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.

Mens body shapes