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.

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.

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.

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.

Design and Implementation of a National SARS-CoV-2 Monitoring Program in England: REACT-1 Study.

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 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection over time, by person and place. Data Collection/Processing. The study team (researchers from Imperial College London and its logistics partner Ipsos) wrote to named individuals aged 5 years and older in random cross-sections of the population of England, using the National Health Service list of patients registered with a general practitioner (near-universal coverage) as a sampling frame. We collected data over 2 to 3 weeks approximately every month across 19 rounds of data collection from May 1, 2020, to March 31, 2022. Data Analysis/Dissemination. We have disseminated the data and study materials widely via the study Web site, preprints, publications in peer-reviewed journals, and the media. We make available data tabulations, suitably anonymized to protect participant confidentiality, on request to the study's data access committee. Public Health Implications. The study provided inter alia real-time data on SARS-CoV-2 prevalence over time, by area, and by sociodemographic variables; estimates of vaccine effectiveness; and symptom profiles, and detected emergence of new variants based on viral genome sequencing. (Am J Public Health. 2023;113(5):545-554. https://doi.org/10.2105/AJPH.2023.307230).

SARS-CoV-2 lineage dynamics in England from September to November 2021: high diversity of Delta sub-lineages and increased transmissibility of AY.4.2.

BACKGROUND: Since the emergence of SARS-CoV-2, evolutionary pressure has driven large increases in the transmissibility of the virus. However, with increasing levels of immunity through vaccination and natural infection the evolutionary pressure will switch towards immune escape. Genomic surveillance in regions of high immunity is crucial in detecting emerging variants that can more successfully navigate the immune landscape. METHODS: We present phylogenetic relationships and lineage dynamics within England (a country with high levels of immunity), as inferred from a random community sample of individuals who provided a self-administered throat and nose swab for rt-PCR testing as part of the REal-time Assessment of Community Transmission-1 (REACT-1) study. During round 14 (9 September-27 September 2021) and 15 (19 October-5 November 2021) lineages were determined for 1322 positive individuals, with 27.1% of those which reported their symptom status reporting no symptoms in the previous month. RESULTS: We identified 44 unique lineages, all of which were Delta or Delta sub-lineages, and found a reduction in their mutation rate over the study period. The proportion of the Delta sub-lineage AY.4.2 was increasing, with a reproduction number 15% (95% CI 8-23%) greater than the most prevalent lineage, AY.4. Further, AY.4.2 was less associated with the most predictive COVID-19 symptoms (p = 0.029) and had a reduced mutation rate (p = 0.050). Both AY.4.2 and AY.4 were found to be geographically clustered in September but this was no longer the case by late October/early November, with only the lineage AY.6 exhibiting clustering towards the South of England. CONCLUSIONS: As SARS-CoV-2 moves towards endemicity and new variants emerge, genomic data obtained from random community samples can augment routine surveillance data without the potential biases introduced due to higher sampling rates of symptomatic individuals.

Epigenetic mechanisms of lung carcinogenesis involve differentially methylated CpG sites beyond those associated with smoking.

Smoking-related epigenetic changes have been linked to lung cancer, but the contribution of epigenetic alterations unrelated to smoking remains unclear. We sought for a sparse set of CpG sites predicting lung cancer and explored the role of smoking in these associations. We analysed CpGs in relation to lung cancer in participants from two nested case-control studies, using (LASSO)-penalised regression. We accounted for the effects of smoking using known smoking-related CpGs, and through conditional-independence network. We identified 29 CpGs (8 smoking-related, 21 smoking-unrelated) associated with lung cancer. Models additionally adjusted for Comprehensive Smoking Index-(CSI) selected 1 smoking-related and 49 smoking-unrelated CpGs. Selected CpGs yielded excellent discriminatory performances, outperforming information provided by CSI only. Of the 8 selected smoking-related CpGs, two captured lung cancer-relevant effects of smoking that were missed by CSI. Further, the 50 CpGs identified in the CSI-adjusted model complementarily explained lung cancer risk. These markers may provide further insight into lung cancer carcinogenesis and help improving early identification of high-risk patients.

Genetic variation in cervical preinvasive and invasive disease: a genome-wide association study.

BACKGROUND: Most uterine cervical high-risk human papillomavirus (HPV) infections are transient, with only a small fraction developing into cervical cancer. Family aggregation studies and heritability estimates suggest a significant inherited genetic component. Candidate gene studies and previous genome-wide association studies (GWASs) report associations between the HLA region and cervical cancer. Adopting a genome-wide approach, we aimed to compare genetic variation in women with invasive cervical cancer and cervical intraepithelial neoplasia (CIN) grade 3 with that in healthy controls. METHODS: We did a GWAS in a cohort of unrelated European individuals using data from UK Biobank, a population-based cohort including 273 377 women aged 40-69 years at recruitment between March 13, 2006, and Oct 1, 2010. We used an additive univariate logistic regression model to analyse genetic variants associated with invasive cervical cancer or CIN3. We sought replication of candidate associations in FinnGen, a large independent dataset of 128 123 individuals. We also did a two-sample mendelian randomisation approach to explore the role of risk factors in the genetic risk of cervical cancer. FINDINGS: We included 4769 CIN3 and invasive cervical cancer case samples and 145 545 control samples in the GWAS. Of 9 600 464 assayed and imputed single-nucleotide polymorphisms (SNPs), six independent variants were associated with CIN3 and invasive cervical cancer. These included novel loci rs10175462 (PAX8; odds ratio [OR] 0·87, 95% CI 0·84-0·91; p=1·07 × 10-9) and rs27069 (CLPTM1L; 0·88, 0·84-0·92; p=2·51 × 10-9), and previously reported signals at rs9272050 (HLA-DQA1; 1·27, 1·21-1·32; p=2·51 × 10-28), rs6938453 (MICA; 0·79, 0·75-0·83; p=1·97 × 10-17), rs55986091 (HLA-DQB1; 0·66, 0·60-0·72; p=6·42 × 10-28), and rs9266183 (HLA-B; 0·73, 0·64-0·83; p=1·53 × 10-6). Three SNPs were replicated in the independent Finnish dataset of 1648 invasive cervical cancer cases: PAX8 (rs10175462; p=0·015), CLPTM1L (rs27069; p=2·54 × 10-7), and HLA-DQA1 (rs9272050; p=7·90 × 10-8). Mendelian randomisation further supported the complementary role of smoking (OR 2·46, 95% CI 1·64-3·69), older age at first pregnancy (0·80, 0·68-0·95), and number of sexual partners (1·95, 1·44-2·63) in the risk of developing cervical cancer. INTERPRETATION: Our results provide new evidence for the genetic susceptibility to cervical cancer, specifically the PAX8, CLPTM1L, and HLA genes, suggesting disruption in apoptotic and immune function pathways. Future studies integrating host and viral, genetic, and epigenetic variation, could further elucidate complex host-viral interactions. FUNDING: NIHR Imperial BRC Wellcome 4i Clinician Scientist Training Programme.

Predictive symptoms for COVID-19 in the community: REACT-1 study of over 1 million people.

BACKGROUND: Rapid detection, isolation, and contact tracing of community COVID-19 cases are essential measures to limit the community spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We aimed to identify a parsimonious set of symptoms that jointly predict COVID-19 and investigated whether predictive symptoms differ between the B.1.1.7 (Alpha) lineage (predominating as of April 2021 in the US, UK, and elsewhere) and wild type. METHODS AND FINDINGS: We obtained throat and nose swabs with valid SARS-CoV-2 PCR test results from 1,147,370 volunteers aged 5 years and above (6,450 positive cases) in the REal-time Assessment of Community Transmission-1 (REACT-1) study. This study involved repeated community-based random surveys of prevalence in England (study rounds 2 to 8, June 2020 to January 2021, response rates 22%-27%). Participants were asked about symptoms occurring in the week prior to testing. Viral genome sequencing was carried out for PCR-positive samples with N-gene cycle threshold value < 34 (N = 1,079) in round 8 (January 2021). In univariate analysis, all 26 surveyed symptoms were associated with PCR positivity compared with non-symptomatic people. Stability selection (1,000 penalized logistic regression models with 50% subsampling) among people reporting at least 1 symptom identified 7 symptoms as jointly and positively predictive of PCR positivity in rounds 2-7 (June to December 2020): loss or change of sense of smell, loss or change of sense of taste, fever, new persistent cough, chills, appetite loss, and muscle aches. The resulting model (rounds 2-7) predicted PCR positivity in round 8 with area under the curve (AUC) of 0.77. The same 7 symptoms were selected as jointly predictive of B.1.1.7 infection in round 8, although when comparing B.1.1.7 with wild type, new persistent cough and sore throat were more predictive of B.1.1.7 infection while loss or change of sense of smell was more predictive of the wild type. The main limitations of our study are (i) potential participation bias despite random sampling of named individuals from the National Health Service register and weighting designed to achieve a representative sample of the population of England and (ii) the necessary reliance on self-reported symptoms, which may be prone to recall bias and may therefore lead to biased estimates of symptom prevalence in England. CONCLUSIONS: Where testing capacity is limited, it is important to use tests in the most efficient way possible. We identified a set of 7 symptoms that, when considered together, maximize detection of COVID-19 in the community, including infection with the B.1.1.7 lineage.

COVID-19 mortality in the UK Biobank cohort: revisiting and evaluating risk factors.

Most studies of severe/fatal COVID-19 risk have used routine/hospitalisation data without detailed pre-morbid characterisation. Using the community-based UK Biobank cohort, we investigate risk factors for COVID-19 mortality in comparison with non-COVID-19 mortality. We investigated demographic, social (education, income, housing, employment), lifestyle (smoking, drinking, body mass index), biological (lipids, cystatin C, vitamin D), medical (comorbidities, medications) and environmental (air pollution) data from UK Biobank (N = 473,550) in relation to 459 COVID-19 and 2626 non-COVID-19 deaths to 21 September 2020. We used univariate, multivariable and penalised regression models. Age (OR = 2.76 [2.18-3.49] per S.D. [8.1 years], p = 2.6 × 10-17), male sex (OR = 1.47 [1.26-1.73], p = 1.3 × 10-6) and Black versus White ethnicity (OR = 1.21 [1.12-1.29], p = 3.0 × 10-7) were independently associated with and jointly explanatory of (area under receiver operating characteristic curve, AUC = 0.79) increased risk of COVID-19 mortality. In multivariable regression, alongside demographic covariates, being a healthcare worker, current smoker, having cardiovascular disease, hypertension, diabetes, autoimmune disease, and oral steroid use at enrolment were independently associated with COVID-19 mortality. Penalised regression models selected income, cardiovascular disease, hypertension, diabetes, cystatin C, and oral steroid use as jointly contributing to COVID-19 mortality risk; Black ethnicity, hypertension and oral steroid use contributed to COVID-19 but not non-COVID-19 mortality. Age, male sex and Black ethnicity, as well as comorbidities and oral steroid use at enrolment were associated with increased risk of COVID-19 death. Our results suggest that previously reported associations of COVID-19 mortality with body mass index, low vitamin D, air pollutants, renin-angiotensin-aldosterone system inhibitors may be explained by the aforementioned factors.

Agnostic Cys34-albumin adductomics and DNA methylation: Implication of N-acetylcysteine in lung carcinogenesis years before diagnosis.

Although smoking and oxidative stress are known contributors to lung carcinogenesis, their mechanisms of action remain poorly understood. To shed light into these mechanisms, we applied a novel approach using Cys34-adductomics in a lung cancer nested case-control study (n = 212). Adductomics profiles were integrated with DNA-methylation data at established smoking-related CpG sites measured in the same individuals. Our analysis identified 42 Cys34-albumin adducts, of which 2 were significantly differentially abundant in cases and controls: adduct of N-acetylcysteine (NAC, p = 4.15 × 10-3 ) and of cysteinyl-glycine (p = 7.89 × 10-3 ). Blood levels of the former were found associated to the methylation levels at 11 smoking-related CpG sites. We detect, for the first time in prospective blood samples, and irrespective of time to diagnosis, decreased levels of NAC adduct in lung cancer cases. Altogether, our results highlight the potential role of these adducts in the oxidative stress response contributing to lung carcinogenesis years before diagnosis.

Prenatal Exposure to Multiple Air Pollutants, Mediating Molecular Mechanisms, and Shifts in Birthweight.

Mechanisms underlying adverse birth and later in life health effects from exposure to air pollution during the prenatal period have not been not fully elucidated, especially in the context of mixtures. We assessed the effects of prenatal exposure to mixtures of air pollutants of particulate matter (PM), PM2.5, PM10, nitrogen oxides, NO2, NOx, ultrafine particles (UFP), and oxidative potential (OP) of PM2.5 on infant birthweight in four European birth cohorts and the mechanistic underpinnings through cross-omics of metabolites and inflammatory proteins. The association between mixtures of air pollutants and birthweight z-scores (standardized for gestational age) was assessed for three different mixture models, using Bayesian machine kernel regression (BKMR). We determined the direct effect for PM2.5, PM10, NO2, and mediation by cross-omic signatures (identified using sparse partial least-squares regression) using causal mediation BKMR models. There was a negative association with birthweight z-scores and exposure to mixtures of air pollutants, where up to -0.21 or approximately a 96 g decrease in birthweight, comparing the 75th percentile to the median level of exposure to the air pollutant mixture could occur. Shifts in birthweight z-scores from prenatal exposure to PM2.5, PM10, and NO2 were mediated by molecular mechanisms, represented by cross-omics scores. Interleukin-17 and epidermal growth factor were identified as important inflammatory responses underlyingair pollution-associated shifts in birthweight. Our results signify that by identifying mechanisms through which mixtures of air pollutants operate, the causality of air pollution-associated shifts in birthweight is better supported, substantiating the need for reducing exposure in vulnerable populations.

Predictive Accuracy of a Polygenic Risk Score-Enhanced Prediction Model vs a Clinical Risk Score for Coronary Artery Disease.

Importance: The incremental value of polygenic risk scores in addition to well-established risk prediction models for coronary artery disease (CAD) is uncertain. Objective: To examine whether a polygenic risk score for CAD improves risk prediction beyond pooled cohort equations. Design, Setting, and Participants: Observational study of UK Biobank participants enrolled from 2006 to 2010. A case-control sample of 15 947 prevalent CAD cases and equal number of age and sex frequency-matched controls was used to optimize the predictive performance of a polygenic risk score for CAD based on summary statistics from published genome-wide association studies. A separate cohort of 352 660 individuals (with follow-up to 2017) was used to evaluate the predictive accuracy of the polygenic risk score, pooled cohort equations, and both combined for incident CAD. Exposures: Polygenic risk score for CAD, pooled cohort equations, and both combined. Main Outcomes and Measures: CAD (myocardial infarction and its related sequelae). Discrimination, calibration, and reclassification using a risk threshold of 7.5% were assessed. Results: In the cohort of 352 660 participants (mean age, 55.9 years; 205 297 women [58.2%]) used to evaluate the predictive accuracy of the examined models, there were 6272 incident CAD events over a median of 8 years of follow-up. CAD discrimination for polygenic risk score, pooled cohort equations, and both combined resulted in C statistics of 0.61 (95% CI, 0.60 to 0.62), 0.76 (95% CI, 0.75 to 0.77), and 0.78 (95% CI, 0.77 to 0.79), respectively. The change in C statistic between the latter 2 models was 0.02 (95% CI, 0.01 to 0.03). Calibration of the models showed overestimation of risk by pooled cohort equations, which was corrected after recalibration. Using a risk threshold of 7.5%, addition of the polygenic risk score to pooled cohort equations resulted in a net reclassification improvement of 4.4% (95% CI, 3.5% to 5.3%) for cases and -0.4% (95% CI, -0.5% to -0.4%) for noncases (overall net reclassification improvement, 4.0% [95% CI, 3.1% to 4.9%]). Conclusions and Relevance: The addition of a polygenic risk score for CAD to pooled cohort equations was associated with a statistically significant, yet modest, improvement in the predictive accuracy for incident CAD and improved risk stratification for only a small proportion of individuals. The use of genetic information over the pooled cohort equations model warrants further investigation before clinical implementation.

Pre-diagnostic blood immune markers, incidence and progression of B-cell lymphoma and multiple myeloma: Univariate and functionally informed multivariate analyses.

Recent prospective studies have shown that dysregulation of the immune system may precede the development of B-cell lymphomas (BCL) in immunocompetent individuals. However, to date, the studies were restricted to a few immune markers, which were considered separately. Using a nested case-control study within two European prospective cohorts, we measured plasma levels of 28 immune markers in samples collected a median of 6 years before diagnosis (range 2.01-15.97) in 268 incident cases of BCL (including multiple myeloma [MM]) and matched controls. Linear mixed models and partial least square analyses were used to analyze the association between levels of immune marker and the incidence of BCL and its main histological subtypes and to investigate potential biomarkers predictive of the time to diagnosis. Linear mixed model analyses identified associations linking lower levels of fibroblast growth factor-2 (FGF-2 p = 7.2 × 10-4 ) and transforming growth factor alpha (TGF-α, p = 6.5 × 10-5 ) and BCL incidence. Analyses stratified by histological subtypes identified inverse associations for MM subtype including FGF-2 (p = 7.8 × 10-7 ), TGF-α (p = 4.08 × 10-5 ), fractalkine (p = 1.12 × 10-3 ), monocyte chemotactic protein-3 (p = 1.36 × 10-4 ), macrophage inflammatory protein 1-alpha (p = 4.6 × 10-4 ) and vascular endothelial growth factor (p = 4.23 × 10-5 ). Our results also provided marginal support for already reported associations between chemokines and diffuse large BCL (DLBCL) and cytokines and chronic lymphocytic leukemia (CLL). Case-only analyses showed that Granulocyte-macrophage colony stimulating factor levels were consistently higher closer to diagnosis, which provides further evidence of its role in tumor progression. In conclusion, our study suggests a role of growth-factors in the incidence of MM and of chemokine and cytokine regulation in DLBCL and CLL.

Sex inequalities in cardiovascular risk prediction.

AIMS: Evaluate sex differences in cardiovascular disease (CVD) risk prediction, including use of i) optimal sex-specific risk predictors and ii) sex-specific risk thresholds. METHODS AND RESULTS: Prospective cohort study using UK Biobank, including 121,724 and 182,632 healthy men and women, respectively, aged 38-73 years at baseline. There were 11,899 (men) and 9,110 (women) incident CVD cases (hospitalization or mortality) with median 12.1 years follow-up. We used recalibrated Pooled Cohort Equations (PCE, 7.5% 10-year risk threshold as per US guidelines), QRISK3 (10% 10-year risk threshold as per UK guidelines) and Cox survival models using sparse sex-specific variable sets (via LASSO stability selection) to predict CVD risk separately in men and women. LASSO stability selection included 12 variables in common between men and women, with three additional variables selected for men and one for women. C-statistics were slightly lower for PCE than QRISK3 and models using stably-selected variables, but were similar between men and women: 0.67 [0.66-0.68], 0.70 [0.69-0.71], and 0.71 [0.70-0.72] in men and 0.69 [0.68-0.70], 0.72 [0.71-0.73], and 0.72 [0.71-0.73] in women for PCE, QRISK3 and models using stably-selected variables, respectively. At current clinically implemented risk thresholds, test sensitivity was markedly lower in women than men for all models: at 7.5% 10-year risk, sensitivity was 65.1% and 68.2% in men and 24.0% and 33.4% in women for PCE and models using stably-selected variables, respectively; at 10% 10-year risk, sensitivity was 53.7% and 52.3% in men and 16.8% and 20.2% in women for QRISK3 and models using stably-selected variables, respectively. Specificity was correspondingly higher in women than men. However, the sensitivity in women at 5% 10-year risk threshold increased to 50.1%, 58.5% and 55.7% for PCE, QRISK3 and models using stably-selected variables, respectively. CONCLUSIONS: Use of sparse sex-specific variables improved CVD risk prediction compared with PCE but not QRISK3. At current risk thresholds, PCE and QRISK3 work less well for women than men but sensitivity was improved in women using a 5% 10-year risk threshold. Use of sex-specific risk thresholds should be considered in any re-evaluation of CVD risk calculators. TRANSLATIONAL PERSPECTIVE: Cardiovascular disease (CVD) risk prediction is an important component of clinical risk management and disease prevention. We find that at risk prediction thresholds used by currently applied risk prediction algorithms (PCE 7.5% 10-year risk threshold in the US and QRISK3 10% risk threshold in the UK), sensitivity of these risk prediction tools is markedly lower in women than in men. This sex inequality implies that women are proportionately less likely to receive appropriate clinical management including lipid-lowering therapy. If the risk prediction threshold is lowered to 5% 10-year risk in women, then sensitivity in women is substantially increased.

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.

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.

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.

Life-course socioeconomic factors are associated with markers of epigenetic aging in a population-based study.

Adverse socioeconomic circumstances negatively affect the functioning of biological systems, but the underlying mechanisms remain only partially understood. Here, we explore the associations between life-course socioeconomic factors and four markers of epigenetic aging in a population-based setting. We included 684 participants (52 % women, mean age 52.6 ± 15.5 years) from a population and family-based Swiss study. We used nine life-course socioeconomic indicators as the main exposure variables, and four blood-derived, second generation markers of epigenetic aging as the outcome variables (Levine's DNAmPhenoAge, DunedinPoAm38, GrimAge epigenetic age acceleration (EAA), and the mortality risk score (MS)). First, we investigated the associations between socioeconomic indicators and markers of epigenetic aging via mixed-effect linear regression models, adjusting for age, sex, participant's recruitment center, familial structure (random-effect covariate), seasonality of blood sampling, and technical covariates. Second, we implemented counterfactual mediation analysis to investigate life-course and intermediate mechanisms underlying the socioeconomic gradient in epigenetic aging. Effect-size estimates were assessed using regression coefficients and counterfactual mediation parameters, along with their respective 95 % confidence intervals. Individuals reporting a low father's occupation, adverse financial conditions in childhood, a low income, having financial difficulties, or experiencing unfavorable socioeconomic trajectories were epigenetically older and had a higher mortality risk score than their more advantaged counterparts. Specifically, this corresponded to an average increase of 1.1-1.5 years for Levine's epigenetic age (ß and 95 %CI range, ß (minimum and maximum): 1.1-1.5 95 %CI[0.0-0.2; 2.3-3.0]), 1.1-1.5 additional years for GrimAge (ß: 1.1-1.5 95 %CI[0.2-0.6; 1.9-3.0]), a 1-3 % higher DunedinPoAm38 age acceleration (ß: 0.01-0.03 95 %CI[0.00; 0.03-0.04]), and a 10-50 % higher MS score (ß: 0.1-0.4 95 %CI[0.0-0.2; 0.3-0.4]) for the aforementioned socioeconomic indicators. By exploring the life-course mechanisms underlying the socioeconomic gradient in epigenetic aging, we found that both childhood and adulthood socioeconomic factors contributed to epigenetic aging, and that detrimental lifestyle factors mediated the relation between socioeconomic circumstances in adulthood and EAA (31-89 % mediated proportion). This study provides emerging evidence for an association between disadvantaged life-course socioeconomic circumstances and detrimental epigenetic aging patterns, supporting the "sensitive-period" life-course model. Counterfactual mediation analyses further indicated that the effect of socioeconomic factors in adulthood operates through detrimental lifestyle factors, whereas associations involving early-life socioeconomic factors were less clear.

Variant-specific symptoms of COVID-19 in a study of 1,542,510 adults in England.

Infection with SARS-CoV-2 virus is associated with a wide range of symptoms. The REal-time Assessment of Community Transmission -1 (REACT-1) study monitored the spread and clinical manifestation of SARS-CoV-2 among random samples of the population in England from 1 May 2020 to 31 March 2022. We show changing symptom profiles associated with the different variants over that period, with lower reporting of loss of sense of smell or taste for Omicron compared to previous variants, and higher reporting of cold-like and influenza-like symptoms, controlling for vaccination status. Contrary to the perception that recent variants have become successively milder, Omicron BA.2 was associated with reporting more symptoms, with greater disruption to daily activities, than BA.1. With restrictions lifted and routine testing limited in many countries, monitoring the changing symptom profiles associated with SARS-CoV-2 infection and effects on daily activities will become increasingly important.