Multiomic Signatures of Traffic-Related Air Pollution in London Reveal Potential Short-Term Perturbations in Gut Microbiome-Related Pathways.

This randomized crossover study investigated the metabolic and mRNA alterations associated with exposure to high and low traffic-related air pollution (TRAP) in 50 participants who were either healthy or were diagnosed with chronic pulmonary obstructive disease (COPD) or ischemic heart disease (IHD). For the first time, this study combined transcriptomics and serum metabolomics measured in the same participants over multiple time points (2 h before, and 2 and 24 h after exposure) and over two contrasted exposure regimes to identify potential multiomic modifications linked to TRAP exposure. With a multivariate normal model, we identified 78 metabolic features and 53 mRNA features associated with at least one TRAP exposure. Nitrogen dioxide (NO2) emerged as the dominant pollutant, with 67 unique associated metabolomic features. Pathway analysis and annotation of metabolic features consistently indicated perturbations in the tryptophan metabolism associated with NO2 exposure, particularly in the gut-microbiome-associated indole pathway. Conditional multiomics networks revealed complex and intricate mechanisms associated with TRAP exposure, with some effects persisting 24 h after exposure. Our findings indicate that exposure to TRAP can alter important physiological mechanisms even after a short-term exposure of a 2 h walk. We describe for the first time a potential link between NO2 exposure and perturbation of the microbiome-related pathways.

Respiratory viral infection promotes the awakening and outgrowth of dormant metastatic breast cancer cells in lungs.

Breast cancer is the second most common cancer globally. Most deaths from breast cancer are due to metastatic disease which often follows long periods of clinical dormancy1. Understanding the mechanisms that disrupt the quiescence of dormant disseminated cancer cells (DCC) is crucial for addressing metastatic progression. Infection with respiratory viruses (e.g. influenza or SARS-CoV-2) is common and triggers an inflammatory response locally and systemically2,3. Here we show that influenza virus infection leads to loss of the pro-dormancy mesenchymal phenotype in breast DCC in the lung, causing DCC proliferation within days of infection, and a greater than 100-fold expansion of carcinoma cells into metastatic lesions within two weeks. Such DCC phenotypic change and expansion is interleukin-6 (IL-6)-dependent. We further show that CD4 T cells are required for the maintenance of pulmonary metastatic burden post-influenza virus infection, in part through attenuation of CD8 cell responses in the lungs. Single-cell RNA-seq analyses reveal DCC-dependent impairment of T-cell activation in the lungs of infected mice. SARS-CoV-2 infected mice also showed increased breast DCC expansion in lungs post-infection. Expanding our findings to human observational data, we observed that cancer survivors contracting a SARS-CoV-2 infection have substantially increased risks of lung metastatic progression and cancer-related death compared to cancer survivors who did not. These discoveries underscore the significant impact of respiratory viral infections on the resurgence of metastatic cancer, offering novel insights into the interconnection between infectious diseases and cancer metastasis.

A prospective study of smoking-related white blood cell DNA methylation markers and risk of bladder cancer.

Bladder cancer, a common neoplasm, is primarily caused by tobacco smoking. Epigenetic alterations including DNA methylation have the potential to be used as prospective markers of increased risk, particularly in at-risk populations such as smokers. We aimed to investigate the potential of smoking-related white blood cell (WBC) methylation markers to contribute to an increase in bladder cancer risk prediction over classical questionnaire-based smoking metrics (i.e., duration, intensity, packyears) in a nested case-control study within the prospective prostate, lung, colorectal, and ovarian (PLCO) Cancer Screening Trial and the alpha-tocopherol, beta-carotene cancer (ATBC) Prevention Study (789 cases; 849 controls). We identified 200 differentially methylated sites associated with smoking status and 28 significantly associated (after correction for multiple testing) with bladder cancer risk among 2670 previously reported smoking-related cytosine-phosphate-guanines sites (CpGs). Similar patterns were observed across cohorts. Receiver operating characteristic (ROC) analyses indicated that cg05575921 (AHHR), the strongest smoking-related association we identified for bladder cancer risk, alone yielded similar predictive performance (AUC: 0.60) than classical smoking metrics (AUC: 0.59-0.62). Best prediction was achieved by including the first principal component (PC1) from the 200 smoking-related CpGs alongside smoking metrics (AUC: 0.63-0.65). Further, PC1 remained significantly associated with elevated bladder cancer risk after adjusting for smoking metrics. These findings suggest DNA methylation profiles reflect aspects of tobacco smoke exposure in addition to those captured by smoking duration, intensity and packyears, and/or individual susceptibility relevant to bladder cancer etiology, warranting further investigation.

Hair-Derived Exposome Exploration of Cardiometabolic Health: Piloting a Bayesian Multitrait Variable Selection Approach.

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.

Cognition and Memory after Covid-19 in a Large Community Sample.

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 those of propensity-score-matching analyses. In a comparison of the group that had unresolved persistent symptoms with the no-Covid-19 group, memory, reasoning, and executive function tasks were associated with the largest deficits (-0.33 to -0.20 SD); these tasks correlated weakly with recent symptoms, including poor memory and brain fog. No adverse events were reported. CONCLUSIONS: Participants with resolved persistent symptoms after Covid-19 had objectively measured cognitive function similar to that in participants with shorter-duration symptoms, although short-duration Covid-19 was still associated with small cognitive deficits after recovery. Longer-term persistence of cognitive deficits and any clinical implications remain uncertain. (Funded by the National Institute for Health and Care Research and others.).

Decoding the exposome: data science methodologies and implications in exposome-wide association studies (ExWASs).

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.

Integrating multiple lines of evidence to assess the effects of maternal BMI on pregnancy and perinatal outcomes.

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, European Research Council, National Institutes of Health, National Institute for Health Research, Research Council of Norway, Wellcome Trust.

Educational patterning in biological health seven years apart: Findings from the Tromsø Study.

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 (ßlow education Tromsø6=0.30 [95 %-CI=0.18-0.43] and ßlow education Tromsø7=0.30 [95 %-CI=0.17-0.42]). They also presented higher longitudinal BHS compared to people with higher education (ßlow 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.

Association of hair polychlorinated biphenyls and multiclass pesticides with obesity, diabetes, hypertension and dyslipidemia in NESCAV study.

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), ß-hexachlorocyclohexane (ß-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.

Proteomic signatures of eosinophilic and neutrophilic asthma from serum and sputum.

BACKGROUND: Eosinophilic 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. METHODS: Proteomic 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. FINDINGS: We 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. INTERPRETATION: Targeted 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. FUNDING: U-BIOPRED has received funding from the Innovative Medicines Initiative (IMI) Joint Undertaking under grant agreement no. 115010, resources of which are composed of financial contributions from the European Union's Seventh Framework Programme (FP7/2007-2013), and European Federation of Pharmaceutical Industries and Associations (EFPIA) companies' in-kind contributions (www.imi.europa.eu). ADEPT was funded by Johnson & Johnson/Janssen pharmaceutical Company.

Automated calibration for stability selection in penalised regression and graphical models.

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.

Metabolomic Signatures of Exposure to Nitrate and Trihalomethanes in Drinking Water and Colorectal Cancer Risk in a Spanish Multicentric Study (MCC-Spain).

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 controls from the Multi Case-Control Spain project. Untargeted metabolomic analysis was conducted in blood samples using ultrahigh-performance liquid chromatography-quadrupole time-of-flight mass spectrometry. A variety of univariate and multivariate association analyses were conducted after data quality control, normalization, and imputation. Linear regression and partial least-squares analyses were conducted for chloroform, brominated THMs, total THMs, and nitrate among controls and for case-control status, together with a N-integration model discriminating colorectal cancer cases from controls through interrogation of correlations between the exposure variables and the metabolomic features. Results revealed a total of 568 metabolomic features associated with at least one water contaminant or colorectal cancer. Annotated metabolites and pathway analysis suggest a number of pathways as potentially involved in the link between exposure to these water contaminants and colorectal cancer, including nicotinamide, cytochrome P-450, and tyrosine metabolism. These findings provide insights into the underlying biological mechanisms and potential biomarkers associated with water contaminant exposure and colorectal cancer risk. Further research in this area is needed to better understand the causal relationship and the public health implications.

Automated calibration of consensus weighted distance-based clustering approaches using sharp.

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 regularized approaches. We propose a procedure for the calibration of the number of clusters (and regularization parameter) by maximizing 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 maximizing 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.

Long-term health impacts of COVID-19 among 242,712 adults in England.

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.

Biological age estimation using circulating blood biomarkers.

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.

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.

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. 2023;113(11):1201-1209. https://doi.org/10.2105/AJPH.2023.307381).

Cardiometabolic risk estimation using exposome data and machine learning.

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 using SHAP, a state-of-the-art explainability method. RESULTS: The proposed ML model presents a comparable performance to the integrative ML model despite using solely exposome information, achieving a ROC-AUC of 0.78±0.01 and 0.77±0.01 for CVD and T2D, respectively. Additionally, for CVD risk prediction, the exposome-based model presents an improved performance over the traditional Framingham risk score. No bias in terms of key sensitive variables was identified. CONCLUSIONS: We identified exposome factors that play an important role in identifying patients at risk of CVD and T2D, such as naps during the day, age completed full-time education, past tobacco smoking, frequency of tiredness/unenthusiasm, and current work status. Overall, this work demonstrates the potential of exposome-based machine learning as a fair CVD and T2D risk assessment tool.

SARS-CoV-2 rapid antibody test results and subsequent risk of hospitalisation and death in 361,801 people.

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.

Variability of the Human Serum Metabolome over 3 Months in the EXPOsOMICS Personal Exposure Monitoring Study.

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.

Risk factors for human papillomavirus infection, cervical intraepithelial neoplasia and cervical cancer: an umbrella review and follow-up Mendelian randomisation studies.

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 predicted lifetime smoking index, and young age at first pregnancy with cervical cancer, highlighting also that observational evidence can hide different inherent biases. This evidence strengthens the need for more frequent HPV screening in people with immunosuppression, further investigation of the vaginal microbiome and access to sexual health services.