![]() ![]() ![]() However, current applications usually do not consider the whole information provided by the set of body measures and instead use only a small part of them often to extract only body indices such as BMI or WTH 13, 14, 15, 16. ![]() The capability of 3D laser scanning anthropometry arises from the vast number of measured body surface dimensions that allow discovery of health risk phenotypes beyond simple, one-dimensional classification schemes based on the waist-to-hip ratio (WTH) or the BMI. Body scanning is utilized in medical application, for example, for cosmetic and reconstructive surgery 10, 11, and increasingly in health research to study anthropometry of hundreds to thousands of participants in epidemiological cohort studies 9, 12 to assess their possible relevance for health risk prediction. Three-dimensional (3D) whole-body laser scanning provides another promising technique for evaluating “external” body shape by granting the opportunity to assess dozens of anthropological body measures at once with high accuracy and within only a few seconds of time 9. Other methods, such as dual-energy X-ray absorptiometry (DXA) and bioelectrical impedance analysis, represent interesting options of estimating “internal” tissue distribution in the human body. For example, upper body and lower body fat depots show opposite associations with risk for diabetes and cardiovascular diseases 6, 7, 8.įat distribution can be analyzed in detail using imaging techniques, such as computed tomography and magnetic resonance imaging, which are relatively expensive methods requiring expert skills and which are therefore difficult for application in large population studies. Health risk obviously associates in a more complex way with human body dimensions and depends, for example, on the relation between fat and muscles and their distributions along the body 4. However, it turned out that about 10% of BMI-defined obese individuals of European ethnicity are healthy in terms of their metabolic state, while another nearly 10% have a normal BMI but are metabolically unhealthy 4, 5. Simple anthropometric measures such as the body mass index (BMI) and waist circumference are often used to define the obesity status of a person. For example, overweight and obesity increase risks for developing metabolic and cardiovascular diseases in an age-dependent manner 3. There is growing evidence that body shape and regional body composition are strong indicators of metabolic health 1, 2. Body size and shape are governed by genetic and environmental factors, including lifestyle with potential impact for health. Human body dimensions and shape vary between individuals in an age-dependent manner. Body typing opens options for personalized anthropometry to better estimate health risk in epidemiological research and future clinical applications. We discuss health risks factors in the context of body shape and its relation to obesity. Physical activity is inversely related to the body mass index and decreases with age, while self-reported incidence for myocardial infarction shows overall the inverse trend. ![]() The incidence of the different body types changes with characteristic Life Course trajectories. Female body shapes change more strongly than male ones. Slim body shapes remain slim and partly tend to become even more lean and fragile, while obese body shapes remain obese. We find that aging results in similar reshaping of female and male bodies despite the large diversity of body types observed in the study. We here applied this body typing concept to describe the diversity of body shapes in an aging population and its association with physical activity and selected health and lifestyle factors. Body scanning delivers multidimensional anthropometric data, which were further processed by machine learning to stratify the participants into body types. We applied whole-body laser scanning to a cohort of 8499 women and men of age 40–80 years within the frame of the LIFE (Leipzig Research Center for Civilization Diseases) study aimed at discovering health risk in a middle European urban population. Anthropometric methods and data are needed to better describe the diversity of the human body in human populations, its age dependence, and associations with health risk. Body shape and composition are heterogeneous among humans with possible impact for health. ![]()
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