title: The determination of three subcutaneous adipose tissue compartments in non-insulin-dependent diabetes mellitus women with artificial neural networks and factor analysis.
abstract: The optical device LIPOMETER allows for non-invasive, quick, precise and safe determination of subcutaneous fat distribution, so-called subcutaneous adipose tissue topography (SAT-Top). In this paper, we show how the high-dimensional SAT-Top information of women with type-2 diabetes mellitus (non-insulin-dependent diabetes mellitus (NIDDM)) and a healthy control group can be analysed and represented in low-dimensional plots by applying factor analysis and special artificial neural networks. Three top-down sorted subcutaneous adipose tissue compartments are determined (upper trunk, lower trunk, legs). NIDDM women provide significantly higher upper trunk obesity and significantly lower leg obesity ('apple' type), as compared with their healthy control group. Further, we show that the results of the applied networks are very similar to the results of factor analysis.
authors.affiliation: Institute for Medical Chemistry and Pregl Laboratory, Karl-Franzens-University of Graz, Harrachgasse 21, A-8010, Graz, Austria. erwin.tafeit@kfunigraz.ac.at
authors.forename: E
authors.initials: E
authors.lastname: Tafeit
authors.forename: R
authors.initials: R
authors.lastname: Möller
authors.forename: K
authors.initials: K
authors.lastname: Sudi
authors.forename: G
authors.initials: G
authors.lastname: Reibnegger
mesh_terms: D000273:Adipose Tissue / Q000473:pathology*; D000368:Aged; D001823:Body Composition / Q000502:physiology; D003924:Diabetes Mellitus, Type 2 / Q000473:pathology*; D005163:Factor Analysis, Statistical; D005260:Female; D006801:Humans; D008875:Middle Aged; D016571:Neural Networks, Computer