Epigenetic Regulation of F2RL3 Associates With Myocardial Infarction and Platelet Function.

BACKGROUND: DNA hypomethylation at the F2RL3 (F2R like thrombin or trypsin receptor 3) locus has been associated with both smoking and atherosclerotic cardiovascular disease; whether these smoking-related associations form a pathway to disease is unknown. F2RL3 encodes protease-activated receptor 4, a potent thrombin receptor expressed on platelets. Given the role of thrombin in platelet activation and the role of thrombus formation in myocardial infarction, alterations to this biological pathway could be important for ischemic cardiovascular disease. METHODS: We conducted multiple independent experiments to assess whether DNA hypomethylation at F2RL3 in response to smoking is associated with risk of myocardial infarction via changes to platelet reactivity. Using cohort data (N=3205), we explored the relationship between smoking, DNA hypomethylation at F2RL3, and myocardial infarction. We compared platelet reactivity in individuals with low versus high DNA methylation at F2RL3 (N=41). We used an in vitro model to explore the biological response of F2RL3 to cigarette smoke extract. Finally, a series of reporter constructs were used to investigate how differential methylation could impact F2RL3 gene expression. RESULTS: Observationally, DNA methylation at F2RL3 mediated an estimated 34% of the smoking effect on increased risk of myocardial infarction. An association between methylation group (low/high) and platelet reactivity was observed in response to PAR4 (protease-activated receptor 4) stimulation. In cells, cigarette smoke extract exposure was associated with a 4.9% to 9.3% reduction in DNA methylation at F2RL3 and a corresponding 1.7-(95% CI, 1.2-2.4, P=0.04) fold increase in F2RL3 mRNA. Results from reporter assays suggest the exon 2 region of F2RL3 may help control gene expression. CONCLUSIONS: Smoking-induced epigenetic DNA hypomethylation at F2RL3 appears to increase PAR4 expression with potential downstream consequences for platelet reactivity. Combined evidence here not only identifies F2RL3 DNA methylation as a possible contributory pathway from smoking to cardiovascular disease risk but from any feature potentially influencing F2RL3 regulation in a similar manner.

Social inequalities in pregnancy metabolic profile: findings from the multi-ethnic Born in Bradford cohort study.

BACKGROUND: Lower socioeconomic position (SEP) associates with adverse pregnancy and perinatal outcomes and with less favourable metabolic profile in nonpregnant adults. Socioeconomic differences in pregnancy metabolic profile are unknown. We investigated association between a composite measure of SEP and pregnancy metabolic profile in White European (WE) and South Asian (SA) women. METHODS: We included 3,905 WE and 4,404 SA pregnant women from a population-based UK cohort. Latent class analysis was applied to nineteen individual, household, and area-based SEP indicators (collected by questionnaires or linkage to residential address) to derive a composite SEP latent variable. Targeted nuclear magnetic resonance spectroscopy was used to determine 148 metabolic traits from mid-pregnancy serum samples. Associations between SEP and metabolic traits were examined using linear regressions adjusted for gestational age and weighted by latent class probabilities. RESULTS: Five SEP sub-groups were identified and labelled 'Highest SEP' (48% WE and 52% SA), 'High-Medium SEP' (77% and 23%), 'Medium SEP' (56% and 44%) 'Low-Medium SEP' (21% and 79%), and 'Lowest SEP' (52% and 48%). Lower SEP was associated with more adverse levels of 113 metabolic traits, including lower high-density lipoprotein (HDL) and higher triglycerides and very low-density lipoprotein (VLDL) traits. For example, mean standardized difference (95%CI) in concentration of small VLDL particles (vs. Highest SEP) was 0.12 standard deviation (SD) units (0.05 to 0.20) for 'Medium SEP' and 0.25SD (0.18 to 0.32) for 'Lowest SEP'. There was statistical evidence of ethnic differences in associations of SEP with 31 traits, primarily characterised by stronger associations in WE women e.g., mean difference in HDL cholesterol in WE and SA women respectively (vs. Highest-SEP) was -0.30SD (-0.41 to -0.20) and -0.16SD (-0.27 to -0.05) for 'Medium SEP', and -0.62SD (-0.72 to -0.52) and -0.29SD (-0.40 to -0.20) for 'Lowest SEP'. CONCLUSIONS: We found widespread socioeconomic differences in metabolic traits in pregnant WE and SA women residing in the UK. Further research is needed to understand whether the socioeconomic differences we observe here reflect pre-conception differences or differences in the metabolic pregnancy response. If replicated, it would be important to explore if these differences contribute to socioeconomic differences in pregnancy outcomes.

The effect of maternal BMI, smoking and alcohol on congenital heart diseases: a Mendelian randomisation study.

BACKGROUND: Congenital heart diseases (CHDs) remain a significant cause of infant morbidity and mortality. Epidemiological studies have explored maternal risk factors for offspring CHDs, but few have used genetic epidemiology methods to improve causal inference. METHODS: Three birth cohorts, including 65,510 mother/offspring pairs (N = 562 CHD cases) were included. We used Mendelian randomisation (MR) analyses to explore the effects of genetically predicted maternal body mass index (BMI), smoking and alcohol on offspring CHDs. We generated genetic risk scores (GRS) using summary data from large-scale genome-wide association studies (GWAS) and validated the strength and relevance of the genetic instrument for exposure levels during pregnancy. Logistic regression was used to estimate the odds ratio (OR) of CHD per 1 standard deviation (SD) higher GRS. Results for the three cohorts were combined using random-effects meta-analyses. We performed several sensitivity analyses including multivariable MR to check the robustness of our findings. RESULTS: The GRSs associated with the exposures during pregnancy in all three cohorts. The associations of the GRS for maternal BMI with offspring CHD (pooled OR (95% confidence interval) per 1SD higher GRS: 0.95 (0.88, 1.03)), lifetime smoking (pooled OR: 1.01 (0.93, 1.09)) and alcoholic drinks per week (pooled OR: 1.06 (0.98, 1.15)) were close to the null. Sensitivity analyses yielded similar results. CONCLUSIONS: Our results do not provide robust evidence of an effect of maternal BMI, smoking or alcohol on offspring CHDs. However, results were imprecise. Our findings need to be replicated, and highlight the need for more and larger studies with maternal and offspring genotype and offspring CHD data.

metaboprep: an R package for preanalysis data description and processing.

MOTIVATION: Metabolomics is an increasingly common part of health research and there is need for preanalytical data processing. Researchers typically need to characterize the data and to exclude errors within the context of the intended analysis. Whilst some preprocessing steps are common, there is currently a lack of standardization and reporting transparency for these procedures. RESULTS: Here, we introduce metaboprep, a standardized data processing workflow to extract and characterize high quality metabolomics datasets. The package extracts data from preformed worksheets, provides summary statistics and enables the user to select samples and metabolites for their analysis based on a set of quality metrics. A report summarizing quality metrics and the influence of available batch variables on the data are generated for the purpose of open disclosure. Where possible, we provide users flexibility in defining their own selection thresholds. AVAILABILITY AND IMPLEMENTATION: metaboprep is an open-source R package available at https://github.com/MRCIEU/metaboprep. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

An Evaluation of an Algorithm for the Selection of Flexible Survival Models for Cancer Immunotherapies: Pass or Fail?

BACKGROUND AND OBJECTIVE: Accurately extrapolating survival beyond trial follow-up is essential in a health technology assessment where model choice often substantially impacts estimates of clinical and cost effectiveness. Evidence suggests standard parametric models often provide poor fits to long-term data from immuno-oncology trials. Palmer et al. developed an algorithm to aid the selection of more flexible survival models for these interventions. We assess the usability of the algorithm, identify areas for improvement and evaluate whether it effectively identifies models capable of accurate extrapolation. METHODS: We applied the Palmer algorithm to the CheckMate-649 trial, which investigated nivolumab plus chemotherapy versus chemotherapy alone in patients with gastroesophageal adenocarcinoma. We evaluated the algorithm's performance by comparing survival estimates from identified models using the 12-month data cut to survival observed in the 48-month data cut. RESULTS: The Palmer algorithm offers a systematic procedure for model selection, encouraging detailed analyses and ensuring that crucial stages in the selection process are not overlooked. In our study, a range of models were identified as potentially appropriate for extrapolating survival, but only flexible parametric non-mixture cure models provided extrapolations that were plausible and accurately predicted subsequently observed survival. The algorithm could be improved with minor additions around the specification of hazard plots and setting out plausibility criteria. CONCLUSIONS: The Palmer algorithm provides a systematic framework for identifying suitable survival models, and for defining plausibility criteria for extrapolation validity. Using the algorithm ensures that model selection is based on explicit justification and evidence, which could reduce discordance in health technology appraisals.

Treatment Effect Waning in Immuno-oncology Health Technology Assessments: A Review of Assumptions and Supporting Evidence with Proposals to Guide Modelling.

Treatment effect waning (TEW) refers to the attenuation of treatment effects over time. Assumptions of a sustained immuno-oncologic treatment effect have been a source of contention in health technology assessment (HTA). We review how TEW has been addressed in HTA and in the wider scientific literature. We analysed company submissions to English language HTA agencies and summarised methods and assumptions used. We subsequently reviewed TEW-related work in the ISPOR Scientific Presentations Database and conducted a targeted literature review (TLR) for evidence of the maintenance of immuno-oncology (IO) treatment effects post-treatment discontinuation. We found no standardised approach adopted by companies in submissions to HTA agencies, with immediate TEW most used in scenario analyses. Independently fitted survival models do however suggest TEW may often be implicitly modelled. Materials in the ISPOR scientific database suggest gradual TEW is more plausible than immediate TEW. The TLR uncovered evidence of durable survival in patients treated with IOs but no evidence that directly addresses the presence or absence of TEW. Our HTA review shows the need for a consistent and appropriate implementation of TEW in oncology appraisals. However, the TLR highlights the absence of direct evidence on TEW in literature, as TEW is defined in terms of relative treatment effects-not absolute survival. We propose a sequence of steps for analysts to use when assessing whether a TEW scenario is necessary and appropriate to present in appraisals of IOs.

Incidence of diabetes after SARS-CoV-2 infection in England and the implications of COVID-19 vaccination: a retrospective cohort study of 16 million people.

BACKGROUND: Some studies have shown that the incidence of type 2 diabetes increases after a diagnosis of COVID-19, although the evidence is not conclusive. However, the effects of the COVID-19 vaccine on this association, or the effect on other diabetes subtypes, are not clear. We aimed to investigate the association between COVID-19 and incidence of type 2, type 1, gestational and non-specific diabetes, and the effect of COVID- 19 vaccination, up to 52 weeks after diagnosis. METHODS: In this retrospective cohort study, we investigated the diagnoses of incident diabetes following COVID-19 diagnosis in England in a pre-vaccination, vaccinated, and unvaccinated cohort using linked electronic health records. People alive and aged between 18 years and 110 years, registered with a general practitioner for at least 6 months before baseline, and with available data for sex, region, and area deprivation were included. Those with a previous COVID-19 diagnosis were excluded. We estimated adjusted hazard ratios (aHRs) comparing diabetes incidence after COVID-19 diagnosis with diabetes incidence before or in the absence of COVID-19 up to 102 weeks after diagnosis. Results were stratified by COVID-19 severity (categorised as hospitalised or non-hospitalised) and diabetes type. FINDINGS: 16 669 943 people were included in the pre-vaccination cohort (Jan 1, 2020-Dec 14, 2021), 12 279 669 in the vaccinated cohort, and 3 076 953 in the unvaccinated cohort (both June 1-Dec 14, 2021). In the pre-vaccination cohort, aHRs for the incidence of type 2 diabetes after COVID-19 (compared with before or in the absence of diagnosis) declined from 4·30 (95% CI 4·06-4·55) in weeks 1-4 to 1·24 (1·14-1.35) in weeks 53-102. aHRs were higher in unvaccinated people (8·76 [7·49-10·25]) than in vaccinated people (1·66 [1·50-1·84]) in weeks 1-4 and in patients hospitalised with COVID-19 (pre-vaccination cohort 28·3 [26·2-30·5]) in weeks 1-4 declining to 2·04 [1·72-2·42] in weeks 53-102) than in those who were not hospitalised (1·95 [1·78-2·13] in weeks 1-4 declining to 1·11 [1·01-1·22] in weeks 53-102). Type 2 diabetes persisted for 4 months after COVID-19 in around 60% of those diagnosed. Patterns were similar for type 1 diabetes, although excess incidence did not persist beyond 1 year after a COVID-19 diagnosis. INTERPRETATION: Elevated incidence of type 2 diabetes after COVID-19 is greater, and persists for longer, in people who were hospitalised with COVID-19 than in those who were not, and is markedly less apparent in people who have been vaccinated against COVID-19. Testing for type 2 diabetes after severe COVID-19 and the promotion of vaccination are important tools in addressing this public health problem. FUNDING: UK National Institute for Health and Care Research, UK Research and Innovation (UKRI) Medical Research Council, UKRI Engineering and Physical Sciences Research Council, Health Data Research UK, Diabetes UK, British Heart Foundation, and the Stroke Association.

COVID-19 and Mental Illnesses in Vaccinated and Unvaccinated People.

Importance: Associations have been found between COVID-19 and subsequent mental illness in both hospital- and population-based studies. However, evidence regarding which mental illnesses are associated with COVID-19 by vaccination status in these populations is limited. Objective: To determine which mental illnesses are associated with diagnosed COVID-19 by vaccination status in both hospitalized patients and the general population. Design, Setting, and Participants: This study was conducted in 3 cohorts, 1 before vaccine availability followed during the wild-type/Alpha variant eras (January 2020-June 2021) and 2 (vaccinated and unvaccinated) during the Delta variant era (June-December 2021). With National Health Service England approval, OpenSAFELY-TPP was used to access linked data from 24 million people registered with general practices in England using TPP SystmOne. People registered with a GP in England for at least 6 months and alive with known age between 18 and 110 years, sex, deprivation index information, and region at baseline were included. People were excluded if they had COVID-19 before baseline. Data were analyzed from July 2022 to June 2024. Exposure: Confirmed COVID-19 diagnosis recorded in primary care secondary care, testing data, or the death registry. Main Outcomes and Measures: Adjusted hazard ratios (aHRs) comparing the incidence of mental illnesses after diagnosis of COVID-19 with the incidence before or without COVID-19 for depression, serious mental illness, general anxiety, posttraumatic stress disorder, eating disorders, addiction, self-harm, and suicide. Results: The largest cohort, the pre-vaccine availability cohort, included 18 648 606 people (9 363 710 [50.2%] female and 9 284 896 [49.8%] male) with a median (IQR) age of 49 (34-64) years. The vaccinated cohort included 14 035 286 individuals (7 308 556 [52.1%] female and 6 726 730 [47.9%] male) with a median (IQR) age of 53 (38-67) years. The unvaccinated cohort included 3 242 215 individuals (1 363 401 [42.1%] female and 1 878 814 [57.9%] male) with a median (IQR) age of 35 (27-46) years. Incidence of most outcomes was elevated during weeks 1 through 4 after COVID-19 diagnosis, compared with before or without COVID-19, in each cohort. Incidence of mental illnesses was lower in the vaccinated cohort compared with the pre-vaccine availability and unvaccinated cohorts: aHRs for depression and serious mental illness during weeks 1 through 4 after COVID-19 were 1.93 (95% CI, 1.88-1.98) and 1.49 (95% CI, 1.41-1.57) in the pre-vaccine availability cohort and 1.79 (95% CI, 1.68-1.90) and 1.45 (95% CI, 1.27-1.65) in the unvaccinated cohort compared with 1.16 (95% CI, 1.12-1.20) and 0.91 (95% CI, 0.85-0.98) in the vaccinated cohort. Elevation in incidence was higher and persisted longer after hospitalization for COVID-19. Conclusions and Relevance: In this study, incidence of mental illnesses was elevated for up to a year following severe COVID-19 in unvaccinated people. These findings suggest that vaccination may mitigate the adverse effects of COVID-19 on mental health.

Impact of vaccination on the association of COVID-19 with cardiovascular diseases: An OpenSAFELY cohort study.

Infection with SARS-CoV-2 is associated with an increased risk of arterial and venous thrombotic events, but the implications of vaccination for this increased risk are uncertain. With the approval of NHS England, we quantified associations between COVID-19 diagnosis and cardiovascular diseases in different vaccination and variant eras using linked electronic health records for ~40% of the English population. We defined a 'pre-vaccination' cohort (18,210,937 people) in the wild-type/Alpha variant eras (January 2020-June 2021), and 'vaccinated' and 'unvaccinated' cohorts (13,572,399 and 3,161,485 people respectively) in the Delta variant era (June-December 2021). We showed that the incidence of each arterial thrombotic, venous thrombotic and other cardiovascular outcomes was substantially elevated during weeks 1-4 after COVID-19, compared with before or without COVID-19, but less markedly elevated in time periods beyond week 4. Hazard ratios were higher after hospitalised than non-hospitalised COVID-19 and higher in the pre-vaccination and unvaccinated cohorts than the vaccinated cohort. COVID-19 vaccination reduces the risk of cardiovascular events after COVID-19 infection. People who had COVID-19 before or without being vaccinated are at higher risk of cardiovascular events for at least two years.

Field-testing to determine power - cadence and torque - cadence profiles in professional road cyclists.

The aim of this study was to evaluate a field-based approach to determine torque-cadence and power-cadence profiles in professional cyclists and establish if this field-based protocol can differentiate between varying rider specialisations. Twenty-four male professional athletes from a World Tour cycling team participated in this investigation (Height = 1.84 ± 0.05 m, Weight = 72.3 ± 5.6 kg, Age = 25 ± 4 y). All riders were subsequently categorised into the following groups: 1) General Classification (GC) group; 2) sprinter group; and 3) classics group. All participants completed a specific sprint protocol in the field which included 6 times 6s sprints with varying gearing, starting cadences, starting speeds and position (i.e. seated vs standing). Power-cadence and torque-cadence profiles were determined based on the sprint outputs. There was a significant main effect of rider specialisation on the measured (sprint) variables (P≤0.03). Body weight, maximum power outputs (1s, 10s and modelled) and maximum torque were highest in the sprinter group, followed by the classics group, followed by the GC group. The protocol was able to differentiate between different rider specialisations (i.e. GC, sprinters, classics). The proposed methodology can contribute to individualising training content in the short-duration domain.HighlightsCommercially available power metres can be used to assess power-cadence and torque cadence relationships in the fieldKey differences are present for the modelled parameters between cyclists of different specialisationsProfiling a cyclist's power-cadence and torque-cadence relationship provides greater insight into the physiological mechanisms behind maximal power production.

Health and Well-Being in Surviving Congenital Heart Disease Patients: An Umbrella Review With Synthesis of Best Evidence.

Background: Advances in the management of congenital heart disease (CHD) patients have enabled improvement in long-term survival even for those with serious defects. Research priorities (for patients, families and clinicians) have shifted from a focus on how to improve survival to exploring long-term outcomes in patients with CHD. A comprehensive appraisal of available evidence could inform best practice to maximize health and well-being, and identify research gaps to direct further research toward patient and clinical need. We aimed to critically appraise all available published systematic reviews of health and well-being outcomes in adult patients with CHD. Methods: We conducted an umbrella review, including any systematic reviews that assessed the association of having vs. not having CHD with any long-term health (physical or mental), social (e.g., education, occupation) or well-being [e.g., quality of life (QoL)] outcome in adulthood (≥18-years). Results: Out of 1330 articles screened, we identified five systematic reviews of associations of CHD with adult outcomes. All but one (which studied QoL) explored health outcomes: one cardiovascular, two mental, and one mortality after transplant. CHD patients had a higher risk of stroke, coronary heart disease and heart failure, with the pooled relative risk (RR) for any outcome of 3.12 (95% CI: 3.01 to 3.24), with substantial heterogeneity (I2 = 99%) explained by the outcome being studied (stronger association for heart failure) and geography (stronger in Europe compared with other regions). CHD patients had a higher risk of anxiety (OR = 2.58 (1.45 to 4.59)], and higher mean scores for depression/anxiety symptoms (difference in means = -0.11 SD (-0.28 to 0.06), I2 = 94%)]. Compared with patients having a cardiac transplant for other (non-CHD) diseases, CHD patients had higher short-term mortality (RR at 30-days post-transplant = 2.18 [1.62 to 2.93)], with moderate heterogeneity (I2 = 41%) explained by previous surgery (higher mortality with prior Fontan/Glenn operation). All domains of QoL were lower in patients with Fontan's circulation than non-CHD adults. Conclusion: Adults with CHD have poorer cardiovascular, mental health and QoL outcomes, and higher short-term mortality after transplant. The paucity of systematic reviews, in particular for outcomes such as education, occupation and lifestyles, highlights the need for this to be made a priority by funders and researchers. Systematic Review Registration: [www.crd.york.ac.uk/prospero], identifier [CRD42020175034].

The Relationship of Maternal Gestational Mass Spectrometry-Derived Metabolites with Offspring Congenital Heart Disease: Results from Multivariable and Mendelian Randomization Analyses.

Background: It is plausible that maternal pregnancy metabolism influences the risk of offspring congenital heart disease (CHD). We sought to explore this through a systematic approach using different methods and data. Methods: We undertook multivariable logistic regression of the odds of CHD for 923 mass spectrometry (MS)-derived metabolites in a sub-sample of a UK birth cohort (Born in Bradford (BiB); N = 2605, 46 CHD cases). We considered metabolites reaching a p-value threshold <0.05 to be suggestively associated with CHD. We sought validation of our findings, by repeating the multivariable regression analysis within the BiB cohort for any suggestively associated metabolite that was measured by nuclear magnetic resonance (NMR) or clinical chemistry (N = 7296, 87 CHD cases), and by using genetic risk scores (GRS: weighted genetic risk scores of single nucleotide polymorphisms (SNPs) that were associated with any suggestive metabolite) in Mendelian randomization (MR) analyses. The MR analyses were performed in BiB and two additional European birth cohorts (N = 38,662, 319 CHD cases). Results: In the main multivariable analyses, we identified 44 metabolites suggestively associated with CHD, including those from the following super pathways: amino acids, lipids, co-factors and vitamins, xenobiotics, nucleotides, energy, and several unknown molecules. Of these 44, isoleucine and leucine were available in the larger BiB cohort (NMR), and for these the results were validated. The MR analyses were possible for 27/44 metabolites and for 11 there was consistency with the multivariable regression results. Conclusions: In summary, we have used complimentary data sources and statistical techniques to construct layers of evidence. We found that pregnancy amino acid metabolism, androgenic steroid lipids, and levels of succinylcarnitine could be important contributing factors for CHD.

Evaluating the direct effects of childhood adiposity on adult systemic metabolism: a multivariable Mendelian randomization analysis.

BACKGROUND: Individuals who are obese in childhood have an elevated risk of disease in adulthood. However, whether childhood adiposity directly impacts intermediate markers of this risk, independently of adult adiposity, is unclear. In this study, we have simultaneously evaluated the effects of childhood and adulthood body size on 123 systemic molecular biomarkers representing multiple metabolic pathways. METHODS: Two-sample Mendelian randomization (MR) was conducted to estimate the causal effect of childhood body size on a total of 123 nuclear magnetic resonance-based metabolic markers using summary genome-wide association study (GWAS) data from up to 24 925 adults. Multivariable MR was then applied to evaluate the direct effects of childhood body size on these metabolic markers whilst accounting for adult body size. Further MR analyses were undertaken to estimate the potential mediating effects of these circulating metabolites on the risk of coronary artery disease (CAD) in adulthood using a sample of 60 801 cases and 123 504 controls. RESULTS: Univariable analyses provided evidence that childhood body size has an effect on 42 of the 123 metabolic markers assessed (based on P < 4.07 × 10-4). However, the majority of these effects (35/42) substantially attenuated when accounting for adult body size using multivariable MR. We found little evidence that the biomarkers that were potentially influenced directly by childhood body size (leucine, isoleucine and tyrosine) mediate this effect onto adult disease risk. Very-low-density lipoprotein markers provided the strongest evidence of mediating the long-term effect of adiposity on CAD risk. CONCLUSIONS: Our findings suggest that childhood adiposity predominantly exerts its detrimental effect on adult systemic metabolism along a pathway that involves adulthood body size.

Do Mass Spectrometry-Derived Metabolomics Improve the Prediction of Pregnancy-Related Disorders? Findings from a UK Birth Cohort with Independent Validation.

Many women who experience gestational diabetes (GDM), gestational hypertension (GHT), pre-eclampsia (PE), have a spontaneous preterm birth (sPTB) or have an offspring born small/large for gestational age (SGA/LGA) do not meet the criteria for high-risk pregnancies based upon certain maternal risk factors. Tools that better predict these outcomes are needed to tailor antenatal care to risk. Recent studies have suggested that metabolomics may improve the prediction of these pregnancy-related disorders. These have largely been based on targeted platforms or focused on a single pregnancy outcome. The aim of this study was to assess the predictive ability of an untargeted platform of over 700 metabolites to predict the above pregnancy-related disorders in two cohorts. We used data collected from women in the Born in Bradford study (BiB; two sub-samples, n = 2000 and n = 1000) and the Pregnancy Outcome Prediction study (POPs; n = 827) to train, test and validate prediction models for GDM, PE, GHT, SGA, LGA and sPTB. We compared the predictive performance of three models: (1) risk factors (maternal age, pregnancy smoking, BMI, ethnicity and parity) (2) mass spectrometry (MS)-derived metabolites (n = 718 quantified metabolites, collected at 26-28 weeks' gestation) and (3) combined risk factors and metabolites. We used BiB for the training and testing of the models and POPs for independent validation. In both cohorts, discrimination for GDM, PE, LGA and SGA improved with the addition of metabolites to the risk factor model. The models' area under the curve (AUC) were similar for both cohorts, with good discrimination for GDM (AUC (95% CI) BiB 0.76 (0.71, 0.81) and POPs 0.76 (0.72, 0.81)) and LGA (BiB 0.86 (0.80, 0.91) and POPs 0.76 (0.60, 0.92)). Discrimination was improved for the combined models (compared to the risk factors models) for PE and SGA, with modest discrimination in both studies (PE-BiB 0.68 (0.58, 0.78) and POPs 0.66 (0.60, 0.71); SGA-BiB 0.68 (0.63, 0.74) and POPs 0.64 (0.59, 0.69)). Prediction for sPTB was poor in BiB and POPs for all models. In BiB, calibration for the combined models was good for GDM, LGA and SGA. Retained predictors include 4-hydroxyglutamate for GDM, LGA and PE and glycerol for GDM and PE. MS-derived metabolomics combined with maternal risk factors improves the prediction of GDM, PE, LGA and SGA, with good discrimination for GDM and LGA. Validation across two very different cohorts supports further investigation on whether the metabolites reflect novel causal paths to GDM and LGA.

Effect of Maternal Prepregnancy/Early-Pregnancy Body Mass Index and Pregnancy Smoking and Alcohol on Congenital Heart Diseases: A Parental Negative Control Study.

Background Congenital heart diseases (CHDs) are the most common congenital anomaly. The causes of CHDs are largely unknown. Higher prenatal body mass index (BMI), smoking, and alcohol consumption are associated with increased risk of CHDs. Whether these are causal is unclear. Methods and Results Seven European birth cohorts, including 232 390 offspring (2469 CHD cases [1.1%]), were included. We applied negative exposure paternal control analyses to explore the intrauterine effects of maternal BMI, smoking, and alcohol consumption during pregnancy, on offspring CHDs and CHD severity. We used logistic regression, adjusting for confounders and the other parent's exposure and combined estimates using a fixed-effects meta-analysis. In adjusted analyses, maternal overweight (odds ratio [OR], 1.15 [95% CI, 1.01-1.31]) and obesity (OR, 1.12 [95% CI, 0.93-1.36]), compared with normal weight, were associated with higher odds of CHD, but there was no clear evidence of a linear increase in odds across the whole BMI distribution. Associations of paternal overweight, obesity, and mean BMI were similar to the maternal associations. Maternal pregnancy smoking was associated with higher odds of CHD (OR, 1.11 [95% CI, 0.97-1.25]) but paternal smoking was not (OR, 0.96 [95% CI, 0.85-1.07]). The positive association with maternal smoking appeared to be driven by nonsevere CHD cases (OR, 1.22 [95% CI, 1.04-1.44]). Associations with maternal moderate/heavy pregnancy alcohol consumption were imprecisely estimated (OR, 1.16 [95% CI, 0.52-2.58]) and similar to those for paternal consumption. Conclusions We found evidence of an intrauterine effect for maternal smoking on offspring CHDs, but no evidence for higher maternal BMI or alcohol consumption. Our findings provide further support for the importance of smoking cessation during pregnancy.

Ascertaining and classifying cases of congenital anomalies in the ALSPAC birth cohort.

Congenital anomalies (CAs) are structural or functional disorders that occur during intrauterine life. Longitudinal cohort studies provide unique opportunities to investigate potential causes and consequences of these disorders. In this data note, we describe how we identified cases of major CAs, with a specific focus on congenital heart diseases (CHDs), in the Avon Longitudinal Study of Parents and Children (ALSPAC). We demonstrate that combining multiple sources of data including data from antenatal, delivery, primary and secondary health records, and parent-reported information can improve case ascertainment. Our approach identified 590 participants with a CA according to the European Surveillance of Congenital Anomalies (EUROCAT) guidelines, 127 of whom had a CHD. We describe the methods that identified these cases and provide statistics on subtypes of anomalies. The data note contains details on the processes required for researchers to access these data.

Metabolomics datasets in the Born in Bradford cohort.

Metabolomics is the quantification of small molecules, commonly known as metabolites. Collectively, these metabolites and their interactions within a biological system are known as the metabolome. The metabolome is a unique area of study, capturing influences from both genotype and environment. The availability of high-throughput technologies for quantifying large numbers of metabolites, as well as lipids and lipoprotein particles, has enabled detailed investigation of human metabolism in large-scale epidemiological studies. The Born in Bradford (BiB) cohort includes 12,453 women who experienced 13,776 pregnancies recruited between 2007-2011, their partners and their offspring. In this data note, we describe the metabolomic data available in BiB, profiled during pregnancy, in cord blood and during early life in the offspring. These include two platforms of metabolomic profiling: nuclear magnetic resonance and mass spectrometry. The maternal measures, taken at 26-28 weeks' gestation, can provide insight into the metabolome during pregnancy and how it relates to maternal and offspring health. The offspring cord blood measurements provide information on the fetal metabolome. These measures, alongside maternal pregnancy measures, can be used to explore how they may influence outcomes. The infant measures (taken around ages 12 and 24 months) provide a snapshot of the early life metabolome during a key phase of nutrition, environmental exposures, growth, and development. These metabolomic data can be examined alongside the BiB cohorts' extensive phenotype data from questionnaires, medical, educational and social record linkage, and other 'omics data.

The effect of maternal pre-/early-pregnancy BMI and pregnancy smoking and alcohol on congenital heart diseases: a parental negative control study.

BACKGROUND: Congenital heart diseases (CHDs) are the most common congenital anomaly. The causes of CHDs are largely unknown. Higher prenatal body mass index (BMI), smoking and alcohol consumption are associated with increased risk of CHDs. Whether these are causal is unclear. METHODS AND RESULTS: Seven European birth cohorts including 232,390 offspring (2,469 CHD cases [1.1%]) were included. We applied negative exposure paternal control analyses to explore the intrauterine effects of maternal BMI, smoking and alcohol consumption during pregnancy, on offspring CHDs and CHD severity. We used logistic regression and combined estimates using a fixed-effects meta-analysis. Analyses of BMI categories resulted in similar increased odds of CHD in overweight (mothers OR: 1.15 (1.01, 1.31) and fathers 1.10 (0.96, 1.27)) and obesity (mothers OR: 1.12 (0.93, 1.36) and fathers 1.16 (0.90, 1.50)). The association of mean BMI with CHD was null. Maternal smoking was associated with increased odds of CHD (OR: 1.11 (0.97, 1.25)) but paternal smoking was not (OR: 0.96 (0.85, 1.07)). The difference increased when removing offspring with genetic/chromosomal defects (mothers OR: 1.15 (1.01, 1.32) and fathers 0.93 (0.83, 1.05)). The positive association with maternal pregnancy smoking appeared to be driven by non-severe CHD cases (OR: 1.22 (1.04, 1.44)). Associations with maternal (OR: 1.16 (0.52, 2.58)) and paternal (OR: 1.23 (0.74, 2.06)) moderate/heavy pregnancy alcohol consumption were similar. CONCLUSIONS: We found evidence of an intrauterine effect for maternal smoking on offspring CHDs, but no evidence for higher maternal BMI or alcohol consumption. Our findings provide further support for why smoking cessation is important during pregnancy.

Prioritizing putative influential genes in cardiovascular disease susceptibility by applying tissue-specific Mendelian randomization.

BACKGROUND: The extent to which changes in gene expression can influence cardiovascular disease risk across different tissue types has not yet been systematically explored. We have developed an analysis pipeline that integrates tissue-specific gene expression, Mendelian randomization and multiple-trait colocalization to develop functional mechanistic insight into the causal pathway from a genetic variant to a complex trait. METHODS: We undertook an expression quantitative trait loci-wide association study to uncover genetic variants associated with both nearby gene expression and cardiovascular traits. Fine-mapping was performed to prioritize possible causal variants for detected associations. Two-sample Mendelian randomization (MR) was then applied using findings from genome-wide association studies (GWAS) to investigate whether changes in gene expression within certain tissue types may influence cardiovascular trait variation. We subsequently used Bayesian multiple-trait colocalization to further interrogate the findings and also gain insight into whether DNA methylation, as well as gene expression, may play a role in disease susceptibility. Finally, we applied our analysis pipeline genome-wide using summary statistics from large-scale GWAS. RESULTS: Eight genetic loci were associated with changes in gene expression and measures of cardiovascular function. Our MR analysis provided evidence of tissue-specific effects at multiple loci, of which the effects at the ADCY3 and FADS1 loci for body mass index and cholesterol, respectively, were particularly insightful. Multiple-trait colocalization uncovered evidence which suggested that changes in DNA methylation at the promoter region upstream of FADS1/TMEM258 may also affect cardiovascular trait variation along with gene expression. Furthermore, colocalization analyses uncovered evidence of tissue specificity between gene expression in liver tissue and cholesterol levels. Applying our pipeline genome-wide using summary statistics from GWAS uncovered 233 association signals at loci which represent promising candidates for further evaluation. CONCLUSIONS: Disease susceptibility can be influenced by differential changes in tissue-specific gene expression and DNA methylation. The approach undertaken in our study can be used to elucidate mechanisms in disease, as well as helping prioritize putative causal genes at associated loci where multiple nearby genes may be co-regulated. Future studies which continue to uncover quantitative trait loci for molecular traits across various tissue and cell types will further improve our capability to understand and prevent disease.