measures. We assessed the within- and between-subject variability of untargeted LC-HRMS measurements in 298 nonfasting human serum samples collected on two occasions from 157 subjects. Samples were collected ca. 107 (IQR: 34) days apart as part of the multicenter EXPOsOMICS Personal Exposure Monitoring study. In total, 4294 metabolic features were detected, and 184 unique compounds could be identified with high confidence. The median intraclass correlation coefficient (ICC) across all metabolic features was 0.51 (IQR: 0.29) and 0.64 (IQR: 0.25) for the 184 uniquely identified compounds. For this group, the median ICC marginally changed (0.63) when we included common confounders (age, sex, and body mass index) in the regression model. When grouping compounds by compound class, the ICC was largest among glycerophospholipids (median ICC 0.70) and steroids (0.67), and lowest for amino acids (0.61) and the O-acylcarnitine class (0.44). ICCs varied substantially within chemical classes. Our results suggest that the metabolome as measured with untargeted LC-HRMS is fairly stable (ICC > 0.5) over 100 days for more than half of the features monitored in our study, to reflect average levels across this time period. Variance across the metabolome will result in differential measurement error across the metabolome, which needs to be considered in the interpretation of metabolome results.
Whitaker M, Davies B, Atchison C, et al., 2023, SARS-CoV-2 rapid antibody test results and subsequent risk of hospitalisation and death in 361,801 people, Nature Communications, Vol: 14, ISSN: 2041-1723
The value of SARS-CoV-2 lateral flow immunoassay (LFIA) tests for estimating individual disease risk is unclear. The REACT-2 study in England, UK, obtained self-administered SARS-CoV-2 LFIA test results from 361,801 adults in January-May 2021. Here, we link to routine data on subsequent hospitalisation (to September 2021), and death (to December 2021). Among those who had received one or more vaccines, a negative LFIA is associated with increased risk of hospitalisation with COVID-19 (HR: 2.73 [95% confidence interval: 1.15,6.48]), death (all-cause) (HR: 1.59, 95% CI:1.07, 2.37), and death with COVID-19 as underlying cause (20.6 [1.83,232]). For people designated at high risk from COVID-19, who had received one or more vaccines, there is an additional risk of all-cause mortality of 1.9 per 1000 for those testing antibody negative compared to positive. However, the LFIA does not provide substantial predictive information over and above that which is available from detailed sociodemographic and health-related variables. Nonetheless, this simple test provides a marker which could be a valuable addition to understanding population and individual-level risk.
Cascarano A, Mur-Petit J, Hernandez-Gonzalez J, et al., 2023, Machine and deep learning for longitudinal biomedical data: a review of methods and applications, ARTIFICIAL INTELLIGENCE REVIEW, ISSN: 0269-2821
Eales O, de Oliveira Martins L, Page A, et al., 2023, Dynamics and scale of the SARS-CoV-2 variant Omicron epidemic in England, Nature Communications, Vol: 13, ISSN: 2041-1723
The SARS-CoV-2 pandemic has been characterised by the regular emergence of genomic variants. With natural and vaccine-induced population immunity at high levels, evolutionary pressure favours variants better able to evade SARS-CoV-2 neutralising antibodies. The Omicron variant (first detected in November 2021) exhibited a high degree of immune evasion, leading to increased infection rates worldwide. However, estimates of the magnitude of this Omicron wave have often relied on routine testing data, which are prone to several biases. Using data from the REal-time Assessment of Community Transmission-1 (REACT-1) study, a series of cross-sectional surveys assessing prevalence of SARS-CoV-2 infection in England, we estimated the dynamics of England’s Omicron wave (from 9 September 2021 to 1 March 2022). We estimate an initial peak in national Omicron prevalence of 6.89% (5.34%, 10.61%) during January 2022, followed by a resurgence in SARS-CoV-2 infections as the more transmissible Omicron sub-lineage, BA.2 replaced BA.1 and BA.1.1. Assuming the emergence of further distinct variants, intermittent epidemics of similar magnitudes may become the ‘new normal’.
Bowden SJ, Doulgeraki T, Bouras E, et al., 2023, Risk factors for human papillomavirus infection, cervical intraepithelial neoplasia and cervical cancer: an umbrella review and follow-up Mendelian randomisation studies, BMC Medicine, Vol: 21, Pages: 1-15, ISSN: 1741-7015
Background: Persistent infection by oncogenic human papillomavirus (HPV) is necessary although not sufficient for development of cervical cancer. Behavioural, environmental, or comorbid exposures may promote or protect against malignant transformation. Randomised evidence is limited and the validity of observational studies describing these associations remains unclear.Methods: In this umbrella review we searched electronic databases to identify meta-analyses of observational studies that evaluated risk or protective factors and the incidence of HPV infection, cervical intra-epithelial neoplasia (CIN), cervical cancer incidence and mortality. Following re-analysis, evidence was classified and graded based on a pre-defined set of statistical criteria. Quality was assessed with AMSTAR-2. For all associations graded as weak evidence or above, with available genetic instruments, we also performed Mendelian randomisation to examine the potential causal effect of modifiable exposures with risk of cervical cancer. The protocol for this study was registered on PROSPERO (CRD42020189995).Results: We included 171 meta-analyses of different exposure contrasts from 50 studies. Systemic immunosuppression including HIV infection (RR=2.20(95%CI=1.89-2.54)) and immunosuppressive medications for inflammatory bowel disease (RR=1.33(95%CI=1.27-1.39)), as well as an altered vaginal microbiome (RR=1.59(95%CI=1.40-1.81)) were supported by strong and highly suggestive evidence for an association with HPV persistence, CIN or cervical cancer. Smoking, number of sexual partners and young age at first pregnancy were supported by highly suggestive evidence and confirmed by Mendelian randomisation.Conclusions: Our main analysis supported the association of systemic (HIV infection, immunosuppressive medications) and local immunosuppression (altered vaginal microbiota) with increased risk for worse HPV and cervical disease outcomes. Mendelian randomisation confirmed the link for genetically predic
Atchison C, Whitaker M, Donnelly C, et al., 2023, Characteristics and predictors of persistent symptoms post COVID-19 in children and young people: a large community cross-sectional study in England, Archives of Disease in Childhood, Vol: 108, ISSN: 0003-9888
Objective: To estimate the prevalence of, and associated risk factors for, persistent symptoms post-COVID-19 among children aged 5–17 years in England.Design: Serial cross-sectional study.Setting: Rounds 10–19 (March 2021 to March 2022) of the REal-time Assessment of Community Transmission-1 study (monthly cross-sectional surveys of random samples of the population in England).Study population: Children aged 5–17 years in the community.Predictors: Age, sex, ethnicity, presence of a pre-existing health condition, index of multiple deprivation, COVID-19 vaccination status and dominant UK circulating SARS-CoV-2 variant at time of symptom onset.Main outcome measures: Prevalence of persistent symptoms, reported as those lasting ≥3 months post-COVID-19.Results: Overall, 4.4% (95% CI 3.7 to 5.1) of 3173 5–11 year-olds and 13.3% (95% CI 12.5 to 14.1) of 6886 12–17 year-olds with prior symptomatic infection reported at least one symptom lasting ≥3 months post-COVID-19, of whom 13.5% (95% CI 8.4 to 20.9) and 10.9% (95% CI 9.0 to 13.2), respectively, reported their ability to carry out day-to-day activities was reduced ‘a lot’ due to their symptoms. The most common symptoms among participants with persistent symptoms were persistent coughing (27.4%) and headaches (25.4%) in children aged 5–11 years and loss or change of sense of smell (52.2%) and taste (40.7%) in participants aged 12–17 years. Higher age and having a pre-existing health condition were associated with higher odds of reporting persistent symptoms.Conclusions: One in 23 5–11 year-olds and one in eight 12–17 year-olds post-COVID-19 report persistent symptoms lasting ≥3 months, of which one in nine report a large impact on performing day-to-day activities.
Brewer H, Chadeau-Hyam M, Johnson E, et al., 2023, Cancer Loyalty Card Study (CLOCS): feasibility outcomes for an observational case-control study focusing on the patient interval in ovarian cancer, BMJ Open, Vol: 13, Pages: 1-10, ISSN: 2044-6055
Objectives: Ovarian cancer symptoms are often non‐specific and can be normalised before patients seek medical help. The Cancer Loyalty Card Study (CLOCS) investigated self‐management behaviours of ovarian cancer patients prior to their diagnosis using loyaltycard data collected by two United Kingdom (UK)‐based high street retailers. Here, we discuss the feasibility outcomes for this novel research. Design: Observational case‐control study. Setting: Control participants were invited to the study using social media and other sources from the general public. Once consented, control participants were required to submit proof of identification (ID) for their loyalty card data to be shared. Cases were identifiedusing unique National Health Service (NHS) numbers (a proxy for ID) and were recruited through 12 NHS tertiary care clinics.Participants: Women in the UK, 18 years or older, with at least one of the participating high street retailers’ loyalty cards. Those with an ovarian cancer diagnosis within two years of recruitment were considered cases, and those without an ovarian cancer diagnosis were considered controls.Primary Outcome Measures: Recruitment rates, demographics of participants and identification of any barriers to recruitment.Results: In total, 182 cases and 427 controls were recruited with significant differences by age, number of people in participants’ households and the geographical region in the UK. However, only 37% (n=160/427) of control participants provided sufficient ID details and81% (130/160) matched retailers’ records. The majority of the participants provided complete responses to the 24‐Item Ovarian Risk Questionnaire.Conclusions: Our findings show that recruitment to a study aiming to understand self‐care behaviours using loyalty card data is challenging but feasible. The general public were willing to share their data for health research. Barriers in data sharing mechanisms need to be addressed to maximise participant rete
Borges MC, Clayton G, Freathy RM, et al., 2023, The impact of BMI on pregnancy and perinatal outcomes in 497,932 women, Publisher: WILEY, Pages: 157-158, ISSN: 1470-0328
Eales O, Haw D, Wang H, et al., 2023, Dynamics of SARS-CoV-2 infection hospitalisation and infection fatality ratios over 23 months in England, PLoS Biology, Vol: 21, Pages: 1-21, ISSN: 1544-9173
The relationship between prevalence of infection and severe outcomes such as hospitalisation and death changed over the course of the COVID-19 pandemic. Reliable estimates of the infection fatality ratio (IFR) and infection hospitalisation ratio (IHR) along with the time-delay between infection and hospitalisation/death can inform forecasts of the numbers/timing of severe outcomes and allow healthcare services to better prepare for periods of increased demand. The REal-time Assessment of Community Transmission-1 (REACT-1) study estimated swab positivity for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection in England approximately monthly from May 2020 to March 2022. Here, we analyse the changing relationship between prevalence of swab positivity and the IFR and IHR over this period in England, using publicly available data for the daily number of deaths and hospitalisations, REACT-1 swab positivity data, time-delay models, and Bayesian P-spline models. We analyse data for all age groups together, as well as in 2 subgroups: those aged 65 and over and those aged 64 and under. Additionally, we analysed the relationship between swab positivity and daily case numbers to estimate the case ascertainment rate of England's mass testing programme. During 2020, we estimated the IFR to be 0.67% and the IHR to be 2.6%. By late 2021/early 2022, the IFR and IHR had both decreased to 0.097% and 0.76%, respectively. The average case ascertainment rate over the entire duration of the study was estimated to be 36.1%, but there was some significant variation in continuous estimates of the case ascertainment rate. Continuous estimates of the IFR and IHR of the virus were observed to increase during the periods of Alpha and Delta's emergence. During periods of vaccination rollout, and the emergence of the Omicron variant, the IFR and IHR decreased. During 2020, we estimated a time-lag of 19 days between hospitalisation and swab positivity, and 26 days between deaths
Elliott P, Whitaker M, Tang D, et al., 2023, Design and implementation of a national SARS-CoV-2 monitoring programme in England: REACT-1 Study, American Journal of Public Health, ISSN: 0090-0036
Data System. The REal-time Assessment of Community Transmission-1 (REACT-1) Study was funded by the Department of Health and Social Care in England to provide reliable and timely estimates of prevalence of SARS-CoV-2 infection by time, person and place.Data Collection/Processing. The data were obtained by writing to named individuals aged 5 years and above in random cross-sections of the population of England, using the National Health Service (NHS) list of patients registered with a general practitioner (>99% coverage) as sampling frame. Data were collected 2-3 weekly approximately every month across 19distinct rounds of data collection from May 1, 2020 to March 31, 2022.Data Analysis/Dissemination. The data and study materials are widely disseminated via the study website, preprints, publications in peer-reviewed journals and the media. Data tabulations suitably anonymised to protect participant confidentiality are available on request to the study’s Data Access Committee.Implications. The study provided inter alia real-time data on SARS-CoV-2 prevalence over time, by area, and by socio-demographic variables; estimates of vaccine effectiveness; symptom profiles and detected emergence of new variants based on viral genome sequencing.
Lee J-H, Dixey P, Bhavsar P, et al., 2023, Precision Medicine Intervention in Severe Asthma (PRISM) study: molecular phenotyping of patients with severe asthma and response to biologics, ERJ Open Research, Vol: 9, ISSN: 2312-0541
Severe asthma represents an important clinical unmet need despite the introduction of biologic agents. Although advanced omics technologies have aided researchers in identifying clinically relevant molecular pathways, there is a lack of an integrated omics approach in severe asthma particularly in terms of its evolution over time. The collaborative Korea–UK research project Precision Medicine Intervention in Severe Asthma (PRISM) was launched in 2020 with the aim of identifying molecular phenotypes of severe asthma by analysing multi-omics data encompassing genomics, epigenomics, transcriptomics, proteomics, metagenomics and metabolomics. PRISM is a prospective, observational, multicentre study involving patients with severe asthma attending severe asthma clinics in Korea and the UK. Data including patient demographics, inflammatory phenotype, medication, lung function and control status of asthma will be collected along with biological samples (blood, sputum, urine, nasal epithelial cells and exhaled breath condensate) for omics analyses. Follow-up evaluations will be performed at baseline, 1 month, 4–6 months and 10–12 months to assess the stability of phenotype and treatment responses for those patients who have newly begun biologic therapy. Standalone and integrated omics data will be generated from the patient samples at each visit, paired with clinical information. By analysing these data, we will identify the molecular pathways that drive lung function, asthma control status, acute exacerbations and the requirement for daily oral corticosteroids, and that are involved in the therapeutic response to biological therapy. PRISM will establish a large multi-omics dataset of severe asthma to identify potential key pathophysiological pathways of severe asthma.
Rothwell JA, Bešević J, Dimou N, et al., 2023, Circulating amino acid levels and colorectal cancer risk in the European Prospective Investigation into Cancer and Nutrition and UK Biobank cohorts, BMC Medicine, Vol: 21, Pages: 1-13, ISSN: 1741-7015
BACKGROUND: Amino acid metabolism is dysregulated in colorectal cancer patients; however, it is not clear whether pre-diagnostic levels of amino acids are associated with subsequent risk of colorectal cancer. We investigated circulating levels of amino acids in relation to colorectal cancer risk in the European Prospective Investigation into Cancer and Nutrition (EPIC) and UK Biobank cohorts. METHODS: Concentrations of 13-21 amino acids were determined in baseline fasting plasma or serum samples in 654 incident colorectal cancer cases and 654 matched controls in EPIC. Amino acids associated with colorectal cancer risk following adjustment for the false discovery rate (FDR) were then tested for associations in the UK Biobank, for which measurements of 9 amino acids were available in 111,323 participants, of which 1221 were incident colorectal cancer cases. RESULTS: Histidine levels were inversely associated with colorectal cancer risk in EPIC (odds ratio [OR] 0.80 per standard deviation [SD], 95% confidence interval [CI] 0.69-0.92, FDR P-value=0.03) and in UK Biobank (HR 0.93 per SD, 95% CI 0.87-0.99, P-value=0.03). Glutamine levels were borderline inversely associated with colorectal cancer risk in EPIC (OR 0.85 per SD, 95% CI 0.75-0.97, FDR P-value=0.08) and similarly in UK Biobank (HR 0.95, 95% CI 0.89-1.01, P=0.09) In both cohorts, associations changed only minimally when cases diagnosed within 2 or 5 years of follow-up were excluded. CONCLUSIONS: Higher circulating levels of histidine were associated with a lower risk of colorectal cancer in two large prospective cohorts. Further research to ascertain the role of histidine metabolism and potentially that of glutamine in colorectal cancer development is warranted.
Brewer HR, Hirst Y, Chadeau-Hyam M, et al., 2023, Association between purchase of over-the-counter medications and ovarian cancer diagnosis in the Cancer Loyalty Card Study (CLOCS): observational case-control study, JMIR Public Health and Surveillance, Vol: 9, ISSN: 2369-2960
BACKGROUND: Over-the-counter (OTC) medications are frequently used to self-care for nonspecific ovarian cancer symptoms prior to diagnosis. Monitoring such purchases may provide an opportunity for earlier diagnosis. OBJECTIVE: The aim of the Cancer Loyalty Card Study (CLOCS) was to investigate purchases of OTC pain and indigestion medications prior to ovarian cancer diagnosis in women with and without ovarian cancer in the United Kingdom using loyalty card data. METHODS: An observational case-control study was performed comparing purchases of OTC pain and indigestion medications prior to diagnosis in women with (n=153) and without (n=120) ovarian cancer using loyalty card data from two UK-based high street retailers. Monthly purchases of pain and indigestion medications for cases and controls were compared using the Fisher exact test, conditional logistic regression, and receiver operating characteristic (ROC) curve analysis. RESULTS: Pain and indigestion medication purchases were increased among cases 8 months before diagnosis, with maximum discrimination between cases and controls 8 months before diagnosis (Fisher exact odds ratio [OR] 2.9, 95% CI 2.1-4.1). An increase in indigestion medication purchases was detected up to 9 months before diagnosis (adjusted conditional logistic regression OR 1.38, 95% CI 1.04-1.83). The ROC analysis for indigestion medication purchases showed a maximum area under the curve (AUC) at 13 months before diagnosis (AUC=0.65, 95% CI 0.57-0.73), which further improved when stratified to late-stage ovarian cancer (AUC=0.68, 95% CI 0.59-0.78). CONCLUSIONS: There is a difference in purchases of pain and indigestion medications among women with and without ovarian cancer up to 8 months before diagnosis. Facilitating earlier presentation among those who self-care for symptoms using this novel data source could improve ovarian cancer patients' options for treatment and improve survival. TRIAL REGISTRATION: ClinicalTrials.gov NCT03994653; https
Garmendia AT, Gkouzionis I, Triantafyllidis CP, et al., 2023, Towards personalised early prediction of Intra-Operative Hypotension following anesthesia using Deep Learning and phenotypic heterogeneity
<jats:title>Abstract</jats:title><jats:p>Intra-Operative Hypotension (IOH) is a haemodynamic abnormality that is commonly observed in operating theatres following general anesthesia and associates with life-threatening post-operative complications. Using Long Short Term Memory (LSTM) models applied to Electronic Health Records (EHR) and time-series intra-operative data in 604 patients that underwent colorectal surgery we predicted the instant risk of IOH events within the next five minutes. K-means clustering was used to group patients based on pre-clinical data. As part of a sensitivity analysis, the model was also trained on patients clustered according to Mean artelial Blood Pressure (MBP) time-series trends at the start of the operation using K-means with Dynamic Time Warping. The baseline LSTM model trained on all patients yielded a test set Area Under the Curve (AUC) value of 0.83. In contrast, training the model on smaller sized clusters (grouped by EHR) improved the AUC value (0.85). Similarly, the AUC was increased by 4.8% (0.87) when training the model on clusters grouped by MBP. The encouraging results of the baseline model demonstrate the applicability of the approach in a clinical setting. Furthermore, the increased predictive performance of the model after being trained using a clustering approach first, paves the way for a more personalised patient stratification approach to IOH prediction using clinical data.</jats:p>
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