Scanning is faster, requires less intensive staff training and provides more information. It can be used in an epidemiologic setting with children and adolescents but some measurements should be considered with caution due to reduced agreement with conventional anthropometry.

Three-dimensional (3D-) body scanning of children and adolescents allows the detailed study of physiological development in terms of anthropometrical alterations which potentially provide early onset markers for obesity. Here, we present a systematic analysis of body scanning data of 2,700 urban children and adolescents in the age range between 5 and 18 years with the special aim to stratify the participants into distinct body shape types and to describe their change upon development. In a first step, we extracted a set of eight representative meta-measures from the data. Each of them collects a related group of anthropometrical features and changes specifically upon aging. In a second step we defined seven body types by clustering the meta-measures of all participants. These body types describe the body shapes in terms of three weight (lower, normal and overweight) and three age (young, medium and older) categories. For younger children (age of 5–10 years) we found a common ‘early childhood body shape’ which splits into three weight-dependent types for older children, with one or two years delay for boys. Our study shows that the concept of body types provides a reliable option for the anthropometric characterization of developing and aging populations.

Background: Pediatric reference intervals for iron-related parameters are determined continuously over time from a highly standardized sample collection by application of the R-package generalized additive models for location, scale and shape (GAMLSS), which is little known in laboratory medicine. Methods: Two thousand seven hundred and seventy-eight samples from Leipzig research center for civilization diseases (LIFE) Child participants at the age of 2.5-19 years were analyzed on a Sysmex XN-9000 for hemoglobin and reticulocytes and on a Roche Cobas 8000 for transferrin and ferritin. Reference intervals were established by repeated model calculation by use of the LMS (λ-μ-σ) method from Cole with specifically weighted subsamples. Results: Continuous and gender-specific reference intervals as well as smoothed percentile curves were