Beyerlein Andreas, Toschke André M, von Kries Rüdiger
Division of Epidemiology, Institute of Social Paediatrics and Adolescent Medicine, Ludwig-Maximilians University of Munich, Munich, Germany.
Obesity (Silver Spring). 2008 Dec;16(12):2730-3. doi: 10.1038/oby.2008.432. Epub 2008 Oct 9.
A protective effect of breastfeeding on overweight (binary) has been reported by meta-analyses using logistic regression, whereas studies using linear regression and BMI (continuous) detected no significant association. To assess the relationship of these differences with different outcome classification, we compared results for linear, logistic, and quantile regression models in a cross-sectional data set of considerable size. Height, weight, and questionnaire data on 9,368 preschool children were collected during school-entry examinations in 1999 and 2002 in Bavaria, Southern Germany. We calculated multivariable linear, logistic, and quantile regression models with outcomes BMI, overweight, obesity, and BMI quantiles (as appropriate). Models considered the covariates breastfeeding (breastfed vs. never breastfed), gender, age, smoking in pregnancy, TV watching, maternal BMI, parental education, and early infant weight gain. No significant association was found in the linear regression model. In the logistic model, a significant association was observed for obesity (odds ratio: 0.72 (95% confidence interval (CI) 0.55, 0.94)). In quantile regression no significant point estimates were observed for the percentiles of 0.4-0.8. However, breastfeeding reduced the BMI of children having values on the 90th and 97th percentiles by -0.23 (95% CI -0.39, -0.07) and -0.26 (95% CI -0.45, -0.07) kg/m(2), respectively, on average. In contrast, breastfeeding was significantly associated with a low shift toward higher BMI values for BMI quantiles of 0.03 and from 0.1 to 0.3. The detection of associations between breastfeeding and childhood body composition might be related to the coding of the response variable (continuous or binary) and the statistical method used (linear, logistic, or quantile regression). Quantile regression should additionally be applied in such studies.
荟萃分析使用逻辑回归报告了母乳喂养对超重(二元变量)的保护作用,而使用线性回归和体重指数(连续变量)的研究未发现显著关联。为了评估这些差异与不同结局分类之间的关系,我们在一个规模相当大的横断面数据集中比较了线性、逻辑和分位数回归模型的结果。1999年和2002年在德国南部巴伐利亚州的入学检查期间,收集了9368名学龄前儿童的身高、体重和问卷数据。我们计算了以体重指数、超重、肥胖和体重指数分位数(视情况而定)为结局的多变量线性、逻辑和分位数回归模型。模型考虑了协变量母乳喂养(母乳喂养与从未母乳喂养)、性别、年龄、孕期吸烟、看电视、母亲体重指数、父母教育程度和婴儿早期体重增加。线性回归模型未发现显著关联。在逻辑模型中,观察到肥胖存在显著关联(比值比:0.72(95%置信区间(CI)0.55,0.94))。在分位数回归中,未观察到0.4 - 0.8分位数的显著点估计值。然而,母乳喂养分别使处于第90和第97百分位数的儿童的体重指数平均降低了 -0.23(95% CI -