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Twenty-four scientists met for the annual Auxological conference held at Krobielowice castle, Poland, to discuss the diverse influences of the environment and of social behavior on growth following last year’s focus on growth and public health concerns (Hermanussen et al., 2022b). Growth and final body size exhibit marked plastic responses to ecological conditions. Among the shortest are the pygmoid people of Rampasasa, Flores, Indonesia, who still live under most secluded insular conditions. Genetics and nutrition are usually considered responsible for the poor growth in many parts of this world, but evidence is accumulating on the prominent impact of social embedding on child growth. Secular trends not only in the growth of height, but also in body proportions, accompany the secular changes in the social, economic and political conditions, with major influences on the emotional and educational circumstances under which the children grow up (Bogin, 2021). Aspects of developmental tempo and aspects of sports were discussed, and the impact of migration by the example of women from Bangladesh who grew up in the UK. Child growth was considered in particular from the point of view of strategic adjustments of individual size within the network of its social group. Theoretical considerations on network characteristics were presented and related to the evolutionary conservation of growth regulating hypothalamic neuropeptides that have been shown to link behavior and physical growth in the vertebrate species. New statistical approaches were presented for the evaluation of short term growth measurements that permit monitoring child growth at intervals of a few days and weeks.
Human growth data analysis and statistics – the 5th Gülpe International Student Summer School
(2023)
The Summer School in Gülpe (Ecological Station of the University of Potsdam) offers an exceptional learning opportunity for students to apply their knowledge and skills to real-world problems. With the guidance of experienced human biologists, statisticians, and programmers, students have the unique chance to analyze their own data and gain valuable insights. This interdisciplinary setting not only bridges different research areas but also leads to highly valuable outputs. The progress of students within just a few days is truly remarkable, especially when they are motivated and receive immediate feedback on their questions, problems, and results. The Summer School covers a wide range of topics, with this year’s focus mainly on two areas: understanding the impact of socioeconomic and physiological factors on human development and mastering statistical techniques for analyzing data such as changepoint analysis and the St. Nicolas House Analysis (SNHA) to visualize interacting variables. The latter technique, born out of the Summer School’s emphasis on gaining comprehensive data insights and understanding major relationships, has proven to be a valuable tool for researchers in the field. The articles in this special issue demonstrate that the Summer School in Gülpe stands as a testament to the power of practical learning and collaboration. Students who attend not only gain hands-on experience but also benefit from the expertise of professionals and the opportunity to engage with peers from diverse disciplines.
The St. Nicolas House Algorithm (SNHA) finds association chains of direct dependent variables in a data set. The dependency is based on the correlation coefficient, which is visualized as an undirected graph. The network prediction is improved by a bootstrap routine. It enables the computation of the empirical p-value, which is used to evaluate the significance of the predicted edges. Synthetic data generated with the Monte Carlo method were used to firstly compare the Python package with the original R package, and secondly to evaluate the predicted network using the sensitivity, specificity, balanced classification rate and the Matthew's correlation coefficient (MCC). The Python implementation yields the same results as the R package. Hence, the algorithm was correctly ported into Python. The SNHA scores high specificity values for all tested graphs. For graphs with high edge densities, the other evaluation metrics decrease due to lower sensitivity, which could be partially improved by using bootstrap,while for graphs with low edge densities the algorithm achieves high evaluation scores. The empirical p-values indicated that the predicted edges indeed are significant.
Animal societies are structured of dominance hierarchy (DH). DH can be viewed as networks and analyzed by graph theory. We study the impact of state-dependent feedback (winner-loser effect) on the emergence of local dominance structures after pairwise contests between initially equal-ranking members (equal resource-holding-power, RHP) of small and large social groups. We simulated pairwise agonistic contests between individuals with and without a priori higher RHP by Monte-Carlo-method. Random pairwise contests between equal-ranking competitors result in random dominance structures (‘Null variant’) that are low in transitive triads and high in pass along triads; whereas state-dependent feedback (‘Winner-loser variant’) yields centralized ‘star’ structured DH that evolve from competitors with initially equal RHP and correspond to hierarchies that evolve from keystone individuals. Monte-Carlo simulated DH following state-dependent feedback show motif patterns very similar to those of a variety of natural DH, suggesting that state-dependent feedback plays a pivotal role in robust self-organizing phenomena that transcend the specifics of the individual. Self-organization based on state-dependent feedback leads to social structures that correspond to those resulting from pre-existing keystone individuals. As the efficiency of centralized social networks benefits both, the individual and the group, centralization of social networks appears to be an important evolutionary goal.
Background: Network models are useful tools for researchers to simplify and understand investigated systems. Yet, the assessment of methods for network construction is often uncertain. Random resampling simulations can aid to assess methods, provided synthetic data exists for reliable network construction.
Objectives: We implemented a new Monte Carlo algorithm to create simulated data for network reconstruction, tested the influence of adjusted parameters and used simulations to select a method for network model estimation based on real-world data. We hypothesized, that reconstructs based on Monte Carlo data are scored at least as good compared to a benchmark.
Methods: Simulated data was generated in R using the Monte Carlo algorithm of the mcgraph package. Benchmark data was created by the huge package. Networks were reconstructed using six estimator functions and scored by four classification metrics. For compatibility tests of mean score differences, Welch’s t-test was used. Network model estimation based on real-world data was done by stepwise selection.
Samples: Simulated data was generated based on 640 input graphs of various types and sizes. The real-world dataset consisted of 67 medieval skeletons of females and males from the region of Refshale (Lolland) and Nordby (Jutland) in Denmark.
Results: Results after t-tests and determining confidence intervals (CI95%) show, that evaluation scores for network reconstructs based on the mcgraph package were at least as good compared to the benchmark huge. The results even indicate slightly better scores on average for the mcgraph package.
Conclusion: The results confirmed our objective and suggested that Monte Carlo data can keep up with the benchmark in the applied test framework. The algorithm offers the feature to use (weighted) un- and directed graphs and might be useful for assessing methods for network construction.
Background: In the animal kingdom body size is often linked to dominance and subsequently the standing in social hierarchy. Similarly, human growth has been associated and linked to socioeconomic factors, including one’s social status. This has already been proposed in the early 1900s where data on young German school girls from different social strata have been compared.
Objectives: This paper aims to summarize and analyze these results and make them accessible for non-German speakers. The full English translation of the historic work of Dikanski (Dikanski, 1914) is available as a supplement. Further, this work aims to compare the historical data with modern references, to test three hypotheses: (1) higher social class is positively associated with body height and weight, (2) affluent people from the used historical data match modern references in weight and height and (3) weight distributions are skewed in both modern and historical populations.
Methods: Comparison of historical data from 1914 with WHO and 1980s German data. The data sets, for both body weight and height for 6.0- and 7.0-year-old girls, were fitted onto centile curves and quantile correlation coefficients were calculated.
Results: In historical data social status is positively associated with body height and weight while both are also normally distributed, which marks a significant difference to modern references.
Conclusion: Social status is positively associated with height, signaling social dominance, making children of affluent classes taller. Children from the historical data do not reach the average height of modern children, even under the best environmental conditions. The children of the upper social class were not skewed in weight distribution, although they had the means to become as obese as modern children.
Students learn by repetition. Repetition is essential, but repetition needs questioning, and questioning the repertoire belongs to the essential tasks of student education. Guiding students to questioning was and is our prime motive to offer our International Student Summer Schools. The data were critically discussed among the students, in the twilight of Just So Stories, common knowledge, and prompted questioning of contemporary solutions. For these schools, the students bring their own data, carry their preliminary concepts, and in group discussions, they may have to challenge these concepts. Catch-up growth is known to affect long bone growth, but different opinions exist to what extent it also affects body proportions. Skeletal age and dental development are considered appropriate measures of maturation, but it appears that both system develop independently and are regulated by different mechanisms. Body weight distributions are assumed to be skewed, yet, historic data disproved this assumption. Many discussions focused on current ideas of global growth standards as a common yardstick for all populations world-wide, with new statistical tools being developed including network reconstruction and evaluation of the reconstructs to determine the confidence of graph prediction methods.
PRI: Re-analysis of a public mass cytometry dataset reveals patterns of effective tumor treatments
(2022)
Recently, mass cytometry has enabled quantification of up to 50 parameters for millions of cells per sample. It remains a challenge to analyze such high-dimensional data to exploit the richness of the inherent information, even though many valuable new analysis tools have already been developed. We propose a novel algorithm "pattern recognition of immune cells (PRI)" to tackle these high-dimensional protein combinations in the data. PRI is a tool for the analysis and visualization of cytometry data based on a three or more-parametric binning approach, feature engineering of bin properties of multivariate cell data, and a pseudo-multiparametric visualization. Using a publicly available mass cytometry dataset, we proved that reproducible feature engineering and intuitive understanding of the generated bin plots are helpful hallmarks for re-analysis with PRI. In the CD4(+)T cell population analyzed, PRI revealed two bin-plot patterns (CD90/CD44/CD86 and CD90/CD44/CD27) and 20 bin plot features for threshold-independent classification of mice concerning ineffective and effective tumor treatment. In addition, PRI mapped cell subsets regarding co-expression of the proliferation marker Ki67 with two major transcription factors and further delineated a specific Th1 cell subset. All these results demonstrate the added insights that can be obtained using the non-cluster-based tool PRI for re-analyses of high-dimensional cytometric data.
Twenty-one scientists met for this year’s virtual conference on Auxology held at the University Potsdam, Germany, to discuss child and adolescent growth during times of fear and emotional stress. Growth within the broad range of normal for age and sex is considered a sign of good general health whereas fear and emotional stress can lead to growth faltering. Stunting is a sign of social disadvantage and poor parental education. Adverse childhood experiences affect child development, particularly in families with low parental education and low socioeconomic status. Negative effects were also shown in Indian children exposed prenatally and in early postnatal life to the cyclone Aila in 2009. Distrust, fears and fake news regarding the current Corona pandemic received particular attention though the effects generally appeared weak. Mean birth weight was higher; rates of low, very and extremely low birth weight were lower. Other topics discussed by the participants, were the influences of economic crises on birth weight, the measurement of self-confidence and its impact on growth, the associations between obesity, peer relationship, and behavior among Turkish adolescents, height trends in Indonesia, physiological neonatal weight loss, methods for assessing biological maturation in sportsmen, and a new method for skeletal age determination. The participants also discussed the association between acute myocardial infarction and somatotype in Estonia, rural-urban growth differences in Mongolian children, socio-environmental conditions and sexual dimorphism, biological mortality bias, and new statistical techniques for describing inhomogeneity in the association of bivariate variables, and for detecting and visualizing extensive interactions among variables.
In nature, dominance is often shown by body size; even in humans many studies report that social status is associated with body height. In today's society, educational status is an important factor for social classification. Since growing children do not have their own educational or social status, they are often affected by the status of their parents. Therefore, the question appears, whether parental educational status measurably affects the growth of a child. If so, is this explainable by the nutritional factors? To test this hypothesis, seven different Indian data sets where reexamined using the St. Nicolas House Analysis. The results show a direct association between parental education and body height (hSDS) of the child, but there was no influence of parental education on the nutritional status. We conclude that education has a direct effect on height that is not mediated via nutrition.
Aim: We aimed to examine the distribution and secular changes of conscript body height in the geographic network of Norway since 1878 and to study its association with the degree of urbanization, and population density. Material and methods: Data on body height of Norwegian military conscripts were provided by the Statistics Norway Department (SSB). The sample comprised eight cohorts with the following measurement years: 1st 1877, 1878 and 1880, 2nd 18951897, 3rd 1915-1917, 4th 1935-1937, 5th 1955-1957, 6th 1975-1977, 7th 1995-1997, and 8th 2009-2011. For determining neighborhood correlations, a network was created consisting of neighboring counties, sharing a common border. Results: Average body height of Norwegian men increased by 10.9 cm between 1878 and 2010, but this trend was heterogeneous. Some counties increased by more than 1 cm per decade (Finmark) others by only 7 mm per decade (Sor-Trondelag). Urban counties and counties with higher population density showed stronger height trends than rural counties. The largest spread in body height between the various counties was observed in 1936 when for the first time people living in the more urban counties got taller than rural people. The height advantage of urban counties however, disappeared after 1996. At this time, also the secular trend in height had come to a halt. The secular trend in height had become obvious after the dissolution of the union between Norway and Sweden in 1905 and World War I, and was strongest between 1936 and 1956. During this period maximum between-county heterogeneity in height existed with body height differences of more than 6 cm between the tallest and the shortest county. The end of this period was characterized by social democratic reforms that flattened the income distribution, eliminated poverty, and ensured social services after World War II. Conclusion: The temporal coincidence between the trends in height, the degree of urbanization and the onset of the political transition of Norway from a Swedish province into an independent democratic wealthy modern European state after World War I and particularly after World War II, and the abatement of this trend after this period of transition had stabilized, suggest social and political components interfering with the regulation of physical growth in humans.
Identification of a super-functional Tfh-like subpopulation in murine lupus by pattern perception
(2020)
Dysregulated cytokine expression by T cells plays a pivotal role in the pathogenesis of autoimmune diseases. However, the identification of the corresponding pathogenic subpopulations is a challenge, since a distinction between physiological variation and a new quality in the expression of protein markers requires combinatorial evaluation. Here, we were able to identify a super-functional follicular helper T cell (Tfh)-like subpopulation in lupus-prone NZBxW mice with our binning approach "pattern recognition of immune cells (PRI)". PRI uncovered a subpopulation of IL-21(+) IFN-gamma(high) PD-1(low) CD40L(high) CXCR5(-) Bcl-6(-) T cells specifically expanded in diseased mice. In addition, these cells express high levels of TNF-alpha and IL-2, and provide B cell help for IgG production in an IL-21 and CD40L dependent manner. This super-functional T cell subset might be a superior driver of autoimmune processes due to a polyfunctional and high cytokine expression combined with Tfh-like properties.
No correlation between short term weight gain and lower leg length gain in healthy German children
(2020)
Background:
Length-for-age is considered the indicator of choice in monitoring the long-term impact of chronic nutritional deficiency. Aim: We hypothesized that short term increments of body weight cross-correlate with increments of the lower leg length.
Sample and methods:
We re-analyzed the association between weekly measurements of weight and of lower leg length in 34 healthy German children, aged 2.9-15.9 years. The data are a subset of measurements originally published in 1988 (Hermanussen et al. 1988a). As the growth measurements were often not equally spaced in time due to interposed holidays and illness, the incremental rates for weight and lower leg length were smoothed using spline functions. Autocorrelation and cross-correlation functions were calculated for weight increments and lower leg length increments.
Results:
Height and weight increments are pulsatile. Autocorrelations indicated that mini growth spurts occur at irregular intervals. Lack of cross-correlations between weight and lower leg length indicated that mini spurts in weight gain do not coincide with mini spurts in length gain even when considering lag times of up to 10 weeks. Short term changes of weight gain and lower leg length gain in healthy children show no temporal association.
Life history theory predicts that experiencing stress during the early period of life will result in accelerated growth and earlier maturation. Indeed, animal and some human studies documented a faster pace of growth in the offspring of stressed mothers. Recent advances in epigenetics suggest that the effects of early developmental stress might be passed across the generations. However, evidence for such intergenerational transmission is scarce, at least in humans. Here we report the results of the study investigating the association between childhood trauma in mothers and physical growth in their children during the first months of life. Anthropometric and psychological data were collected from 99 mothers and their exclusively breastfed children at the age of 5 months. The mothers completed the Early Life Stress Questionnaire to assess childhood trauma. The questionnaire includes questions about the most traumatic events that they had experienced before the age of 12 years. Infant growth was evaluated based on the anthropometric measurements of weight, length, and head circumference. Also, to control for the size of maternal investment, the composition of breast milk samples taken at the time of infant anthropometric measurements was investigated. The children of mothers with higher early life stress tended to have higher weight and bigger head circumference. The association between infant anthropometrics and early maternal stress was not affected by breast milk composition, suggesting that the effect of maternal stress on infant growth was independent of the size of maternal investment. Our results demonstrate that early maternal trauma may affect the pace of growth in the offspring and, in consequence, lead to a faster life history strategy. This effect might be explained via changes in offspring epigenetics.
Background: The polymorphism in FTO gene (rs9939609) is known to be associated with higher BMI and body fat mass content. However, environmental factors can modify this effect. The purpose of the present study was to investigate an association between sport specialization and the rs9939609 SNP in FTO gene in the cohort of professional and amateur young athletes. Methods: A total number of 250 young individuals 8-18 years old living in Moscow or Moscow district participated in the study. Individuals were divided into 3 groups in accordance with their physical activity level: control group (n = 49), amateurs (n = 67) and professionals (n = 137). Amateur and professional athletes were subdivided into groups according to their sport specialization. Quantile regression was used as a regression model, where the dependent (outcome) variable was BMI, along with percentage of body fat mass, and the independent variables (predictors) were the rs9939609 SNP in FTO gene, physical activity (active versus inactive), sport specialization (aerobic, intermittent sports and martial arts), nationality, level of sport experience (in years), gender and percentage of free fat mass content. Results: The regression analysis revealed that physical activity and sport specialization had greater impact compared to FTO allele in the group of physically active individuals. Physical activity, in particular aerobic, had negative associations with body fat mass and BMI. The rs9939609 SNP in FTO gene is associated with physical activity and aerobic activity. The magnitude of association becomes significantly larger at the upper quantiles of the body fat mass distribution. Conclusion: Physical activity and sport specialization explained more variance in body composition of physically active young individuals compared to the FTO polymorphism. Effect of interaction of physical activity, in particular aerobic, with the FTO polymorphism on body composition of young athletes was found.
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.
Meeting Reports
(2019)
Thirty-one scientists met at Aschauhof, Germany to discuss the role of beliefs and self-perception on body size. In view of apparent growth stimulatory effects of dominance within the social group that is observed in social mammals, they discussed various aspects of competitive growth strategies and growth adjustments. Presentations included new data from Indonesia, a cohort-based prospective study from Merida, Yucatan, and evidence from recent meta-analyses and patterns of growth in the socially deprived. The effects of stress experienced during pregnancy and adverse childhood events were discussed, as well as obesity in school children, with emphasis on problems when using z-scores in extremely obese children. Aspects were presented on body image in African-American women, and body perception and the disappointments of menopause in view of feelings of attractiveness in different populations. Secular trends in height were presented, including short views on so called 'racial types' vs bio-plasticity, and historic data on early-life nutritional status and later-life socioeconomic outcomes during the Dutch potato famine. New tools for describing body proportions in patients with variable degrees of phocomelia were presented along with electronic growth charts. Bio-statisticians discussed the influence of randomness, community and network structures, and presented novel tools and methods for analyzing social network data.
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: 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.