title: Detecting body fat-A weighty problem BMI versus subcutaneous fat patterns in athletes and non-athletes.
abstract: We aimed to describe the relationship between BMI and the subcutaneous adipose tissue topography within young athletes and non-athletic controls, to comparatively evaluate the diagnostic powers of subcutaneous adipose tissue thicknesses at different body sites, furthermore to explore appropriate cut-offs to discriminate between athletes and controls. Measurements were determined in 64 males and 42 females, who were subsequently separated into two even groups (athletes and non-athletes). The optical device LIPOMETER was applied at standardised body sites to measure the thickness of subcutaneous adipose tissue layers. To calculate the power of the different body sites and the BMI to discriminate between athletes and non-athletes, receiver operating characteristic curve analysis was performed. In men, the neck (optimal cut-off value 2.3 mm) and trunk (optimal cut-off value 15.5 mm) provided the strongest discrimination power: with 90.6% (58 of 64) of the subjects being correctly classified into athletes or non-athletes. Discrimination power of the BMI values was 64.1% (41 of 64 were correctly classified). In women, the upper back (optimal cut-off value 3.3 mm) and arms (optimal cut-off value 15.9 mm) provided the strongest discrimination power with 88.1% (37 of 42 being correctly classified). When using BMI to discriminate between athletes and non-athletes only 52.4% (22 of 42) were correctly classified. These results suggest that compared to BMI levels, subcutaneous fat patterns are a more accurate way of discriminating between athletes and non-athletes. In particular the neck and the trunk compartment in men and the upper back and arms compartment in women, were the best sites to discriminate between young athletes and non-athletes on the basis of their fat patterns.
authors.affiliation: Medical University Vienna, Clinic for Internal Medicine III, Department of Endocrinology and Metabolism, Vienna, Austria ; Medical University Graz, Inst. of Pathophysiology and Immunology, Graz, Austria ; SIPCAN Special Institute for Preventive Cardiology and Nutrition, Salzburg, Austria ; Medical University Graz, Inst. of Physiological Chemistry, Graz, Austria.
authors.forename: Renate
authors.initials: R
authors.lastname: Kruschitz
authors.forename: Sandra J
authors.initials: SJ
authors.lastname: Wallner-Liebmann
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Hamlin
authors.forename: Maximilian
authors.initials: M
authors.lastname: Moser
authors.forename: Bernhard
authors.initials: B
authors.lastname: Ludvik
authors.forename: Wolfgang J
authors.initials: WJ
authors.lastname: Schnedl
authors.forename: Erwin
authors.initials: E
authors.lastname: Tafeit
mesh_terms: D015502:Absorptiometry, Photon; D000273:Adipose Tissue / Q000378:metabolism*; D000293:Adolescent; D000328:Adult; D000886:Anthropometry / Q000295:instrumentation / Q000379:methods; D056352:Athletes; D001823:Body Composition; D015992:Body Mass Index; D003430:Cross-Sectional Studies; D005260:Female; D006801:Humans; D008297:Male; D012372:ROC Curve; D015203:Reproducibility of Results; D050151:Subcutaneous Fat / Q000378:metabolism*; D013550:Swimming; D014057:Tomography, X-Ray Computed; D055815:Young Adult