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Background: Multiple linear correlations between parameters can be shown in correlation matrices. Correlations can be ranked, but can also be visualized in network graphs. Yet, translating a correlation matrix into a network graph is not trivial. In view of a popular child game, we propose to name this method St. Nicolas House Analysis. Material and methods: We present a new method (St. Nicolas House Analysis) that helps translating correlation matrices into network graphs. The performance of this and other network reconstruction methods was tested in randomly created virtual scale-free networks, networks consisting of bands or hubs, using balanced classification rate and the F1-Score for correctly predicting existing and non-existing edges. Thereafter we analyzed anthropometric data and information on parental education, obtained from an anthropometric survey in 908 Indonesian boys and 808 Indonesian girls. Seven parameters were analyzed: child height standard deviation score (hSDS), child BMI standard deviation scores (BMI_SDS), mid-upper-arm circumference (MUAC), mean thickness of subscapular and triceps skinfold (mean SF), and elbow breadth; as well as maternal and paternal education (years of schooling). The parameters were considered as the nodes of the network; the edges represent the correlations between the nodes. Results: Performance measures, balanced classification rate and the F1-score, showed that St. Nicolas’ House Analysis was superior to methods using sophisticated correlation value thresholds and methods based on partial correlations for analyzing bands and hubs. We applied this method also in an Indonesia data set. Ranking correlations showed the direct association between parental education and child growth. Conclusion: St. Nicolas House Analysis confirmed that growth of Indonesian school children directly depends on maternal education, with no evidence that this effect is mediated by the state of nutrition.
Today, serious health problems as overweight and obesity are not just constricted to the developed world, but also increase in the developing countries (Prentice 2006, Ramachandram et al. 2002). Focusing on this issue, BMI and percentage of body fat were compared in 2094 schoolchildren from two cross-sectional studies from India and Germany investigated in 2008 and 2009. The German children are in all age groups significantly taller, whereas the Indian children show higher values in BMI (e.g. 12 years: Indian: around 22 kg/m(2); German: around 19 kg/m(2)) and in the percentage of body fat (e.g. 12 years: Indian: around 27 %; German: around 18-20%) in most of the investigated age groups. The Indian children have significantly higher BMI between 10 and 13 (boys) respectively 14 years (girls). Indian children showed significant higher percentage of body fat between 10 and 15 years (boys) and between 8 and 16 years (girls). The difference in overweight between Indian and German children was strongest at 11 (boys) and 12 (girls) years: 70 % of the Indian but 20% of the German children were classified as overweight. In countries such as India that undergo nutritional transition, a rapid increase in obesity and overweight is observed. In contrast to the industrialized countries, the risk of overweight in developing countries is associated with high socioeconomic status. Other reasons of the rapid increase of overweight in the developing countries caused by different environmental or genetic factors are discussed.