ABDOMINAL BODY COMPOSITION DIFFERENCES
NFL FOOTBALL PLAYERS
IN
TYLER A. BOSCH,1 T. PEPPER BURRUSS,2 NATE L. WEIR,2 KURT A. FIELDING,2 BRYAN E. ENGEL,2
TODD D. WESTON,3 AND DONALD R. DENGEL1,4
1
Department of Medicine, University of Minnesota Medical School, Minneapolis, Minnesota; 2Green Bay Packers Professional
Football Team, Green Bay, Wisconsin; 3GE Healthcare, Madison, Wisconsin; and 4School of Kinesiology, University of
Minnesota, Minneapolis, Minnesota
ABSTRACT
Bosch, TA, Burruss, TP, Weir, NL, Fielding, KA, Engel, BE,
Weston, TD, and Dengel, DR. Abdominal body composition
differences in NFL football players. J Strength Cond Res 28(12):
3313–3319, 2014—The purpose of this study was to examine
visceral fat mass as well as other measures abdominal body composition in National Football League (NFL) players before the start
of the season. Three hundred and seventy NFL football players
were measured before the start of the season using dual-energy xray absorptiometry. Regional fat and lean mass was measured for
each player. Players were categorized into 3 groups based on
positions that mirror each other: linemen; linebackers/tight ends/
running backs and wide receivers/defensive backs. Significant differences were observed between the position groups for both lean
and fat regional measurements. However, the magnitude of difference was much greater for fat measures than lean measures.
Additionally, a threshold was observed (;114 kg) at which there
is a greater increase in fat accumulation than lean mass accumulation. The increase in fat accumulation is distributed to the abdominal region where thresholds were observed for subcutaneous
abdominal fat accumulation (12.1% body fat) and visceral abdominal fat accumulation (20.1% body fat), which likely explains the
regional fat differences between groups. The results of this study
suggest that as players get larger, there is more total fat than total
lean mass accumulation and more fat is distributed to the abdominal region. This is of importance as increased fat mass may be
detrimental to performance at certain positions. The thresholds
observed for increased abdominal fat accumulation should be
monitored closely given recent research observed that abdominal
obesity predicts lower extremity injury risk and visceral adipose
tissue’s established association with cardiometabolic risk.
KEY WORDS obesity, DXA, athletes, visceral fat
Address correspondence to Tyler A. Bosch, [email protected].
28(12)/3313–3319
Journal of Strength and Conditioning Research
Ó 2014 National Strength and Conditioning Association
INTRODUCTION
B
ody size is always a discussion topic in professional
football players, players rise or fall in the draft based
on size characteristics, or players reporting to camp
overweight. Additionally, there is constant discussion about adding mass to a players frame. Rarely though are
these changes quantified in terms of the type of mass (fat or
muscle) being gained or lost and where the changes (region)
are occurring. Previously, we have reported on positional differences in total body composition in National Football League
(NFL) players (2). Although the majority of NFL players
would be considered overweight or obese based on body mass
index (BMI), their percent body fat is much closer to a normal
or lean range. We and others observed the similarity in body
composition of positions that mirror each other (i.e., offensive
lineman vs. defensive linemen; wide receivers vs. defensive
backs) (2,5–7,13,18). To date, studies in this population have
focused on total body composition measurements. To the best
of our knowledge, this is the first study to measure the abdominal and other regional body composition, including visceral
adipose tissue (VAT), in NFL players. Recent advancements
now allow dual-energy x-ray absorptiometry (DXA) to measure android and gynoid regions. Furthermore, DXA can differentiate between subcutaneous abdominal adipose tissue
(SAAT) and VAT accumulation within the abdominal region.
Visceral adipose tissue is an established marker for cardiometabolic risk (3,4,9,11,17,21), independent of subcutaneous
abdominal fat and total body fat. More recently, abdominal
obesity has been associated with increased risk of lower-body
musculoskeletal injuries. These measurements could provide
valuable insight into what type of tissue is being accumulated
to account for positional differences that have been previously
established for the entire body. Current methods of change in
mass such as weight, BMI, and bioelectrical impedance are
limited in their ability to distinguish between lean and fat mass
or measure regional composition. Being able to reliably quantify regional composition changes would be of great value to
coaches and trainers trying to get players to gain or lose
weight. The purpose of this study was to examine abdominal
body composition, including visceral mass in NFL position
groups before the season. We hypothesized that regional lean
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Regional Fat in Football Players
gynoid areas. The base of the
android region is placed at the
TABLE 1. Descriptive and total body composition measurements mean (6SD) for
iliac crests with the height of
the sample.*
the region of interest being
Linemen (n = 123) LB/TE/RB (n = 122) WR/DB (n = 125)
determined by calculating 20%
of the distance between the
A
A
A
23.8 (2.2)
23.6 (2.0)
Age (y)
24.0 (2.4)
iliac crests and the participants’
Height (cm)
191.9A (3.7)
186.7B (5.7)
183.8C (3.9)
chin (19). The gynoid region is
Weight (kg)
137.1A (11.7)
109.6B (6.6)
92.3C (6.2)
BMI (kg$m22)
37.3A (3.5)
31.5B (1.9)
27.3C (1.8)
located midpelvis to midthigh;
Years played (y)
2.4A (2.3)
2.2A (1.9)
2.1A (2.0)
the upper limit is set below the
Percent fat (%)
27A (6)
17B (4)
12C (3)
iliac crest a distance 1.5 times
Total fat (kg)
36.4A (10.0)
17.6B (4.5)
10.9C (3.4)
the height of the android
Total lean (kg)
95.9A (5.0)
87.3B (4.7)
77.1C (4.4)
region. The lower limit is set
*BMI = body mass index. If the variables share a letter within each row, they are not
a distance of 2 times the height
significantly different than one another at a = 0.05. Linemen = offensive and defensive linemen
of the android region (19). Vispositions; LB/TE/RB = linebackers, tight ends, and running backs; WR/DB = wide receivers,
ceral adipose tissue was deterdefensive backs (includes safety and cornerbacks).
mined in the android region by
subtracting subcutaneous fat
from total fat (12). Subcutaneous fat was determined using an algorithm and measureand fat mass measurements would be significantly different
ments of total abdominal thickness and the width of the
between position groups.
subcutaneous fat layer along the lateral extent of the abdoMETHODS
men along with empirically derived geometric constants to
estimate the subcutaneous fat in the android region (12).
Experimental Approach to the Problem
Participants were scanned using standard imaging and posiPlayers were instructed to be at hemostasis before all testing
tioning protocols. Height and weight were measured by
sessions. When possible, scans were done in the morning on
a standard wall stadiometer and medical beam scale,
off days during physical examinations or before practice. A
respectively.
full body scan was acquired using a GE Healthcare Lunar
iDXA (GE Healthcare Lunar, Madison, WI, USA). The
Subjects
iDXA is capable of scanning participants who weight up to
We assessed NFL players from the Green Bay Packers
450 lbs, which makes it ideal for this population as none of
professional football team from 2006 to 2011 (ages: 20–40
them were in excess of this cutoff. Scans were analyzed using
years). Players were either active on the roster, free-agents,
encore software version 13.6, revision 2. No hardware or
or prospective draft choices. One thousand three hundred
software changes were made during the duration of the
and twenty-eight scans were performed during this time
study. Two regions of interest were determined after the scan
period. Three hundred and seventy NFL players (age: 20–
to measure fat and lean composition within the android and
35 years) had 1 measurement
between April and August. If
players had more than 1 scan,
the scan used for analysis was
TABLE 2. Abdominal body composition measurements mean (6SD) for the
randomly chosen using a presample.*
designated
randomization
Linemen (n = 123) LB/TE/RB (n = 122) WR/DB (n = 125)
scheme. Informed consent
was obtained from each partic8.3B (2.6)
4.8C (2.0)
Trunk fat (kg)
19.9A (6.3)
A
B
C
ipant from the Green Bay
Trunk lean (kg)
42.7 (3.2)
39.0 (2.7)
34.5 (2.0)
Packers professional football
1.2B (0.5)
0.6C (0.3)
Android fat (kg)
3.4A (1.3)
Android lean (kg)
6.3A (0.5)
5.6B (0.5)
4.8C (0.4)
team. The University of MinGynoid fat (kg)
6.0A (1.7)
3.0B (0.9)
1.7C (0.7)
nesota Institutional Review
Gynoid lean (kg)
16.0A (1.2)
14.4B (1.0)
12.5C (1.0)
Board approved this study.
Visceral fat (kg)
1.2A (0.6)
0.3B (0.2)
0.3B (0.1)
Participants were categorized
*If the variables share a letter within each row, they are not significantly different than one
by position into 1 of 7 categoanother at a = 0.05. Linemen = offensive and defensive linemen positions; LB/TE/RB =
ries: defensive backs (DB),
linebackers, tight ends, and running backs; WR/DB = wide receivers, defensive backs
defensive lineman (DL), line(includes safety and cornerbacks).
backers (LB), offensive lineman
(OL), running backs (RB), tight
3314
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TABLE 3. Whole body mass estimates and regional body composition by position group.*
OL (n = 65) DL (n = 58) LB (n = 55) TE (n = 31) RB (n = 36) WR (n = 56) DB (n = 69)
BMI (kg$m22)
Percent fat (%)
Trunk fat (kg)
Trunk lean (kg)
Android fat (kg)
Android lean (kg)
Gynoid fat (kg)
Gynoid lean (kg)
Visceral fat (kg)
37.9A
28.8A
22.2A
43.3A
3.9A
6.4A
6.3A
16.1A
1.3A
(2.1)
(3.7)
(4.0)
(3.5)
(0.9)
(0.5)
(1.2)
(1.0)
(0.5)
36.5A
25.2B
17.3B
42.0A
2.9B
6.2AC
5.7A
15.9A
0.9B
(4.5)
(6.9)
(7.3)
(2.8)
(1.5)
(0.5)
(2.1)
(1.4)
(0.6)
31.6C
17.0C
8.4C
38.9C
1.2C
5.6B
3.1B
14.3B
0.3C
(1.6)
(3.2)
(2.2)
(2.5)
(0.4)
(0.5)
(0.8)
(0.9)
(0.2)
30.6C
16.8C
8.9C
41.0A
1.3C
5.9C
3.1B
15.0B
0.3C
(1.6)
(3.8)
(2.7)
(2.2)
(0.5)
(0.4)
(0.8)
(1.0)
(0.2)
32.0C
16.0C
7.6C
37.5C
1.1C
5.4B
2.7B
14.0B
0.4C
(2.3)
(4.0)
(3.0)
(2.3)
(0.6)
(0.5)
(1.1)
(1.0)
(0.2)
27.3D
12.5D
5.0D
34.9D
0.6D
4.9D
1.8C
12.7C
0.3C
(1.8)
(3.1)
(1.9)
(1.9)
(0.3)
(0.4)
(0.6)
(1.1)
(0.1)
27.4D
12.1D
4.7D
34.1D
0.6D
4.7D
1.6C
12.4C
0.3C
(1.8)
(3.3)
(2.0)
(2.0)
(0.3)
(0.4)
(0.7)
(0.9)
(0.1)
*If the positions do not share a letter within the same row, they are significantly different (p # 0.05 adjusted for multiple comparisons). OL = offensive linemen; DL = defensive linemen; LB = linebacker; TE = tight end; RB = running back; WR = wide receiver;
DB = defensive back; BMI = body mass index.
ends (TE), and wide receivers (WR). They were then placed
into groups of positions that mirror each other: linemen,
LB/TE/RB, and WR/DB. This was done to increase the
power for testing comparisons between groups. These
groups were determined based on our previous work that
observed similar body composition between positions that
mirror each other.
Statistical Analyses
Descriptive statistics were calculated using mean 6 SD by
position group. An analysis of variance (ANOVA) was used
to test if positional group mean values were equal to each
other. Tukey’s HSD (honest significant difference) method
was used to compare each positional group mean against the
next to correct for type I error from performing multiple
comparisons (p = 0.01). Analysis of variance and Tukey’s
HSD were also used to measure positional difference for
regional measurements. We used segmented linear regression to determine whether fat mass and lean mass accumulation changes with increasing body weight. To determine
when abdominal fat accumulation begins in relation to percent body fat, segmented linear regression was used to determine breakpoints in the association between VAT,
subcutaneous abdominal fat, and percent body fat. The
slopes above and below the identified breakpoints we analyzed by ANOVA to determine whether they were significantly different (p = 0.05). Boxplots were used to present the
median (black line), range of total fat mass, total lean mass,
SAAT, and VAT by position group. The boxplot displays the
middle 50% of the data (box), range of the data (dashed
lines), and possible outliers (open circles). All analysis was
completed using R (R Foundation for Statistical Computing,
Vienna, Austria).
Figure 1. A) Mean mass vs. body weight with the change in slope at
;114 kg of body weight. B) Fat mass vs. body weight with the change in
slope at ;114 kg of body weight.
RESULTS
Table 1 presents the characteristics and total body composition
measurements for each position group for the cross-sectional
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Regional Fat in Football Players
Figure 2. Boxplots by position group of: (A) total lean mass; (B) total fat mass; (C) SAAT mass; and (D) VAT mass. Linemen = offensive and defensive linemen
positions; LB/TE/RB = linebackers, tight ends, and running backs; WR/DB = wide receivers, defensive backs (includes safety and cornerbacks).
sample. Each position group had spent similar time in the
NFL at the time of the scan. According to standard BMI
classifications, the linemen position group would be classified as severely obese (BMI .35 kg$m22), the LB/TE/
RB position group would be classified as moderately obese
(BMI, 30–34.9 kg$m22), and the WR/DB position group
would be classified as overweight (BMI, 25–29.9 kg$m22).
Not 1 group had a mean BMI that was considered to be
normal. Unlike the BMI classifications, only the linemen
are classified as obese (.24%) using standard percent
body fat classifications (8). The other 2 position groups
would be classified as acceptable (15–20%) or healthy
(11–14%) (8).
Table 2 presents the trunk body composition measurements for each position group. The linemen position group
has significantly more fat and lean mass (p # 0.05) across all
trunk composition variables compared with the other position groups. Compared with the WR/DB group, the LB/
3316
the
TE/RB group has significantly more fat and lean mass for all
variables except VAT.
Table 3 presents the trunk body composition measurements for each position. Offensive lineman and DL are similar
on all lean measurements but differed on many fat measurements. LB, TE, and RB were similar on all measurements
except for trunk lean mass and android lean mass. For those
2 measurements, TE were similar to OL and DL. Wide receivers and DB were similar on all measurements and significantly different than all other positions. Offensive lineman
had significantly more visceral fat than DL. There was no
difference in visceral fat between LB, TE, RB, WR, or DB.
Figure 1A presents the relationship of lean mass and
weight. A significant breakpoint in the slope was identified
at 114.8 kg (95% CI = 111.4–118.2). The estimated slope
before the breakpoint is 613.2 g (95% CI = 565.5–661.0).
The estimated slope after the breakpoint is 215.1 g (95%
CI = 170.0–260.3). Figure 1B presents the relationship of
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Figure 3 presents the relationship between SAAT and total
percent fat mass. A significant breakpoint was identified at
12.1% (95% CI = 11.3–12.9). The estimated slope before this
breakpoint was not significantly different than zero (23.2 g,
95% CI = 261.5 to 55.03). The estimated slope after the
breakpoint was 139.3 g (95% CI = 134.7–143.8). An ANOVA
determined a significant difference between the slopes of
subcutaneous accumulation before and after this breakpoint
(p , 0.001). Figure 4 presents the relationship between VAT
mass and total percent body fat. A significant breakpoint was
identified at 20.1% body fat (95% CI = 18.9–21.3). The estimated slope before the breakpoint is not significantly different than zero (2.7 g [95% CI = 27.6 to 13.0]). The estimated
slope after the breakpoint was 99.0 g (95% CI = 89.5–109.6).
An ANOVA determined a significantly different slope in
VAT accumulation before and after the ;20% threshold.
DISCUSSION
Figure 3. Relationship of subcutaneous abdominal fat (SAAT) and
percent body fat with a change in slope at ;12% body fat.
fat mass and weight. A significant breakpoint in the slope
was identified at 113.9 kg (95% CI = 110.3–117.4). The estimated slope before the breakpoint was 364.5 g (95% CI =
313–416.1). The estimated slope after the breakpoint was
759.8 g (95% CI = 715–804.6) (Figure 1A,B).
Figure 2A–D presents a boxplot of total fat mass, total lean
mass, SAAT, and VAT by position groups. Linemen had
a significantly higher range of fat mass, whereas there was
much more overlap between position groups for lean mass.
Linemen have a much greater range of SAAT compared
with other positions. Similarly, linemen have a much greater
range of VAT values compared with other position groups.
Figure 4. Relationship of visceral adipose tissue by percent body fat
with a change in slope at ;20% body fat.
National Football League players are a unique population
because their body composition is so different than the
average population. Their BMI classifications are all
extremely high, yet their percent fat classifications are
relatively normal or lean in a majority of players. This
suggests that the amount of lean mass all players have is
much higher than the average population. This is evident in
the lean mass differences between position groups. Although
significantly different, the magnitude of difference between
position groups for lean mass variables is, on average,
between 10 and 15%. Conversely, the average fat mass
difference between position groups is close to 200%, or a 2fold difference. This was evident in Figure 1, lean mass accumulation decreases after ;114 kg (;250 lbs), whereas fat
mass continues to increase. There is a shift in the type of
tissue accumulated after that break point; before that point,
increases in weight result in more lean mass than fat mass
accumulation. However, after ;114 kg, more fat is accumulated than lean mass. This balance is of importance because
body composition is associated with physical performance
and injury risk, increasing the mass of a player needs to be
closely monitored to ensure the increases are a result of lean
tissue and not fat tissue, which could inhibit performance.
Increases in body weight result in a proportional increase in
the forces that articular, ligamentous, and muscular structures
must resist (15). As such, joints would have a difficult time
accommodating this increase in force with this disproportionate accumulation of fat mass to lean mass after ;114 kg.
Not surprisingly, as weight increased, abdominal fat
accumulation also increased; however, the increase was
more exponential than linear. This would suggest that as
weight increases, more fat is being distributed to the
abdominal region. When fat accumulates in the abdominal
region, it can be stored in the visceral region or the
subcutaneous region. In addition to increased injury risk
(15,16), VAT is an independent risk factor for cardiovascular
disease and insulin resistance (3,4,9,11,17,21). In this
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Regional Fat in Football Players
population, this accumulation occurred at different thresholds, suggesting that excess fat will be preferentially distributed to the SAAT region before VAT region. Subcutaneous
abdominal adipose tissue accumulation begins around ;12%
body fat and continues to increase as adiposity increases.
Visceral adipose tissue accumulation does not begin until
;20% body fat, which supports that excess fat is preferentially
stored subcutaneously. These thresholds would explain why
regional fat mass was different, as we hypothesized, between
the position groups for all measures except visceral fat.
Although there is no difference in VAT mass between LB/
TE/RB and WR/DB groups, the linemen group had
significantly higher VAT mass than the other 2 groups.
These differences are a result of the differences in percent
body fat between groups. The linemen had a higher average
percent body fat than the other 2 groups. This average value
was above 20% where VAT accumulation increases linearly
with percent body fat. Linemen would be classified as obese
based on percent body fat. In addition, there does not seem
to be a direct relationship between subcutaneous abdominal
fat mass and VAT mass until 20%. This suggests that the
body will prevent distribution to VAT until all other depots
have been filled. Recent evidence has linked an increase in
abdominal adiposity with increased risk of lower extremity
injuries (16), which is consistent with the association
between increased BMI and injury rates in athletes
(6,10,22). The mechanism behind this is yet to be identified,
but these data would suggest that an increase in abdominal
fat could play a role in the associated increased injury risk.
The positional differences in VAT accumulation may have
health consequences as well. Linemen have higher cardiovascular risk factors during their career (20) and increased
prevalence of metabolic syndrome and cardiovascular disease after they retire (1,14).
Interestingly, compared with the WR/DB group, the LB/
TE/RB group has 2 times as much android fat mass, yet
their VAT mass is similar. These observations can be
explained by the relationship of each regional depot with
total percent mass. The LB/TE/RB and WR/DB groups
were below this threshold, which likely resulted in minimal
VAT accumulation. Additionally, the significantly lower
percent body fat for the WR/DB group explains the
difference in android fat mass with the LB/TE/RB group.
To the best of our knowledge, this is the first study to
observe distinct cut-points of linear accumulation for
regional fat depots. This would be beneficial, especially for
positions that rely on speed and quickness as increased
abdominal fat accumulation could hinder performance.
Our previous study observed similarities in positions that
mirror each other for measures of total body composition
and bone mass and differences compared with positions
that do not mirror. Our results demonstrate a similar pattern
with respect to regional body composition. However,
differences do exist between OL and DL for most fat
measurements, which is consistent with the total fat
3318
the
difference we reported previously. In addition to that, this
study observed that increases in weight above 114 kg results
in more fat mass being accumulated than lean mass. This
increase in fat mass is being accumulated in the abdominal
region in both subcutaneous and visceral regions. This
disproportionate increase in fat mass compared with lean
mass could inhibit performance and put players at increased
risk for injury.
PRACTICAL APPLICATIONS
Dual-energy x-ray absorptiometry allows coaches, trainers,
and players the unique opportunity to observe how increases
in weight are being achieved. Is it an increase in lean or fat
mass and more importantly, where is that mass being
accumulated? The results of this study suggest that the
differences in weight between position groups are a result of
dramatic differences in fat mass, specifically abdominal fat
mass. We have furthermore demonstrated a shift is tissue
type accumulation (fat mass . lean mass) after 114 kg.
Although it may be advantageous to for some positions to
have more mass regardless of what type of tissue is being
accumulated, most positions rely on speed and quickness,
which could be inhibited by increased fat accumulation. Furthermore, accumulation of excess fat within the abdominal
region could dramatically increase the risk of musculoskeletal injuries. Dual-energy x-ray absorptiometry can differentiate regional accumulation to monitor weight gain. More
importantly, for players who are overweight, DXA would
be able to reliably determine what type of tissue is being lost
during weight loss as the goal would be to maintain lean
mass while decreasing fat mass. The results of this study
suggest that DXA provides a distinct advantage for monitoring weight change as it can distinguish the tissue type and
region of accumulation or loss. Furthermore, the increased
visceral accumulation in linemen observed in this study may
explain the increased prevalence of cardiovascular disease
(20) and metabolic syndrome in linemen compared with
other positions after retirement (1,14).
ACKNOWLEDGMENTS
The results of this study do not constitute endorsement of
the products by the authors of the National Strength and
Conditioning Association. T. D. Weston is an employee for
GE Healthcare that manufactures the iDXA used to collect
body composition data. T. P. Burruss, N. L. Weir, B. E. Engel,
and K. A. Fielding work for the Green Bay Packers where
the data were collected.
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