Obesity
Diagnosing Obesity by Body Mass Index in Chronic
Kidney Disease
An Explanation for the “Obesity Paradox?”
Rajiv Agarwal, Jennifer E. Bills, Robert P. Light
Downloaded from http://hyper.ahajournals.org/ by guest on June 16, 2017
Abstract—Although obesity is associated with poor outcomes, among patients with chronic kidney disease (CKD), obesity
is related to improved survival. These results may be related to poor diagnostic performance of body mass index (BMI)
in assessing body fat content. Accordingly, among 77 patients with CKD and 20 controls, body fat percentage was
estimated by air displacement plethysmography (ADP), skinfold thickness, and body impedance analysis. Defined by
BMI ⱖ30 kg/m2, the prevalence of obesity was 20% in controls and 65% in patients with CKD. Defined by ADP, the
prevalence increased to 60% among controls and to 90% among patients with CKD. Although sensitivity and positive
predictive value of BMI to diagnose obesity were 100%, specificity was 72%, but the negative predictive value was only
30%. BMI correctly classified adiposity in 75%. Regardless of the presence or absence of CKD, subclinical obesity
(defined as BMI ⬍30 kg/m2 but excess body fat by ADP) was often missed in people with low lean body mass. The
adjusted odds ratio for subclinical obesity per 1 kg of reduced lean body mass by ADP was 1.14 (95% CI: 1.06 to 1.23;
P⬍0.001). Skinfold thickness measurements correctly classified 94% of CKD patients, but bioelectrical impedance
analyzer–assessed body fat estimation did so in only 65%. Air displacement plethysmography–, skinfold thickness–, and
bioelectrical impedance analyzer–assessed body fat all provided reproducible estimates of adiposity. Skinfold thickness
measurements may be a better test to classify obesity among those with CKD. Given the low negative predictive value
of BMI for obesity, our study may provide an explanation of the “obesity paradox.” (Hypertension. 2010;56:893-900.)
Key Words: obesity 䡲 epidemiology 䡲 chronic kidney disease 䡲 skinfold thickness 䡲 body impedance analysis
䡲 body composition assessment
O
ne in 3 adult in the United States is obese, and the
epidemic of obesity continues to grow.1 Obesity is
associated with a variety of adverse health consequences,
such as cardiovascular disease, dyslipidemia, diabetes mellitus, and shortened life span in the general population, yet
among patients with chronic kidney disease (CKD), obesity is
paradoxically associated with better outcomes.2 This paradoxical association has been mostly found among patients
on hemodialysis, but data among people with CKD not yet
on dialysis also point out the same paradoxical association.3 The “reverse epidemiology” of obesity has not been
explained by conventional cardiovascular risk factors.
Whether this is because of a true association or because of
poor diagnostic performance of clinical methods to assess
obesity is unclear.
The World Health Organization defines obesity as the
presence of excess body fat.4 Body fat is considered excessive
when it is ⬎25% in men and ⬎35% in women.4 Body fat is
difficult to measure directly. Therefore, in clinical practice,
body mass index (BMI) is commonly used to diagnose
obesity. However, BMI can be influenced by muscle mass,
and its ability to diagnose obesity can vary considerably by
predictors of muscle mass, such as age, sex, and race. Among
patients with CKD, who often are elderly and frail, lean body
mass may be reduced. Furthermore, volume overload that
often accompanies CKD by itself can influence BMI estimation. Among patients with CKD, therefore, BMI may not
accurately reflect excess body fat. We hypothesized that BMI
is a poor surrogate for adiposity among those with CKD.
Furthermore, if BMI is found to have poor diagnostic performance, then alternative methods must be sought to better
assess body fat content.
The gold standard for body composition assessment is air
displacement plethysmography (ADP).5 ADP uses wholebody densitometry to determine body composition. It is based
on the same reference standard operating principle as underwater weighing, except that air displacement, instead of water
displacement, is used to provide quick and convenient results.
In comparison with other body composition assessment
methods, ADP has several advantages. For example, it
Received August 4, 2010; first decision August 21, 2010; revision accepted August 31, 2010.
From the Indiana University School of Medicine (R.A., J.E.B., R.P.L.) and Richard L. Roudebush Veterans’ Administration Medical Center (R.A.),
Indianapolis, Ind.
Correspondence to Rajiv Agarwal, Indiana University and Veterans’ Administration Medical Center, 1481 West 10th St, Indianapolis, IN 46202.
E-mail [email protected]
© 2010 American Heart Association, Inc.
Hypertension is available at http://hyper.ahajournals.org
DOI: 10.1161/HYPERTENSIONAHA.110.160747
893
894
Hypertension
November 2010
Downloaded from http://hyper.ahajournals.org/ by guest on June 16, 2017
eliminates the radiation exposure inherent in dual energy x-ray
absorptiometry, as well as the difficulties associated with underwater submersion in hydrostatic weighing. However, ADP
requires specialized expensive equipment and requires the subject to wear compression underwear to perform body composition assessment. ADP, therefore, remains largely a research
technique and a reference standard.
Two other techniques are relatively easy to perform.
Skinfold thickness (SFT) measurement is performed through
calipers, and an experienced operator can perform this in a
few minutes. Likewise, body impedance analysis (BIA) is
simple and inexpensive. Accordingly we assessed these 2
research techniques to assess body fat content and compared
it to the reference standard of ADP.
We hypothesized that BMI does not assess obesity well
among patients with CKD. We reasoned that if BMI is a poor
indicator of body fat, it would suggest that the association of
BMI with outcomes may not be because of obesity. The purpose
of our study was, therefore, to compare obesity assessed by BMI
with the gold standard of ADP. To improve the assessment of
obesity, we also assessed fat percentage assessed by SFT and
BIA compared with ADP.
They were then instructed to exhale lightly 3 times. After thoracic
gas volume was measured, they exited the ADP system and were
examined for the presence of pedal edema. Body fat percentage was
calculated using sex- and race-specific equations, as follows: (1) for
nonblacks we used the Siri equation6; (2) for black men we used the
Shutte equation7; and (3) for black women we used the Ortiz
equation.8
Skinfold Thickness Measurements
Skinfold thickness was measured using a Lange Skinfold Caliper
(Beta Technologies) on both sides of the body in triplicate at 4
locations: biceps, triceps, subscapular, and supra iliac. The measurements were made by pinching the skin with the thumb and index
finger, as follows. Biceps skinfold thickness was measured at the
midpoint of the arm with the patient sitting with arms relaxed in the
supine position resting on thighs. Triceps skinfold thickness was
measured at the midpoint between the acromion and the olecranon
process in the sitting position with arms crossed at a 90° bend,
resting on thighs. The subscapular skinfold was measured with the
patient standing with arms to the side. The shoulder blade was found
and followed down to where it started to curve. The skin was pinched
and measured with the calipers. The supra iliac skinfold was also
measured with the patient standing. The skin above the right
hipbone along the midaxillary line was measured. The average
skinfold thickness measurement from each of the 4 sites was used
in the Durnin and Womersely equation to predict the percentage
of body fat.9
Materials and Methods
We studied patients with CKD stages 3 and 4 and blood pressure
⬍140/90 mm Hg in the seated position in the clinic. Patients with
overt proteinuria with stage 2 CKD were also included in the
study. Patients were recruited from the Roudebush Veterans’
Administration Medical Center and Wishard Memorial Hospital
(Indianapolis, IN). As a control group, 20 veterans without CKD
were recruited from the medicine clinic of the Roudebush
Veterans’ Administration Medical Center. To participate, these
non-CKD controls had to be nonsmokers with BMI ⬍40 kg/m2,
estimated glomerular filtration rate ⬎60 mL/min per 1.73 m2, and
no history of CKD, myocardial infarction, stroke, diabetes mellitus, or hypertension. All of the body composition measurements
were made after an overnight fast on the same day. To assess
test-retest reliability, among 40 patients, all of the measurements
were repeated after 4 to 8 weeks of the initial measurement. The
study protocol was approved by the Institutional Review Board
for protection of human subjects of the Indiana University and the
research and development committee of the Roudebush Veterans’
Administration Medical Center. All of the study subjects gave
written informed consent.
Air Displacement Plethysmography
ADP was measured using the BOD POD Gold Standard Body
Composition Tracking System (Life Measurement, Inc). The ADP
system consisted of the air plethysmograph, a digital scale, and
computer software (BOD POD version 4.2⫹). Each participant was
asked to change into compression shorts (for men) or a swimsuit (for
women) and a swim cap and to remove any jewelry. Body mass was
measured to the nearest 0.001 kg using the electronic scale before the
body volume measurement. Height was taken using a Seca 222
measuring rod (Seca Group). The subject stood with his or her back
to the measuring rod, and the measuring slide was pushed onto the
head so that the measuring slide abutted without bending. The height
was read to the nearest millimeter. The subject then entered the ADP
chamber and was instructed not to move. There were 2 body volume
readings, with the door being opened in between each reading. In the
event of a discrepancy between the 2 readings, a third reading was
taken. To measure thoracic gas volume, the subject, after a prompt,
inhaled and exhaled multiple times while holding his or her nose and
making a tight seal around the tube attached to the ADP system.
Waist:Hip Ratio
Using a measuring tape, waist and hip circumferences were measured to the nearest 0.1 cm. The tape was snug but not so tight that
it compressed the underlying soft tissue. Waist circumference was
measured with the subject standing comfortably with his or her
weight distributed evenly with feet ⬇25 to 30 cm apart. The
measurement was taken midway between the inferior margin of the
last rib and the crest of the ilium in a horizontal plane. Hip
circumference was measured with the subject standing with his or
her arms at the side and feet together. The person taking the
measurement sat at the side of the subject so that the level of
maximum extension of the buttocks could be seen. The measuring
tape was placed around the buttocks in a horizontal plane at the
maximum extension.
Body Impedance Analysis
Body impedance was measured using a bioelectrical impedance
analyzer (BIA) (RJL Systems). Subjects were asked to lie down on
the bed with their right shoe and sock removed. They were instructed
to keep still with feet apart and their hands not touching their body.
Electrodes were placed at 4 locations on the right side of the body as
follows: (1) right wrist adjacent to an imaginary line bisecting the
ulnar head; (2) base of the right middle finger; (3) right ankle
adjacent to an imaginary line bisecting the inside ankle (medial
malleolus); and (4) base of the index toe. Leads were attached to the
electrodes as shown in the user manual and plugged into the BIA.
Numbers corresponding to resistance, reactance, impedance, and
phase angle were recorded in triplicate. Equations used to assess
body composition from population-based studies were used to assess
body fat.10
Data Analysis
Using ADP measurements, obesity was defined as body fat percentage ⬎25% in men and ⬎35% in women according to the World
Health Organization.4 Using these criteria, obesity was present in all
but 6 of the patients. Given that obesity was nearly universal in our
population, receiver operating characteristic curve analysis was not
performed. We assessed the relationship between BMI and ADP
Agarwal et al
Table 1.
Obesity Paradox and BMI
895
Baseline Characteristics of the Study Population
Clinical Characteristic
Control
CKD
All
n (%)
20 (21)
77 (79)
97 (100)
Estimated glomerular filtration rate,
mL/min per 1.73 m2
73.3⫾7.4
39.5⫾12.5
46.5⫾18.0
⬍0.0001
Age, y
59.5⫾10.0
67.2⫾11.0
65.6⫾11.2
⬍0.01
Race, n (%)
0.9
White
16 (80)
58 (75)
74 (76)
Black
3 (15)
15 (19)
18 (19)
American Indian/Alaskan
0 (0)
1 (1)
1 (1)
Other/unknown
1 (5)
3 (4)
4 (4)
18 (90)
72 (94)
90 (93)
Never
7 (35)
15 (19)
22 (23)
Former
13 (65)
48 (62)
61 (63)
Current
0 (0)
14 (18)
14 (14)
Never
1 (5)
7 (9)
8 (8)
Former
6 (30)
36 (47)
42 (43)
Men
Tobacco use, n (%)
Downloaded from http://hyper.ahajournals.org/ by guest on June 16, 2017
Current
0.6
0.07
Alcohol use, n (%)
Baecke activity score
P
0.3
13 (65)
34 (44)
47 (48)
11.04⫾14.74
5.04⫾5.90
6.63⫾9.36
51 (66)
51 (53)
Diabetes mellitus, n (%)
0.02
Etiology of CKD, n (%)
Hypertensive nephrosclerosis
32 (42)
32 (33)
Diabetes mellitus
24 (31)
24 (25)
3 (4)
3 (3)
Analgesic induced kidney injury
1 (1)
1 (1)
Ischemic nephropathy
2 (3)
2 (2)
Glomerulonephritis
4 (5)
4 (4)
Adult autosomal polycystic
kidney disease
Obstructive uropathy
3 (4)
3 (3)
Other
3 (4)
3 (3)
Unknown
Urine protein:creatinine ratio,
median (interquartile range)
Edema, n (%)
5 (6)
5 (5)
0.002 (0.001 to 0.004)
0.01 (0.001 to 0.370)
0.06 (0.004 to 0.290)
0.04
2 (10)
36 (47)
38 (39)
⬍0.0001
body fat percentage by examining correlations between the 2
variables. We next examined the relationship between BMI and ADP
lean mass. Lean mass was calculated by multiplying the percentage
of lean mass by body weight.
We defined subclinical obesity as the presence of high body fat
percentage but BMI ⬍30 kg/m2. We defined overt obesity as the
presence of high body fat percentage and BMI ⬎30 kg/m2. To
explore the determinants of subclinical obesity, we assessed
potential explanatory variables (as shown in Table 3). We
hypothesized that muscle mass may be reduced in patients with
subclinical obesity. Accordingly, we used lean mass assessed by
ADP to reflect muscle mass. Mid-arm circumference and triceps
skinfold thickness were also used to calculate bone-free arm
muscle area. Corrected arm muscle areas were calculated from
triceps skinfold thickness and midarm circumference using the
formulas used by Schmidt et al.11
To examine the relationship of SFT and BIA we used BlandAltman plots and Lin concordance correlation coefficients. We
finally examined the relationship between weight and body fat using
a multivariate regression analysis.
Test-retest reliability was assessed for ADP, SFT, BIA, and BMI
on 2 occasions 4 to 8 weeks apart using the Lin concordance
correlation coefficient12 and Bland-Altman plots.13
Results
Table 1 shows the baseline characteristics of the patients. The
sample included predominantly men and was composed of
older individuals. Table S1 (please see the online Data
Supplement at http://hyper.ahajournals.org) shows baseline
characteristics by CKD stage. Notably, compared with nonCKD controls and stage 3A CKD, the physical activity score
was reduced at stages 3B and 4 CKD. Table 2 shows the body
composition assessment by various techniques. Patients with
CKD had a greater BMI and had greater adiposity. However,
the lean body mass, on average, was comparable between
controls and those with CKD. Table S2 shows body composition by CKD stage. Increasing adiposity was noted with
896
Hypertension
November 2010
Table 2.
Body Composition Assessment
Control
CKD
All
P
Body mass index, kg/m2
Body Composition Assessment
27.4⫾3.9
32.1⫾5.5
31.1⫾5.5
⬍0.001
Waist circumference, cm
100.5⫾11.6
113.8⫾15.0
111.1⫾15.3
⬍0.001
Hip circumference, cm
104.7⫾8.1
111.2⫾13.1
109.8⫾12.5
0.04
Arm circumference, cm
31.8⫾2.6
34.5⫾4.7
34.0⫾4.5
0.02
Waist:hip ratio
0.96⫾0.07
1.03⫾0.07
1.01⫾0.07
⬍0.001
Arm muscle area
46.0⫾10.5
52.6⫾16.4
51.3⫾15.6
0.1
0 (0)
5 (6)
5 (5)
Muscle mass by exam, n (%)
0.3
Wasted
19 (95)
71 (92)
90 (93)
ADP (% fat)
28.9⫾7.7
36.2⫾8.4
34.7⫾8.8
⬍0.001
SFT (% fat)
33.3⫾5.5
36.2⫾6.1
35.6⫾6.0
0.06
BIA (% fat)
23.1⫾5.6
27.1⫾7.2
26.2⫾7.1
0.02
ADP lean mass, kg
61.5⫾8.5
60.6⫾10.8
60.8⫾10.3
0.7
BMI as a Screening Tool for Obesity
50.0
As defined by BMI ⱖ30 kg/m2, the prevalence of obesity in
non-CKD controls was 20%. By comparison, the prevalence
of obesity among CKD patients was 65%. However, applying
the gold standard of ADP-measured body fat, the prevalence
of obesity increased to 60% among non-CKD patients and to
an astounding 90% among patients with CKD.
Figure 1 shows that ADP-assessed percentage of body fat
or adiposity was related to BMI (r⫽0.67; P⬍0.01). At higher
levels of adiposity there was greater variation in BMI. For
example, at 40% body fat, BMI could vary from ⬍30 kg/m2
CKD
n=0
to ⬎40 kg/m2. No patient in our study was so muscular that
BMI was increased and misclassified him or her as obese
(n⫽0 in top left quadrant of Figure 1). Thus, BMI ⱖ30 kg/m2
had 100% specificity and 100% positive predictive value for
obesity. However, using the same threshold of BMI to
classify obesity, 30% of patients with CKD would not be
classified as obese, although they would meet the definition
of obesity by the gold standard test of obesity (negative
predictive value of BMI 30%). Accordingly, BMI as a
screening tool to detect obesity would miss 30% of the
patients. BMI was 72% sensitive in detecting obesity and
correctly classified adiposity in 75% of the patients.
Among people without CKD, as in patients with CKD,
BMI ⱖ30 kg/m2 had 100% specificity and 100% positive
predictive value for obesity. However, the sensitivity of this
n=50
Control
n=0
n=4
CKD Stage 2
CKD Stage 3B
30.0
30.0
BMI (kg/m2)
BMI (kg/m2)
40.0
40.0
Control
CKD Stage 3A
CKD Stage 4
r = 0.67
p < 0.01
10.0
20.0
30.0
40.0
ADP (fat %)
50.0
8
n=8
n=8
8
20.0
n=19
19
n=8
8
20.0
Downloaded from http://hyper.ahajournals.org/ by guest on June 16, 2017
higher CKD stages most clearly by ADP assessment and less
so by BIA assessment.
50.0
Adequate
10.0
20.0
30.0
40.0
ADP (fat %)
50.0
Figure 1. Relationship of BMI to ADP-assessed percentage of body fat among patients with CKD (left) and controls (right). Arrow markers denote female subjects. Vertical line denotes the threshold of body fat percentage for obesity for men and horizontal line the
threshold for obesity by BMI among men and women. The numbers (n) relate to subjects in each quadrant. For women, “n” was calculated with threshold of obesity at body fat of ⱖ35%. The correlation coefficient (r) reflects the combined results of CKD and controls.
r = 0.57
p < 0.01
30 0
30.0
40 0
40.0
ADP (fat %)
r = 0.44
p < 0.01
10 0
10.0
50 0
50.0
897
20 0
20.0
30 0
30.0
40 0
40.0
ADP (fat %)
50 0
50.0
Difference
e
-20 -10 0 10
20 0
20.0
Difference
e
-20 -10 0 10
0 20
10 0
10.0
Obesity Paradox and BMI
BIA (fat %)
B
10.0 20.0
0 30.0 40.0 50.0
SFT (fat %)
S
10.0 20.0
0 30.0 40.0 50.0
Agarwal et al
10
20
30
Mean
40
50
10
20
Downloaded from http://hyper.ahajournals.org/ by guest on June 16, 2017
Control
CKD Stage 2
CKD Stage 3A
CKD Stage 4
CKD Stage 3B
30
Mean
40
50
Figure 2. Diagnostic performance of SFT and BIA in diagnosing adiposity. Adiposity was assessed by ADP percentage of fat. The top
panel shows the Lin concordance correlation coefficient. The dotted diagonal lines in the top panel are the lines of identity. Bottom
panels show the Bland-Altman plots.
threshold to detect obesity was only 33% and negative
predictive value only 50%. BMI correctly classified adiposity
in 60% of the patients.
Relationship of BMI to Lean Body Mass and
Comparison With SFT and BIA
Other than being related to percentage of body fat, BMI was
also related to ADP-assessed lean body mass (Figure S1). The
relationship between lean body mass and BMI (r⫽0.53;
P⬍0.01) was similar to the relationship between percentage
of body fat and BMI (r⫽0.67; P⬍0.01). In contrast to
significant BMI and lean body mass relationship, the relationships of lean body mass to either SFT-assessed percentage of fat (Figure 2, middle) or BIA-assessed percentage of
fat (Figure 2, bottom) were not significant.
Correlates of Subclinical Obesity
To explore the determinants of subclinical obesity, we assessed potential explanatory variables, as shown in Table S3.
Bivariate analysis showed that patients with subclinical obesity were less often diabetic, but other demographic characteristics were well matched. We hypothesized that muscle
mass may be reduced in patients with subclinical obesity.
Compared with those with overt obesity, among those with
subclinical obesity, ADP-assessed lean mass was 9.4 kg less
(P⬍0.0001; Table 3). Similarly, arm circumference and
bone-free arm muscle area were significantly different between groups. Adjusted for age, CKD, and diabetes mellitus,
a multivariable logistic regression model revealed that, compared with overt obesity, the odds for subclinical obesity per
1 kg of reduced lean body mass by ADP were 1.14 (95% CI:
1.06 to 1.23; P⬍0.001). Similarly, the odds for subclinical
obesity per 1-cm2 reduced bone-free arm muscle area were
1.08 (95% CI: 1.03 to 1.13; P⫽0.001).
Diagnostic Performance of SFT and BIA in
Assessing Obesity
Because BMI performed poorly to detect obesity, we assessed
the diagnostic performance of 2 simple and readily available
tests, skin-fold thickness and BIA to assess adiposity.
SFT-Assessed Obesity
The following were the diagnostic test performance results
among non-CKD controls: sensitivity 100%, specificity 13%,
positive predictive value 63%, and negative predictive value
100%, and 65% were correctly classified (Figure S2, right).
For CKD patients, the diagnostic test performance results
were as follows: sensitivity 99%, specificity 50%, positive
predictive value 94%, and negative predictive value 80%, and
94% were correctly classified (Figure S2, left).
Compared with the line of identity, the relationship between SFT-assessed body fat percentage and adiposity was
flatter (Figure 2, top left). Unlike the BMI-adiposity relationship shown in Figure 1, there was no megaphone shape to the
scatter plot. Thus, the error in the assessment of adiposity was
similarly distributed at all levels of body fat.
SFT-assessed body fat did not, on average, overestimate
or underestimate body fat percentage (Figure 2, bottom
left). The limits of agreement were wide; individual
estimates could be off by 13%. At higher levels of
adiposity, SFT-assessed body fat underestimated percentage of body fat. At lower levels of adiposity, it overestimated percentage of body fat.
898
Hypertension
November 2010
Table 3.
Body Composition Differences Between Subclinical and Overt Obesity
Obesity State
Clinical Characteristic
Subclinical
Overt
Total
P
ADP, % fat
33.1⫾4.7
39.7⫾5.8
37.5⫾6.3
⬍0.0001
SFT, % fat
33.2⫾4.8
38.4⫾4.4
36.7⫾5.1
⬍0.0001
BIA, % fat
23.0⫾5.7
29.5⫾6.4
27.2⫾6.8
⬍0.0001
Body mass index, kg/m2
26.9⫾2.0
35.1⫾3.8
32.4⫾5.1
⬍0.0001
Waist circumference, cm
101.1⫾7.7
121.3⫾11.4
114.6⫾14.0
⬍0.0001
Hip circumference, cm
102.7⫾5.8
117.1⫾11.3
112.3⫾11.9
⬍0.0001
Arm circumference, cm
31.6⫾2.9
36.4⫾3.9
34.9⫾4.3
⬍0.0001
Waist:hip ratio
0.99⫾0.07
1.04⫾0.07
1.02⫾0.07
⬍0.01
Arm muscle area
43.9⫾11.5
57.7⫾15.5
53.4⫾15.7
⬍0.001
ADP lean mass, kg
54.9⫾8.0
64.3⫾10.4
61.2⫾10.6
⬍0.0001
2 (7)
3 (6)
5 (6)
23 (85)
51 (94)
74 (91)
Muscle mass by exam, n (%)
0.7
Wasted
BIA-Assessed Obesity
The following were the diagnostic test performance results
among non-CKD controls: sensitivity 42%, specificity 88%,
positive predictive value 83%, and negative predictive value
50%, and 60% were correctly classified (Figure S3, right).
For CKD patients, the results were as follows: sensitivity
63%, specificity 86%, positive predictive value 98%, and
negative predictive value 20%, and 65% were correctly
classified (Figure S3, left).
Compared with the line of identity, the relationship
between BIA-assessed body fat percentage and adiposity
was flatter (Figure 2, top right). Unlike the BMI-adiposity
relationship shown in Figure 1, there was no megaphone
shape to the scatter plot. Thus, the error in the assessment
50.0
BIA
Lin's Concordance = 0.868
95% CI (0.790, 0.947)
30.0
40.0
20.0
0
30.0
10.0
20.0
10.0
50.0
CKD Stage 2
CKD Stage 3B
10.0
20.0 30.0 40.0
Measurement 1
50.0
10
20
30
40
50
10.0
20.0
30.0
40.0
50.0
10
20
30
40
50
5
0
-5
-15 -10
-15 -10
-5
0
Difference
D
0
5
-5
5
10
10
15
Control
CKD Stage 3A
CKD Stage 4
40.0
15
30.0
15
20.0
10
10.0
10.0
Lin's Concordance = 0.818
95% CI (0.704, 0.932)
40.0
0
50.0
SFT
easurement 2
Me
20.0 30.0 40.0
50.0
ADP
Lin's Concordance = 0.947
95% CI (0.914, 0.981)
-15 -10
Downloaded from http://hyper.ahajournals.org/ by guest on June 16, 2017
Adequate
10
20
30
Mean
40
50
Figure 3. Test-retest reliability of AD, SFT, and BIA in assessing body fat at ⬇2 months. The top panel shows the Lin concordance
correlation coefficient. The dotted diagonal lines in the top panel are the lines of identity. Bottom panels show the Bland-Altman plots.
Test-retest reliability was best for ADP, followed by SFT and BIA.
Agarwal et al
of adiposity was similarly distributed at all of the levels of
body fat.
BIA-assessed body fat, on average, underestimated body
fat percentage by 9% (Figure 2, bottom left). The limits of
agreement were wide; individual estimates could be off by
⬇11%. At higher levels of adiposity, BIA-assessed body fat
underestimated percentage of body fat. At lower levels of
adiposity, it overestimated percentage of body fat. Thus, the
concordance correlation coefficient was lower (0.44) compared with SFT (0.56).
Test-Retest Reliability of ADP-, SFT-, and
BIA-Assessed Body Fat
Downloaded from http://hyper.ahajournals.org/ by guest on June 16, 2017
Among 40 participants, we repeated the assessment of body
composition by all 3 of the techniques within 2 months. The
results of the test-retest reliability are shown in Figure 3.
Agreement between 2 measurements was best in the case of
ADP (concordance correlation coefficient: 0.947), followed
by SFT, and least for BIA.
Discussion
The major findings of our study are the following: (1) the
prevalence of obesity among patients with CKD is high but is
even higher when measured directly; (2) normal BMI does
not exclude obesity, although a high BMI rules it in; (3)
regardless of presence or absence of CKD, subclinical obesity
is often missed in people with low lean body mass; (4)
skinfold thickness measurements can exclude obesity with a
high degree of certainty; BIA-assessed body fat estimation
does not rule out obesity; (5) SFT-assessed body fat percentage can detect the majority of subclinical obesity; and (6)
ADP-, SFT-, and BIA-assessed body fat all provide reproducible estimates of adiposity.
Similar results were reported by Romero-Corral et al14
among patients with coronary artery disease. Using ADPassessed body fat at the gold standard, BMI of ⱖ30 kg/m2
had excellent specificity (95%) and positive predictive value
(97%) in detecting obesity. The authors reported poor sensitivity (43%) and negative predictive value (59%) of BMI in
diagnosing obesity, which is in keeping with our findings.
BMI could not distinguish reliably between fat mass and lean
body mass in their study, as also noted in our study.
Our data also support the studies of Beddhu et al. These
investigators demonstrated that the survival advantage of
high BMI among CKD patients on long-term dialysis was
limited to those with normal or increased muscle mass.
Patients with high BMI and high body fat had increased
all-cause and cardiovascular mortality. In contrast to Beddhu
et al,15 who estimated muscle mass from 24-hour urine
creatinine, we directly measured lean body mass by the
gold-standard measurement of ADP. Our study not only
supports their observations but extends them in calling
attention to body fat excess among those with low BMI. Our
study, therefore, calls into question the accuracy of BMI in
predicting body fat. These results support the findings of
Postorino et al,16 who found that, whereas BMI was inversely
related to mortality among dialysis patients, surrogate mea-
Obesity Paradox and BMI
899
sures of abdominal obesity and segmental fat distribution
were directly associated with mortality. Skinfold thickness
measurement appears to be an attractive and simple-toimplement technique when screening for obesity among those
with CKD. It correctly classified adiposity in 94% of the
patients.
Pathophysiological studies have revealed that, among patients with CKD, obesity is not an innocuous bystander and
may directly or indirectly damage the kidney.17 Evidence for
the direct damaging effect of obesity is the following.
Because of heightened sympathetic activity, high levels of
angiotensin II, and hyperinsulinemia, obesity is often accompanied by glomerular hyperfiltration and increased proximal
tubular sodium resorption. Enhanced proximal salt reabsorption determines a reduced delivery of sodium to the macula
densa. This then causes afferent vasodilatation and enhanced
renin synthesis. As a result of high local angiotensin II levels,
the efferent arteriole is constricted in the obese, and glomerulomegaly and focal glomerulosclerosis ensue. Evidence is
emerging that fat cells may trigger inflammation in the
kidney indirectly by producing inflammatory cytokines,
which may further aggravate renal dysfunction.
There are several limitations of our study. We did not study
a random sample of the CKD population but only those
willing to participate in these studies. There were few women
and mostly elderly patients in our sample. Given the crosssectional nature of our study, we cannot infer causality. A
larger cohort with longitudinal follow-up may begin to better
address the questions of what might explain the reverse
epidemiology of obesity in CKD.
Perspectives
Using BMI to detect obesity among those with CKD may
miss a large number of people with excess body fat. Often
these people have low muscle mass. Using a caliper to
measure skinfold thickness may detect these patients in a
more reliable way. Given the low negative predictive value of
BMI for obesity, our study may provide an explanation of the
“obesity paradox.” Perhaps better measurements of risk
factors, such as blood pressure, by ambulatory or home BP
monitoring18 and obesity by ADP can reverse the “reverse
epidemiology”2 and provide support to the notion that neither
being hypertensive nor being fat may be good, even for those
with CKD.
Sources of Funding
This work was supported by a Veterans’ Administration Merit
Review grant to R.A.
Disclosures
None.
References
1. Ogden CL, Carroll MD, Curtin LR, McDowell MA, Tabak CJ, Flegal
KM. Prevalence of overweight and obesity in the United States,
1999 –2004. JAMA. 2006;295:1549 –1555.
900
Hypertension
November 2010
Downloaded from http://hyper.ahajournals.org/ by guest on June 16, 2017
2. Kalantar-Zadeh K, Abbott KC, Salahudeen AK, Kilpatrick RD, Horwich
TB. Survival advantages of obesity in dialysis patients. Am J Clin Nutr.
2005;81:543–554.
3. Kovesdy CP, Anderson JE, Kalantar-Zadeh K. Paradoxical association
between body mass index and mortality in men with CKD not yet on
dialysis. Am J Kidney Dis. 2007;49:581–591.
4. World Health Organization Expert Committee on Physical Status: the Use
and Interpretation of Anthropometry. Physical Status: The Use and Interpretation of Anthropometry–Report of a WHO Expert Committee.
Geneva, Switzerland: World Health Organization; 1995.
5. Fields DA, Goran MI, McCrory MA. Body-composition assessment via
air-displacement plethysmography in adults and children: a review. Am J
Clin Nutr. 2002;75:453– 467.
6. Siri WE. Body composition from fluid spaces and density: analysis of
methods. In: Brozek J, Henschel A, eds. Techniques for Measuring Body
Composition. Washington, DC: National Academy of Science; 1961:
223–2944.
7. Schutte JE, Townsend EJ, Hugg J, Shoup RF, Malina RM, Blomqvist CG.
Density of lean body mass is greater in blacks than in whites. J Appl
Physiol. 1984;56:1647–1649.
8. Ortiz O, Russell M, Daley TL, Baumgartner RN, Waki M, Lichtman S,
Wang J, Pierson RN Jr, Heymsfield SB. Differences in skeletal muscle
and bone mineral mass between black and white females and their
relevance to estimates of body composition. Am J Clin Nutr. 1992;
55:8 –13.
9. Durnin JV, Womersley J. Body fat assessed from total body density and
its estimation from skinfold thickness: measurements on 481 men and
women aged from 16 to 72 years. Br J Nutr. 1974;32:77–97.
10. Sun SS, Chumlea WC, Heymsfield SB, Lukaski HC, Schoeller D, Friedl
K, Kuczmarski RJ, Flegal KM, Johnson CL, Hubbard VS. Development
of bioelectrical impedance analysis prediction equations for body composition with the use of a multicomponent model for use in epidemiologic
surveys. Am J Clin Nutr. 2003;77:331–340.
11. Schmidt R, Dumler F, Cruz C, Lubkowski T, Kilates C. Improved
nutritional follow-up of peritoneal dialysis patients with bioelectrical
impedance. Adv Perit Dial. 1992;8:157–159.
12. Lin LI. A concordance correlation coefficient to evaluate reproducibility.
Biometrics. 1989;45:255–268.
13. Bland JM, Altman DG. Statistical methods for assessing agreement
between two methods of clinical measurement. Lancet. 1986;1:307–310.
14. Romero-Corral A, Somers VK, Sierra-Johnson J, Jensen MD, Thomas RJ,
Squires RW, Allison TG, Korinek J, Lopez-Jimenez F. Diagnostic performance of body mass index to detect obesity in patients with coronary
artery disease. Eur Heart J. 2007;28:2087–2093.
15. Beddhu S, Pappas LM, Ramkumar N, Samore M. Effects of body size and
body composition on survival in hemodialysis patients. J Am Soc
Nephrol. 2003;14:2366 –2372.
16. Postorino M, Marino C, Tripepi G, Zoccali C. Abdominal obesity and
all-cause and cardiovascular mortality in end-stage renal disease. J Am
Coll Cardiol. 2009;53:1265–1272.
17. Zoccali C. Overweight, obesity and metabolic alterations in chronic
kidney disease. Prilozi. 2009;30:17–31.
18. Agarwal R. Blood pressure and mortality among hemodialysis patients.
Hypertension. 2010;55:762–768.
Diagnosing Obesity by Body Mass Index in Chronic Kidney Disease: An Explanation for
the ''Obesity Paradox?''
Rajiv Agarwal, Jennifer E. Bills and Robert P. Light
Downloaded from http://hyper.ahajournals.org/ by guest on June 16, 2017
Hypertension. 2010;56:893-900; originally published online September 27, 2010;
doi: 10.1161/HYPERTENSIONAHA.110.160747
Hypertension is published by the American Heart Association, 7272 Greenville Avenue, Dallas, TX 75231
Copyright © 2010 American Heart Association, Inc. All rights reserved.
Print ISSN: 0194-911X. Online ISSN: 1524-4563
The online version of this article, along with updated information and services, is located on the
World Wide Web at:
http://hyper.ahajournals.org/content/56/5/893
Data Supplement (unedited) at:
http://hyper.ahajournals.org/content/suppl/2010/09/24/HYPERTENSIONAHA.110.160747.DC1
Permissions: Requests for permissions to reproduce figures, tables, or portions of articles originally published
in Hypertension can be obtained via RightsLink, a service of the Copyright Clearance Center, not the Editorial
Office. Once the online version of the published article for which permission is being requested is located,
click Request Permissions in the middle column of the Web page under Services. Further information about
this process is available in the Permissions and Rights Question and Answer document.
Reprints: Information about reprints can be found online at:
http://www.lww.com/reprints
Subscriptions: Information about subscribing to Hypertension is online at:
http://hyper.ahajournals.org//subscriptions/
Diagnosing obesity by body mass index in chronic kidney
disease—an explanation for the “obesity paradox”?
Rajiv Agarwal M.D.
Jennifer E. Bills, B.A.
Robert P. Light, B.S.
Data supplement
Indiana University School of Medicine and Richard L. Roudebush Veterans
Administration Medical Center, Indianapolis, IN
Correspondence:
Rajiv Agarwal M.D.
Professor of Medicine,
Indiana University and VAMC,
1481 West 10th Street,
Indianapolis, IN 46202
Phone 317-988 2241
Email: [email protected]
Table S1: Baseline characteristics of the study population
by CKD Stage
CKD Stage
Contr
Clinical characteristic
ol
2
3A
20
19
n
(21%)
5 (5%) (20%)
73.3 ±
66.1 ±
51.5
eGFR (mL/min/1.73m2)
7.4
3.5
± 4.0
59.5 ±
58.8 ±
66.6
Age (y)
10.0
3.6
± 13.7
p
3B
33
(34%)
4
20
*
24.5
<
0.0001
0
.02
0
.6
(21%)
37.6 ±
3.7
± 3.8
69.3 ±
9.8
66.4
± 10.8
Race
16
White
(80%)
Black
(15%)
3
(60%)
3
Amer Indian/Alaskan
Other/Unknown
Male
17
(89%)
1
(20%)
21
(64%)
2
(11%)
9
(27%)
0
0 (0%) (0%)
1
0
1 (5%) (20%)
(0%)
18
5
18
(90%)
(100%)
(95%)
0 (0%)
17
(85%)
3
(15%)
1
(3%)
0
(0%)
2
(6%)
0
(0%)
30
(91%)
19
(95%)
0
.9
0
Tobacco Use
.4
7
Never
(35%)
1
(20%)
13
Former
(65%)
2
(11%)
4
(80%)
7
(21%)
(61%)
5
Current
0 (0%)
0 (0%) (26%)
(25%)
20
12
(63%)
5
12
(60%)
6
(18%)
3
(15%)
0
Alcohol Use
.3
3
Never
Former
1 (5%)
6
(30%)
13
Current
Baecke Activity Score
(65%)
11.04
± 14.74
Diabetes Mellitus
0 (0%) (16%)
1
6
(20%)
(32%)
4
10
(80%)
(53%)
4.61 ±
10.7
6.62
6 ± 9.13
2
12
(40%)
(63%)
2
(6%)
2
(10%)
17
(52%)
12
(60%)
14
(42%)
6
(30%)
3.76 ±
3.53
2.62
± 2.30
24
(73%)
0
.03
13
(65%)
0
.5
0
Etiology of CKD
Hypertensive
Nephrosclerosis
.1
1
(20%)
8
(42%)
15
(45%)
5
Diabetes Mellitus
Adult Autosomal Polycystic
Kidney Disease
Analgesic Induced Kidney
Injury
0 (0%) (26%)
13
(39%)
0
0 (0%) (0%)
(6%)
Nephrotic Glomerulonephritis
Obstructive Uropathy
Other
Unknown
Urine Protein/Creatinine Ratio
(median, IQR)
Edema
.002
(.001, .004)
2
(5%)
(0%)
0 (0%) (0%)
1
0
(20%)
(0%)
1
1
(20%)
(5%)
1
1
(20%)
(5%)
1
3
(20%)
(16%)
.003
.05
(.002, .004)
(.005, .17)
3
8
1
0
0
Ischemic Nephropathy
6
(30%)
2
1
0 (0%) (5%)
8
(40%)
0
(0%)
0
(0%)
2
(10%)
2
(6%)
1
(5%)
0
(0%)
1
(5%)
1
(3%)
0
(0%)
0
1
(0%)
(5%)
.10
(.05, .37)
16
.30
(.03, .96)
9
0
.07
0
(10%)
(60%)
(42%)
* p value indicates the differences between groups that include controls
and CKD stages
(48%)
(45%)
.05
Table S2: Body composition assessment
by CKD stage
C
Body composition assessment
Body Mass Index (kg/m2)
Waist Circumference (cm)
ontrol
27
.4 ± 3.9
10
0.5 ±
11.6
Waist-Hip Ratio
10
4.7 ± 8.1
31
.8 ± 2.6
0.
96 ±
0.07
Arm Muscle Area
46
.0 ± 10.5
Hip Circumference (cm)
Arm Circumference (cm)
CKD Stage
3
2 A
3
3
2.8 ± 4.8 2.2 ± 5.6
1
1
14.6 ±
12.5 ±
16.5
14.9
1
1
11.3 ±
09.0 ±
13.0
11.9
3
3
5.8 ± 4.5 4.7 ± 4.6
1.
1.
03 ±
03 ±
0.05
0.06
6
5
2.6 ±
2.4 ±
12.4
12.8
3
p
B
4
3
3
1.5 ± 4.9 3.0 ± 6.5
1
1
13.0 ±
16.3 ±
14.9
15.7
1
1
09.6 ±
15.8 ±
10.6
17.2
3
3
3.7 ± 4.2 5.4 ± 5.7
1.
1.
03 ±
01 ±
0.08
0.06
5
5
0.1 ±
4.5 ±
17.2
18.8
*
0
.01
<
0.01
0
.08
0
.08
<
0.01
0
.2
0
Muscle Mass by Exam
.4
0
Wasted
(0%)
Air displacement
plethysmography (ADP) (% Fat)
0
(0%)
1
9
(95%)
(100%)
3
28
3 4.4 ±
.9 ± 7.7
3.5 ± 9.4 10.5
19
Adequate
0
(0%)
5
(100%)
3
2
(9%)
(10%)
2
9 (88%)
1
8 (90%)
3
3
6.0 ± 7.1 9.0 ± 8.0 0.01
<
33
3
3
3
Skin fold thickness (% Fat)
.3 ± 5.5
5.0 ± 8.4 6.1 ± 6.7 6.3 ± 5.7
Body impedance analysis (%
23
2
2
2
Fat)
.1 ± 5.6
8.8 ± 4.2 5.2 ± 8.5 6.4 ± 5.2
5
61
6
6 9.6 ±
ADP Lean Mass (kg)
.5 ± 8.5
8.3 ± 8.9 2.1 ± 9.1 11.6
* p value indicates the differences between groups that include
controls and CKD stages
3
6.4 ± 5.8 .4
2
9.7 ± 8.6 .05
5
9.0 ±
.4
10.9
0
0
0
Table S3: Clinical differences between subclinical and
overt obesity
Obesity State
Sub
Clinical characteristic
clinical
Overt
27
54
n
(33%)
(67%)
19
47
Presence of CKD
(70%)
(87%)
46.6
42.2 ±
eGFR (mL/min/1.73m2)
± 21.2
14.6
68.3
65.1 ±
Age (y)
± 12.2
10.7
Total
81
(100%)
66
(81%)
.07
43.7
± 17.1
.3
66.2
± 11.2
.2
p
0
0
0
0
Race
.8
20
White
(74%)
Black
(19%)
Amer Indian/Alaskan
(0%)
Other/Unknown
(7%)
41
(76%)
5
10
(19%)
0
1
1
(1%)
2
(4%)
26
(96%)
15
(19%)
(2%)
2
Male
61
(75%)
4
(5%)
51
(94%)
77
(95%)
0
.7
0
.7
Tobacco Use
8
Never
(30%)
12
(22%)
16
Former
Current
(59%)
(25%)
34
(63%)
3
20
50
(62%)
8
11
(11%)
(15%)
(14%)
0
Alcohol Use
.4
1
Never
(4%)
Former
(48%)
Current
(48%)
7
(13%)
13
22
(41%)
13
± 3.22
Diabetes Mellitus
(41%)
35
(43%)
25
(46%)
5.12
Baecke Activity Score
8
(10%)
38
(47%)
6.01 ±
7.39
11
5.74
± 6.38
36
(67%)
0
.6
0
47
(58%)
.03
0
Etiology of CKD
Hypertensive
Nephrosclerosis
.04
8
(30%)
23
(43%)
6
Diabetes Mellitus
(22%)
Adult Autosomal Polycystic
3
Kidney Disease
(11%)
Analgesic Induced Kidney
0
Injury
(0%)
1
Ischemic Nephropathy
(4%)
Nephrotic
1
Glomerulonephritis
(4%)
8
Obstructive Uropathy
(30%)
0
Other
(0%)
0
Unknown
(0%)
31
(38%)
16
(30%)
22
(27%)
0
(0%)
3
(4%)
1
(2%)
1
(1%)
1
(2%)
2
(2%)
2
(4%)
3
(4%)
4
(7%)
12
(15%)
1
(2%)
1
(1%)
2
(4%)
2
(2%)
0
No CKD
Urine Protein/Creatinine
Ratio
Edema
4
4
(0%)
(7%)
(5%)
.075
(.004, .29)
10
(37%)
.063
(.006, .334)
27
(50%)
.068
(.005, .300)
37
(46%)
0
.3
0
.3
Figure S1: Relationship of ADP-assessed lean
body mass to BMI, skinfold thickness (SFT)
assessed fat and body impedance analysis (BIA)
assessed fat. Between BMI and lean mass the
relationship was significant. Between SFT and lean
body mass or between BIA and lean body mass there
was no relationship.
Figure S2: Relationship of skinfold thickness (SFT) assessed percent body fat to ADP assessed percent
body fat among patients with CKD (left) and controls (right). Red markers denote female subjects. Vertical line
denotes the threshold of body fat percent for obesity by ADP for men and horizontal line the threshold for
obesity by SFT for men. The numbers (n) relate to subjects in each quadrant. For women, n was calculated with
threshold of obesity at body fat of 35% or more. The Lin’s concoradance correlation coefficient (r) reflects the
combined results of CKD and controls.
Figure S3: Relationship of body impedance analysis (BIA) assessed percent body fat to ADP assessed
percent body fat among patients with CKD (left) and controls (right). Red markers denote female subjects.
Vertical line denotes the threshold of body fat percent for obesity by ADP for men and horizontal line the
threshold for obesity by BIA for men. The numbers (n) relate to subjects in each quadrant. For women, n was
calculated with threshold of obesity at body fat of 35% or more. The Lin’s concoradance correlation coefficient (r)
reflects the combined results of CKD and controls.