Associations of Maternal Educational Level, Proximity to Greenspace During Pregnancy, and Gestational Diabetes With Body Mass Index From Infancy to Early Adulthood: A Proof-of-Concept Federated Analysis in 18 Birth Cohorts.

International sharing of cohort data for research is important and challenging. We explored the feasibility of multi-cohort federated analyses by examining associations between three pregnancy exposures (maternal education, exposure to green vegetation and gestational diabetes) with offspring BMI from infancy to 17 years. We used data from 18 cohorts (n=206,180 mother-child pairs) from the EU Child Cohort Network and derived BMI at ages 0-1, 2-3, 4-7, 8-13 and 14-17 years. Associations were estimated using linear regression via one-stage IPD meta-analysis using DataSHIELD. Associations between lower maternal education and higher child BMI emerged from age 4 and increased with age (difference in BMI z-score comparing low with high education age 2-3 years = 0.03 [95% CI 0.00, 0.05], 4-7 years = 0.16 [95% CI 0.14, 0.17], 8-13 years = 0.24 [95% CI 0.22, 0.26]). Gestational diabetes was positively associated with BMI from 8 years (BMI z-score difference = 0.18 [CI 0.12, 0.25]) but not at younger ages; however associations attenuated towards the null when restricted to cohorts which measured GDM via universal screening. Exposure to green vegetation was weakly associated with higher BMI up to age one but not at older ages. Opportunities of cross-cohort federated analyses are discussed.

Correction: Gestational age at birth and body size from infancy through adolescence: An individual participant data meta-analysis on 253,810 singletons in 16 birth cohort studies.

[This corrects the article DOI: 10.1371/journal.pmed.1004036.].

Differences in birth weight between immigrants' and natives' children in Europe and Australia: a LifeCycle comparative observational cohort study.

OBJECTIVE: Research on adults has identified an immigrant health advantage, known as the 'immigrant health paradox', by which migrants exhibit better health outcomes than natives. Is this health advantage transferred from parents to children in the form of higher birth weight relative to children of natives? SETTING: Western Europe and Australia. PARTICIPANTS: We use data from nine birth cohorts participating in the LifeCycle Project, including five studies with large samples of immigrants' children: Etude Longitudinale Française depuis l'Enfance-France (N=12 494), the Raine Study-Australia (N=2283), Born in Bradford-UK (N=4132), Amsterdam Born Children and their Development study-Netherlands (N=4030) and the Generation R study-Netherlands (N=4877). We include male and female babies born to immigrant and native parents. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcome is birth weight measured in grams. Different specifications were tested: birth weight as a continuous variable including all births (DV1), the same variable but excluding babies born with over 4500 g (DV2), low birth weight as a 0-1 binary variable (1=birth weight below 2500 g) (DV3). Results using these three measures were similar, only results using DV1 are presented. Parental migration status is measured in four categories: both parents natives, both born abroad, only mother born abroad and only father born abroad. RESULTS: Two patterns in children's birth weight by parental migration status emerged: higher birth weight among children of immigrants in France (+12 g, p<0.10) and Australia (+40 g, p<0.10) and lower birth weight among children of immigrants in the UK (-82 g, p<0.05) and the Netherlands (-80 g and -73 g, p<0.001) compared with natives' children. Smoking during pregnancy emerged as a mechanism explaining some of the birth weight gaps between children of immigrants and natives. CONCLUSION: The immigrant health advantage is not universally transferred to children in the form of higher birth weight in all host countries. Further research should investigate whether this cross-national variation is due to differences in immigrant communities, social and healthcare contexts across host countries.

Gestational age at birth and body size from infancy through adolescence: An individual participant data meta-analysis on 253,810 singletons in 16 birth cohort studies.

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 positively associated with BMI in the first decade of life, with the greatest increase in mean BMI z-score during early infancy (0.02, 95% confidence interval (CI): 0.00; 0.05, p < 0.05) per week of increase in GA, while in adolescence, preterm individuals reached similar levels of BMI (0.00, 95% CI: -0.01; 0.01, p 0.9) as term counterparts. The association between GA and overweight revealed a similar pattern of association with an increase in odds ratio (OR) of overweight from late infancy through mid-childhood (OR 1.01 to 1.02) per week increase in GA. By adolescence, however, GA was slightly negatively associated with the risk of overweight (OR 0.98 [95% CI: 0.97; 1.00], p 0.1) per week of increase in GA. Although based on only four cohorts (n = 32,089) that reached the age of adolescence, data suggest that individuals born very preterm may be at increased odds of overweight (OR 1.46 [95% CI: 1.03; 2.08], p < 0.05) compared with term counterparts. Findings were consistent across cohorts and sensitivity analyses despite considerable heterogeneity in cohort characteristics. However, residual confounding may be a limitation in this study, while findings may be less generalisable to settings in low- and middle-income countries. CONCLUSIONS: This study based on data from infancy through adolescence from 16 cohort studies found that GA may be important for body size in infancy, but the strength of association attenuates consistently with age. By adolescence, preterm individuals have on average a similar mean BMI to peers born at term.

Dynamic prediction model to identify young children at high risk of future overweight: Development and internal validation in a cohort study.

BACKGROUND: Primary prevention of overweight is to be preferred above secondary prevention, which has shown moderate effectiveness. OBJECTIVE: To develop and internally validate a dynamic prediction model to identify young children in the general population, applicable at every age between birth and age 6, at high risk of future overweight (age 8). METHODS: Data were used from the Prevention and Incidence of Asthma and Mite Allergy birth cohort, born in 1996 to 1997, in the Netherlands. Participants for whom data on the outcome overweight at age 8 and at least three body mass index SD scores (BMI SDS) at the age of ≥3 months and ≤6 years were available, were included (N = 2265). The outcome of the prediction model is overweight (yes/no) at age 8 (range 7.4-10.5 years), defined according to the sex- and age-specific BMI cut-offs of the International Obesity Task Force. RESULTS: After backward selection in a Generalized Estimating Equations analysis, the prediction model included the baseline predictors maternal BMI, paternal BMI, paternal education, birthweight, sex, ethnicity and indoor smoke exposure; and the longitudinal predictors BMI SDS, and the linear and quadratic terms of the growth curve describing a child's BMI SDS development over time, as well as the longitudinal predictors' interactions with age. The area under the curve of the model after internal validation was 0.845 and Nagelkerke R2 was 0.351. CONCLUSIONS: A dynamic prediction model for overweight was developed with a good predictive ability using easily obtainable predictor information. External validation is needed to confirm that the model has potential for use in practice.

Development of a prediction model to target screening for high blood pressure in children.

Targeted screening for childhood high blood pressure may be more feasible than routine blood pressure measurement in all children to avoid unnecessary harms, overdiagnosis or costs. Targeting maybe based e.g. on being overweight, but information on other predictors may also be useful. Therefore, we aimed to develop a multivariable diagnostic prediction model to select children aged 9-10 years for blood pressure measurement. Data from 5359 children in a population-based prospective cohort study were used. High blood pressure was defined as systolic or diastolic blood pressure ≥ 95th percentile for gender, age, and height. Logistic regression with backward selection was used to identify the strongest predictors related to pregnancy, child, and parent characteristics. Internal validation was performed using bootstrapping. 227 children (4.2%) had high blood pressure. The diagnostic model included maternal hypertensive disease during pregnancy, maternal BMI, maternal educational level, parental hypertension, parental smoking, child birth weight standard deviation score (SDS), child BMI SDS, and child ethnicity. The area under the ROC curve was 0.73, compared to 0.65 when using only child overweight. Using the model and a cut-off of 5% for predicted risk, sensitivity and specificity were 59% and 76%; using child overweight only, sensitivity and specificity were 47% and 84%. In conclusion, our diagnostic prediction model uses easily obtainable information to identify children at increased risk of high blood pressure, offering an opportunity for targeted screening. This model enables to detect a higher proportion of children with high blood pressure than a strategy based on child overweight only.

Childhood prediction models for hypertension later in life: a systematic review.

BACKGROUND: Hypertension, even during childhood, increases the risk of developing atherosclerosis and cardiovascular disease. Therefore, starting prevention of hypertension early in the life course could be beneficial. Prediction models might be useful for identifying children at increased risk of developing hypertension, which may enable targeted primordial prevention of cardiovascular disease. OBJECTIVE: To provide an overview of childhood prediction models for future hypertension. METHODS: Embase and Medline were systematically searched. Studies were included that were performed in the general population, and that reported on development or validation of a multivariable model for children to predict future high blood pressure, prehypertension or hypertension. Data were extracted using the CHARMS checklist for prediction modelling studies. RESULTS: Out of 12 780 reviewed records, six studies were included in which 18 models were presented. Five studies predicted adulthood hypertension, and one predicted adolescent prehypertension/hypertension. BMI and current blood pressure were most commonly included as predictors in the final models. Considerable heterogeneity existed in timing of prediction (from early childhood to late adolescence) and outcome measurement. Important methodological information was often missing, and in four studies information to apply the model in new individuals was insufficient. Reported area under the ROC curves ranged from 0.51 to 0.74. As none of the models were validated, generalizability could not be confirmed. CONCLUSION: Several childhood prediction models for future hypertension were identified, but their value for practice remains unclear because of suboptimal methods, limited information on performance, or the lack of external validation. Further validation studies are indicated.

Repeatedly measured predictors: a comparison of methods for prediction modeling.

BACKGROUND: In literature, not much emphasis has been placed on methods for analyzing repeatedly measured independent variables, even less so for the use in prediction modeling specifically. However, repeated measurements could especially be interesting for the construction of prediction models. Therefore, our objective was to evaluate different methods to model a repeatedly measured independent variable and a long-term fixed outcome variable into a prediction model. METHODS: Six methods to handle a repeatedly measured predictor were applied to develop prediction models. Methods were evaluated with respect to the models' predictive quality (explained variance R 2 and the area under the curve (AUC)) and their properties were discussed. The models included overweight and BMI-standard deviation score (BMI-SDS) at age 10 years as outcome and seven BMI-SDS measurements between 0 and 5.5 years as longitudinal predictor. Methods for comparison encompassed developing models including: all measurements; a single (here: the last) measurement; a mean or maximum value of all measurements; changes between subsequent measurements; conditional measurements; and growth curve parameters. RESULTS: All methods, except for using the maximum or mean, resulted in prediction models for overweight of similar predictive quality, with adjusted Nagelkerke R 2 ranging between 0.230 and 0.244 and AUC ranging between 0.799 and 0.807. Continuous BMI-SDS prediction showed similar results. CONCLUSIONS: The choice of method depends on hypothesized predictor-outcome associations, available data, and requirements of the prediction model. Overall, the growth curve method seems to be the most flexible method capable of incorporating longitudinal predictor information without loss in predictive quality.