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Supplemental material, Supplementary_material for The Longitudinal Relationship Between Screen Time, Sleep and a Diagnosis of Attention-Deficit/Hyperactivity Disorder in Childhood by Birgit Levelink, Marjolein van der Vlegel, Monique Mommers, Jessica Gubbels, Edward Dompeling, Frans J.M. Feron, Dorothea M.C.B. van Zeben-van der Aa, Petra Hurks and Carel Thijs in Journal of Attention Disorders

Additional file 3. Attirition analyses.

Additional file 2. STROBE checklist.

Figure S2. Gene count distribution in Dutch children. Black indicates all individuals, n = 281; red indicates lean children (BMI z-score

Figure S1. Compositional and functional comparison between Dutch children and adults. Relative abundance of major phyla in Dutch children (a) and adults (b). (c) Relative abundance of COG (clusters of orthologous groups) categories across each sample in Dutch children and adults. (PDF 298 kb)

Figure S4. Comparison of gut microbial functional potentials between enterotypes. (a) Differentially enrichment of KEGG modules between enterotypes. Dashed lines indicate a reporter score of 1.96, corresponding to 95% confidence in a normal distribution. (b) Heatmap showing that the relative abundance profiles of 8 selected KOs involved in key functions of metabolic pathways for carbohydrate metabolism (K00845, K01051 and K00873) and amino acid biosynthesis (K01738, K00928, K00058, K00651and K00765) distinguishes E3 from E1 and E2. (PDF 358 kb)

Figure S6. Correlations between continuous phenotypic parameters. (a) Spearman’s rank correlations between continuous phenotypic parameters in the entire cohort (n = 281). (b) Spearman’s correlations between continuous phenotypic parameters in E1 (n = 143). (c) Spearman’s rank correlations between continuous phenotypic parameters in E2 (n = 74). (d) Spearman’s rank correlations between continuous phenotypic parameters in E3 (n = 64). The “*” indicates significant correlation with adjusted P

Figure S8. Evaluation of enterotying protocols for Dutch children. (a-c) Evaluation of optimal cluster number by using the DMM protocol (a), the PAM-JSD protocol (b) and the PAM-BC protocol (c). The optimal number of clusters was calculated using Laplace approximation for the DMM protocol (a) and the Calinskiâ Harabasz index for the PAM-based protocols (b-c). Cluster stability using the DMM (d), the PAM-JSD (e) and the PAM-BC protocols (f). The X axis indicates resampling number and the Y axis indicates the consistency of resampling relative to the original result based on 281 samples. (PDF 221 kb)

Figure S5. Correlations between continuous phenotypic parameters and species profile in the entire cohort. (a) Spearman’s rank correlations between early events, pre-school lifestyle and species profile (n = 281). (b) Spearman’s rank correlations between blood parameters and species profiles (n = 281). P values were adjusted for each parameter. The “*” indicates significant correlation with adjusted P

Figure S4. Comparison of gut microbial functional potentials between enterotypes. (a) Differentially enrichment of KEGG modules between enterotypes. Dashed lines indicate a reporter score of 1.96, corresponding to 95% confidence in a normal distribution. (b) Heatmap showing that the relative abundance profiles of 8 selected KOs involved in key functions of metabolic pathways for carbohydrate metabolism (K00845, K01051 and K00873) and amino acid biosynthesis (K01738, K00928, K00058, K00651and K00765) distinguishes E3 from E1 and E2. (PDF 358 kb)

Figure S7. Correlations between Streptococcus species profile and selected phenotypic parameters in enterotypes. Heatmap showing the Spearman’s rank correlations between Streptococcus species and selected phenotypic parameters including free fatty acids levels and the intake of total carbohydrate, total fat, dietary fiber, and plant-based protein. P values were adjusted for each parameter. The “*” indicates significant correlation with adjusted P 0.05. FFA, free fatty acids. (PDF 197 kb)

Figure S5. Correlations between continuous phenotypic parameters and species profile in the entire cohort. (a) Spearman’s rank correlations between early events, pre-school lifestyle and species profile (n = 281). (b) Spearman’s rank correlations between blood parameters and species profiles (n = 281). P values were adjusted for each parameter. The “*” indicates significant correlation with adjusted P

Figure S6. Correlations between continuous phenotypic parameters. (a) Spearman’s rank correlations between continuous phenotypic parameters in the entire cohort (n = 281). (b) Spearman’s correlations between continuous phenotypic parameters in E1 (n = 143). (c) Spearman’s rank correlations between continuous phenotypic parameters in E2 (n = 74). (d) Spearman’s rank correlations between continuous phenotypic parameters in E3 (n = 64). The “*” indicates significant correlation with adjusted P

Figure S3. Comparison of gut microbial compositional and functional structure between enterotypes. (a-c) Comparison of gene count, gene-based Shannon diversity and reads mapping ratio to the taxonomic annotated genes between enterotypes. (d-f) Comparison of gene count, KO-based Shannon diversity and reads mapping ratio to the KO annotated genes between enterotypes. Dunn’s post hoc test, *, P

Figure S7. Correlations between Streptococcus species profile and selected phenotypic parameters in enterotypes. Heatmap showing the Spearman’s rank correlations between Streptococcus species and selected phenotypic parameters including free fatty acids levels and the intake of total carbohydrate, total fat, dietary fiber, and plant-based protein. P values were adjusted for each parameter. The “*” indicates significant correlation with adjusted P 0.05. FFA, free fatty acids. (PDF 197 kb)

Figure S8. Evaluation of enterotying protocols for Dutch children. (a-c) Evaluation of optimal cluster number by using the DMM protocol (a), the PAM-JSD protocol (b) and the PAM-BC protocol (c). The optimal number of clusters was calculated using Laplace approximation for the DMM protocol (a) and the Calinskiâ Harabasz index for the PAM-based protocols (b-c). Cluster stability using the DMM (d), the PAM-JSD (e) and the PAM-BC protocols (f). The X axis indicates resampling number and the Y axis indicates the consistency of resampling relative to the original result based on 281 samples. (PDF 221 kb)

Figure S3. Comparison of gut microbial compositional and functional structure between enterotypes. (a-c) Comparison of gene count, gene-based Shannon diversity and reads mapping ratio to the taxonomic annotated genes between enterotypes. (d-f) Comparison of gene count, KO-based Shannon diversity and reads mapping ratio to the KO annotated genes between enterotypes. Dunn’s post hoc test, *, P

Introduction: The objective of this study was to assess relationships between children&rsquo, s physical environment and afterschool leisure time physical activity (PA) and active transport. Methods: Children aged 10&ndash, 12 years participated in a 7-day accelerometer and Global Positioning Systems (GPS) protocol. Afterschool leisure time PA and active transport were identified based on location- and speed-algorithms based on accelerometer, GPS and Geospatial Information Systems (GIS) data. We operationalized children&rsquo, s exposure to the environment by combining home, school and the daily transport environment in individualized daily activity-spaces. Results: In total, 255 children from 20 Dutch primary schools from suburban areas provided valid data. This study showed that greenspaces and smaller distances from the children&rsquo, s home to school were associated with afterschool leisure time PA and walking. Greater distances between home and school, as well as pedestrian infrastructure were associated with increased cycling. Conclusion: We demonstrated associations between environments and afterschool PA within several behavioral contexts. Future studies are encouraged to target specific behavioral domains and to develop natural experiments based on interactions between several types of the environment, child characteristics and potential socio-cognitive processes.

Background: Preterm birth, low birth weight, and infant catch-up growth seem associated with an increased risk of respiratory diseases in later life, but individual studies showed conflicting results. Objectives: We performed an individual participant data meta-analysis for 147,252 children of 31 birth cohort studies to determine the associations of birth and infant growth characteristics with the risks of preschool wheezing (1-4 years) and school-age asthma (5-10 years). Methods: First, we performed an adjusted 1-stage random-effect meta-analysis to assess the combined associations of gestational age, birth weight, and infant weight gain with childhood asthma. Second, we performed an adjusted 2-stage random-effect meta-analysis to assess the associations of preterm birth (gestational age < 37 weeks) and low birth weight (< 2500 g) with childhood asthma outcomes. Results: Younger gestational age at birth and higher infant weight gain were independently associated with higher risks of preschool wheezing and school-age asthma (P, Supported by the European Community's Seventh Framework Programme FP7/20072013, project CHICOS. The research leading to these results has received funding from the European Respiratory Society and the European Community's Seventh Framework Programme FP7/2007-2013-Marie Curie Actions under grant agreement RESPIRE, PCOFUND-GA-2008-229571.

Figure S1. Flow chart of participating cohorts and individuals. Table S1. Cohort-specific methods of data collection for maternal anthropometrics and gestational age. Table S2. Box-Cox t model specifications for each maternal pre-pregnancy body mass index group. Table S3. Gestational weight measurements per participating cohort and maternal pre-pregnancy body mass index group. Figure S2. Sample size according to gestational age for each maternal pre-pregnancy body mass index group. Figure S3. Predicted z scores for the average weight gain according to gestational age for each maternal pre-pregnancy body mass index group. Table S4. Week-specific Box-Cox t model parameters and selected percentiles of gestational weight gain for maternal pre-pregnancy underweight. Table S5. Week-specific Box-Cox t model parameters and selected percentiles of gestational weight gain for maternal pre-pregnancy normal weight. Table S6. Week-specific Box-Cox t model parameters and selected percentiles of gestational weight gain for maternal pre-pregnancy overweight. Table S7. Week-specific Box-Cox t model parameters and selected percentiles of gestational weight gain for maternal pre-pregnancy obesity grade 1. Table S8. Week-specific Box-Cox t model parameters and selected percentiles of gestational weight gain for maternal pre-pregnancy obesity grade 2. Table S9. Week-specific Box-Cox t model parameters and selected percentiles of gestational weight gain for maternal pre-pregnancy obesity grade 3. Table S10. Local institutional ethical review boards per cohort. (DOCX 631 kb)

Aims.We assessed the relationship of maternal complications including hypertensive disorders and diabetes in pregnancy, and pre-pregnancy body mass index (BMI) with wheezing symptoms in infants, combining data from birth cohorts participating in the CHICOS (Developing a Child Cohort Research Strategy for Europe) project. Methods. Eligible cohorts met the following criteria: recruitment from 1990 onwards, maternal complications recorded at any time during pregnancy, and wheezing recorded in the first 12-24 months of life. Associations of maternal complications and ever (≥ one episode), and recurrent (≥4 episodes) wheezing were assessed for each cohort, adjusting for maternal country of birth, education, asthma, smoking in pregnancy, parity, and age. Crude, adjusted and mutually adjusted risks ratios (aRR) were pooled using a random-effects meta-analysis. Results. The analysis included 93245 subjects. Ever wheezing ranged from 20 to 47 %, and recurrent wheezing from 3 to 14%. Pre-eclampsia and obesity (BMI≥ 30; ref: BMI: 18.5-24) were associated with an increased risk of ever wheezing (aRR, 1.09; 95% CI: 1.02, 1.17 and 1.12; 95% CI: 1.07, 1.18, respectively). Both estimates increased slightly for recurrent wheezing. Maternal pre-pregnancy overweight (BMI: 25-29) was associated with an increased risk of wheezing, both ever and recurrent (aRR: 1.09, 95% CI: 1.06, 1.11 and 1.20: 1.13, 1.26, respectively). Neither hypertension nor diabetes were found to be associated with ever or recurrent wheezing. Conclusions. Pre-pregnancy overweight, obesity and pre-eclampsia are associated with a slightly increased risk of wheezing in infants.

Figure S1. Flow chart of participating cohorts and individuals. Table S1. Cohort-specific methods of data collection for maternal anthropometrics and gestational age. Table S2. Box-Cox t model specifications for each maternal pre-pregnancy body mass index group. Table S3. Gestational weight measurements per participating cohort and maternal pre-pregnancy body mass index group. Figure S2. Sample size according to gestational age for each maternal pre-pregnancy body mass index group. Figure S3. Predicted z scores for the average weight gain according to gestational age for each maternal pre-pregnancy body mass index group. Table S4. Week-specific Box-Cox t model parameters and selected percentiles of gestational weight gain for maternal pre-pregnancy underweight. Table S5. Week-specific Box-Cox t model parameters and selected percentiles of gestational weight gain for maternal pre-pregnancy normal weight. Table S6. Week-specific Box-Cox t model parameters and selected percentiles of gestational weight gain for maternal pre-pregnancy overweight. Table S7. Week-specific Box-Cox t model parameters and selected percentiles of gestational weight gain for maternal pre-pregnancy obesity grade 1. Table S8. Week-specific Box-Cox t model parameters and selected percentiles of gestational weight gain for maternal pre-pregnancy obesity grade 2. Table S9. Week-specific Box-Cox t model parameters and selected percentiles of gestational weight gain for maternal pre-pregnancy obesity grade 3. Table S10. Local institutional ethical review boards per cohort. (DOCX 631 kb)

Background Maternal obesity and excessive gestational weight gain may have persistent effects on offspring fat development. However, it remains unclear whether these effects differ by severity of obesity, and whether these effects are restricted to the extremes of maternal body mass index (BMI) and gestational weight gain. We aimed to assess the separate and combined associations of maternal BMI and gestational weight gain with the risk of overweight/obesity throughout childhood, and their population impact. Methods and findings We conducted an individual participant data meta-analysis of data from 162,129 mothers and their children from 37 pregnancy and birth cohort studies from Europe, North America, and Australia. We assessed the individual and combined associations of maternal pre-pregnancy BMI and gestational weight gain, both in clinical categories and across their full ranges, with the risks of overweight/obesity in early (2.0–5.0 years), mid (5.0–10.0 years) and late childhood (10.0–18.0 years), using multilevel binary logistic regression models with a random intercept at cohort level adjusted for maternal sociodemographic and lifestyle-related characteristics. We observed that higher maternal pre-pregnancy BMI and gestational weight gain both in clinical categories and across their full ranges were associated with higher risks of childhood overweight/obesity, with the strongest effects in late childhood (odds ratios [ORs] for overweight/obesity in early, mid, and late childhood, respectively: OR 1.66 [95% CI: 1.56, 1.78], OR 1.91 [95% CI: 1.85, 1.98], and OR 2.28 [95% CI: 2.08, 2.50] for maternal overweight; OR 2.43 [95% CI: 2.24, 2.64], OR 3.12 [95% CI: 2.98, 3.27], and OR 4.47 [95% CI: 3.99, 5.23] for maternal obesity; and OR 1.39 [95% CI: 1.30, 1.49], OR 1.55 [95% CI: 1.49, 1.60], and OR 1.72 [95% CI: 1.56, 1.91] for excessive gestational weight gain). The proportions of childhood overweight/obesity prevalence attributable to maternal overweight, maternal obesity, and excessive gestational weight gain ranged from 10.2% to 21.6%. Relative to the effect of maternal BMI, excessive gestational weight gain only slightly increased the risk of childhood overweight/obesity within each clinical BMI category (p-values for interactions of maternal BMI with gestational weight gain: p = 0.038, p < 0.001, and p = 0.637 in early, mid, and late childhood, respectively). Limitations of this study include the self-report of maternal BMI and gestational weight gain for some of the cohorts, and the potential of residual confounding. Also, as this study only included participants from Europe, North America, and Australia, results need to be interpreted with caution with respect to other populations. Conclusions In this study, higher maternal pre-pregnancy BMI and gestational weight gain were associated with an increased risk of childhood overweight/obesity, with the strongest effects at later ages. The additional effect of gestational weight gain in women who are overweight or obese before pregnancy is small. Given the large population impact, future intervention trials aiming to reduce the prevalence of childhood overweight and obesity should focus on maternal weight status before pregnancy, in addition to weight gain during pregnancy., Vincent Jaddoe and colleagues report that a high BMI before pregnancy, rather than excessive gestational weight gain, is more likely to contribute to childhood obesity., Author summary Why was this study done? Maternal pre-pregnancy obesity and excessive gestational weight gain are important risk factors of various pregnancy and birth complications. An accumulating body of evidence suggests that maternal obesity and excessive gestational weight gain also have persistent effects on offspring fat development. It is not clear whether these effects exist across the full ranges of maternal pre-pregnancy body mass index and gestational weight gain, what their combined effects are, and what the population impact of these effects is. What did the researchers do and find? We conducted an individual participant data meta-analysis using data from 162,129 mothers and their children from 37 pregnancy and birth cohorts from Europe, North America, and Australia to assess the separate and combined associations of maternal pre-pregnancy body mass index and gestational weight gain with the risk of overweight/obesity throughout childhood, and their population impact. We observed not only that maternal pre-pregnancy overweight and obesity were associated with an increased risk of childhood overweight/obesity, but that this risk increased gradually over the full range of maternal BMI. Similarly, the risk of childhood overweight/obesity increased across the full range of gestational weight gain. We estimated that 21.7% to 41.7% of childhood overweight/obesity prevalence could be attributed to maternal overweight and obesity together, whereas 11.4% to 19.2% could be attributed to excessive gestational weight gain. The additional effect of excessive gestational weight gain on the risk of childhood overweight/obesity was small among women who are already overweight or obese before pregnancy. What do these findings mean? Maternal pre-pregnancy BMI and, to a smaller extent, gestational weight gain are important modifiable risk factors of childhood overweight/obesity with considerable population impact. Future intervention trials aiming to reduce the prevalence of childhood overweight and obesity should focus on maternal weight status before pregnancy, in addition to weight status during pregnancy.

ObjectiveTo assess the separate and combined associations of maternal pre‐pregnancy body mass index (BMI) and gestational weight gain with the risks of pregnancy complications and their population impact.DesignIndividual participant data meta‐analysis of 39 cohorts.SettingEurope, North America, and Oceania.Population265 270 births.MethodsInformation on maternal pre‐pregnancy BMI, gestational weight gain, and pregnancy complications was obtained. Multilevel binary logistic regression models were used.Main outcome measuresGestational hypertension, pre‐eclampsia, gestational diabetes, preterm birth, small and large for gestational age at birth.ResultsHigher maternal pre‐pregnancy BMI and gestational weight gain were, across their full ranges, associated with higher risks of gestational hypertensive disorders, gestational diabetes, and large for gestational age at birth. Preterm birth risk was higher at lower and higher BMI and weight gain. Compared with normal weight mothers with medium gestational weight gain, obese mothers with high gestational weight gain had the highest risk of any pregnancy complication (odds ratio 2.51, 95% CI 2.31– 2.74). We estimated that 23.9% of any pregnancy complication was attributable to maternal overweight/obesity and 31.6% of large for gestational age infants was attributable to excessive gestational weight gain.ConclusionsMaternal pre‐pregnancy BMI and gestational weight gain are, across their full ranges, associated with risks of pregnancy complications. Obese mothers with high gestational weight gain are at the highest risk of pregnancy complications. Promoting a healthy pre‐pregnancy BMI and gestational weight gain may reduce the burden of pregnancy complications and ultimately the risk of maternal and neonatal morbidity.Tweetable abstractPromoting a healthy body mass index and gestational weight gain might reduce the population burden of pregnancy complications.

BackgroundGestational weight gain differs according to pre-pregnancy body mass index and is related to the risks of adverse maternal and child health outcomes. Gestational weight gain charts for women in different pre-pregnancy body mass index groups enable identification of women and offspring at risk for adverse health outcomes. We aimed to construct gestational weight gain reference charts for underweight, normal weight, overweight, and grades 1, 2 and 3 obese women and to compare these charts with those obtained in women with uncomplicated term pregnancies.MethodsWe used individual participant data from 218,216 pregnant women participating in 33 cohorts from Europe, North America, and Oceania. Of these women, 9065 (4.2%), 148,697 (68.1%), 42,678 (19.6%), 13,084 (6.0%), 3597 (1.6%), and 1095 (0.5%) were underweight, normal weight, overweight, and grades 1, 2, and 3 obese women, respectively. A total of 138, 517 women from 26 cohorts had pregnancies with no hypertensive or diabetic disorders and with term deliveries of appropriate for gestational age at birth infants. Gestational weight gain charts for underweight, normal weight, overweight, and grade 1, 2, and 3 obese women were derived by the Box-Cox t method using the generalized additive model for location, scale, and shape.ResultsWe observed that gestational weight gain strongly differed per maternal pre-pregnancy body mass index group. The median (interquartile range) gestational weight gain at 40weeks was 14.2kg (11.4-17.4) for underweight women, 14.5kg (11.5-17.7) for normal weight women, 13.9kg (10.1-17.9) for overweight women, and 11.2kg (7.0-15.7), 8.7kg (4.3-13.4) and 6.3kg (1.9-11.1) for grades 1, 2, and 3 obese women, respectively. The rate of weight gain was lower in the first half than in the second half of pregnancy. No differences in the patterns of weight gain were observed between cohorts or countries. Similar weight gain patterns were observed in mothers without pregnancy complications.ConclusionsGestational weight gain patterns are strongly related to pre-pregnancy body mass index. The derived charts can be used to assess gestational weight gain in etiological research and as a monitoring tool for weight gain during pregnancy in clinical practice., ABCDr This work was supported by the Netherlands Organization for Health Research and Development (ZonMw) (TOP grant, 40-00812-98-11010).r ALSPACr The UK Medical Research Council and Wellcome (Grant ref.: 102215/2/13/2) and the University of Bristol provide core support fog ALSPAC. This study has received support from the US National Institute of Health (R01 DK10324) and European Research Council under the European Union's Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement no 669545. DA Lawlor works in a unit that receives UK MRC funding (MQ_UU_12013/5) and is an NIHR senior investigator (NF-Sl-0611-10196).r AOB/Fr All Our Families is funded through Alberta Innovates Interdisciplinary Team Grant 4200700595, the Alberta Children's Hospital Foundation, and the Max Bell Foundation.r DNBCr The Danish National Research Foundation has established the Danish Epidemiology Science Centre that initiated and created the Danish National Birth Cohort. The cohort is furthermore a result of a major grant from this foundation. Additional support for the Danish National Birth Cohort is obtained from the Pharmacy Foundation, the Egmont Foundation, the March of Dimes Birth Defects Foundation, the Augustinus Foundation, and the Health Foundation. The DNBC 7-year follow-up is supported by the Lundbeck Foundation (195/04) and the Danish Medical Research Council (SWF 0646).r EDENr The EDEN Study was supported by Foundation for medical research (FRM), National Agency for Research (ANR), National Institute for Research in Public health (IRESP: TGIR cohorte sante 2008 program), French Ministry of Health (DGS), French Ministry of Research, INSERM Bone and Joint Diseases National Research (PRO-A) and Human Nutrition National Research Programs, Paris-Sud LJniversity, Nestle, French National Institute for Population Health Surveil lance (InVS), French National Institute for h-lealth Education (APES), the European Union FP7 programmes (FP7/2007-2013, HELIX, ESCAPE, ENRIECO, Medall projects), Diabetes National Re.search Program (through a collaboration with the French Association of Diabetic Patients (AFD)), French Agency for Environmental Health Safety (now ANSES), Mutuelle Generale de l'Education Nationale a complementary health inswance (MGEN), French national agency for food security, French-speaking association for the study of diabetes and metabolism (ALFEDIAM).r FCOUr FCOU study is supported by the US National Institutes of Health Fogarty International Center, US NIEHS, US CDC, US PA, and National Academy of Medical Sciences of Ukraine.r GASPIIr Ministry of Health.r GECKO Drenther The GECKO Drenthe birth cohort was funded by an unrestricted grant of Hutchison Whampoa Ld, Hong Kong and supported by the University of Groningen, Well Baby Clinic Foundation Icare, Noodlease, Paediatric Association Of The Netherlands and Youth Health Care Drenthe.r Generation Rr 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. Research leading to these results has received funding from the European Union's Seventh Framework Programme (FP7/2007-2013), project ErlyNutrition under grant agreement no. 289346, the European Union's Horizon 2020 research and innovation programme under grant agreement no.; 633595 (DynalHEALTH)and the European Union's Horzon 2020 research and Innovation programme under grant agreement 733206 (LifeCycle Project). Romy Gaillard received funding from the Dutch Heart Foundation (grant number 2017T013) and the Dutch Diabetes Foundation (grant number 2017.81.002). Vincent Jaddoe received grants from the Netherlands Organization for Health Research and Development (VIDI 016.136.361) and the European Research Council (Consolidator Grant, ERC-2014-CoG-648916).r Generation XXIr Generation XX: was funded by Pmgrama Operational de Saude-Saude XXI, Quadro Comunitario de Apoio III and Administracao Regional de Saude Norte (Regional Department of Ministry of Health). This study was funded by FEDER through the Operational Programme Competitiveness and Internationalization and national funding from the Foundation for Science and Technology FCT (Portuguese Minstry of Science, Technology and Higher Education) (POCI-01-0145-FEDER-016837), under the project PathMOB.: Risco cardiomwtabolico na infancia: desde o inicio da vida ao fim da infancia (Ref. FCT PIDc/DTP-EP1/3306/2014) and the Unidade de Investigacao em Epiclemiologia-Instituto de Saude Publica da Universidade do Porte (EPIUnit) (POCI-01-0145-FEDER-006862; Ref. UlD/DTP/04750/2013). AC Santos holds a FG Investigator contract IF/01060/2015.r GENESISr The study was supported by a research grant from Friesland Foods Hellas. Gen3G Gen3G was supported by a Fonds de recherche du Quebec en sante (FRQ-S) operating grant (grant 4206971; a Canadian Institute of Health Reseach (CIHR) Operating grant (grant WOE) 115071); a Diabete Quebec grant and a Canadian Diabetes Association operating grant (grant #OG-3-08-2622-JA).r GINIplusr The GINIplus study was mainly supported for the first 3 years of the Federal Ministry for Education, Science, Research and Technology I:interventional arm) and Helmholtz Zentrum Munich (former GSF) (observational arm). The 4-year, 6-year, 10-year, and 15-year follow-up examinations of the GINIplus Study were covered from the respective budgets of the 5 study centers Helmoltz Zentrum Munich (former GSF), Research Institute at Marien-Hospital Wesel, LMU Munich, TU Munich and from 6 years onwards also from IUF-Leibniz Research-Institute for Environmental Medicine at the University of Dusseldorp and a grant from the Federal Ministry for Environment (IUF Dus seldoit FKZ 20462296). Further, the 15 year follow-up examination of the GINIplus study was supported by the Commission of the European Communities, the 7th Framework Program: MeDALL project, and as well by the companies Mead Johnson and Nestle.r HUMISr European Community's Seventh Framework Programme (P7/2007-2013) under grant agreements Early Nutrition no. 289346 and by funds from the Norwegian Research Council's MILPAAHEL programme, project no. 213148. INMA-Sabadellr This study was funded by grants from the Institute de Salad Carlos III (Red INMA G03/176) and the Genera tat de Catalunya-CIRIT (1999SGR 00241).r INMA-Valenciar This study was funded by Grants from UE (P7-ENV-2011 cod 282957 and HEALTH2010.2.4.5-1), Spain: ISCIII (G03/176; FIS-FEDER: P109/02647, PI11/01007, PI11/02591, PI11/02038, PI1.3/1944-, PI13/2032, P114/00891, PI14/01687, and PI16/1288; Miguel Servet-FEDER CP11/00178, CP15/00025, and CPII16/00051), and Generalitat Valenciana: FISABIO (UGP 15-230, UGP-15-244, and UGP-15-249).r INMA-Gipuzkoar This study was funded by grants from the Institute. de Salad Carlos III (Red INMA G03/176).; r KOALAr Data collection for the KOALA study from pregnancy up to age 1 year was financially supported by grants from Royal Friesland Foods (Leeuwarden); Trio dos Foundation (Zeist); Phoenix Foundation; Raphael Foundation; Iona Foundation, Foundation for the Advancement of Heilpedagogie (all in the Netherlands).r Krakow Cohortr The study received funding from a NIEHS R01 grants entitled: Vulnerability of the Fetus/Infant to PAH, PM2.5 and FTS and Developmental effects of early-life exposure to airborne PAC (R01ES010165 and R01ES015282) and from The Lundin Foundation, The John and Wendy Neu Family Foundation, The Gladys and Roland Harriman Foundation and an Anonymous Foundation.r LISAplusr The LISAplus study was mainly supported by grants from the Federal Ministry for Education Science, Research and Technology and in addition from Helmholtz Zentrum Munich (former GSF), Helmholtz Centre for Environmental Research-UFZ, Leipzig, Research Institute at Marien-Hospital Wesel, Pediatric Practice, Bad Honnef for the first 2 years. The 4-year, 6-year, 10-year, and 15-year follow-up examinations of the LISA-plus Study were covered from the respective budgets of the involved partners (Helmholtz Zentrum Munich (former GSF), Helmholtz Centre for Enironmental Research-UFZ, Leipzig, Research Institute at Marien-Hospital Wesel Pediatric Practice, Bad Honnef, IUr-Leibniz-Research Institute for Environmental Medicine at the University of Dusseldorf) and in addition by a grant from the Federal Ministry for Environment (IUF-Dussedorf, FKZ 204622961. Further, the 15-year follow-up examination of the LISAplus study was supported by the Commission of the FaMpean Communities and the 7th Framework Program: McDALL project.r LUKAS The grants from the Academy of Finland (grants 139021;287675); the Juho Vainio Foundation; the Foundation for Pediatric Research; EVE/VTR-funding; Paivikki and Saran Sohlberg Foundation; The Finnish Cultural Foundation; European Union QLK4-CT-2001-00250; and by the National Institute for Health and Welfare, Finland.r MoBar The Norwegian Mother and Child Cohort Study is supported by the Norwegian [Ministry of Health and Care. Services and the Ministry of Education and Research, NIH/NIEHS (contract no. N01-ES-75558), NIH/NINDS (grant. no. 1 UO1 NS 047537-01 and grant no. 2 UO1 NS 047537-06A1).r NINFEAr The NINFEA cohort was partially funded by the Compagnia San Paolo Fundation and by the Piedmont Region.r PELAGIEr The Pelagic cohort was supported by the French National Research Agency (ANR-2010-PRSP-007) and the French Research Institute for Public Health (AMC11004NSA-DGS).r PIAMAr The PIAMA study was supported by the Netherlands Organization for Health Research and Development; The Netherlands Organization for Scientific Research; The Netherlands Asthma Fund; The Netherlands Ministry of Spatial Planning, Housing, and the Environment; and The Netherlands Ministry of Health, Welfare, and Sport.r Piccolipiur The Piccolipiu project was financially supported by the Italian National Center for Disease Prevention and Control (CCM grants years 2010 and 2014) and by the Italian Ministry of Health (art 12 and 12 bis D.Igs 502/92).r PRIDE Studyr The PRIDE Study is supported by grants from the Netherlands Organization for Health Research and Development, the Radboud Institute for Health Sciences, and the Lung Foundation Netherlands.r Project Vivar National Institutes of Health (R01 HD034568, UGOD023286).; r RAINE Studyr The Western Australian Pregnancy Cohort (Raine Study) has been funded by program and project grants from the Australian National Health and Medical Research Council, the Commonwealth Scientific and Industrial Research Organization, Healthway, and the Lions Eye Institute in Western Australia. The University of Western Australia (UWA), Curtin University, the Raine Medical Research Foundation, the Telethon Kids Institute, the Women's and Infant's Research Foundation (KEMH), Murdoch University, The University of Notre Dame Australia, and Edith Cowan University provide funding for the Core Management of the Raine Study.r REPRO_PLr National Science Centre, Poland, under the grant DEC-2014/15/3/NZ7/00998, FP7 HEALS Grant No. 603946 and the Ministry of Science and Higher Education, under grant agreement no. 3068/7.PR./2014/2.r RHEAr The Rhea project was financially supported by European projects (EU FP6-2003-Food-3-NewGeneris, EU FP&.STREP Hiwate, EU FP7 ENV.2007.1222 Project No. 211250 Escape, EU FP7-2008-ENV-1.2.1.4 Envirogenomarkers, EU FP7-HEALTH-2009-single stage CHICOS, EU FP7 ENV.2008.1.2.1.6. Proposal No. 226285 ENIIIECO, EU-FP/-HEALTH-2012 Proposal No 308333 HELIX) and the Greer: Ministry of Health (Program of Prevention of obesity and ileuMdevelopmental disorders in preschool children, in Heraklion District, Crete, Greece: 2011-2014; Rhea Plus: Primary Prevention Program of Environmental Risk Factors for Reproductive Health, and Child Health 2012-15).r Slovak PCB studyr Support was provided by US National Institutes of Health grants R01 CA096525, R03 TW007152, P30 E5001247, and K12 ES019852.r STEPSr This study was supported by the University of Turku, Abo Akademi University, the Turku University Hospital, and the City of Turku, as well as by the Academy of Finland (grants 121569 and 123571), the Juno Vainio Foundation, the Yrjo Jahnsson Foundation, the TorKu.r SWSr The SWS is supported by grants from the Medical Research Council, National Institute for Health Research Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton National Health Service Foundation Trust, and the European Union's Seventh Framework Programme. (P7/2007-2013), project EarlyNutrifion (grant 289346). Study participants were drawn from a cohort study funded by the Medical Research council and the Dunhill Medical Trust.