Analysing body composition as compositional data: An exploration of the relationship between body composition, body mass and bone strength.

Human body composition is made up of mutually exclusive and exhaustive parts (e.g. %truncal fat, %non-truncal fat and %fat-free mass) which are constrained to sum to the same total (100%). In statistical analyses, individual parts of body composition (e.g. %truncal fat or %fat-free mass) have traditionally been used as proxies for body composition, and have been linked with a range of health outcomes. But analysis of individual parts omits information about the other parts, which are intrinsically co-dependent because of the constant sum constraint of 100%. Further, body mass may be associated with health outcomes. We describe a statistical approach for body composition based on compositional data analysis. The body composition data are expressed as logratios to allow relative information about all the compositional parts to be explored simultaneously in relation to health outcomes. We describe a recent extension to the logratio approach to compositional data analysis which allows absolute information about the total of the compositional parts (body mass) to be considered alongside relative information about body composition. The statistical approach is illustrated by an example that explores the relationships between adults' body composition, body mass and bone strength.

The adiposity of children is associated with their lifestyle behaviours: a cluster analysis of school-aged children from 12 nations.

BACKGROUND: The relationship between children's adiposity and lifestyle behaviour patterns is an area of growing interest. OBJECTIVES: The objectives of this study are to identify clusters of children based on lifestyle behaviours and compare children's adiposity among clusters. METHODS: Cross-sectional data from the International Study of Childhood Obesity, Lifestyle and the Environment were used. PARTICIPANTS: the participants were children (9-11 years) from 12 nations (n = 5710). MEASURES: 24-h accelerometry and self-reported diet and screen time were clustering input variables. Objectively measured adiposity indicators were waist-to-height ratio, percent body fat and body mass index z-scores. ANALYSIS: sex-stratified analyses were performed on the global sample and repeated on a site-wise basis. Cluster analysis (using isometric log ratios for compositional data) was used to identify common lifestyle behaviour patterns. Site representation and adiposity were compared across clusters using linear models. RESULTS: Four clusters emerged: (1) Junk Food Screenies, (2) Actives, (3) Sitters and (4) All-Rounders. Countries were represented differently among clusters. Chinese children were over-represented in Sitters and Colombian children in Actives. Adiposity varied across clusters, being highest in Sitters and lowest in Actives. CONCLUSIONS: Children from different sites clustered into groups of similar lifestyle behaviours. Cluster membership was linked with differing adiposity. Findings support the implementation of activity interventions in all countries, targeting both physical activity and sedentary time.

Recycling of plastic waste: Presence of phthalates in plastics from households and industry.

Plastics recycling has the potential to substitute virgin plastics partially as a source of raw materials in plastic product manufacturing. Plastic as a material may contain a variety of chemicals, some potentially hazardous. Phthalates, for instance, are a group of chemicals produced in large volumes and are commonly used as plasticisers in plastics manufacturing. Potential impacts on human health require restricted use in selected applications and a need for the closer monitoring of potential sources of human exposure. Although the presence of phthalates in a variety of plastics has been recognised, the influence of plastic recycling on phthalate content has been hypothesised but not well documented. In the present work we analysed selected phthalates (DMP, DEP, DPP, DiBP, DBP, BBzP, DEHP, DCHP and DnOP) in samples of waste plastics as well as recycled and virgin plastics. DBP, DiBP and DEHP had the highest frequency of detection in the samples analysed, with 360µg/g, 460µg/g and 2700µg/g as the maximum measured concentrations, respectively. Among other, statistical analysis of the analytical results suggested that phthalates were potentially added in the later stages of plastic product manufacturing (labelling, gluing, etc.) and were not removed following recycling of household waste plastics. Furthermore, DEHP was identified as a potential indicator for phthalate contamination of plastics. Close monitoring of plastics intended for phthalates-sensitive applications is recommended if recycled plastics are to be used as raw material in production.

Values below detection limit in compositional chemical data.

Samples representing part of a whole, usually called compositional data in statistics, are commonplace in analytical chemistry--say chemical data in percentage, ppm, or µg g(-1). Their distinctive feature is that there is an inherent relationship between all the analytes constituting a chemical sample as they only convey relative information. Some compositional data analysis principles and the log-ratio based methodology are outlined here in practical terms. Besides, one often finds that some analytes are not present in sufficient concentration in a sample to allow the measuring instruments to effectively detect them. These non-detects are usually labelled as "