Circulating Metabolites Associated with Alcohol Intake in the European Prospective Investigation into Cancer and Nutrition Cohort.

Identifying the metabolites associated with alcohol consumption may provide insights into the metabolic pathways through which alcohol may affect human health. We studied associations of alcohol consumption with circulating concentrations of 123 metabolites among 2974 healthy participants from the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Alcohol consumption at recruitment was self-reported through dietary questionnaires. Metabolite concentrations were measured by tandem mass spectrometry (BIOCRATES AbsoluteIDQTM p180 kit). Data were randomly divided into discovery (2/3) and replication (1/3) sets. Multivariable linear regression models were used to evaluate confounder-adjusted associations of alcohol consumption with metabolite concentrations. Metabolites significantly related to alcohol intake in the discovery set (FDR q-value < 0.05) were further tested in the replication set (Bonferroni-corrected p-value < 0.05). Of the 72 metabolites significantly related to alcohol intake in the discovery set, 34 were also significant in the replication analysis, including three acylcarnitines, the amino acid citrulline, four lysophosphatidylcholines, 13 diacylphosphatidylcholines, seven acyl-alkylphosphatidylcholines, and six sphingomyelins. Our results confirmed earlier findings that alcohol consumption was associated with several lipid metabolites, and possibly also with specific acylcarnitines and amino acids. This provides further leads for future research studies aiming at elucidating the mechanisms underlying the effects of alcohol in relation to morbid conditions

Association of Selenoprotein and Selenium Pathway Genotypes with Risk of Colorectal Cancer and Interaction with Selenium Status

Selenoprotein genetic variations and suboptimal selenium (Se) levels may contribute to the risk of colorectal cancer (CRC) development. We examined the association between CRC risk and genotype for single nucleotide polymorphisms (SNPs) in selenoprotein and Se metabolic pathway genes. Illumina Goldengate assays were designed and resulted in the genotyping of 1040 variants in 154 genes from 1420 cases and 1421 controls within the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Multivariable logistic regression revealed an association of 144 individual SNPs from 63 Se pathway genes with CRC risk. However, regarding the selenoprotein genes, only TXNRD1 rs11111979 retained borderline statistical significance after adjustment for correlated tests (P-ACT = 0.10; P-ACT significance threshold was P <0.1). SNPs in Wingless/Integrated (Wnt) and Transforming growth factor (TGF) beta-signaling genes (FRZB, SMAD3, SMAD7) from pathways affected by Se intake were also associated with CRC risk after multiple testing adjustments. Interactions with Se status (using existing serum Se and Selenoprotein P data) were tested at the SNP, gene, and pathway levels. Pathway analyses using the modified Adaptive Rank Truncated Product method suggested that genes and gene x Se status interactions in antioxidant, apoptosis, and TGF-beta signaling pathways may be associated with CRC risk. This study suggests that SNPs in the Se pathway alone or in combination with suboptimal Se status may contribute to CRC development.Peer reviewe

Plasma microRNAs as biomarkers of pancreatic cancer risk in a prospective cohort study

Accepted manuscript version. Published version available in International Journal of Cancer 2017, 141 (5):905–915 .Noninvasive biomarkers for early pancreatic ductal adenocarcinoma (PDAC) diagnosis and disease risk stratification are greatly needed. We conducted a nested case-control study within the Prospective Investigation into Cancer and Nutrition (EPIC) cohort to evaluate prediagnostic microRNAs (miRs) as biomarkers of subsequent PDAC risk. A panel of eight miRs (miR-10a, -10b, -21-3p, -21-5p, -30c, -106b, -155 and -212) based on previous evidence from our group was evaluated in 225 microscopically confirmed PDAC cases and 225 controls matched on center, sex, fasting status and age/date/time of blood collection. MiR levels in prediagnostic plasma samples were determined by quantitative RT-PCR. Logistic regression was used to model levels and PDAC risk, adjusting for covariates and to estimate area under the receiver operating characteristic curves (AUC). Plasma miR-10b, -21-5p, -30c and -106b levels were significantly higher in cases diagnosed within 2 years of blood collection compared to matched controls (all p-values <0.04). Based on adjusted logistic regression models, levels for six miRs (miR-10a, -10b, -21-5p, -30c, -155 and -212) overall, and for four miRs (-10a, -10b, -21-5p and -30c) at shorter follow-up time between blood collection and diagnosis (≤5 yr, ≤2 yr), were statistically significantly associated with risk. A score based on the panel showed a linear dose-response trend with risk (p-value = 0.0006). For shorter follow-up (≤5 yr), AUC for the score was 0.73, and for individual miRs ranged from 0.73 (miR-212) to 0.79 (miR-21-5p)

Net contribution and predictive ability of the CUN-BAE body fatness index in relation to cardiometabolic conditions

BACKGROUND: The CUN-BAE (Clínica Universidad de Navarra-Body adiposity estimator) index is an anthropometric index based on age, sex and body mass index (BMI) for a refined prediction of body fatness in adults. CUN-BAE may help detect metabolically unhealthy individuals with otherwise normal weight according to BMI or waist circumference (WC). The aim of this study was to evaluate whether CUN-BAE, independent of its components (BMI, age and sex), was associated with cardiometabolic conditions including arterial hypertension, diabetes mellitus and metabolic syndrome (MetS). METHODS: The ENRICA study was based on a cross-sectional sample of non-institutionalized men and women representative of the adult Spanish population. Body weight, height, and WC were measured in all participants. The residual of CUN-BAE (rCUN-BAE), i.e. the part of the index not explained by its components, was calculated. The associations of CUN-BAE, rCUN-BAE, BMI and WC with hypertension, diabetes and MetS were analysed by multivariate logistic regression, and the Akaike information criterion (AIC) was calculated. RESULTS: The sample included 12,122 individuals. rCUN-BAE was associated with hypertension (OR 1.14, 95% CI 1.07-1.21) and MetS (OR 1.48, 1.37-1.60), but not with diabetes (OR 1.05, 0.94-1.16). In subjects with a BMI?<?25 kg/m2, CUN-BAE was significantly associated with all three outcome variables. CUN-BAE was more strongly associated with the cardiometabolic conditions than BMI and WC and fit similar AICs. CONCLUSIONS: The CUN-BAE index for body fatness was positively associated with hypertension, diabetes and MetS in adults independent of BMI or WC. CUN-BAE may help to identify individuals with cardiometabolic conditions beyond BMI, but this needs to be confirmed in prospective settings.Funding: The ENRICA study was funded and financed by Sanofi-Aventis. Specific funding for this analysis came from the governmental Spain FIS PI12/1166 and PI11/01379 projects and from the “UAM Chair in Epidemiology and Control of Cardiovascular Risk”

Development and validation of circulating CA125 prediction models in postmenopausal women.

BACKGROUND: Cancer Antigen 125 (CA125) is currently the best available ovarian cancer screening biomarker. However, CA125 has been limited by low sensitivity and specificity in part due to normal variation between individuals. Personal characteristics that influence CA125 could be used to improve its performance as screening biomarker. METHODS: We developed and validated linear and dichotomous (≥35 U/mL) circulating CA125 prediction models in postmenopausal women without ovarian cancer who participated in one of five large population-based studies: Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO, n = 26,981), European Prospective Investigation into Cancer and Nutrition (EPIC, n = 861), the Nurses' Health Studies (NHS/NHSII, n = 81), and the New England Case Control Study (NEC, n = 923). The prediction models were developed using stepwise regression in PLCO and validated in EPIC, NHS/NHSII and NEC. RESULT: The linear CA125 prediction model, which included age, race, body mass index (BMI), smoking status and duration, parity, hysterectomy, age at menopause, and duration of hormone therapy (HT), explained 5% of the total variance of CA125. The correlation between measured and predicted CA125 was comparable in PLCO testing dataset (r = 0.18) and external validation datasets (r = 0.14). The dichotomous CA125 prediction model included age, race, BMI, smoking status and duration, hysterectomy, time since menopause, and duration of HT with AUC of 0.64 in PLCO and 0.80 in validation dataset. CONCLUSIONS: The linear prediction model explained a small portion of the total variability of CA125, suggesting the need to identify novel predictors of CA125. The dichotomous prediction model showed moderate discriminatory performance which validated well in independent dataset. Our dichotomous model could be valuable in identifying healthy women who may have elevated CA125 levels, which may contribute to reducing false positive tests using CA125 as screening biomarker

Menstrual Factors, Reproductive History, Hormone Use, and Urothelial Carcinoma Risk: AProspective Study in the EPIC Cohort

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The effect of precipitation and application rate on dicyandiamide persistence and efficiency in two Irish grassland soils

peer-reviewedThe nitrification inhibitor dicyandiamide (DCD) has had variable success in reducing nitrate (NO3-) leaching and nitrous oxide (N2O) emissions from soils receiving nitrogen (N) fertilisers. Factors such as soil type, temperature and moisture have been linked to the variable efficacy of DCD. Since DCD is water soluble it can be leached from the rooting zone where it is intended to inhibit nitrification. Intact soil columns (15 cm diameter by 35 cm long) were taken from luvic gleysol and haplic cambisol grassland sites and placed in growth chambers. DCD was applied at 15 or 30 kg DCD ha-1, with high or low precipitation. Leaching of DCD, mineral N and the residual soil DCD concentrations were determined over eight weeks High precipitation increased DCD in leachate and decreased recovery in soil. A soil x DCD rate interaction was detected for the DCD unaccounted (proxy for degraded DCD). In the cambisol degradation of DCD was high (circa 81%) and unaffected by DCD rate. In contrast DCD degradation in the gleysol was lower and differentially affected by rate, 67 and 46% for the 15 and 30 kg ha-1 treatments, respectively. Differences DCD degradation rates between soils may be related to differences in organic matter content and associated microbiological activity. Variable degradation rates of DCD in soil, unrelated to temperature or moisture, may contribute to varying DCD efficacy. Soil properties should be considered when tailoring DCD strategies for improving nitrogen use efficiency and crop yields, through the reduction of reactive nitrogen loss.This research was financially supported under the National Development Plan, through the Research Stimulus Fund, administered by the Department of Agriculture, Food and the Marine under grants 07519 and 07545

Net contribution and predictive ability of the CUN-BAE body fatness index in relation to cardiometabolic conditions.

The CUN-BAE (Clínica Universidad de Navarra-Body adiposity estimator) index is an anthropometric index based on age, sex and body mass index (BMI) for a refined prediction of body fatness in adults. CUN-BAE may help detect metabolically unhealthy individuals with otherwise normal weight according to BMI or waist circumference (WC). The aim of this study was to evaluate whether CUN-BAE, independent of its components (BMI, age and sex), was associated with cardiometabolic conditions including arterial hypertension, diabetes mellitus and metabolic syndrome (MetS). The ENRICA study was based on a cross-sectional sample of non-institutionalized men and women representative of the adult Spanish population. Body weight, height, and WC were measured in all participants. The residual of CUN-BAE (rCUN-BAE), i.e. the part of the index not explained by its components, was calculated. The associations of CUN-BAE, rCUN-BAE, BMI and WC with hypertension, diabetes and MetS were analysed by multivariate logistic regression, and the Akaike information criterion (AIC) was calculated. The sample included 12,122 individuals. rCUN-BAE was associated with hypertension (OR 1.14, 95% CI 1.07-1.21) and MetS (OR 1.48, 1.37-1.60), but not with diabetes (OR 1.05, 0.94-1.16). In subjects with a BMI  The CUN-BAE index for body fatness was positively associated with hypertension, diabetes and MetS in adults independent of BMI or WC. CUN-BAE may help to identify individuals with cardiometabolic conditions beyond BMI, but this needs to be confirmed in prospective settings

Breakfast Size and Prevalence of Metabolic Syndrome in the European Prospective Investigation into Cancer and Nutrition (EPIC) Spanish Cohort

Background: Recent evidence suggest that energy distribution during the daytimecould be a potential determinant for the development of metabolic syndrome (MetS). Objective: To cross-sectionally assess the association between breakfast size and the prevalence of MetS in Spanish adults. Methods: Our study included a subset of 3644 participants from the European Prospective Investigation into Cancer and Nutrition Spain study recontacted between 2017-2018. Information on diet, sociodemographic, lifestyle, sleep quality, and chronotype was collected using standardized questionnaires, while anthropometric and blood pressure data were measured in a face-to-face personal interview by a nurse. MetS was defined according to the Adult Treatment Panel III (ATPIII) definition by measuring serum levels of total cholesterol, tryglycerides and glucose. Breakfast size was calculated as: (energy from breakfast/total energy intake) * 2000 kcal. To evaluate the association between breakfast size and MetS prevalence, a multivariable logistic regression model adjusted by potential confounders was used to estimate OR and 95% CI. Results: Prevalence of MetS in our study was 40.7%. The mean breakfast size was 306.6 * 2000 kcal (15% of the total daily energy intake), with 14 (0.4%) participants skipping breakfast. Participants in the highest quartile of breakfast size had a lower MetS prevalence compared to participants in the lowest quartile (ORQ4vsQ1 = 0.62; 95% CI = 0.51-0.76; p-trend < 0.001). No modification of the estimated ORs by sex, breakfast time, and number of eating occasions per day were observed. Conclusion: Our results suggest that higher breakfast size is associated with lower prevalence of MetS in Spanish adults, supporting the importance of a high energy breakfast. Further prospective studies are necessary to confirm these findings