Risk factors for childhood overweight: shift of the mean body mass index and shift of the upper percentiles: results from a cross-sectional study

Beyerlein, Andreas; Toschke, André Michael; Kries, Rüdiger von

doi:10.1038/ijo.2009.301

Cited by 57 publications

(79 citation statements)

References 39 publications

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“…By using quantile regression, we obtained similar results as Beyerlein et al [19,20] with regard to timeconstant risk factors. Apart from age, other risk factors also exert their effect in a different way on upper quantiles of the BMI distribution than on the mean.…”

Section: Discussionsupporting

confidence: 85%

“…For cross-sectional BMI data, quantile regression methods have been used to model a Z-score of the BMI depending on covariates [19,20], which was obtained by transforming raw BMI values based on age-and sex-specific reference charts. Here, we directly model raw BMI quantiles and include age and sex as covariates; and we thereby avoid the decision for a specific reference chart.…”

Section: Introductionmentioning

confidence: 99%

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Boosting Structured Additive Quantile Regression for Longitudinal Childhood Obesity Data

Fenske

Fahrmeir

Hothorn

et al. 2013

The International Journal of Biostatistics

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Childhood obesity and the investigation of its risk factors has become an important public health issue. Our work is based on and motivated by a German longitudinal study including 2,226 children with up to ten measurements on their body mass index (BMI) and risk factors from birth to the age of 10 years. We introduce boosting of structured additive quantile regression as a novel distribution-free approach for longitudinal quantile regression. The quantile-specific predictors of our model include conventional linear population effects, smooth nonlinear functional effects, varying-coefficient terms, and individual-specific effects, such as intercepts and slopes. Estimation is based on boosting, a computer intensive inference method for highly complex models. We propose a component-wise functional gradient descent boosting algorithm that allows for penalized estimation of the large variety of different effects, particularly leading to individual-specific effects shrunken toward zero. This concept allows us to flexibly estimate the nonlinear age curves of upper quantiles of the BMI distribution, both on population and on individual-specific level, adjusted for further risk factors and to detect age-varying effects of categorical risk factors. Our model approach can be regarded as the quantile regression analog of Gaussian additive mixed models (or structured additive mean regression models), and we compare both model classes with respect to our obesity data.

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Section: Discussionsupporting

confidence: 85%

Section: Introductionmentioning

confidence: 99%

Boosting Structured Additive Quantile Regression for Longitudinal Childhood Obesity Data

Fenske

Fahrmeir

Hothorn

et al. 2013

The International Journal of Biostatistics

View full text Add to dashboard Cite

show abstract

“…Since the effects of a number of risk factors for obesity appear to be stronger on the upper parts of the BMI distribution as recently shown (45), use of the mean BMI is a conservative approach in assessing the effects of maternal smoking on offspring's overweight.…”

Section: Discussionmentioning

confidence: 99%

Is low birth weight in the causal pathway of the association between maternal smoking in pregnancy and higher BMI in the offspring?

et al. 2011

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This is the author-created version of the article. The final publication is available at http://www.springerlink.com/content/v50765612w23x7p8/fulltext.html. AbstractIntroduction: A number of cross-sectional and prospective studies suggested a priming effect of maternal smoking in pregnancy on offspring's obesity. It has been hypothesized that this association might be explained by low birth weight and subsequent catch-up growth in the causal pathway. We therefore examined the role of birth weight in children exposed vs. not exposed to cigarette smoking in utero on later body mass index (BMI).

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“…Children of obese parents, specifically mothers, are consistently found to be at increased risk of obesity (Hernandez-Valero et al, 2007;Beyerlein et al, 2010). Additional predictors for childhood obesity include parental LSES, low education, long work hours and smoking (Huerta et al, 2006;Beyerlein et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Maternal weight misperceptions and smoking are associated with overweight and obesity in low SES preschoolers

et al. 2011

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Objectives: To identify modifiable risk factors for obesity among low socioeconomic status (LSES) children. Methods: Cross-sectional data were obtained from 238 4-7-year-old children and 224 mothers from LSES preschools. Anthropometric measurements were obtained; mothers were interviewed about sociodemographic characteristics, health behaviors, perceptions and beliefs. Results: The combined prevalence of overweight and obesity (OWOB) among children was 29.8% based on the new World Health Organization (WHO) growth standard. Prevalence of OWOB (body mass index X25) among mothers was 51.8%. Mean age, sleeping hours, gender distribution and poverty level were similar between normal and OWOB children. Over 82% of mothers underestimated their child's weight status. Of the 62 OWOB children, 74.2% were perceived by their mothers as having 'normal weight' (NW) and 8% were perceived as 'thin'. Mothers perceived 67 out of 158 NW children (42.4%) as 'thin' (Po0.001). Mediation analysis indicated that 10% of the effect of maternal underestimation on child's OWOB may be mediated through child's daily sedentary hours (P ¼ 0.06). In a multivariable logistic-regression analysis controlling for maternal obesity, knowledge regarding breakfast's importance and child's daily sedentary hours, maternal underestimation of the child's weight status (odds ratio ¼ 7.33; 95% confidence interval (CI):2.41-22.37; Po0.0001) and parental smoking (odds ratio ¼ 3.25; 95% CI: 1.26-8.40; P ¼ 0.015) were the only significant factors associated with OWOB in LSES children. Conclusions: Maternal perception of child's weight status and parental smoking are associated with childhood OWOB among LSES children. These parameters can help identify children at risk for obesity. Maternal perception may be amenable to intervention.

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Risk factors for childhood overweight: shift of the mean body mass index and shift of the upper percentiles: results from a cross-sectional study

Cited by 57 publications

References 39 publications

Boosting Structured Additive Quantile Regression for Longitudinal Childhood Obesity Data

Boosting Structured Additive Quantile Regression for Longitudinal Childhood Obesity Data

Is low birth weight in the causal pathway of the association between maternal smoking in pregnancy and higher BMI in the offspring?

Maternal weight misperceptions and smoking are associated with overweight and obesity in low SES preschoolers

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