Search

Background: We investigated average body height in the central provinces of the Russian empire in the middle of XIX century in view of the concept of "community effects on height". We analyzed body height correlations between neighboring districts at this time. We added information about secular changes in body height during the 19th century of this territory. Material and methods: The study used height data of conscripts, which were born in the years 1853-1863, and age 21 at the time of measurement. The territory of seven provinces was considered as a network with 105 nodes, each node representing one district with information on average male body height. In order to define neighboring districts three different approaches were used: based on the "common borders" method, based on Euclidean distances (from 60 to 120 km), based on real road connections. Results: Small but significant correlation coefficients were observed between 1st order districts in the network based on Euclidean distance of 100 km (r = 0.256, p-value = 0.006) and based on "the common borders" approach (r = 0.25, p-value = 0.02). Wherein no significant correlations were observed in the network based on road connections and between second order neighbors regardless of the method. Conclusion: Height correlation coefficients between 1st order neighboring districts observed in the Russian districts were very similar to values observed in the Polish study (r = 0.24). The considered Russian territory and the territory of Poland have a lot in common. They consist of both plains without mountains. In contradistinction to Poland the transport infrastructure in Russia was weakly developed in the middle of XIX century. In addition, the mobility of people was limited by serfdom. In this context the absent of significant correlation of second order neighbors can be explained by low population density and lack of migration and communication between the districts.

Background: Recent research reported height biased migration of taller individuals and a Monte Carlo simulation showed that such preferential migration of taller individuals into network hubs can induce a secular trend of height. In the simulation model taller agents in the hubs raise the overall height of all individuals in the network by a community effect. However, it could be seen that the actual network structure influences the strength of this effect. In this paper the background and the influence of the network structure on the strength of the secular trend by migration is investigated. Material and methods: Three principal network types are analyzed: networks derived from street connections in Switzerland, more regular fishing net like networks and randomly generated ones. Our networks have between 10 and 152 nodes and between 20 and 307 edges connecting the nodes. Depending on the network size between 5.000 and 90.000 agents with an average height of 170 cm (SD 6.5 cm) are initially released into the network. In each iteration new agents are regenerated based on the actual average body height of the previous iteration and, to a certain proportion, corrected by body heights in the neighboring nodes. After generating new agents, a certain number of them migrated into neighbor nodes, the model let preferentially taller agents migrate into network hubs. Migration is balanced by back migration of the same number of agents from nodes with high centrality measures to less connected nodes. The latter is random as well, but not biased by the agents height. Furthermore the distribution of agents per node and their correlation to the centrality of the nodes is varied in a systematic manner. After 100 iterations, the secular trend, i.e. the gain in body height for the different networks, is investigated in relation to the network properties. Results: We observe an increase of average agent body height after 100 iterations if height biased migration is enabled. The increase rate depends on the height of the neighboring factor, the population distribution, the relationship between population in the nodes and their centrality as well as on the network topology. Networks with uniform like distributions of the agents in the nodes, uncorrelated associations between node centrality and agent number per node, as well as very heterogeneous networks with very different node centralities lead to biggest gains in average body height. Conclusion: Our simulations show, that height biased migration into network hubs can possibly contribute to the secular trend of height increase in the human population. The strength of this "tall by migration" event depends on the actual properties of the underlying network. There is a possible significance of this mechanism for social networks, when hubs are represented by individuals and edges as their personal relationships. However, the required high number of iterations to achieve significant effects in more natural network structures in our models requires further studies to test the relevance and real effect sizes in real world scenarios.

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.

Background: Since the 19th century, sexual dimorphism has been known to be sensitive to adverse environmental conditions. Migrants are sensitive to economic and political affluence, and tend to adjust in height towards height of their hosts. We aim to study growth, sexual dimorphism, and height of native and migrant children in three primary schools in Nabire, Papua, Indonesia. Material and Methods 1: Cross-sectional data on height were obtained from 186 children (59 and 56 native Papuan girls and boys, respectively; 38 and 33 trans-migrant girls and boys respectively; who are aged 7.0-7.7 (mean 7.45) years from three primary schools in Nabire, Papua, Indonesia. The Indonesian National Growth Charts were used as references for height. Results 1: With 118.4 cm (SD 6.7) cm, trans-migrant 7-year old boys were almost 3 cm taller compared to their native-age-matched-peers (115.7 cm (5.0 SD) cm, p < 0.05). The case was different among the girls. Trans-migrant girls were not significantly taller than native girls. Material and Method 2: We re-analyzed the height data from over 300,000 infants, children and adolescents, age 0-18 year, from 34 Indonesian provinces. The data were obtained from the latest Indonesian 2013 National Basic Health Survey (NHBS), and have previously been used to construct National Indonesian Growth Charts. St. Nicolas House Analysis was used to translate correlation matrices into network graphs and visualizing chains of associations between parameters that were multiply correlated. Results 2: St. Nicolas House Analysis suggested that the growth of male sex were more sensitive to population crowding, and that crowding contributes to the greater height dimorphism in taller populations. Conclusion: Economic prosperity and increased population density stimulate male adult height to a greater extent than female adult height, and increase the sexual dimorphism in height among the tall populations of densely populated wealthy countries.

Introduction: Body height is influenced by biological factors such as genetics, nutrition and health, but also by the social network, and environmental and economical factors. During centuries, the Japanese society has developed on islands. This setting provides ideal natural conditions for studying the influence of social networks on human height. Material and methods: We investigated body height of male Japanese students aged 17.5 years obtained in 47 prefectures, from the Japanese school health survey of the years 1955, 1975, 1995, and 2015. Results: Japanese students increased in height from 163.23 cm in 1955 to 170.84 cm in 1995, with no further increase thereafter (170.63 cm in 2015). Students living in neighboring prefectures were similar in height. The correlation of height between neighboring prefectures ranged between r = 0.79 and r = 0.49 among first degree neighbors, between r = 0.49 and r = 0.21 among second degree neighbors and dropped to insignificance among third degree neighbors indicating psychosocial effects of the community on body height. Tall stature and short stature prefectures did not remain tall or short throughout history. Autocorrelations of height within the same prefectures decreased from the 20 years periods of 1955-1975, 1975-1995 and 1995-2015 (r = 0.52, r = 0.61, r = 0.63, respectively) to the 40 years periods of 1955-1995 and 1975-2015 (r = 0.49, r = 0.52), down to the 60 years period of 1955-2015 (r = 0.27), indicating significant volatility of height. Conclusion: Body height of 17.5 years old Japanese students increased since 1955. Body height depended on height of the neighboring prefecture, but was volatile with decreasing autocorrelation during a period of 60 years.

The aim of the study was to investigate the correlation of heights of conscripts living in neighboring districts in Poland. The study used 10% of a nationally representative sample of 26,178 males 18.5-19.5 years old examined during the National survey of Polish conscripts conducted in 2001. The sample represented all regions and social strata of the country and included 354 different districts within 16 voivodships (provinces). Analyses were performed with the R statistical software. A small but significant correlation (0.24, p < 0.0001) was observed for height between 1st order neighboring districts. Correlations decreased with increased distances between neighboring districts, but remained significant for 7th node neighbors (0.18, p < 0.0001). Regarding voivodships (provinces), average height showed a geographical trend from the northwest (relatively tall) to the southeast (relatively short), and the correlation was stronger for first order neighboring provinces (0.796, p < 0.001). This study revealed clusters of tall people and short people, providing a support for hypothesis of the community effect in height. Small correlations between 1st order neighbors than in another country (Switzerland) may be associated with differences in geography, since in Poland there are no natural barriers (e.g., mountains) and road infrastructure is well-developed.

Background: We investigated height of Norwegian conscripts in view of the hypothesis of a "community effect on height" using autocorrelation analysis of district heights within a time-span of 20 years at the end of the 19th century and correlations between neighboring districts at this time. Material and methods: After digitalizing available body height data of Norwegian draftees in 1877-1878, 1880 (averaged as 1878), and 1895-1897 (averaged as 1896) we calculated the magnitude of autocorrelation of body height within the same municipality at different time points. Furthermore, we generated three different neighborhood networks, (1) based on Euclidean distances, (2) a minimum spanning tree build on those distances, (3) a network founded on real world road connections. The networks were used to determine the correlation between body height of neighboring districts depending on the number of edges required to connect two municipalities. Results: The autocorrelation value for body heights was around r = 0.5 (for all p < 0.001) in the years 1878 and 1896. The correlation between neighboring districts varied in the Euclidean distance based network between 0.47 and 0.27 approximately for both years in a sorted order, descending from nearest (0-50 km) to farthest (150-200 km, for all p < 0.001). First order neighbors in the minimum spanning tree network correlation was 0.36 in 1878 and 0.42 in 1896 (for all p < 0.001). The values of neighbor correlation in the road connection based network ranged in 1878 from 0.42 (first order neighbors) to 0.17 (forth order neighbors, for all p < 0.01) and in 1896 from 0.46 (first order neighbors) to 0.12 (forth order neighbors, for all p < 0.05). Conclusion: This initial study of Norwegian conscript height data from the 19th century showed significant medium sized effects for the within district autocorrelation between 1878 and 1896 as well as medium neighborhood correlation, slightly lower in comparison to a recent study regarding Swiss conscripts. Digitalizing more data from other years in this and later time spans as well as using older road and ship connections instead of the actual road data might stabilize and improve those findings.

Aim: Influence of nutrition in human growth failure, especially stunting, is a well-accepted idea. The present study assesses the influence of nutrition and non-nutritional factors on height growth in a short stature population. Material and methods: The present study was conducted among the children and adolescents of Sikkim, India. The sample size was 538 (boys and girls) of age 2-18 years. The anthropometric indices mid upper arm circumference-for-age Z-scores (MUACZ) and BMI-for-age Z-scores (BAZ) were utilised as proxy of nutritional status and growth was assessed using height-for-age Z-scores (HAZ). Associations were assessed using correlation, St. Nicolas house analysis (SNHA), principal component analysis (PCA) and regression. Results: Nutritional status of the participating children and adolescents as assessed by MUACZ and BAZ were largely normal. Despite variation in HAZ from -4 to +2 there was no influence of the nutritional indices on height. Further, there was clear lack of association between HAZ and socio-economic variables in the present study. Conclusion: The findings of the present study suggest nutrition is not the primary regulator of human growth. The possible influence of community effects on height is discussed.

Background: The association between stature and social dominance is known. Dominance within social groups and current politics are related issues. We therefore aimed to compare estimates of the opinion of a population about their current political issues, with physical growth. Material and methods: We used data on the 2012 and the 2014 elections for the Japanese House of Representatives and the percent proportion of votes of the 47 prefectures of Japan, and regional data on body height of 17.5 year old men and women. Information on capita income, possession of mobile phones, urban/rural population ratio, and age distribution were added to capture socioeconomic factors. Four political parties were present in most of the 47 prefectures: the Liberal Democratic Party (LDP), the Democratic Party of Japan (DPJ), the New Komeito Party (Kom) that is known for their social network community, and the Japanese Communist Party (JCP). Results: A dense network of associations exists between height, age distribution, per capita income, number of smartphones, and voting results. Male and female body height was inversely related with the proportion of votes for New Komeito Party. Average stature decreases by one mm per percent votes for this political party. Medium strong positive associations were found for male body height and voting results of the DPJ and for female body height with the JCP election results. Conclusion: In modern Japan, popular preferences for conservative political structures coincide with shorter stature.

Background: In a recent Monte Carlo simulation, the clustering of body height of Swiss military conscripts within a spatial network with characteristic features of the natural Swiss geography was investigated. In this study I examined the effect of migration of tall individuals into network hubs on the dynamics of body height within the whole spatial network. The aim of this study was to simulate height trends. Material and methods: Three networks were used for modeling, a regular rectangular fishing net like network, a real world example based on the geographic map of Switzerland, and a random network. All networks contained between 144 and 148 districts and between 265-307 road connections. Around 100,000 agents were initially released with average height of 170 cm, and height standard deviation of 6.5 cm. The simulation was started with the a priori assumption that height variation within a district is limited and also depends on height of neighboring districts (community effect on height). In addition to a neighborhood influence factor, which simulates a community effect, body height dependent migration of conscripts between adjacent districts in each Monte Carlo simulation was used to re-calculate next generation body heights. In order to determine the direction of migration for taller individuals, various centrality measures for the evaluation of district importance within the spatial network were applied. Taller individuals were favored to migrate more into network hubs, backward migration using the same number of individuals was random, not biased towards body height. Network hubs were defined by the importance of a district within the spatial network. The importance of a district was evaluated by various centrality measures. In the null model there were no road connections, height information could not be delivered between the districts. Results: Due to the favored migration of tall individuals into network hubs, average body height of the hubs, and later, of the whole network increased by up to 0.1 cm per iteration depending on the network model. The general increase in height within the network depended on connectedness and on the amount of height information that was exchanged between neighboring districts. If higher amounts of neighborhood height information were exchanged, the general increase in height within the network was large (strong secular trend). The trend in the homogeneous fishnet like network was lowest, the trend in the random network was highest. Yet, some network properties, such as the heteroscedasticity and autocorrelations of the migration simulation models differed greatly from the natural features observed in Swiss military conscript networks. Autocorrelations of district heights for instance, were much higher in the migration models. Conclusion: This study confirmed that secular height trends can be modeled by preferred migration of tall individuals into network hubs. However, basic network properties of the migration simulation models differed greatly from the natural features observed in Swiss military conscripts. Similar network-based data from other countries should be explored to better investigate height trends with Monte Carlo migration approach.

Height and skeletal morphology strongly relate to life style. Parallel to the decrease in physical activity and locomotion, modern people are slimmer in skeletal proportions. In German children and adolescents, elbow breadth and particularly relative pelvic breadth (50th centile of bicristal distance divided by body height) have significantly decreased in recent years. Even more evident than the changes in pelvic morphology are the rapid changes in body height in most modern countries since the end-19th and particularly since the mid-20th century. Modern Japanese mature earlier; the age at take-off (ATO, the age at which the adolescent growth spurt starts) decreases, and they are taller at all ages. Preece-Baines modelling of six national samples of Japanese children and adolescents, surveyed between 1955 and 2000, shows that this gain in height is largely an adolescent trend, whereas height at take-off (HTO) increased by less than 3 cm since 1955; adolescent growth (height gain between ATO and adult age) increased by 6 cm. The effect of globalization on the modern post-war Japanese society ("community effect in height") on adolescent growth is discussed., Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe, 869

Background: Human populations differ in height. Recent evidence suggests that social networks play an important role in the regulation of adolescent growth and adult height. We further investigated the effect of physical connectedness on height. Material and methods: We considered Switzerland as a geographic network with 169 nodes (district capitals) and 335 edges (connecting roads) and studied effects of connectedness on height in Swiss conscript from 1884-1891, 1908-1910, and 2004-2009. We also created exponential-family random graph models to separate possible unspecific effects of geographic vicinity. Results: In 1884-1891, in 1908-1910, and in 2004-2009, 1st, 2nd and 3rd order neighboring districts significantly correlate in height (p

BACKGROUND/OBJECTIVES Recent evidence suggests clustering of human body height. We want to assess the consequences of connectedness in a spatial network on height clustering in an artificial society. SUBJECTS/METHODS We used an agent-based computer modelling technique (Monte Carlo simulation) and compared simulated height in a spatial network with characteristics of the observed geographic height distribution of three historic cohorts of Swiss military conscripts (conscripted in 1884-1891; 1908-1910; and 2004-2009). RESULTS Conscript height shows several characteristic features: (1) height distributions are overdispersed. (2) Conscripts from districts with direct inter-district road connections tend to be similar in height. (3) Clusters of tall and clusters of short stature districts vary over time. Autocorrelations in height between late 19th and early 21st century districts are low. (4) Mean district height depends on the number of connecting roads and on the number of conscripts per district. Using Monte Carlo simulation, we were able to generate these natural characteristics in an artificial society. Already 5% height information from directly connected districts is sufficient to simulate the characteristics of natural height distribution. Very similar observations in regular rectangular networks indicate that the characteristics of Swiss conscript height distributions do not so much result from the particular Swiss geography but rather appear to be general features of spatial networks. CONCLUSIONS Spatial connectedness can affect height clustering in an artificial society, similar to that seen in natural cohorts of military conscripts, and strengthen the concept of connectedness being involved in the regulation of human height.

Principal components analysis (PCA) is a standard tool in multivariate data analysis to reduce the number of dimensions, while retaining as much as possible of the data's variation. Instead of investigating thousands of original variables, the first few components containing the majority of the data's variation are explored. The visualization and statistical analysis of these new variables, the principal components, can help to find similarities and differences between samples. Important original variables that are the major contributors to the first few components can be discovered as well.This chapter seeks to deliver a conceptual understanding of PCA as well as a mathematical description. We describe how PCA can be used to analyze different datasets, and we include practical code examples. Possible shortcomings of the methodology and ways to overcome these problems are also discussed.