Project description:The prevalence of food hypersensitivity in the UK is still largely open to debate. Additionally its pathogenesis is also unclear although it is known that there are differing phenotypes. Determining its prevalence, along with identifying those factors associated with its development will help to assess its clinical importance within the national setting and also add to the debate on appropriate prevention strategies.A population based birth cohort study conducted in Hampshire, UK as part of the EuroPrevall birth cohort study. 1140 infants were recruited with 823 being followed up until 2 years of age. Infants with suspected food reactions were assessed including specific IgE measurement and skin prick testing. Diagnosis of food hypersensitivity was by positive double-blind, placebo-controlled food challenge (DBPCFC) where symptoms up to 48 h after the end of the food challenge were considered indicative of a food hypersensitivity. Factors associated with food hypersensitivity and its two phenotypes of IgE-mediated and non-IgE-mediated disease were modelled in a multivariable logistic regression analysis.Cumulative incidence of food hypersensitivity by 2 years of age was 5.0 %. The cumulative incidence for individual food allergens were hens' egg 2.7 % (1.6-3.8); cows' milk 2.4 % (1.4-3.5); peanut 0.7 % (0.1-1.3); soy 0.4 % (0.0-0.8); wheat 0.2 % (0.0-0.5) and 0.1 % (0.0-0.32) for fish. The cumulative incidence of IgE-mediated food allergy was 2.6 % with 2.1 % reacting to hens' egg. For non-IgE-mediated food allergy the cumulative incidence was 2.4 % (cows' milk 1.7 %). Predictors for any food hypersensitivity were wheeze, maternal atopy, increasing gestational age, age at first solid food introduction and mean healthy dietary pattern score. Predictors for IgE mediated allergy were eczema, rhinitis and healthy dietary pattern score whereas for non-IgE-mediated food allergy the predictors were dog in the home, healthy dietary pattern score, maternal consumption of probiotics during breastfeeding and age at first solid food introduction.Just under half the infants with confirmed food hypersensitivity had no demonstrable IgE. In an exploratory analysis, risk factors for this phenotype of food hypersensitivity differed from those for IgE-mediated food allergy except for a healthy infant diet which was associated with less risk for both phenotypes.

Project description:All subjects were recruited at Centennial Women?s Hospital and the Perinatal Research Center in Nashville, TN beginning in 2003. Pregnant women were enrolled during their first clinical visit after obtaining informed consent as described previously. Demographic and clinical data specific to the fetus was collected from clinical records. Gestational age of the neonate was determined by maternal reporting of the last menstrual period and corroboration by ultrasound dating. Accurate knowledge of gestational age (GA) is essential for proper monitoring and care of neonates. However, accurate GA measures are often not available. DNA methylation has previously been shown to associate with GA, and has been used to accurately predict chronological age in adults. In the current study, we examine whether DNA methylation in cord blood can be used to predict gestational age at birth. Results: We found that GA can be accurately predicted from DNA methylation of neonatal cord blood and blood spot samples (DNAm GA), using 148 CpG sites selected through elastic net regression in six training datasets (N=207). We evaluated predictive accuracy in six testing datasets (N=1,202), and found that the accuracy of DNAm GA meets or exceeds accuracy of gestational age estimates based on established methods. We also found an increased DNAm GA, relative to clinical GA, was associated with increased birthweight percentile (p=.00057), adjusting for GA, sex, and ancestry, suggesting that DNAm GA could represent developmental age more accurately than clinical estimates of GA. Conclusions: Further development of this predictor could provide a method of accurate neonatal estimation of GA for use in resource-limited populations, or in cases where GA cannot be estimated clinically. When clinical estimates are available, the predictor can be used to test hypotheses related to developmental age and other early life circumstances, and may provide increased accuracy beyond clinical estimates.

Project description:All subjects were recruited at Centennial Women?s Hospital and the Perinatal Research Center in Nashville, TN beginning in 2003. Pregnant women were enrolled during their first clinical visit after obtaining informed consent as described previously. Demographic and clinical data specific to the fetus was collected from clinical records. Gestational age of the neonate was determined by maternal reporting of the last menstrual period and corroboration by ultrasound dating. Accurate knowledge of gestational age (GA) is essential for proper monitoring and care of neonates. However, accurate GA measures are often not available. DNA methylation has previously been shown to associate with GA, and has been used to accurately predict chronological age in adults. In the current study, we examine whether DNA methylation in cord blood can be used to predict gestational age at birth. Results: We found that GA can be accurately predicted from DNA methylation of neonatal cord blood and blood spot samples (DNAm GA), using 148 CpG sites selected through elastic net regression in six training datasets (N=207). We evaluated predictive accuracy in six testing datasets (N=1,202), and found that the accuracy of DNAm GA meets or exceeds accuracy of gestational age estimates based on established methods. We also found an increased DNAm GA, relative to clinical GA, was associated with increased birthweight percentile (p=.00057), adjusting for GA, sex, and ancestry, suggesting that DNAm GA could represent developmental age more accurately than clinical estimates of GA. Conclusions: Further development of this predictor could provide a method of accurate neonatal estimation of GA for use in resource-limited populations, or in cases where GA cannot be estimated clinically. When clinical estimates are available, the predictor can be used to test hypotheses related to developmental age and other early life circumstances, and may provide increased accuracy beyond clinical estimates. 36 Umbilical cord blood samples were collected in EDTA tubes soon after placental delivery. Blood samples were centrifuged at 3,000 RPM to separate plasma, and buffy coats were aliquoted and stored at -80oC. DNA was extracted using the DNeasy Kit (Qiagen). DNA methylation was interrogated for each sample using the HumanMethylation450 BeadChip (Illumina).

Project description:BackgroundPatients with chronic constipation (CC) show altered gut microbial composition.ObjectivesTo compare the fecal microbiota with different constipation subtypes and to identify potential influencing factors.DesignThis is a prospective cohort study.MethodsThe stool samples of 53 individuals with CC and 31 healthy individuals were analyzed using 16S rRNA sequencing. The associations between microbiota composition and colorectal physiology, lifestyle factors, and psychological distress were analyzed.ResultsIn all, 31 patients with CC were classified as having slow-transit constipation, and 22 were classified under normal-transit constipation. The relative abundance of Bacteroidaceae was lower, and the relative abundance of Peptostreptococcaceae, Christensenellaceae, and Clostridiaceae was higher in slow-transit than in normal-transit group. In all, 28 and 25 patients with CC had dyssynergic defecation (DD) and non-DD, respectively. The relative abundance of Bacteroidaceae and Ruminococcaceae was higher in DD than in non-DD. Rectal defecation pressure was negatively correlated with the relative abundance of Prevotellaceae and Ruminococcaceae but positively correlated with that of Bifidobacteriaceae in CC patients. Multiple linear regression analysis suggested that depression was a positive predictor of Lachnospiraceae relative abundance, and sleep quality was an independent predictor of decreased relative abundance of Prevotellaceae.ConclusionPatients with different CC subtypes showed different characteristics of dysbiosis. Depression and poor sleep were the main factors that affected the intestinal microbiota of patients with CC.Plain language summaryCharacteristics of fecal microbiota in different constipation subtypes and association with colon physiology, lifestyle factors, and psychological status Patients with chronic constipation (CC) show altered gut microbial composition. Previous studies in CC are limited by lacking of subtype stratification, which is reflected in the lack of agreement in findings across the large number of microbiome studies. We analyzed stool microbiome of 53 CC patients and 31 healthy individuals using 16S rRNA sequencing. We found that the relative abundance of Bacteroidaceae was lower, and the relative abundance of Peptostreptococcaceae, Christensenellaceae, and Clostridiaceae was higher in slow-transit than in normal-transit CC patients. The relative abundance of Bacteroidaceae and Ruminococcaceae was higher in dyssynergic defecation (DD) than in non-DD patients with CC. In addition, depression was a positive predictor of Lachnospiraceae relative abundance, and sleep quality was an independent predictor of decreased relative abundance of Prevotellaceae in all CC patients. This study emphasizes patients with different CC subtypes have different characteristics of dysbiosis. Depression and poor sleep may be the main factors that affect the intestinal microbiota of patients with CC.

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Lifestyle, psychological stress, and incidence of adolescent constipation: results from the Toyama birth cohort study.

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Lifestyle, psychological stress, and incidence of adolescent constipation: results from the Toyama birth cohort study.

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