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In the published article, there was an error. The data processing was carried out without setting the fixed propionamide modification for cysteines. In the sample preparation carried out in our study, proteins were reduced and alkylated before tryptic digestion and analysis. This resulted in the modification of the cysteine residues within the sequence. After the nontargeted analysis, this modification should have been taken into account in the settings of the data processing in order to correctly identify all tryptic peptides. The error was that this modification was not set in the software, which resulted in false negative identification of peptides with cysteine residues. Consequently, the identification of proteins was only based on tryptic peptides with unmodified cysteines in their sequence, and proteins rich in cysteines were therefore also identified less. After discovering this error, we reprocessed the raw data, setting the fixed modification in the data processing software. Although this reprocessing resulted in increased identification of cysteine-rich proteins and, consequently, in differences in numbers, tables, figures, and supplementary material, the discussion and conclusions of the article remain the same. Corrections have been made to Materials and methods, Statistical methods, Missing data. This sentence previously stated: In practice this resulted in a minimum of 66 and a median of 215 valid values. The corrected sentence appears below: In practice this resulted in a minimum of 49 and a median of 209 valid values. Corrections have been made to Materials and methods, Statistical methods, Principal component analysis. This sentence previously stated: For unsupervised data exploration, Principal Component Analysis (PCA) (40) was applied on the 300 × 647 data matrix (samples × proteins), using the FactoMineR package for R (41). The corrected sentence appears below:.

Background AU Preterm: Pleaseconfirmthatallheadinglevelsarerepresentedcorrectly birth is the leading cause of perinatal morbidity and:mortality and is associated with adverse developmental and long-term health outcomes, including several cardiometabolic risk factors and outcomes. However, evidence about the association of preterm birth with later body size derives mainly from studies using birth weight as a proxy of prematurity rather than an actual length of gestation. We investigated the association of gestational age (GA) at birth with body size from infancy through adolescence. Methods and findings We conducted a two-stage individual participant data (IPD) meta-analysis using data from 253,810 mother–child dyads from 16 general population-based cohort studies in Europe (Denmark, Finland, France, Italy, Norway, Portugal, Spain, the Netherlands, United Kingdom), North America (Canada), and Australasia (Australia) to estimate the association of GA with body mass index (BMI) and overweight (including obesity) adjusted for the following maternal characteristics as potential confounders: education, height, prepregnancy BMI, ethnic background, parity, smoking during pregnancy, age at child’s birth, gestational diabetes and hypertension, and preeclampsia. Pregnancy and birth cohort studies from the LifeCycle and the EUCAN-Connect projects were invited and were eligible for inclusion if they had information on GA and minimum one measurement of BMI between infancy and adolescence. Using a federated analytical tool (DataSHIELD), we fitted linear and logistic regression models in each cohort separately with a complete-case approach and combined the regression estimates and standard errors through random-effects study-level meta-analysis providing an overall effect estimate at early infancy (>0.0 to 0.5 years), late infancy (>0.5 to 2.0 years), early childhood (>2.0 to 5.0 years), mid-childhood (>5.0 to 9.0 years), late childhood (>9.0 to 14.0 years), and a

BACKGROUND: Preterm birth is the leading cause of perinatal morbidity and mortality and is associated with adverse developmental and long-term health outcomes, including several cardiometabolic risk factors and outcomes. However, evidence about the association of preterm birth with later body size derives mainly from studies using birth weight as a proxy of prematurity rather than an actual length of gestation. We investigated the association of gestational age (GA) at birth with body size from infancy through adolescence.METHODS AND FINDINGS: We conducted a two-stage individual participant data (IPD) meta-analysis using data from 253,810 mother-child dyads from 16 general population-based cohort studies in Europe (Denmark, Finland, France, Italy, Norway, Portugal, Spain, the Netherlands, United Kingdom), North America (Canada), and Australasia (Australia) to estimate the association of GA with body mass index (BMI) and overweight (including obesity) adjusted for the following maternal characteristics as potential confounders: education, height, prepregnancy BMI, ethnic background, parity, smoking during pregnancy, age at child's birth, gestational diabetes and hypertension, and preeclampsia. Pregnancy and birth cohort studies from the LifeCycle and the EUCAN-Connect projects were invited and were eligible for inclusion if they had information on GA and minimum one measurement of BMI between infancy and adolescence. Using a federated analytical tool (DataSHIELD), we fitted linear and logistic regression models in each cohort separately with a complete-case approach and combined the regression estimates and standard errors through random-effects study-level meta-analysis providing an overall effect estimate at early infancy (>0.0 to 0.5 years), late infancy (>0.5 to 2.0 years), early childhood (>2.0 to 5.0 years), mid-childhood (>5.0 to 9.0 years), late childhood (>9.0 to 14.0 years), and adolescence (>14.0 to 19.0 years). GA was positivel

Background: The infant fecal microbiome is known to impact subsequent asthma risk, but the environmental exposures impacting this association, the role of the maternal microbiome, and how the microbiome impacts different childhood asthma phenotypes are unknown. Methods: Our objective was to identify associations between features of the prenatal and early-life fecal microbiomes and child asthma phenotypes. We analyzed fecal 16 s rRNA microbiome profiling and fecal metabolomic profiling from stool samples collected from mothers during the third trimester of pregnancy (n = 120) and offspring at ages 3–6 months (n = 265), 1 (n = 436) and 3 years (n = 506) in a total of 657 mother–child pairs participating in the Vitamin D Antenatal Asthma Reduction Trial. We used clinical data from birth to age 6 years to characterize subjects with asthma as having early, transient or active asthma phenotypes. In addition to identifying specific genera that were robustly associated with asthma phenotypes in multiple covariate-adjusted models, we clustered subjects by their longitudinal microbiome composition and sought associations between fecal metabolites and relevant microbiome and clinical features. Results: Seven maternal and two infant fecal microbial taxa were robustly associated with at least one asthma phenotype, and a longitudinal gut microenvironment profile was associated with early asthma (Fisher exact test p =.03). Though mode of delivery was not directly associated with asthma, we found substantial evidence for a pathway whereby cesarean section reduces fecal Bacteroides and microbial sphingolipids, increasing susceptibility to early asthma. Conclusion: Overall, our results suggest that the early-life, including prenatal, fecal microbiome modifies risk of asthma, especially asthma with onset by age 3 years.

Myrtha E Reyna-Vargas,1 Arpita Parmar,2 Diana L Lefebvre,3 Meghan B Azad,4– 6 Allan B Becker,4,5 Stuart E Turvey,7 Theo J Moraes,1 Wendy Lou,8 Padmaja Subbarao,1,8 Malcolm R Sears,3 Piushkumar J Mandhane,9 Indra Narang1,10 1Translational Medicine Program, The Hospital for Sick Children, Toronto, ON, Canada; 2Sculich School of Medicine and Dentistry, Western University, London, ON, Canada; 3Department of Medicine, McMaster University, Hamilton, ON, Canada; 4Department of Pediatrics and Child Health, University of Manitoba, Winnipeg, MB, Canada; 5Developmental Origins of Chronic Diseases in Children Network (DEVOTION), Children’s Hospital, Winnipeg, MB, Canada; 6Department of Food and Human Nutritional Sciences, University of Manitoba, Winnipeg, MB, Canada; 7Department of Pediatrics, BC Children’s Hospital, University of British Columbia, Vancouver, BC, Canada; 8Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada; 9Department of Pediatrics, University of Alberta, Edmonton, AB, Canada; 10Faculty of Medicine, University of Toronto, Toronto, ON, CanadaCorrespondence: Indra Narang, The Hospital for Sick Children, 555 University Ave-Hill Wing Room #4534, Toronto, ON, M5G 1X8, Canada, Email indra.narang@sickkids.caIntroduction: Decreased sleep duration and increased screen time as early as preschool age may contribute to overweight and obesity. The effects of bedtime together with nocturnal sleep duration remain unclear with a paucity of data evaluating these associations longitudinally. We aim to evaluate the independent and joint effects of sleep duration, sleep bedtime, and screen time at 3 years of age on BMI status, particularly overweight and obesity by age 5 years.Methods: Data from 2185 participants of the CHILD Cohort Study were analyzed longitudinally using generalized estimating equations (GEE). Models included changes in overweight/obesity status from 3 to 5 years of age as outcome, and nocturnal sleep duration, bedtime, and dail

Background: The human milk proteome comprises a vast number of proteins with immunomodulatory functions, but it is not clear how this relates to allergy of the mother or allergy development in the breastfed infant. This study aimed to explore the relation between the human milk proteome and allergy of both mother and child. Methods: Proteins were analyzed in milk samples from a subset of 300 mother-child dyads from the Canadian CHILD Cohort Study, selected based on maternal and child allergy phenotypes. For this selection, the definition of “allergy” included food allergy, eczema, allergic rhinitis, and asthma. Proteins were analyzed with non-targeted shotgun proteomics using filter-aided sample preparation (FASP) and nanoLC-Orbitrap-MS/MS. Protein abundances, based on label-free quantification, were compared using multiple statistical approaches, including univariate, multivariate, and network analyses. Results: Using univariate analysis, we observed a trend that milk for infants who develop an allergy by 3 years of age contains higher abundances of immunoglobulin chains, irrespective of the allergy status of the mother. This observation suggests a difference in the milk’s immunological potential, which might be related to the development of the infant’s immune system. Furthermore, network analysis showed overall increased connectivity of proteins in the milk of allergic mothers and milk for infants who ultimately develop an allergy. This difference in connectivity was especially noted for proteins involved in the protein translation machinery and may be due to the physiological status of the mother, which is reflected in the interconnectedness of proteins in her milk. In addition, it was shown that network analysis complements the other methods for data analysis by revealing complex associations between the milk proteome and mother-child allergy status. Conclusion: Together, these findings give new insights into how the human milk proteome, through differences in