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Background: The EU LifeCycle Project was launched in 2017 to combine, harmonize, and analyze data from more than 250,000 participants across Europe and Australia, involving cohorts participating in the EU-funded LifeCycle Project. The purpose of this cohort description is to provide a detailed overview of the major measures within mental health domains that are available in 17 European and Australian cohorts participating in the LifeCycle Project. Methods: Data on cognitive, behavioral, and psychological development has been collected on participants from birth until adulthood through questionnaire and medical data. We developed an inventory of the available data by mapping individual instruments, domain types, and age groups, providing the basis for statistical harmonization across mental health measures. Results: The mental health data in LifeCycle contain longitudinal and cross-sectional data from birth throughout the life course, covering domains across a wide range of behavioral and psychopathology indicators and outcomes, including executive function, depression, ADHD, and cognition. These data span a unique combination of qualitative data collected through behavioral/cognitive/mental health questionnaires and examination, as well as data from biological samples and indices in the form of imaging (MRI, fetal ultrasound) and DNA methylation data. Harmonized variables on a subset of mental health domains have been developed, providing statistical equivalence of measures required for longitudinal meta-analyses across instruments and cohorts. Conclusion: Mental health data harmonized through the LifeCycle project can be used to study life-course trajectories and exposure-outcome models that examine early life risk factors for mental illness and develop predictive markers for later-life disease.

PDF file - 120K, This is a more detailed discussion of the microarray data.

Cells undergoing malignant transformation often exhibit a shift in cellular metabolism from oxidative phosphorylation to glycolysis. This glycolytic shift, called the Warburg effect, provides a mechanistic basis for targeting glycolysis to suppress carcinogenesis through the use of dietary caloric restriction and energy restriction-mimetic agents (ERMA). We recently reported the development of a novel class of ERMAs that exhibits high potency in eliciting starvation-associated cellular responses and epigenetic changes in cancer cells though glucose uptake inhibition. The lead ERMA in this class, OSU-CG5, decreases the production of ATP and NADH in LNCaP prostate cancer cells. In this study, we examined the effect of OSU-CG5 on the severity of preneoplastic lesions in male transgenic adenocarcinoma of the mouse prostate (TRAMP) mice. Daily oral treatment with OSU-CG5 at 100 mg/kg from 6 to 10 weeks of age resulted in a statistically significant decrease in the weight of urogenital tract and microdissected dorsal, lateral, and anterior prostatic lobes relative to vehicle controls. The suppressive effect of OSU-CG5 was evidenced by marked decreases in Ki67 immunostaining and proliferating cell nuclear antigen (PCNA) expression in the prostate. OSU-CG5 treatment was not associated with evidence of systemic toxicity. Microarray analysis indicated a central role for Akt, and Western blot analysis showed reduced phosphorylation and/or expression levels of Akt, Src, androgen receptor, and insulin-like growth factor-1 receptor in prostate lobes. These findings support further investigation of OSU-CG5 as a potential chemopreventive agent. Cancer Prev Res; 6(3); 232–41. ©2012 AACR.

PDF file - 213K, Supplemental Table 1 is a table that lists the average sum of the adjusted lesion scores (SALS) for each lobe of the prostate, as well as the average score for the most severe and most common lesion in each lobe. Supplemental tables 2 through 5 provide detailed analysis of the microarray data.

BackgroundCommon pregnancy and perinatal complications are associated with offspring cardiometabolic risk factors. These complications may influence multiple metabolic traits in the offspring and these associations might differ with offspring age.MethodsWe used data from eight population-based cohort studies to examine and compare associations of pre-eclampsia (PE), gestational hypertension (GH), gestational diabetes (GD), preterm birth (PTB), small (SGA) and large (LGA) for gestational age (vs. appropriate size for gestational age (AGA)) with up to 167 plasma/serum-based nuclear magnetic resonance-derived metabolic traits encompassing lipids, lipoproteins, fatty acids, amino acids, ketones, glycerides/phospholipids, glycolysis, fluid balance, and inflammation. Confounder-adjusted regression models were used to examine associations (adjusted for maternal education, parity age at pregnancy, ethnicity, pre/early pregnancy body mass index and smoking, and offspring sex and age at metabolic trait assessment), and results were combined using meta-analysis by five age categories representing different periods of the offspring life course: neonates (cord blood), infancy (mean ages: 1.1–1.6 years), childhood (4.2–7.5 years); adolescence (12.0–16.0 years), and adulthood (22.0–67.8 years).ResultsOffspring numbers for each age category/analysis varied from 8925 adults (441 PTB) to 1181 infants (135 GD); 48.4% to 60.0% were females. Pregnancy complications (PE, GH, GD) were each associated with up to three metabolic traits in neonates (P≤0.001) with some evidence of persistence to older ages. PTB and SGA were associated with 32 and 12 metabolic traits in neonates respectively, which included an adjusted standardised mean difference of −0.89 standard deviation (SD) units for albumin with PTB (95% CI: −1.10 to −0.69, P=1.3×10−17) and −0.41 SD for total lipids in medium HDL with SGA (95% CI: −0.56 to −0.25, P=2.6×10−7), with some evidence of persistence to older ages. LGA was inversely associated with 19 metabolic traits including lower levels of cholesterol, lipoproteins, fatty acids, and amino acids, with associations emerging in adolescence, (e.g. −0.11 SD total fatty acids, 95% CI: −0.18 to −0.05, P=0.0009), and attenuating with older age across adulthood Background: Common pregnancy and perinatal complications are associated with offspring cardiometabolic risk factors. These complications may influence multiple metabolic traits in the offspring and these associations might differ with offspring age. Methods: We used data from eight population-based cohort studies to examine and compare associations of pre-eclampsia (PE), gestational hypertension (GH), gestational diabetes (GD), preterm birth (PTB), small (SGA) and large (LGA) for gestational age (vs. appropriate size for gestational age (AGA)) with up to 167 plasma/serum-based nuclear magnetic resonance-derived metabolic traits encompassing lipids, lipoproteins, fatty acids, amino acids, ketones, glycerides/phospholipids, glycolysis, fluid balance, and inflammation. Confounder-adjusted regression models were used to examine associations (adjusted for maternal education, parity age at pregnancy, ethnicity, pre/early pregnancy body mass index and smoking, and offspring sex and age at metabolic trait assessment), and results were combined using meta-analysis by five age categories representing different periods of the offspring life course: neonates (cord blood), infancy (mean ages: 1.1–1.6 years), childhood (4.2–7.5 years); adolescence (12.0–16.0 years), and adulthood (22.0–67.8 years). Results: Offspring numbers for each age category/analysis varied from 8925 adults (441 PTB) to 1181 infants (135 GD); 48.4% to 60.0% were females. Pregnancy complications (PE, GH, GD) were each associated with up to three metabolic traits in neonates (P≤0.001) with some evidence of persistence to older ages. PTB and SGA were associated with 32 and 12 metabolic traits in neonates respectively, which included an adjusted standardised mean difference of −0.89 standard deviation (SD) units for albumin with PTB (95% CI: −1.10 to −0.69, P=1.3×10−17) and −0.41 SD for total lipids in medium HDL with SGA (95% CI: −0.56 to −0.25, P=2.6×10−7), with some evidence of persistence to older ages. LGA was inversely associated with 19 metabolic traits including lower levels of cholesterol, lipoproteins, fatty acids, and amino acids, with associations emerging in adolescence, (e.g. −0.11 SD total fatty acids, 95% CI: −0.18 to −0.05, P=0.0009), and attenuating with older age across adulthood. Conclusions: These reassuring findings suggest little evidence of wide-spread and long-term impact of common pregnancy and perinatal complications on offspring metabolic traits, with most associations only observed for newborns rather than older ages, and for perinatal rather than pregnancy complications.

Postoperative radiation therapy (RT) is an underused standard-of-care intervention for patients with prostate cancer and recurrence/adverse pathologic features after radical prostatectomy. Although stereotactic body RT (SBRT) is a well-studied and convenient option for definitive treatment, data on the postprostatectomy setting are extremely limited. The purpose of this study was to evaluate short-term physician-scored genitourinary (GU) and gastrointestinal (GI) toxicities and patient-reported outcomes after postprostatectomy SBRT.The SCIMITAR trial was a phase 2, dual-center, open-label, single-arm trial that enrolled patients with postoperative prostate-specific antigen0.03 ng/mL or adverse pathologic features. Coprimary endpoints were 4-year biochemical recurrence-free survival, physician-scored acute and late GU and GI toxicities by the Common Terminology Criteria for Adverse Events (version 4.03) scale, and patient-reported quality-of-life (QOL) outcomes, as represented by the Expanded Prostate Cancer Index-26 and the International Prostate Symptom Score. Patients received SBRT 30 to 34 Gy/5 fractions to the prostate bed ± bed boost ± pelvic nodes with computed tomography (CTgRT) or magnetic resonance imaging guidance (MRgRT) in a nonrandomized fashion. Physician-scored toxicities and patient-reported QOL outcomes were collected at baseline and at 1, 3, and 6 months of follow-up. Univariable and multivariable analyses were performed to evaluate predictors of toxicities and QOL outcomes.One hundred participants were enrolled (CTgRT, n = 69; MRgRT, n = 31). The median follow-up was 29.5 months (CTgRT: 33.3 months, MRgRT: 22.6 months). The median (range) prostate bed dose was 32 (30-34) Gy. Acute and late grade 2 GU toxicities were both 9% while acute and late grade 2 GI toxicities were 5% and 0%, respectively. Three patients had grade 3 toxicity (n = 1 GU, n = 2 GI). No patient receiving MRgRT had grade 3 GU or grade ≥2 GI toxicity. Compared with CTgRT, MRgRT was associated with a 30.5% (95% confidence interval, 11.6%-49.5%) reduction in any-grade acute GI toxicity (P = .006). MRgRT was independently associated with improved any-grade GI toxicity and improved bowel QOL.Postprostatectomy SBRT was well tolerated at short-term follow-up. MRgRT may decrease GI toxicity. Longer toxicity and/or efficacy follow-up and randomized studies are needed.

The Horizon2020 LifeCycle Project is a cross-cohort collaboration which brings together data from multiple birth cohorts from across Europe and Australia to facilitate studies on the influence of early-life exposures on later health outcomes. A major product of this collaboration has been the establishment of a FAIR (findable, accessible, interoperable and reusable) data resource known as the EU Child Cohort Network. Here we focus on the EU Child Cohort Network's core variables. These are a set of basic variables, derivable by the majority of participating cohorts and frequently used as covariates or exposures in lifecourse research. First, we describe the process by which the list of core variables was established. Second, we explain the protocol according to which these variables were harmonised in order to make them interoperable. Third, we describe the catalogue developed to ensure that the network's data are findable and reusable. Finally, we describe the core data, including the proportion of variables harmonised by each cohort and the number of children for whom harmonised core data are available. EU Child Cohort Network data will be analysed using a federated analysis platform, removing the need to physically transfer data and thus making the data more accessible to researchers. The network will add value to participating cohorts by increasing statistical power and exposure heterogeneity, as well as facilitating cross-cohort comparisons, cross-validation and replication. Our aim is to motivate other cohorts to join the network and encourage the use of the EU Child Cohort Network by the wider research community. The LifeCycle project received funding from the European Union’s Horizon 2020 research and innovation programme (Grant Agreement No. 733206 LifeCycle). All study specific acknowledgements and funding are presented in the supplementary material. This manuscript reflects only the author’s view and the Commission is not responsible for any use that may be made of the information it contains.

MicroRNA-34 (miR-34) is widely studied for its role in tumor suppression, DNA regulation, and cell cycle regulation. During DNA damage, miR-34 is directly upregulated by p53, inhibiting proliferation and promoting DNA repair. Accordingly, radiation therapy induces upregulation of miR-34 expression, coordinating several downstream radiation-induced stress responses. Recent work has demonstrated the existence of a stable, unphosphorylated pool of miR-34, which is rapidly phosphorylated and activated via loading onto Argonaute after ionizing radiation, independent of de novo transcription. Since Argonaute binding to the 5’ phosphate is also important for miRNA stability, we hypothesize that a separate mechanism must exist to stabilize the unphosphorylated pool of miR-34. Mass spectrometry performed on co-precipitates of biotinylated mimics of unphosphorylated miR-34 identified predominantly nuclear proteins, with a notable enrichment for proteins localized in nuclear paraspeckles. Nuclear paraspeckles are a phase-separated nuclear subcompartment comprised of several key structural proteins and a long noncoding RNA NEAT1_2 that prevents mixing of internal contents with the rest of the nucleoplasm and are canonically implicated in regulating gene expression by sequestering inosine-modified mRNA. To further investigate the role of nuclear paraspeckles in the stability or sequestering of unphosphorylated miR-34, stable cell lines with deletion of a a motif required for stability of paraspeckles through disruption of the lncRNA NEAT1_2 were created (A549 NEAT1 dTH). Interestingly, luciferase reporter assays for miR-34 activity in the NEAT1 dTH lines showed a general loss of miR-34 activity after radiation compared to wildtype cells which exhibit radiation induced activation of miR-34. This loss of activity occurred both immediately after radiation, which is attributable to phosphorylation of the unphosphorylated pool, and at later time points after radiation, which has been attributed to de novo miRNA production. Ongoing research will monitor colocalization of proteins known to activate the unphosphorylated pool with paraspeckles to determine if paraspeckles coordinate activation of unphosphorylated miR-34, as well as will investigate the potential impact of disruption of paraspeckles on de novo miR-34 production. A better understanding of the interplay between nuclear paraspeckles and stress-responsive miRNA production may provide a means to selectively modify cancer stress responses and uncover novel mechanisms in miRNA production and coordination. Citation Format: Graham H. Read, David W. Salzman, Julian Whitelegge, Whitaker Cohn, Tiffany Yang, Cynthia Tsang, Ihsan A. Turk, Joanne B. Weidhaas. Analyzing nuclear paraspeckle-dependent protection of unphosphorylated MicroRNA [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2815.

Background It has been hypothesised that greater maternal adiposity before or during pregnancy causes greater offspring adiposity in childhood and adulthood, via causal intrauterine or periconceptional mechanisms. Previous Mendelian randomization (MR) estimates were imprecise, with wide confidence intervals that included potentially important protective or adverse effects, and may have been biased by collider effects or imperfect adjustment for genetic inheritance. Here we use an improved MR approach to investigate whether associations between maternal pre-/early pregnancy body mass index (BMI) and offspring adiposity from birth to adolescence are causal, or are instead due to confounding. Methods and findings We undertook confounder adjusted multivariable (MV) regression and Mendelian randomization (MR) using mother-offspring pairs from two UK cohorts: Avon Longitudinal Study of Parents and Children (ALSPAC) and Born in Bradford (BiB). In ALSPAC and BiB the outcomes were birthweight (BW; N = 9339) and BMI at age 1 (N = 8659) and 4 years (N = 7575), and in ALSPAC only we investigated BMI at 10 (N = 4476) and 15 years (N = 4112) and dual-energy X-ray absorptiometry (DXA) determined fat mass index (FMI) from age 10– 18 years (N = 2659 to 3855). We compared MR results from several polygenic risk scores (PRS), calculated from maternal non-transmitted alleles at between 29 and 80,939 single nucleotide polymorphisms (SNPs). MV and MR showed a consistent positive association of maternal BMI with BW, but for adiposity at most older ages MR estimates were weaker than MV estimates. In MV regression a one standard deviation (SD) higher maternal BMI was associated with a 0.13 (95% confidence interval [CI]: 0.10, 0.16) SD increase in offspring BW. The corresponding MR estimate from the strongest PRS (including up to 80,939 SNPs) was 0.14 (95% CI: 0.05, 0.23), with no difference between the two estimates (Pdifference = 0.84). For 15 year BMI the MV and MR estimates (80,939 SNPs) were 0.32 (95% CI: 0.29, 0.36) and 0.13 (95% CI: 0.01, 0.24) respectively (Pdifference = 1.0e-3). Results for FMI were similar to those for adolescent BMI. As the number of SNPs included in the PRS increased, the MR confidence intervals narrowed and the effect estimates for adolescent adiposity became closer to the MV estimates. Sensitivity analyses suggested the stronger effects with more SNPs were explained by horizontal pleiotropic bias away from zero. Consequently, the unbiased difference between the MV and MR estimates is probably greater than shown in our main analyses. Furthermore, MR estimates from IVs with fewer SNPs provided no strong evidence for a causal effect on adolescent adiposity. Conclusions Our results suggest that higher maternal pre-/early-pregnancy BMI is not a key driver of higher adiposity in the next generation. Thus, they support interventions that target the whole population for reducing overweight and obesity, rather than a specific focus on women of reproductive age.

FPGAs are increasingly common in modern applications, and cloud providers now support on-demand FPGA acceleration in data centers. Applications in data centers run on virtual infrastructure, where consolidation, multi-tenancy, and workload migration enable economies of scale that are fundamental to the provider's business. However, a general strategy for virtualizing FPGAs has yet to emerge. While manufacturers struggle with hardware-based approaches, we propose a compiler/runtime-based solution called Synergy. We show a compiler transformation for Verilog programs that produces code able to yield control to software at sub-clock-tick granularity according to the semantics of the original program. Synergy uses this property to efficiently support core virtualization primitives: suspend and resume, program migration, and spatial/temporal multiplexing, on hardware which is available today. We use Synergy to virtualize FPGA workloads across a cluster of Altera SoCs and Xilinx FPGAs on Amazon F1. The workloads require no modification, run within 3-4x of unvirtualized performance, and incur a modest increase in FPGA fabric utilization.

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.

BackgroundWeight perception may differ by ethnicity but is not well-understood, particularly among migrants to the United Kingdom (UK). It is also unknown whether a figure rating scale (FRS) or perceived weight question (PWQ) is more accurate for assessing body weight perception.MethodsAt 24-months postpartum, women in the Born in Bradford cohort (Bradford, UK) completed the 9-item Stunkard FRS and a 7-category PWQ. Both scales were condensed to weight categories representing the World Health Organisation cut-offs. Weighted kappa statistics assessed agreement between measured and perceived weight categories. χ2 and Fisher’s exact tests assessed categories of misperception by ethnicity and migration and logistic regression was used to examine odds of underestimation.ResultsThirty percent of white British and 23% of Pakistani-origin women had obesity. Agreement between measured and perceived weight categories were similar for white British women using either a visual scale or weight question (FRS: κ [95%CI]: 0.61 [0.56, 0.65]; PWQ: 0.61 [0.55, 0.68)]. Overall agreement was lower for Pakistani-origin women with the visual scale performing better (FRS (0.58 [0.52, 0.63]) vs PWQ (0.47 [0.40, 0.54]). Pakistani-origin women, particularly those born outside the UK, were more likely to underestimate their body weight compared to white British women; this was greater with the PWQ (18% vs 10%; pConclusionWe observed ethnic differences in weight misperception with Pakistani-origin women more likely to underestimate their weight compared to white British women. Our findings suggest visual scales, rather than perceived weight questions, are more appropriate for the self-assessment of body weight.

The impact of long COVID is increasingly recognised, but risk factors are poorly characterised. We analysed questionnaire data on symptom duration from 10 longitudinal study (LS) samples and electronic healthcare records (EHR) to investigate sociodemographic and health risk factors associated with long COVID, as part of the UK National Core Study for Longitudinal Health and Wellbeing.MethodsAnalysis was conducted on 6,899 adults self-reporting COVID-19 from 45,096 participants of the UK LS, and on 3,327 cases assigned a long COVID code in primary care EHR out of 1,199,812 adults diagnosed with acute COVID-19. In LS, we derived two outcomes: symptoms lasting 4+ weeks and symptoms lasting 12+ weeks. Associations of potential risk factors (age, sex, ethnicity, socioeconomic factors, smoking, general and mental health, overweight/obesity, diabetes, hypertension, hypercholesterolaemia, and asthma) with these two outcomes were assessed, using logistic regression, with meta-analyses of findings presented alongside equivalent results from EHR analyses.ResultsFunctionally limiting long COVID for 12+ weeks affected between 1.2% (age 20), and 4.8% (age 63) of people reporting COVID-19 in LS. The proportion reporting symptoms overall for 12+ weeks ranged from 7.8 (mean age 28) to 17% (mean age 58) and for 4+ weeks 4.2% (age 20) to 33.1% (age 56). Age was associated with a linear increase in long COVID between age 20-70. Being female (LS: OR=1.49; 95%CI:1.24-1.79; EHR: OR=1.51 [1.41-1.61]), poor pre-pandemic mental health (LS: OR=1.46 [1.17-1.83]; EHR: OR=1.57 [1.47-1.68]) and poor general health (LS: OR=1.62 [1.25-2.09]; EHR: OR=1.26; [1.18-1.35]) were associated with higher risk of long COVID. Individuals with asthma also had higher risk (LS: OR=1.32 [1.07-1.62]; EHR: OR=1.56 [1.46-1.67]), as did those categorised as overweight or obese (LS: OR=1.25 [1.01-1.55]; EHR: OR=1.31 [1.21-1.42]) though associations for symptoms lasting 12+ weeks were less pronounced. Non-white ethnic minority groups had lower 4+ week symptom risk (LS: OR=0.32 [0.22-0.47]), a finding consistent in EHR. Associations were not observed for other risk factors. Few participants in the studies had been admitted to hospital (0.8-5.2%).ConclusionsLong COVID is clearly distributed differentially according to several sociodemographic and pre-existing health factors. Establishing which of these risk factors are causal and predisposing is necessary to further inform strategies for preventing and treating long COVID.

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.

Purpose Participation in community programmes by the Roma community is low, whilst this community presents with high risk of poor health and low levels of wellbeing. To improve rates of participation in programmes, compatibility must be achieved between implementation efforts and levels of readiness in the community. The Community Readiness Model (CRM) is a widely used toolkit which provides an indication of how prepared and willing a community is to take action on specific issues. The purpose of this paper is to present findings from a CRM assessment for the Eastern European Roma community in Bradford, UK, on issues related to nutrition and obesity. Design/methodology/approach The authors interviewed key respondents identified as knowledgeable about the Roma community using the CRM. This approach applies a mixed methodology incorporating readiness scores and qualitative data. A mean community readiness score was calculated enabling researchers to place the community in one of nine possible stages of readiness. Interview transcripts were analysed using a qualitative framework analysis to generate the contextual information. Findings An overall score consistent with vague awareness was achieved, which indicates a low level of community readiness. This score suggests that there will be a low likelihood of participation in currently available nutrition and obesity programmes. Originality/value To our knowledge, this is the first study to apply the CRM in the Roma community for any issue. The authors present the findings for each of the six dimensions that make up the CRM together with salient qualitative findings.

Maternal risk factors, such as body mass index (BMI), age, smoking, parity and ethnicity, are associated with risk of pregnancy-related disorders. However, many women who experience gestational diabetes (GDM), gestational hypertension (GHT), pre-eclampsia (PE), have a spontaneous preterm birth (sPTB) or an offspring born small/large for gestational age (SGA/LGA), do not display any of these 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=2,000 and n=1,000) 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 used ten-fold cross-validation and penalised regression to create prediction models. We compared the predictive performance of 3 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 training and testing 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 (combined risk factor and metabolite model). The combined 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, with AUC ∼0.5. 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 improve 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. Developing these prediction tools could enable tailoring antenatal care to improve earlier and more accurate identification of high-risk women.

Sufficient feeding is essential for animals’ survival, which requires a cognitive capability to facilitate food seeking, but the neurobiological processes regulating food seeking are not fully understood. Here we show that stimulation of agouti-related peptide-expressing (AgRP) neurons triggers a long-term depression (LTD) of spontaneous excitatory post-synaptic current (sEPSC) in adjacent pro-opiomelanocortin (POMC) neurons and in most of their distant synaptic targets, including neurons in the paraventricular nucleus of the thalamus (PVT). The AgRP-induced sEPCS LTD can be enhanced by fasting but blunted by satiety signals, e.g. leptin and insulin. Mice subjected to food-seeking tasks develop similar neural plasticity in AgRP-innervated PVT neurons. Further, ablation of the majority of AgRP neurons, or only a subset of AgRP neurons that project to the PVT, impairs animals’ ability to associate spatial and contextual cues with food availability during food seeking. A similar impairment can be also induced by optogenetic inhibition of the AgRP→PVT projections. Together, these results indicate that the AgRP→PVT circuit is necessary for food seeking.

Early life stages are vulnerable to environmental hazards and present important windows of opportunity for lifelong disease prevention. This makes early life a relevant starting point for exposome studies. The Advancing Tools for Human Early Lifecourse Exposome Research and Translation (ATHLETE) project aims to develop a toolbox of exposome tools and a Europe-wide exposome cohort that will be used to systematically quantify the effects of a wide range of community- and individual-level environmental risk factors on mental, cardiometabolic, and respiratory health outcomes and associated biological pathways, longitudinally from early pregnancy through to adolescence. Exposome tool and data development include as follows: (1) a findable, accessible, interoperable, reusable (FAIR) data infrastructure for early life exposome cohort data, including 16 prospective birth cohorts in 11 European countries; (2) targeted and nontargeted approaches to measure a wide range of environmental exposures (urban, chemical, physical, behavioral, social); (3) advanced statistical and toxicological strategies to analyze complex multidimensional exposome data; (4) estimation of associations between the exposome and early organ development, health trajectories, and biological (metagenomic, metabolomic, epigenetic, aging, and stress) pathways; (5) intervention strategies to improve early life urban and chemical exposomes, co-produced with local communities; and (6) child health impacts and associated costs related to the exposome. Data, tools, and results will be assembled in an openly accessible toolbox, which will provide great opportunities for researchers, policymakers, and other stakeholders, beyond the duration of the project. ATHLETE's results will help to better understand and prevent health damage from environmental exposures and their mixtures from the earliest parts of the life course onward. ISGlobal acknowledges support from the Spanish Ministry of Science, Innovation and Universities through the “Centro de Excelencia Severo Ochoa 2019–2023” Program (CEX2018-000806-S) and support from the Generalitat de Catalunya through the Centres de Recerca de Catalunya Program. We acknowledge collaboration with European projects, specifically: Human Early Life Exposome (HELIX) (FP7 grant agreement number 308333), LifeCycle (H2020 grant agreement number 733206), and Connecting Europe and Canada in personalized health (EUCAN-Connect) (H2020 Grant Agreement number 824989). The Born in Bradford (BiB) cohort is only possible because of the enthusiasm and commitment of the Children and Parents in BiB. We are grateful to all the participants, health professionals, and researchers who have made BiB happen. The BiB cohort is only possible because of the enthusiasm and commitment of the children and parents in BiB. We are grateful to all the participants, health professionals, schools, and researchers who have made BiB happen. BiB has received funding from the Wellcome Trust (101597), a joint grant from the UK Medical Research Council and UK Economic and Social Science Research Council (MR/N024391/1), a British Heart Foundation Clinical Study grant (CS/16/4/32482). The Norwegian Mother, Father and Child Cohort Study is supported by the Norwegian Ministry of Health and Care Services and the Ministry of Education and Research. We are grateful to all the participating families in Norway who take part in this ongoing cohort study. The Danish National Birth Cohort was established with a significant grant from the Danish National Research Foundation. Additional support was obtained from the Danish Regional Committees, the Pharmacy Foundation, the Egmont Foundation, the March of Dimes Birth Defects Foundation, the Health Foundation, and other minor grants. The Danish National Birth Cohort Biobank has been supported by the Novo Nordisk Foundation and the Lundbeck Foundation. Follow-up of mothers and children have been supported by the Danish Medical Research Council (SSVF 0646, 271-08-0839/06-066023, O602-01042B, 0602-02738B); the Lundbeck Foundation (195/04, R100-A9193); The Innovation Fund Denmark 0603-00294B (09-067124); the Nordea Foundation (02-2013-2014); Aarhus Ideas (AU R9-A959-13-S804); University of Copenhagen Strategic Grant (IFSV 2012); and the Danish Council for Independent Research (DFF—4183-00594 and DFF—4183-00152). The general design of the Generation R Study is made possible by financial support from the Erasmus MC, University Medical Center, Rotterdam, Erasmus University Rotterdam, Netherlands Organization for Health Research and Development (ZonMw), Netherlands Organisation for Scientific Research (NWO), Ministry of Health, Welfare and Sport, and Ministry of Youth and Families. This project received funding from the European Union’s Horizon 2020 research and innovation programme (LIFECYCLE, grant agreement number 733206, 2016; EUCAN-Connect grant agreement number 824989; ATHLETE, grant agreement number 874583). We gratefully acknowledge the contribution of participants, research collaborators, general practitioners, hospitals, midwives, and pharmacies in Rotterdam. The INfancia y Medio Ambiente cohort (INMA) Sababell study was funded by grants from Instituto de Salud Carlos III (Red INMA G03/176; CB06/02/0041; PI041436; PI081151 incl. Fondo Europeo de Desarollo Regional (FEDER) funds; PI12/01890 incl. FEDER funds; CP13/00054 incl. FEDER funds; PI15/00118 incl. FEDER funds; CP16/00128 incl. FEDER funds; PI16/00118 incl. FEDER funds; PI16/00261 incl. FEDER funds; PI17/01340 incl. FEDER funds, PI18/00547 incl. FEDER funds, PI20/01695 incl. FEDER funds), Centro de Investigación Biomédica en Red - Epidemiología y Salud Pública (CIBERESP), Generalitat de Catalunya-Comissió Interdepartamental de Recerca i Innovació Tecnològia 1999SGR 00241, Generalitat de Catalunya-Agencia de Gestión de Ayudas Universitarias y de Investigación (2009 SGR 501, 2014 SGR 822), Fundació La Marató de TV3 (090430), Spanish Ministry of Economy and Competitiveness (SAF2012-32991 incl. FEDER funds), Agence Nationale de Securite Sanitaire de l’Alimentation de l’Environnement et du Travail (1262C0010; EST-2016 RF-21, EST-19 RF-04), and the European Commission (261357, 308333, 603794 and 634453). The INMA Valencia study was supported by grants from Instituto de Salud Carlos III (FIS-FEDER: 13/2032, 13/1944, 14/00891, 16/1288, 17/00663, and 19/1338; Miguel Servet-FSE: CP15/0025 and MSII16/00051, 00051, and MSII20/0006), Alicia Koplowitz Foundation 2017, Generalitat Valenciana (AICO/2020/285), and Spanish Association Against Cancer (AECC) “Seed Ideas” 2019 (IDEAS19098LOPE). INMA Gipuzkoa was funded by grants from Instituto de Salud Carlos III (FIS-PI06/0867, FIS-PI09/00090, FIS-PI13/02187, and FIS-PI18/01142 incl. FEDER funds), CIBERESP, Department of Health of the Basque Government (2005111093, 2009111069, 2013111089, and 2015111065), and the Provincial Government of Gipuzkoa (DFG06/002, DFG08/001, and DFG15/221) and annual agreements with the municipalities of the study area (Zumarraga, Urretxu, Legazpi, Azkoitia y Azpeitia y Beasain). The Sepages cohort would like to thank the Grenoble University Hospital (CHU-GA) biobank (bb-0033-00069). We thank the Sepages Study group (E. Eyriey, P. Hoffmann, E. Hullo, J. Lepeule, C. Llerena, S. Lyon-Caen, X. Morin, A. Morlot, C. Philippat, I. Pin, J. Quentin, V. Siroux, R. Slama) and the participants of the Suivi de l’Exposition à la Pollution Atmosphérique durant la Grossesse et Effets sur la Santé (SEPAGES) study. We acknowledge support from Auvergne-Rhône-Alpes Région, Soutien aux coopérations universitaires et scientifiques internationales fund to support collaborations between Catalunya and Auvergne-Rhône-Alpes. SEPAGES cohort was supported by the European Research Council (consolidator grant N 311765-E-DOHaD, Principal Investigator [PI], R. Slama), by French National Research Agency (ANR), the ANR (Pregnancy, Air Pollution, Epigenetics and Respiratory health project ANR-12-PDOC-0029-01, PI, J. Lepeule; A Longitudinal Analysis of Effects of Early Life Exposure to Phenols on Health in Humans project, 14-CE21-0007-01, PI, R. Slama; Gut Microbiota in early childhood and Maternal Environmental exposures project, PI, R. Slama; Prenatal Exposure to Tobacco smoking and Air Pollution and Effects on offspring respiratory and neurodevelopmental outcomes ANR 18-CE36-005, PI, J. Lepeule). The Central European Longitudinal Study of Pregnacy and Childhood: The Next Generation (TNG) cohort is supported by the RECETOX research infrastructure (the Ministry of Education, Youth and Sports of the Czech Republic: LM2018121) and the CETOCOEN EXCELLENCE Teaming 2 project of European Union (EU) Horizon 2020 (857560) and the MEYS of the Czech Republic (02.1.01/0.0/0.0/18_ 046/0015975). The Barcelona Life Study Cohort has received funding from European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme under grant agreement number (785994) (Prenatal exposure to urban air pollution and pre and post-natal brain development project) and from the Health Effects Institute (HEI) under Agreement number 4959-RFPA15-1/18-1 (FRONTIER project). Genotyping in the HELIX study was supported by project PI17/01225 (Instituto de Salud Carlos III, co-funded by European Union [European Regional Development Fund], “A way to make Europe”) and the Centro Nacional de Genotipado-CEGEN (PRB2-ISCIII). The Nascita e Infanzia: gli Effetti dell’Ambiente cohort was partially funded by the Compagnia SanPaolo Foundation and the Piedmont Region. The Piccolipiù project was funded by the Italian National Center for Disease Prevention and Control (National Centre for Disease Prevention and Control grants years 2010 and 2014) and by the Italian Ministry of Health (art 12 and 12 bis D.lgs 502/92). The Perturbateurs Endocriniens: Étude Longitudinale sur les Anomalies de la Grossesse, l’Infertilité et l’Enfance study is supported by National Institute of Health and Medical Research and has received multiple funds from the ANR, the French Agency for Food, Environmental and Occupational Health and Safety (ANSES), the Fondation de France, the National Institute for Public Health Surveillance (InVS), the French, Ministry of Labor, the French Ministry of Health, and the French Ministry of Ecology. DataSHIELD is funded under a group of projects that underpin a program of development and application of secure methods for co-analysis, data sharing, and visualization in the Population Health Sciences Institute at Newcastle University (United Kingdom). These include: the “Connected Health Cities project” (North East and North Cumbria) funded by the UK Department of Health (RES/0150/7943/202); the “EUCanConnect project” (European Commission H2020 Flagship Collaboration with Canada); the “58FORWARDS project” (Fostering new Opportunities for Researchers via Wider Access to Research Data and Samples) funded jointly by the Wellcome Trust and the Medical Research Council (108439/Z/15/Z); and the “METADAC project” (Managing Ethico-social, Technical and Administrative issues in Data ACcess) funded jointly by the Medical Research Council, the Wellcome Trust, and the Economic and Social Research Council (MR/N01104X/1 and MR/N01104X/2). We acknowledge the Molecular Genetics Information System team, including Fleur Kelpin, Tommy de Boer, Mariska Slofstra, Connor Stroomberg, Jelmer Veen, Jeroen van Veen, Fernanda de Andrade, Marije van der Geest, Dieuwke Roelofs-Prins, Dennis Hendriksen, Bart Charbon, Joeri van der Velde, Max Postema, Erik Schaberg, Christiaan Hilbrands, Alexander Kellmann, and Luuk Dijkhuis.

BackgroundCongenital heart diseases (CHDs) are the most common congenital anomaly. The causes of CHDs are largely unknown, but intrauterine mechanisms appear to be important. Higher prenatal body mass index (BMI), smoking and alcohol consumption are associated with increased risk of CHDs. Whether these are causal is unclear.MethodsSeven European birth cohorts including 232,390 offspring 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 and compared maternal-specific confounder-adjusted associations to similarly adjusted associations of paternal exposures. If there is a causal intrauterine effect of a maternal pregnancy exposure, we would expect to see a maternal-specific association, with no (or weaker) association with the paternal exposure.ResultsThere were 2,469 CHD cases (1.1%). The association of mean BMI with CHD was null and similar in mothers (adjusted OR per 1kg/m2: 1.00 (95%CI: 0.99, 1.02)) and fathers (OR: 1.01 (0.99, 1.03)). The 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)). 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.ConclusionsWe have shown that maternal, but not paternal, pregnancy smoking is associated with an increase in odds of offspring CHDs, primarily non-severe CHDs, providing evidence of an intrauterine effect. We found no strong evidence of an intrauterine effect of higher maternal BMI or alcohol consumption on offspring CHDs. Our findings provide further evidence for why smoking cessation is important during pregnancy.

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.

BACKGROUND Bisphenol A (BPA) and phthalates metabolites are linked to a variety of adverse health consequences but studies have not explored their association with growth trajectories. OBJECTIVE Explore body mass index (BMI) trajectories for tertile exposures to BPA and phthalates metabolites in the third trimester of pregnancy. METHODS We constructed BMI (kg/m2 ) trajectories from birth to 14 years in a birth cohort of 249 children from Mexico City using tertiles of third trimester maternal urinary concentrations of BPA and phthalates metabolites. Fractional age polynomials and mixed effects models were fit separately by sex. Predicted models were plotted for each metabolite tertile with the covariates mother's education and BMI centered at average values. RESULTS Highest predicted BMI trajectories for female children were observed for third tertile exposure to the phthalate metabolite mono(2-ethyl-5-carboxypentyl) phthalate. In male children, first tertile exposure to mono-isobutyl phthalate and monobenzyl phthalate and second tertile exposure to mono(2-ethylhexyl) phthalate and mono(2-ethyl-5-hydroxyhexyl) phthalate predicted the highest BMI trajectory by adolescence. There was no relationshsip between BPA and child growth trajectory. CONCLUSIONS These results suggest sex-specific differences in BMI trajectories by levels of metabolite exposure. Additional studies are needed to consider growth through adolescence in assessing the association of pregnancy exposures on child's BMI.