Since James S. Garrow proposed the body mass index (BMI, kg/m²) as a measure of fatness in 1985 [1], its use in science and within clinical practice has risen exponentially over the years. Especially in sports science the BMI and assessment of body fat to determine optimal body weight has increased [2]. Body weight and body composition are important performance factors in many sports [3]. A centralized subcutaneous fat distribution has been associated with decreased aerobic capacity in men [4]. In both athletic and non-athletic populations the estimates of body composition characteristics are used to identify health status [5]. Nevertheless the use of body weight by itself and/or BMI has been criticized, particularly in athletic populations [6]–[8]. The BMI indicates a somewhat stronger yet still moderate association with body fat and disease risk compared to estimates based on stature and body mass [9], [10]. Although BMI is correlated (r = 0.60–0.82) with percentage total body fat (TBF%) [11], there is a lack of research regarding the usefulness of BMI as a surrogate for TBF%, especially in young adults and athletes. The BMI does not discriminate between the different components of the body and cannot describe the fat distribution over the body. Individuals with high fat-free mass (FFM) relative to height, like athletes and younger adults, might have a high BMI but they are not necessarily obese [2], [6], [8].

In general, there is little consensus on the use of body fat percentage criteria to define obesity or excess body fat levels [12]. The American College of Sports Medicine (ACSM) [13] recommended on the basis of data reported by Gallagher and colleagues [14] a TBF% over 33% in women and 20% in men as acceptable cut-points for overfatness, corresponding to a BMI of 25 kg/m², in athletes. Recently published TBF% cut-offs from Heo et al. [15] which are comparable with those of Gallagher et al. [14] tend to be higher, especially in younger groups regardless of age, sex and ethnicity. Heo et al. [15] assume that 35–37% TBF% in women and 23–25% TBF% in men corresponds to a BMI of 25 kg/m² in young African Americans and white adults (aged 18–29).

Compared with the general adult population, the influence of a large muscle mass on BMI in athletes and young adults misclassify these individuals as overweight and obese [16]. Probably more important in assessing the health risks of excessive fat stores is the distribution of fat over the body [17]. Therefore, the use of TBF% and subcutaneous fat patterns may be more effective than BMI in assessing fatness and obesity in physically active individuals and young adults.

The computerized optical device named the Lipometer (Moeller Messtechnik, Graz, EU patent number 0516251) allows a non-invasive, quick, precise and safe determination of the thickness of subcutaneous adipose tissue (SAT) layers at any chosen site of the human body.

As far as we know there has been no study that has assessed the relationship between BMI and SAT-Top in young athletes and non-athletes. Therefore the purpose of this study was to prove our hypothesis that compared to BMI levels the subcutaneous fat patterns are a better screening tool to characterize fatness in athletes compared to non-athletes. A secondary aim of this study was to provide appropriate subcutaneous adipose tissue measuring points and cut-offs that allow in a quick and precise way to discriminate between athletes and non-athletic controls.

Male athletes and non-athletes were similar in terms of age, height, weight and BMI, however, male athletes showed a 50.5% lower Total SAT thickness (33.8±13.4 mm) compared to male non-athletes (68.3±36.6 mm, p<0.001). All SAT layer thicknesses at the 15 body sites from neck to calf were significantly lower in the male athletes compared to the male non-athletes (Table 1). This was also the case for the additional variables (the four compartment measurements and TBF %).

Even though the female athletes had significantly higher BMI (p = 0.016) and weight (p = 0.011), their Total SAT thickness was 41.1% lower (78.8±42.1 mm) compared to their non-athlete counterparts (133.7±48.7 mm, p<0.001). SAT at all measured body sites, for all body compartments and TBF% was significant lower in the female athletes compared to the non-athletes except for the lower abdomen, hip and rear thigh ( = gynoid fat pattern) (Table 2).

ROC curves and the corresponding area indices were calculated for height, weight, BMI, TBF%, Total SAT, SAT-layer thicknesses at all 15 body sites and for the 4 compartments. The optimal cut-off values were analysed for body sites with a p-value of ≤0.05 and BMI. Results are presented in Table 3 and 4, and show the area indices for these variables one of the two assumptions that either small or large values provide stronger evidence for positivity ( = athletes).

The best discriminators between male and female athletes and non-athletes are presented as ROC curves in Figure 1 and 2 respectively. In men the neck (Figure 3) measurement (AI = 0.952, sensitivity = 96.9%, specificity = 84.4%, optimal cut-off value 2.3 mm) and the trunk compartment (AI = 0.960, sensitivity = 84.4%, specificity = 96.9, optimal cut-off value 15.5 mm) provided the strongest discrimination power (90.6% [ = 58 of 64 of the subjects were correctly classified as athletes or controls]). The data showed no significant difference between the BMI of athletes and non-athletes (AI = 0.623, discrimination power: 64.1% [41 of 64 correctly classified subjects]) (Table 3). In women the upper back (Figure 4) measurement (AI = 0.888, sensitivity = 81.0%, specificity = 95.2%, optimal cut-off value 3.3 mm) and the arms compartment (AI = 0.923, sensitivity = 100.0%, specificity = 76.2%, optimal cut-off value 15.9 mm) provided the strongest discrimination power (88.1% [ = 37 of 42 correctly classified subjects]). Female athletes had significantly higher BMI, nevertheless the BMI AI was low (AI = 0.717, discrimination power: 52.4% [ = 22 of 42 correctly classified subjects]) (Table 4).

Our data shows that athletes and non-athletes of both sexes can be distinguished very clearly by their subcutaneous fat patterns. In spite of comparable BMI in the males, and even significantly higher BMI in the female athlete group, the measured SAT-Top values were significantly lower in the athletes compared to non-athletes in both groups. Male and female athletes showed approximately 50–60% lower Total SAT thickness compared to non-athletes. The ability of BMI to accurately reflect the amount of body fat across athletic and non-athletic populations has been assessed previously [6], [26]. Nevill et al. [6] report a 5–32% lower total skinfold thickness (measured by callipers) in male and 5–29% lower skinfold thickness in female athletes compared to their non-athletic controls. Furthermore, when Witt and Bush [26] examined the relationship between BMI and body fat in college athletes, the authors found that only 20% of women and 4% of men with BMI ≥25 kg/m² were above the 85th percentile for skinfold measurements. Ode and colleagues [7] analysed the sensitivity, specificity, and predictive values for BMI as a measure of body fatness (measured via air displacement plethysmography) and found low sensitivity between BMI and body fat percentage for athletic populations.

Other investigators have examined the diagnostic ability of BMI in relation to TBF% in adults [11], [24], [27]–[32]. Our data as well as results of previous researchers show that BMI is a relatively poor indicator for the amount of body fat in young athletes and non-athletes. However, because of the lack of an established TBF% criterion for health status and the differences in study design, it is difficult to compare the results of our study with this previous research. Many of these studies used different methods for measuring TBF%, including DXA [27], [28], [30] skinfolds [29] and hydrodensitometry [11], [31], [32]. The different TBF% cut points used to identify over fatness included 25% [11], [28], 30% [29], [31], 33% [32], 35% [30] and 38% [27] for females, and either 20% [11], [28] or 25% [29], [30], [32] for males. With the exception of one study that assessed postmenopausal women [27], each study assessed both males and females. The majority of studies included young, middle-aged and older adults [11], [28]–[31], whereas an additional study focused primarily on young and middle-aged adults [32]. Within the postmenopausal women, BMI seemed to be a good diagnostic test for overfatness [27], however, the remaining research consistently indicated BMI had low sensitivity (0.06–0.60) and high specificity (0.86–1.0) as a measure of TBF% in both males and females [11], [28]–[32].

The results of our current study suggest that BMI is not an accurate predictor of overfatness in young athletes and non-athletes, indicated by the large differences between Lipometer-determined subcutaneous adipose tissue thicknesses and BMI values. Due to a larger muscle mass among the male and female athletes, BMI incorrectly classified normal fat athletes as overfat [33]. Therefore, our results indicate that the subcutaneous fat patterns are a better screening tool to characterize fatness and moreover for detailed fat distribution in physically active young non-athletes. This is particularly noteworthy, given that fatness is more influenced by sport (and therefore physical training) than is the patterning of fat [34]. Our results of the ROC curve analysis showed that in men the neck body site and the trunk compartment have the highest discrimination power between the groups of athletes and non-athletes (Figure 1). In women the highest discrimination power was achieved at the upper back body site, and the arms compartment (Figure 2). Also in previous published papers [19], [35] the neck body site became apparent as a good discriminator between normal weight healthy subjects and normal weight type-2 diabetes subjects. The above findings confirm the danger of using BMI in epidemiological studies, especially when a significant proportion of subjects come from a younger athletic population. When we monitor trends in fatness over time and between populations, a more valid method of assessing fatness is likely to be obtained using surface anthropometry such as the measurement of the neck or trunk compartment for males and the upper back or arms compartment for females with the Lipometer. Other methods to assess the body composition frequently lack precision and reproducibility (calliper techniques), entail the risk of radiation exposure (computed tomography (CT), dual energy X-ray absorptiometry (DXA)), depend on hydrational status (bioimpedance) are inconvenient and time-consuming for the patient (hydrodensitometry) and/or are expensive (nuclear magnetic resonance, CT, air displacement plethysmography) [36]. The Lipometer offers a new practical approach for body fat measurement.