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Background: Family history (FH) of pancreatic cancer (PC) has been associated with an increased risk of PC, but little is known regarding the role of inherited/environmental factors or that of FH of other comorbidities in PC risk. We aimed to address these issues using multiple methodological approaches.Methods: Case-control study including 1431 PC cases and 1090 controls and a reconstructed-cohort study (N = 16 747) made up of their first-degree relatives (FDR). Logistic regression was used to evaluate PC risk associated with FH of cancer, diabetes, allergies, asthma, cystic fibrosis and chronic pancreatitis by relative type and number of affected relatives, by smoking status and other potential effect modifiers, and by tumour stage and location. Familial aggregation of cancer was assessed within the cohort using Cox proportional hazard regression.Results: FH of PC was associated with an increased PC risk [odds ratio (OR) = 2.68; 95% confidence interval (CI): 2.27-4.06] when compared with cancer-free FH, the risk being greater when ≥ 2 FDRs suffered PC (OR = 3.88; 95% CI: 2.96-9.73) and among current smokers (OR = 3.16; 95% CI: 2.56-5.78, interaction FHPC*smoking P-value = 0.04). PC cumulative risk by age 75 was 2.2% among FDRs of cases and 0.7% in those of controls [hazard ratio (HR) = 2.42; 95% CI: 2.16-2.71]. PC risk was significantly associated with FH of cancer (OR = 1.30; 95% CI: 1.13-1.54) and diabetes (OR = 1.24; 95% CI: 1.01-1.52), but not with FH of other diseases.Conclusions: The concordant findings using both approaches strengthen the notion that FH of cancer, PC or diabetes confers a higher PC risk. Smoking notably increases PC risk associated with FH of PC. Further evaluation of these associations should be undertaken to guide PC prevention strategies. [ABSTRACT FROM AUTHOR]

Background Mice models suggest epigenetic inheritance induced by parental allergic disease activity. However, we know little of how parental disease activity before conception influences offspring's asthma and allergy in humans. Objective We aimed to assess the associations of parental asthma severity, bronchial hyperresponsiveness ( BHR), and total and specific IgEs, measured before conception vs. after birth, with offspring asthma and hayfever. Methods The study included 4293 participants (mean age 34, 47% men) from the European Community Respiratory Health Survey ( ECRHS) with information on asthma symptom severity, BHR, total and specific IgEs from 1991 to 1993, and data on 9100 offspring born 1972-2012. Adjusted relative risk ratios ( aRRR) for associations of parental clinical outcome with offspring allergic disease were estimated with multinomial logistic regressions. Results Offspring asthma with hayfever was more strongly associated with parental BHR and specific IgE measured before conception than after birth [ BHR: aRRR = 2.96 (95% CI: 1.92, 4.57) and 1.40 (1.03, 1.91), respectively; specific IgEs: 3.08 (2.13, 4.45) and 1.83 (1.45, 2.31), respectively]. This was confirmed in a sensitivity analysis of a subgroup of offspring aged 11-22 years with information on parental disease activity both before and after birth. Conclusion & Clinical Relevance Parental BHR and specific IgE were associated with offspring asthma and hayfever, with the strongest associations observed with clinical assessment before conception as compared to after birth of the child. If the hypothesis is confirmed in other studies, parental disease activity assessed before conception may prove useful for identifying children at risk for developing asthma with hayfever. [ABSTRACT FROM AUTHOR]

Abstract: The aim of this study is to find and test a predictive model that could be suitable to estimate the outdoor NO2 concentrations at individual level, by integrating ecological measurements recorded by local monitoring stations with individual information collected by a questionnaire. For this purpose, the data from the Italian centres of the European Community Respiratory Health Survey II (ECRHS II) has been used. Outdoor NO2 concentrations were measured using NO2 passive sampling tubes (PS-NO2), exposed outdoor for 14 days, between January 2001 and January 2003. Simultaneously, average NO2 concentrations were collected from all the monitoring stations of the three centres (MS-NO2). Individual measurements carried out with passive samplers were compared with the corresponding NO2 2-week concentrations obtained as the average of all local (background and traffic) monitoring stations (MS-NO2). A multiple linear regression model was fitted to the data using the 2-week PS-NO2 concentrations as the response variable and questionnaire information and MS-NO2 concentrations as predictors. The model minimizing the root mean square error (RMSE), obtained from a ten-fold cross validation, was selected. The model with the best predictive ability included centre, season of the survey, MS-NO2 concentrations, type and age of building, residential area and reported intensity of heavy-duty traffic and explained the 68.9% of the variance. The non-parametric correlation between PS-NO2 and the concentrations estimated by the model is 0.81 (95% CI: 0.77–0.85). This study shows that over short periods (2 weeks) a good prediction of home outdoor exposure to NO2 can be achieved by simply combining routinely collected ecological data with dwelling characteristics and self-reported intensity of heavy traffic. Further studies are needed to extend this prediction to long-term exposure. [Copyright &y& Elsevier]

Background: Pancreatic ductal adenocarcinoma (PDAC) is usually diagnosed in late adulthood; therefore, many patients suffer or have suffered from other diseases. Identifying disease patterns associated with PDAC risk may enable a better characterization of high-risk patients. Methods: Multimorbidity patterns (MPs) were assessed from 17 self-reported conditions using hierarchical clustering, principal component and factor analyses in 1705 PDAC cases and 1084 controls from a European population. Their association with PDAC was evaluated using adjusted logistic regression models. Time since diagnosis of morbidities to PDAC diagnosis/ recruitment was stratified into recent (<3 years) and long term (⩾3 years). The MPs and PDAC genetic networks were explored with DisGeNET bioinformatics-tool which focuses on gene-diseases associations available in curated databases. Results: Three MPs were observed: gastric (heartburn, acid regurgitation, Helicobacter pylori infection and ulcer), metabolic syndrome (obesity, type-2 diabetes, hypercholesterolemia and hypertension) and atopic (nasal allergies, skin allergies and asthma). Strong associations with PDAC were observed for ⩾2 recently diagnosed gastric conditions [odds ratio (OR), 6.13; 95% confidence interval CI 3.01-12.5)] and for ⩾3 recently diagnosed metabolic syndrome conditions (OR, 1.61; 95% CI 1.11-2.35). Atopic conditions were negatively associated with PDAC (high adherence score OR for tertile III, 0.45; 95% CI, 0.36-0.55). Combining type-2 diabetes with gastric MP resulted in higher PDAC risk for recent (OR, 7.89; 95% CI 3.9-16.1) and long-term diagnosed conditions (OR, 1.86; 95% CI 1.29-2.67). A common genetic basis between MPs and PDAC was observed in the bioinformatics analysis. Conclusions: Specific multimorbidities aggregate and associate with PDAC in a time-dependent manner. A better characterization of a high-risk population for PDAC may help in the early diagnosis of this cancer. The common genetic basis between MP and PDAC points to a mechanistic link between these conditions. [ABSTRACT FROM AUTHOR]