Dataset Information

Early Determinants of Childhood Blood Pressure at the Age of 6 Years: The GECKO Drenthe and ABCD Study Birth Cohorts.

ABSTRACT: Background There is still uncertainty about the nature and relative impact of early determinants on childhood blood pressure. This study explored determinants of blood pressure at the age of 6 years in 2 Dutch birth cohorts. Methods and Results Results of hierarchical multiple linear regression analyses in GECKO (Groningen Expert Center for Kids With Obesity) Drenthe study (n=1613) were replicated in ABCD (Amsterdam Born Children and Their Development) study (n=2052). All analyses were adjusted for child's age, sex, height, and body mass index (BMI), and maternal education and subsequently performed in the combined sample. No associations were found between maternal smoking during pregnancy and childhood blood pressure. In the total sample, maternal prepregnancy BMI was positively associated with systolic blood pressure (SBP) (? [95% CI], 0.09 [0.02-0.16] mm Hg) and diastolic blood pressure (? [95% CI], 0.11 [0.04-0.17] mm Hg). Children of women with hypertension had higher SBP (? [95% CI], 0.98 [0.17-1.79] mm Hg). Birth weight standardized for gestational age was inversely associated with SBP (? [95% CI], -6.93 [-9.25 to -4.61] mm Hg) and diastolic blood pressure (? [95% CI], -3.65 [-5.70 to -1.61] mm Hg). Longer gestational age was associated with lower SBP (? [95% CI] per week, -0.25 [-0.42 to -0.08] mm Hg). Breastfeeding for 1 to 3 months was associated with lower SBP (? [95% CI], -0.96 [-1.82 to -0.09] mm Hg) compared with no or <1 month of breastfeeding. Early BMI gain from the age of 2 to 6 years was positively associated with SBP (? [95% CI], 0.41 [0.08-0.74] mm Hg) and diastolic blood pressure (? [95% CI], 0.37 [0.07-0.66] mm Hg), but no effect modification by birth weight was found. Conclusions Higher maternal prepregnancy BMI, maternal hypertension, a relatively lower birth weight for gestational age, shorter gestational age, limited duration of breastfeeding, and more rapid early BMI gain contribute to higher childhood blood pressure at the age of 6 years.

SUBMITTER: Xie T

PROVIDER: S-EPMC7763711 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Early Determinants of Childhood Blood Pressure at the Age of 6 Years: The GECKO Drenthe and ABCD Study Birth Cohorts.

Xie Tian T Falahi Fahimeh F Schmidt-Ott Tabea T Vrijkotte Tanja G M TGM Corpeleijn Eva E Snieder Harold H

Journal of the American Heart Association 20201110 22

Background There is still uncertainty about the nature and relative impact of early determinants on childhood blood pressure. This study explored determinants of blood pressure at the age of 6 years in 2 Dutch birth cohorts. Methods and Results Results of hierarchical multiple linear regression analyses in GECKO (Groningen Expert Center for Kids With Obesity) Drenthe study (n=1613) were replicated in ABCD (Amsterdam Born Children and Their Development) study (n=2052). All analyses were adjusted ...[more]

PMID: 33167754

Similar Datasets

Project description:BackgroundExcessive weight gain during childhood is a strong predictor for adult overweight, but it remains unknown which growth measures in infancy (0-2 years of age), besides predictors known at birth, are the strongest predictors for excessive weight gain between 2 and 5-7 years of age.MethodsThe Amsterdam Born Children and their Development (ABCD) study formed the derivation cohort, and the Groningen Expert Center for Kids with Obesity (GECKO) Drenthe study formed the validation cohort. Change (Δ) in body mass index (BMI) z-score between 2 and 5-7 years was the outcome of interest. The growth measures considered were weight, weight-for-length (WfL), and body mass index (BMI). Formats considered for each growth measure were values at 1, 6, 12, and 24 months, at the BMI peak, the change between aforementioned ages, and prepeak velocity. 10 model structures combining different variable formats and including predictors at birth were derived for each growth measure, resulting in 30 linear regression models. A Parsimonious Model considering all growth measures and a Birth Model considering none were also derived.ResultsThe derivation cohort consisted of 3139 infants of which 373 (11.9%) had excessive gain in BMI z-score (> 0.67). The validation cohort contained 2201 infants of which 592 (26.9%) had excessive gain. Across the 3 growth measures, 5 model structures which included measures related to the BMI peak and prepeak velocity (derivation cohort area under the curve [AUC] range = 0.765-0.855) achieved more accurate estimates than 3 model structures which included growth measure change over time (0.706-0.795). All model structures which used BMI were superior to those using weight or WfL. The AUC across all models was on average 0.126 lower in the validation cohort. The Parsimonious Model's AUCs in the derivation and validation cohorts were 0.856 and 0.766, respectively, compared to 0.690 and 0.491, respectively, for the Birth Model. The respective false positive rates were 28.2% and 20.1% for the Parsimonious Model and 70.0% and 74.6% for the Birth Model.ConclusionModels' performances varied significantly across model structures and growth measures. Developing the optimal model requires extensive testing of the many possibilities.

Project description:BackgroundA better understanding of lifestyle behaviours of children < 7 years and the relation with childhood overweight is needed. The aim of our prospective study was to examine how lifestyle patterns in young children are associated with the development of childhood overweight. As ecological models suggest focusing on not only the child as an individual, but also their environment, we also considered the role of socio-economic status (SES) and spatial clustering of lifestyle and body mass index (BMI).MethodsIn 1792 children (aged 3-6 years) participating in the GECKO Drenthe cohort, diet, screen time, outdoor play and sleep were assessed by questionnaires and moderate-to-vigorous physical activity and sedentary time by accelerometry (Actigraph GT3X). At 10-11 years, height and weight were measured to calculate age- and sex-specific standardized BMI z-scores (zBMI). Lifestyle patterns were identified using principal component analysis. To assess spatial clustering for the lifestyle patterns and zBMI, we calculated the Global Moran's I statistic. Linear- and logistic regression models, taking into account SES, were performed to examine the association between the lifestyle patterns and the development of overweight. For the spatial analyses, we added spatial terms for the determinants, the outcome, and the error term.ResultsThree lifestyle patterns were identified: (1) 'high activity', (2) 'low screen time, high sleep and healthy diet', and (3) 'high outdoor play'. No associations were observed between the 'high activity' or 'high outdoor play' patterns at young age with the development of childhood overweight (all p > 0.05). In contrast, children who adhered to the 'low screen time, high sleep and healthy diet' pattern had lower odds to become overweight and a lower zBMI at 10-11 years (odds ratio [95% CI] = 0.766 [0.65; 0.90]). These findings remained similar after taking SES into account. Regarding the spatial analyses, we found spatial clustering of zBMI, but no spatial clustering of the lifestyle patterns.ConclusionsLow screen time, high sleep duration and a healthy diet cluster into a pattern that seems favourable in the prevention of childhood overweight, independent of individual SES. The spatial analyses suggest that there are likely other neighbourhood factors that contribute to the spatial clustering of childhood overweight.

Project description:BackgroundMental health problems in young people have become a global health burden. The positive effects of physical activity on mental health in adults are well known but still not clear in children. The aim of this study was to investigate to what extent physical activity in early childhood would affect mental health in middle childhood.MethodsFrom the Dutch GECKO Drenthe birth cohort, 850 children (51.5% boys) were enrolled in this analysis. Physical activity and sedentary time were measured at age 5-6 using ActiGraph GT3X. Mental health was assessed using the Strengths and Difficulties Questionnaire (SDQ) at age 5-6 and age 10-11. Multiple linear regression models were used to estimate the associations between physical activity, sedentary time and SDQ subscales, stratified by gender, adjusting for age, BMI, maternal education level, family size, accelerometer wear time and season, and additionally adjusting for SDQ scores at age 5-6 to take tracking of mental health over time into account.ResultsGreater physical activity volume at age 5-6 was associated with lower peer problems scores at age 10-11 in boys and girls. An increase in MVPA was associated with lower peer problems scores in boys (b = -0.445, -0.713 to -0.176) and girls (b = -0.354, -0.601 to -0.107), however, increased sedentary time was linked to higher peer problems scores in boys (b = 1.18, 0.455 to 1.906) and girls (b = 0.870, 0.191 to 1.550). For hyperactivity, higher levels of physical activity volume and MVPA were associated with higher hyperactivity scores in boys. Increased sedentary time was related to lower hyperactivity scores in boys. Further adjustment for SDQ scores at age 5-6 attenuated associations between physical activity and hyperactivity in boys but hardly changed the relationships with peer problems. No significant associations between physical activity and other SDQ subscales or total difficulties scores were observed, neither in boys nor in girls.ConclusionsChildren who are more physically active at age 5-6 have fewer peer problems at age 10-11, and for boys, greater activity levels at age 5-6 could be an indicator of hyperactivity at age 10-11.

Dataset Information

Early Determinants of Childhood Blood Pressure at the Age of 6 Years: The GECKO Drenthe and ABCD Study Birth Cohorts.

Publications

Early Determinants of Childhood Blood Pressure at the Age of 6 Years: The GECKO Drenthe and ABCD Study Birth Cohorts.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets