This study tested whether weight loss through a hypocaloric diet could reduce arterial stiffness in overweight and obese middle-aged and older adults. 36 subjects were randomly assigned to a weight loss or control group. After 12 weeks, the weight loss group lost on average 7.1 kg compared to 0.7 kg in the control group. Arterial stiffness measures (β-stiffness index and carotid-femoral pulse wave velocity) decreased in the weight loss group but not the control group. Reductions in arterial stiffness correlated with reductions in total body and abdominal adiposity. The findings suggest that weight loss can reduce arterial stiffness in overweight and obese middle-aged adults.

1. Arterial Destiffening With Weight Loss in Overweight and
Obese
Middle-Aged and Older Adults
A. Laura Dengo, Elizabeth A. Dennis, Jeb S. Orr, Elaina L.
Marinik, Elizabeth Ehrlich,
Brenda M. Davy, and Kevin P. Davy
From the Human Integrative Physiology Laboratory, Department
of Human Nutrition, Foods, and
Exercise, Virginia Polytechnic Institute and State University,
Blacksburg, Va.
Abstract
We tested the hypothesis that weight loss via a hypocaloric diet
would reduce arterial stiffness in
overweight and obese middle-aged and older adults. Thirty-six
individuals were randomly
assigned to a weight loss (n=25; age: 61.2±0.8 years; body mass
index: 30.0±0.6 kg/m2) or a
control (n=11; age: 66.1±1.9 years; body mass index: 31.8±1.4
kg/m2) group. Arterial stiffness
was measured via carotid artery ultrasonography combined with
applanation tonometry and
carotid-femoral pulse wave velocity via applanation tonometry
at baseline and after the 12-week
intervention. Body weight, body fat, abdominal adiposity, blood
pressure, β-stiffness index, and
carotid-femoral pulse wave velocity were similar in the 2
groups at baseline (all P>0.05). Body
weight (−7.1±0.7 versus −0.7±1.1 kg), body fat, and abdominal
adiposity decreased in the weight
loss group but not in the control group (all P<0.05). Brachial

2. systolic and diastolic blood pressures
declined (P<0.05) only in the weight loss group. Central
systolic and pulse pressures did not
change significantly in either group. β-Stiffness index
(−1.24±0.22 versus 0.52±0.37 U) and
carotid-femoral pulse wave velocity (−187±29 versus 15±42
cm/s) decreased in the weight loss
group but not in the control group (all P<0.05). The reductions
in carotid-femoral pulse wave
velocity were correlated with reductions in total body and
abdominal adiposity (r=0.357– 0.602;
all P<0.05). However, neither total body nor abdominal
adiposity independently predicted
reductions in arterial stiffness indices. In summary, our findings
indicate that weight loss reduces
arterial stiffness in overweight/obese middle-aged and older
adults, and the magnitudes of these
improvements are related to the loss of total and abdominal
adiposity.
Keywords
arterial structure; arterial compliance; aging; pulse wave
velocity; caloric restriction
Advancing age is associated with stiffening of the large elastic
arteries of the cardiothoracic
region.1,2 Total body and abdominal adiposity increase with
advancing age,3 and excess fat
accumulation, particularly in the abdominal visceral region, is
associated with accelerated
large artery stiffening in middle-aged and older adults.4,5
Importantly, large artery stiffening
contributes to the age-related rise in systolic blood pressure
(BP; SBP)6 and is an
independent predictor of total and cardiovascular mortality
among older adults.7

3. © 2010 American Heart Association, Inc.
Correspondence to Kevin P. Davy, Human Integrative
Physiology Laboratory, Department of Human Nutrition, Foods,
and Exercise,
Virginia Polytechnic Institute and State University, Blacksburg,
VA 24061. [email protected]
Disclosures
None.
NIH Public Access
Author Manuscript
Hypertension. Author manuscript; available in PMC 2011 April
1.
Published in final edited form as:
Hypertension. 2010 April ; 55(4): 855–861.
doi:10.1161/HYPERTENSIONAHA.109.147850.
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The results of several studies suggest that weight loss may be
efficacious in reducing large
artery stiffness in the cardiothoracic region.8–12 To date, only
1 randomized, controlled trial
has been conducted to address this issue.8 Balkestein et al8
reported that weight loss
increased carotid artery distensibility and that exercise did not
result in an additive effect.
However, the lack of an adequate control group, small sample
size, and focus on primarily
young and middle-aged males limits generalizability and
precludes a clear understanding of
the impact of weight loss on arterial stiffness. This is a critical
void given that weight loss is
the cornerstone of obesity management and there are currently
few strategies available for
reducing arterial stiffness. Accordingly, we tested the
hypothesis that weight loss via a
hypocaloric diet alone would reduce arterial stiffness in

5. overweight and obese middle-aged
and older adults. We further hypothesized that the reduction in
arterial stiffness with weight
loss, if observed, would be associated with the magnitude of
reduction in total body or
abdominal adiposity.
Methods
Subjects
Thirty-six men (n=15) and women (n=21) 55 to 75 years of age
volunteered to participate in
the study. All of the subjects were sedentary to recreationally
active and free of overt
disease. None of the subjects were smokers or taking
medications that affect body weight or
appetite. The Virginia Polytechnic Institute and State University
Institutional Review Board
approved the protocol. The nature, purpose, risks, and benefits
were explained to each
subject before obtaining informed consent.
Intervention
After baseline testing, subjects were randomly assigned to a
weight loss (n=25) intervention
or a control group (n=11). The subjects randomized to the
weight loss intervention followed
a hypocaloric diet (1200 to 1500 kcal) based on the US
Department of Agriculture food
guide pyramid guidelines10 and were instructed to maintain
their habitual physical activity
level. The control group was instructed to maintain their current
body weight, habitual
physical activity level, and dietary intake. During the 12-week
weight loss intervention
period, subjects met with a dietitian and had their body weight

6. measured weekly. Each
subject was weight stable for ≥2 weeks before follow-up testing.
All of the measurements
were performed between 8:00 AM and 11:00 AM after a 12-hour
fast and having performed no
vigorous physical activity for the previous 48 hours. All of the
subjects reported being free
of acute illness during the week before testing.
Measurements
Body weight was measured to the nearest 0.1 kg on a digital
scale (Scale-Tronix model
5002). Height was measured using a stadiometer. Waist
circumference was measured at the
umbilical level with a spring-loaded Gulick measuring tape.
Body composition was
determined via dual energy x-ray absorptiometry (GE Lunar
Prodigy Advance, software
version 8.10e). Abdominal fat distribution was measured using
computed tomography
(HiSpeed CT/I, GE Medical), as described previously.13 Total,
subcutaneous, and visceral
fat areas were quantified using commercially available software
(SliceOmatic, version 4.3,
Tomovision). Resting heart rate was obtained from lead II of an
ECG. Habitual dietary
intake and physical activity were assessed via self-reported 4-
day food intake records and
accelerometry (GT1M, Actigraph Inc), respectively. Energy and
macronutrient intake were
assessed using nutritional analysis software (NDS-R 6.0,
University of Minnesota). Plasma
lipid and lipoprotein concentrations were measured in a
commercial laboratory using
conventional methods. Plasma glucose concentration was
measured using a YSI glucose

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analyzer (model 2300, Yellow Springs Instruments). Plasma
insulin concentration was
quantified using a commercially available ELISA kit (Linco
Research, Inc).
β-Stiffness index (β-SI)14 was measured as described
previously.15,16 Briefly, left common
carotid artery diameters were obtained 1 to 2 cm from the
carotid bulb with an ultrasound
unit equipped with a high-resolution linear array transducer (3
to 11 MHz). Systolic and
diastolic carotid diameters were quantified offline using
commercially available software
(Vascular Research Tools 5, Medical Imaging Applications,
LLC). Carotid waveforms were
obtained using applanation tonometry of the contralateral
common carotid artery and
calibrated to brachial diastolic BP (DBP) and mean arterial BP
obtained by automated
sphygmomanometry in the supine posture. Brachial pulse
pressure (PP) was calculated as
the difference between SBP and DBP. Central SBP and PP were
obtained from the peak and
the difference between the peak and nadir of the calibrated
waveform, respectively.
Amplifications of SBP and PP were calculated as the difference
between central and
brachial SBP and PP, respectively.
Carotid-femoral (C–F) pulse wave velocity (PWV)
measurements were obtained after 20
minutes of quiet rest in the supine position, as described

9. previously.16 Pulse waveforms
were obtained via applanation tonometry (Probe SPT-301,
Millar Instruments) and recorded
simultaneously at the right C–F arteries. The linear distance
between the carotid and femoral
arteries at the highest point on the patient between the recording
sites was measured with a
tape measure to the nearest 0.5 cm. The C–F distance did not
change (P>0.05) with the
weight loss intervention, and the baseline and postinterventions
distances were highly
correlated (Spearman ρ=0.886; P<0.05). In addition, the effects
of weight loss on C–F PWV
were virtually identical when the postintervention distance was
substituted for the baseline
distance in the calculation of baseline C–F PWV (data not
shown). C–F recordings of
waveforms over 10 to 20 cardiac cycles were analyzed using
signal processing software
(Windaq, Dataq Instruments). PWV was calculated by dividing
travel distance by travel
time from foot to foot of the pulse waves. Although C–F PWV
is a close surrogate for aortic
PWV, we also report estimated aortic PWV using the equation
developed by Vermeersch et
al17 for comparison.
Statistical Analysis
Independent sample t tests were used to compare subject
characteristics and dependent
variables at baseline in the weight loss and control groups. χ2
analysis was used to compare
the overall frequency of medication use between the 2 groups.
Repeated-measures ANOVA
was used to compare changes in subject characteristics and
dependent variables over time

10. between the 2 groups. Our study was not powered to test sex
differences. As such, the
pooled data are presented. Simple correlations were performed
to assess relations among
variables of interest. Linear regression was used to test the
independent effects of measures
of total body and abdominal adiposity on the reduction in
arterial stiffness (C–F PWV).
Furthermore, a test of mediation using linear regression was
used to determine whether the
change in C–F PWV with treatment was mediated by the
reduction in mean BP.18 Mediation
would be supported if weight loss significantly predicted the
change in C–F PWV, weight
loss significantly predicted the change in mean BP, and mean
BP significantly predicted C–
F PWV. This approach was also used to determine whether the
change in C–F PWV with
treatment was mediated by the reduction in sodium intake. All
of the data are expressed as
mean±SE. The significance level was set a priori at the P<0.05
level.
Results
Subject characteristics at baseline and after the intervention are
shown in Table 1. There
were no significant differences in body weight, body
composition, abdominal fat
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distribution, BP, or lipid and lipoprotein concentrations between
the 2 groups at baseline.
Plasma glucose and insulin concentrations were also similar
(P>0.05) in the 2 groups. Body

12. weight decreased (−7.1±0.7 versus −0.7±0.4 kg; P<0.05) after
the intervention in the weight
loss compared with the control group because of reductions in
both body fat (≤4.6±0.6
versus −0.1±0.4 kg; P<0.05) and fat free mass (−1.5±0.3 versus
0.1±0.4 kg; P<0.05; Table
1). Total abdominal fat decreased (−101±14 versus −8±13 cm2;
P<0.05) in the weight loss
group because of reductions in abdominal subcutaneous (−55±9
versus −9±14 cm2; P<0.05)
and abdominal visceral fat (−44±8 versus −3±7 cm2; P<0.05).
Brachial SBP and DBP, as
well as heart rate, decreased (P<0.05) in the weight loss group,
but no significant changes
were observed in the control group (Table 1). Central SBPs and
PPs declined in both groups,
but the magnitude of reduction did not differ significantly. SBP
and PP amplification did not
change in either group (data not shown). In addition, total
cholesterol, low-density
lipoprotein cholesterol, and triglycerides decreased only in the
weight loss group (all
P<0.05). High-density lipoprotein cholesterol did not change
(P>0.05) in either group.
Glucose (P<0.05) and insulin (P=0.05) concentrations decreased
in the weight loss group
but not in the control group.
Medication use in the 2 groups is shown in Table 2. All of the
individuals had been on their
current regiment for ≥8 months, and no changes were made
during the weight loss
intervention. None of the individuals were taking >1
medication. The frequency of
medication use did not differ between the 2 groups (P>0.05).

13. Habitual physical activity and dietary intake at baseline and
after the intervention are shown
in Table 3. Habitual physical activity and dietary intake were
similar (P>0.05) at baseline in
the weight loss and control groups. There was no significant
change in habitual physical
activity after the intervention in either group. Energy intake
decreased (P<0.05) after the
intervention in the weight loss group but not the control group.
There was no significant
reduction in the percentage of fat or carbohydrate intake after
the intervention. However, the
percentage of protein intake increased (P<0.05) in the weight
loss group but not the control
group. Saturated, monounsaturated, polyunsaturated, and trans-
fatty acid intake, as well as
cholesterol intake, declined (all P<0.05) in the weight loss
group, but there were no such
significant changes observed in the control group after the
intervention. Alcohol intake did
not change (P<0.05) after the intervention in either group.
Sodium and potassium intakes
decreased (P<0.05) after the intervention in both groups; the
reduction tended to be greater
in the weight loss group (both P<0.05). Magnesium intake did
not change (P>0.05) after the
intervention in either group.
β-Stiffness index, arterial compliance, and C–F PWV (and
aortic PWV) at baseline and after
the intervention are shown in Table 4. There were no
differences in these arterial stiffness
indices between the 2 groups at baseline. The reductions in β-SI
(−1.24±0.22 versus
0.52±0.37 U; Figure 1A) and C–F PWV (−187±29 versus 15±42
cm/s; Figure 1C) and

14. increases in arterial compliance (0.0125±0.0038 versus
−0.0056±0.0061 mm2/mm
Hg×10−1; Figure 1B) after the intervention were greater (all
P<0.05) in the weight loss
compared with the control group, respectively.
In the pooled sample, the magnitude of change in β-SI was
correlated with the percentage of
initial weight loss (Figure 2A), change in body mass index, and
change body fat percentage
(Table 5). The magnitude of change in C–F PWV was correlated
with the percentage of
initial weight loss (Figure 2B), the magnitude of reduction in
visceral fat (Figure 2C) and
waist circumference (Figure 2D), and the change in the absolute
amount of weight loss,
body mass index, body fat percentage, total fat mass, total
abdominal fat, and subcutaneous
abdominal fat (Table 5). However, linear regression analysis
revealed that only the changes
in body mass index (model 1: β=68.4; P<0.05), total weight loss
(model 2: β=21.6; P<0.05),
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percentage of weight loss (model 3: β=19.4; P<0.05), and total
fat loss (model 4: β=23.6;
P<0.05) independently predicted the reduction in C–F PWV
when indices of total and
abdominal adiposity were considered for inclusion. In addition,
the magnitude of reduction
in C–F PWV with weight loss was correlated with the changes
in triglyceride, total
cholesterol, and low-density lipoprotein cholesterol
concentrations (Table 5). The magnitude

16. of reduction in arterial compliance was correlated with the
change in fat free mass. There
were no other significant correlates in the magnitudes of
reduction in β-SI, arterial
compliance, or C–F PWV.
In an attempt to gain further insight into the potential impact of
BP lowering, we compared
β-SI and C–F PWV in subjects above and below the median
reduction in mean BP with
weight loss (n=12 per group for β-SI and n=11 per group for C–
F PWV). The median
reduction in mean BP for the weight loss group was −7 mm Hg.
The reductions in β-SI
(−1.20±0.28 versus −1.29±0.41 U) and C–F PWV (−198±43
versus −175±50 cm/s) were
similar in individuals with larger (−11±1 versus −4±1 mm Hg)
compared with smaller
reductions in mean BP, respectively.
In our mediation analysis (see the Statistical Analysis section),
weight loss predicted the
change in C–F PWV (β=201.68; P<0.05). However, weight loss
was not a predictor of the
change in mean BP (β=4.53; P>0.05). Therefore, mediation of
the reduction in C–F PWV
with weight loss by changes in mean BP was not supported.
The relation between change in mean arterial pressure and
change in C–F PWV in the
weight loss and control groups is shown in Figure 3. There was
no significant correlation
between change in mean arterial pressure and change in C–F
PWV in the pooled sample
(r=0.129) or among the individual groups (r=−0.17, P>0.05 and
r=0.405, P>0.05 for weight

17. loss and control groups, respectively).
We also compared the reduction in β-SI and C–F PWV in
subjects above (n=10 for β-SI;
n=11 for C–F PWV) and below (n=12 for β-SI; n=11 for C–F
PWV) the median reduction in
dietary sodium intake. The median reduction in dietary sodium
was −733 mg/d. There were
no differences in the magnitude of reduction in either β-SI
(−1.01±0.32 versus −1.44±0.39
U; P>0.05) or C–F PWV (−192±51 versus −157±38 cm/s;
P>0.05) in the groups. Mediation
of the change in C–F PWV with weight loss by the change in
sodium intake was not
supported by linear regression analysis.
Discussion
The major finding of the present study is that intentional weight
loss via hypocaloric diet
alone reduces arterial stiffness in overweight and obese middle-
aged and older adults. The
magnitude of this improvement in arterial stiffness was related
to the magnitude of reduction
in total body and abdominal adiposity. However, only
reductions in indices of weight loss or
total fat loss independently predicted reductions in arterial
stiffness. Importantly, the
improvement in arterial stiffness was observed with a modest
amount of weight loss, that is,
the 5% to 10% weight loss believed necessary to reduce the risk
of cardiovascular diseases.
19
Although previous studies have attempted to address this
issue,8–12 our study is the first to
demonstrate with a randomized, controlled design that

18. intentional weight loss via
hypocaloric diet alone (ie, without increases in physical
activity) reduces large artery
stiffness. Importantly, our findings extend the results of
previous studies to overweight and
obese middle-aged and older adults, a population with
accelerated arterial stiffening and at
increased risk of adverse cardiovascular events.
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In the present study, only reductions in indices of weight loss or
total fat loss independently
predicted reduction in arterial stiffness. As such, our results
suggest that weight loss,
irrespective of reductions in abdominal adiposity, is associated
with favorable reductions in
arterial stiffness.
The mechanisms responsible for the reduction in arterial
stiffness with weight loss observed
are not clear. However, several possibilities exist. First, it is
possible that changes in the
elastic material content of the arterial wall may have occurred
and contributed to the
reductions in arterial stiffness with weight loss. Future studies
in animal models will be
necessary to address this important issue.
Second, collagen cross-linking that occurs in large elastic
arteries as a consequence of
nonenzymatic glycation of collagen is a major cause of age-
related arterial stiffening.
Whether short-term weight loss might also reduce arterial
stiffness by reducing collagen

20. cross-links is unclear.
Finally, the ability to reduce arterial stiffness over a relatively
short time frame, such as
occurred in the present study, is thought to be the result of
changes in local, humoral, or
neural modulation of smooth muscle tone.20 Thus, improved
nitric oxide bioavailability,
reductions in angiotensin II, reductions in sympathetic neural
activity, and/or other factors
may contribute to the favorable changes in arterial stiffness
observed with weight loss.
There are some limitations of the present study that should be
acknowledged. First, our
sample size was small, and the age range of our subjects was
restricted to 55 to 75 years.
Thus, we were not able to test sex or age differences in the
arterial destiffening response to
our intervention. However, given the known age and sex
differences in body fat distribution
and potential depot specific responses to weight loss, future
studies will be necessary.
Second, our weight loss intervention was relatively short in
duration. Thus, whether the
reductions in arterial stiffness would be sustained over time is
unclear.
Third, we cannot exclude the possibility that the observed
reductions in arterial stiffness
were the result of the BP-lowering effects of weight loss and/or
the reduction in sodium
intake accompanying calorie restriction. However, we observed
significant reductions in β-
SI, a presumed BP-independent measure of arterial stiffness.14

21. In addition, changes in SBP,
DBP, or mean BP (or sodium intake) were not related to
changes in any of our measures of
arterial stiffness. Furthermore, there were no significant
differences in the magnitude of
reduction in either β-SI or C–F PWV in individuals above
compared with below the median
reduction in mean BP or sodium intake. Taken together, our
findings suggest that the
reduction in arterial stiffness with weight loss is, at least in
part, independent of the
reductions in BP and sodium intake.
Finally, our study was not designed to address the hemodynamic
consequences of arterial
destiffening with weight loss. However, this will be an
important objective for the future.
We recently reported that high-dose atorvastatin (80 mg once
daily) reduces arterial stiffness
in overweight and obese middle-aged and older adults.16 The
post-treatment levels of
arterial stiffness achieved in both our previous study16 and the
present study suggest that
arterial stiffness remains elevated compared with healthy young
individuals.15 Whether
weight loss (or therapeutic lifestyle change in general)
combined with statin therapy (or
other drugs) can reduce arterial stiffness in an additive or
synergistic manner is unclear.
Future studies are needed to address this important issue,
because greater reductions in
arterial stiffness should translate into superior cardiovascular
risk reduction.
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23. In summary, the findings from the present study suggest that
modest weight loss by
hypocaloric diet alone is efficacious in reducing arterial
stiffness in overweight and obese
middle-aged and older adults. The reductions in arterial
stiffness appear to be determined, at
least in part, by the magnitude of total and abdominal fat loss.
Future studies will be
necessary to determine the impact of long-term weight
maintenance and the efficacy of
combined arterial destiffening therapies.
Perspectives
Large artery stiffening is a potent risk factor for cardiovascular
mortality among older
adults. The findings of our present study suggest that weight
loss by hypocaloric diet alone
is efficacious in reducing large artery stiffness in overweight
and obese middle-aged and
older adults. The observed reductions in C–F PWV (ie, ≈150 to
200 cm/s) would translate
into a reversal of age-related arterial stiffening by ≈15 to 20
years.1,2 These observations
provide additional support for recommending weight loss to
middle-aged and older adults
who are overweight or obese. The reductions in arterial stiffness
observed in the present
study are similar in magnitude to the arterial destiffening effect
that we observed previously
with high-dose atorvastatin therapy in a similar population.16
Taken together, these
observations suggest that a combination of these therapies could
be particularly efficacious
in reversing arterial aging. Future studies will be necessary to

24. address this important issue.
Acknowledgments
We thank the participants for their time, effort, and commitment
to the study.
Sources of Funding
This study was supported by an American Heart Association
Grant-in-Aid (to K.P.D.).
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28. 17. Vermeersch SJ, Rietzschel ER, De Buyzere ML, Van Bortel
LM, Gillebert TC, Verdonck PR,
Laurent S, Segers P, Boutouyrie P. Distance measurements for
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Figure 1.
Change in β-SI after control and weight loss intervention (A).
Change in arterial compliance
after control and weight loss intervention (B). Change in C–F
PWV after control and weight
loss intervention (C). Values are mean±SE *P<0.05 vs control.
Dengo et al. Page 9

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31. Figure 2.
Relation between changes in β-SI and percentage of weight loss
in the pooled sample (A);
relation between changes in C–F PWV and percentage of weight
loss (B); relation between
changes in C–F PWV and changes in visceral fat (C); and
relation between changes in C–F
PWV and changes in waist circumference (D).
Dengo et al. Page 10
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Figure 3.
Relation between changes in mean arterial pressure and change
in C–F PWV.
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Table 1
Subject Characteristics Before and After the Control or Weight
Loss Intervention
Control (5 Women
and 6 Men)
Weight Loss (16 Women
and 9 Men)
Variable Before After Before After
Age, y 66.1±1.8 61.2±0.8
Body weight, kg 91.0±4.8 90.4±4.9 84.6±2.6 77.5±2.2*†‡
Body mass index, kg/m2 31.8±1.4 31.5±1.5 30.0±0.6

35. 27.5±0.6*†‡
Waist circumference, cm 109.1±3.4 109.2±3.6 101.6±1.6
95.0.±1.3*†‡
Body fat, % 39.3±3.0 39.3±2.9 40.7±1.5 37.7±1.7*‡
Total fat mass, kg 32.5±2.5 32.4±2.4 32.7±1.5 28.0±1.6*‡
Fat free mass, kg 51.0±4.0 50.9±4.0 47.7±1.9 46.3±1.9*‡
Total abdominal fat, cm2 611±58 603±51 570±28 469±27*‡
Abdominal subcut. fat, cm2 391±44 382±38 391±24 336±23*‡
Abdominal visceral fat, cm2 188±18 186±17 177±15 133±12*‡
Brachial SBP, mmHg 132±3 128±2 126±1 119±2*†
Brachial DBP, mmHg 74±2 73±1 74±1 69±2*‡
Brachial MAP, mmHg 93±2 92±2 91±1 85±2*§‡
Brachial PP, mm Hg 58±2 55±3 52±1 50±2†
Carotid SBP, mm Hg 120±3 117±2 115±2 111±2*
Carotid PP, mm Hg 45±2 43±3 40±1 41±1
Heart rate, bpm 57±2 57±3 62±2 57±1*
Triglycerides, mg/dL 97±17 108±22 110±9 95±10‡
Total cholesterol, mg/dL 192±5 193±4 207±8 190±8‡
HDL cholesterol, mg/dL 51±7 50±7 47±3 46±2

36. LDL cholesterol, mg/dL 122±6 122±5 139±7 126±7
Glucose, mg/dL 87±5 90±5 89±3 85±3‡
Insulin, pmol/L 45±8 45±9 33±3 28±4§
All values are expressed as mean±SE. HDL indicates high-
density lipoprotein; LDL, low-density lipoprotein; VLDL, very
low-density lipoprotein;
MAP, mean arterial pressure; subcut., subcutaneous.
*
Data show the effect of time, P<0.05.
†
Data show the effect of group, P<0.05.
‡
Data show the effect of time×group interaction, P<0.05.
§
Data show the group effect, P=0.05.
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Table 2
Frequency of Medication Use
Group Medication Women Men
Control HRT 2 …
Plavix … 1

38. HCTZ 1 …
Lisinopril … 1
Weight loss HRT 2 …
Flomax … 1
Avodart … 1
Dyazide … 1
Lipitor … 3
Crestor 1 …
Fosamax 2 …
HRT indicates hormone replacement therapy.
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Table 3
Physical Activity and Dietary Intake Before and After the
Control or Weight Loss Intervention
Control Weight Loss
Variable Before After Before After
PA counts/d ×103 229±32 219±24 253±28 288±21
Energy, kcal 1897±103 1770±154 1998±100 1382±73*‡

40. Fat, % 37±2 38±1 36±1 32±2
Carbohydrates, % 46±3 46±2 47±2 50±3
Protein, % 16±1 16±1 16±1 18±1*‡
Alcohol, % 3.5±1.0 2.5±1.2 4.1±1.3 3.6±1.0
Cholesterol, mg 226±27 244±37 272±25 164±18*‡
SFA, g 24±2 24±3 25±2 15±1*‡
MUFA, g 30±3 29±3 30±2 19±2*‡
PUFA, g 19±2 16±2 17±1 11±1*†
TFA, g 5±1 5±1 5±1 3±1*
Sodium, mg 3304±276 3034±285 3203±185 2364±144*
Potassium, mg 2717±151 2627±221 3104±170 2536±133*
Magnesium, mg 305±26 298±35 339±19 309±18
All of the values are expressed as mean±SE. PA indicates
physical activity; SFA, saturated fatty acids; MUFA,
monounsaturated fatty acids;
PUFA, polyunsaturated fatty acids; TFA, trans fatty acids.
*
Data show the effect of time, P<0.05.
†
Data show the effect of group, P<0.05.

41. ‡
Data show the effect of time×group interaction, P<0.05.
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Table 4
Indices of Arterial Stiffness Before and After the Control or
Weight Loss Intervention
Control Weight Loss
Variable Before After Before After
β-SI, U 11.73±0.89 12.25±0.82 10.68±0.58 9.44±0.52†
AC, mm2/
mm Hg
×10−1
0.126±0.011 0.120±0.010 0.120±0.014 0.133±0.012†
C–F PWV,
cm/s
1176±76 1190±77 1155±46 968±36*†
Aortic PWV,
cm/s
818±54 883±62 833±35 706±25*†
All values are expressed as mean±SE. Aortic PWV was
estimated using the equation developed by Vermeersch et al.17
AC indicates arterial

43. compliance.
*
Data show the effect of time, P<0.05.
†
Data show the effect of time×group interaction, P<0.05.
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Table 5
Correlations Between Change Indices of Arterial Stiffness With
the Intervention and Other Variables
Variable C–F PWV β-SI
Arterial
Compliance
Weight loss, kg 0.602* … …
Weight loss, % 0.592* 0.340* …
Body mass index, kg/m2 0.618* 0.296 …
Waist circumference, cm 0.519* … …
Body fat, % 0.386* 0.326 …
Total fat mass, kg 0.482* … …
Fat free mass, kg … … −0.357*
Total abdominal fat, cm2 0.461* … …

45. Abdominal subcutaneous fat, cm2 0.360* … …
Abdominal visceral fat, cm2 0.357* … …
Heart rate, bpm … … …
Triglycerides, mg/dL 0.298 … …
Total cholesterol, mg/dL 0.431* … …
LDL cholesterol, mg/dL 0.356* … …
LDL indicates low-density lipoprotein.
*
P<0.05.
Hypertension. Author manuscript; available in PMC 2011 April
1.
A v a i l a b l e o n l i n e a t w w w . s c i e n c e d i r e c t . c o
m
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1098-3015$36.00 – s
Published by Elsevie
http://dx.doi.org/10
E-mail: ddkim62
* Address correspo

46. NE Pacific Street, Bo
j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c
a t e / j v a l
Systematic Review
Estimating the Medical Care Costs of Obesity in the United
States: Systematic Review, Meta-Analysis, and
Empirical Analysis
David D. Kim, MS1,*, Anirban Basu, PhD1,2
1Department of Health Services, University of Washington,
Seattle, WA, USA; 2The National Bureau of Economic
Research,
Cambridge, MA, USA
A B S T R A C T
Background: The prevalence of adult obesity exceeds 30% in
the United
States, posing a significant public health concern as well as a
substantial
financial burden. Although the impact of obesity on medical
spending is
undeniably significant, the estimated magnitude of the cost of
obesity
has varied considerably, perhaps driven by different study
method-
ologies. Objectives: To document variations in study design and
meth-
odology in existing literature and to understand the impact of
those
variations on the estimated costs of obesity. Methods: We
conducted a
systematic review of the twelve recently published articles that
reported
costs of obesity and performed a meta-analysis to generate a
pooled
estimate across those studies. Also, we performed an original
analysis

47. to understand the impact of different age groups, statistical
models, and
confounder adjustment on the magnitude of estimated costs
using the
nationally representative Medical Expenditure Panel Surveys
from 2008-
2010. Results: We found significant variations among cost
estimates in
ee front matter Copyright & 2016, International S
r Inc.
.1016/j.jval.2016.02.008
@uw.edu.
ndence to: David D. Kim, MS, Department of Health
x 357660, Seattle, WA 98195.
the existing literature. The meta-analysis found that the annual
medical spending attributable to an obese individual was $1901
($1239-$2582) in 2014 USD, accounting for $149.4 billion at
the
national level. The two most significant drivers of variability in
the cost estimates were age groups and adjustment for obesity-
related comorbid conditions. Conclusions: It would be important
to
acknowledge variations in the magnitude of the medical cost of
obesity driven by different study design and methodology.
Research-
ers and policy-makers need to be cautious on determining
appropriate
cost estimates according to their scientific and political
questions.
Keywords: economic burden, medical care costs, obesity,
United States.

48. Copyright & 2016, International Society for
Pharmacoeconomics and
Outcomes Research (ISPOR). Published by Elsevier Inc.
Introduction
The prevalence of obesity, which is defined as a body mass
index
(BMI) of greater than 30, has increased dramatically in the
United
States since the late 1990s [1]. So much so that recently obesity
has been officially recognized as a disease by the American
Medical Association, an action that could put more emphasis
on the health condition by doctors and insurance companies so
as to minimize its adverse effects. Currently, rates of obesity
exceed 30% in most sex and adult age groups, whereas its
prevalence among children and adolescents, defined as a BMI of
more than 95th percentile, has reached 17% [2].
The alarming rates of the high prevalence of obesity have
posed a significant public health concern as well as a
substantial
financial burden on our society because obesity is known to be a
risk factor for many chronic diseases, such as type 2 diabetes,
cancer, hypertension, asthma, myocardial infarction, stroke, and
other conditions�[3,4]. To understand the economic burden of
obesity, several studies have attempted to estimate the attribut-
able costs of obesity, following the burden-of-illness literature
on
other disease areas [5–9]. A previous cost-of-illness study esti-
mated that health care spending attributable to the rising
prevalence of obesity has increased by 27% between 1987 and
2001 [10]. In gross terms, the annual medical costs of obesity
were
estimated to be $40 billion in 2006 [11]. The latest study using
an
instrumental variable (IV) approach even showed that the esti-

49. mated medical costs related to obesity could reach $209.7
billion,
which is twice higher than the previous estimate, $86 billion
[12].
As evidenced by the aforementioned estimates, although the
impact of obesity on the medical care spending is undeniably
significant, the estimated magnitude of the medical care costs
attributable to obesity has varied considerably, perhaps driven
by
different study methodologies, including data, statistical
models,
confounder adjustment, and target populations. In this article,
ociety for Pharmacoeconomics and Outcomes Research
(ISPOR).
Services, School of Public Health, University of Washington,
1959
http://dx.doi.org/10.1016/j.jval.2016.02.008
http://dx.doi.org/10.1016/j.jval.2016.02.008
http://dx.doi.org/10.1016/j.jval.2016.02.008
http://crossmark.crossref.org/dialog/?doi=10.1016/j.jval.2016.0
2.008&domain=pdf
http://crossmark.crossref.org/dialog/?doi=10.1016/j.jval.2016.0
2.008&domain=pdf
http://crossmark.crossref.org/dialog/?doi=10.1016/j.jval.2016.0
2.008&domain=pdf
mailto:[email protected]
http://dx.doi.org/10.1016/j.jval.2016.02.008
V A L U E I N H E A L T H 1 9 ( 2 0 1 6 ) 6 0 2 – 6 1 3 603
we approach these issues systematically with two goals: 1) to
conduct a systematic review and meta-analysis of recently
published articles that estimated the medical costs associated

50. with obesity between 2008 and 2012 and to document the
variations in study methodologies and 2) to demonstrate the
importance of study methodologies by performing an original
analysis to examine the impact of age group, confounder adjust-
ment, and statistical methods on the cost estimates of obesity
through the empirical analysis of a nationally representative US
population. Especially, we also examined the impact of obesity-
related diseases (ORDs) on the medical costs of obesity to show
that most, if not all, of those costs are attributable to ORDs.
We believe that it would be important to recognize significant
variations among estimates of obesity-attributable costs in the
existing literature and to understand the impact of study meth-
odology on the magnitude of these estimates so that researchers
and policymakers are able to determine the appropriate estimate
and methods according to their scientific and political
questions.
Methods
A Systematic Review and Meta-Analysis
Literature search
We searched the MEDLINE and Cochran database to identify
articles related to medical costs of obesity using keywords
“obesity AND (cost OR expenditure) AND healthcare)) AND
“united states.” To account for the unique health care system
and the impact of costs attributable to obesity in the United
States, we limited the search to studies conducted in the US
settings. We initially identified 567 articles from the search,
then
narrowed down to 16 articles for in-depth reviews. Following
the
extensive reviews, we excluded three studies that did not
provide
explicit methods and/or aggregate annual costs per person, in
addition to a previously conducted systematic review [13–16].

51. Finally, we included 12 studies in this study for the systematic
review [17–26]. Appendix Figure A in Supplemental Materials
found at http://dx.dor.org/10.1016/j.jval.2016.02.008 provides
details
on search strategies for identifying studies included in this
review.
Improve comparability across studies
To improve comparability across heterogeneous studies, we
performed appropriate adjustments to convert estimates from
each study into annual per-person costs among all obese pop-
ulation (BMI Z 30).
First, we converted cost estimates to 2014 USD to adjust for
the inflation over time using annual average consumer price
index for medical care [27]. One study reported the quarter-per-
person medical costs, and we annualized the cost estimate [17].
All the 12 studies reported direct medical costs, including the
out-
of-pocket costs for inpatient, noninpatient (outpatient, emer-
gency room, and other), and prescription drug spending.
Then, we aggregated all BMI-specific estimates into a single
composite estimate of costs attributable to all obese individuals.
Among the 12 studies, 8 studies defined obesity as a BMI of
greater
than 30 whereas 4 studies implemented more comprehensive
obesity categories, defined as class I obesity (30 o BMI r 35),
class
II obesity (35 o BMI r 40), and class III obesity (40 o BMI)
[21,22,24,26]. Two of the four studies combined class II and
class III
obesity into a single category because of the sample size issue
[22,26]. To generate comparable cost estimates, we calculated a
weighted average among subgroup-specific estimates on the
basis of
the number of each subgroup reported in each of the four

52. studies.
In addition, three studies estimated sex-specific costs of
obesity [18,20,23], and one study provided race (non-Hispanic
whites vs. blacks) stratified results [26]. Another study reported
both sex and race (non-Hispanic whites vs. blacks) stratified
estimates [19]. Based on the sample size of each stratum
presented in each study, only the weighted average estimates
for aggregating sex and race categories are presented in Table 2.
Evaluating quality of studies
We evaluated the quality of studies on the basis of four criteria:
the
use of nationally representative samples, longitudinal data sets,
analysis of adults of all ages, and appropriate confounding
factor
adjustments. A previous systematic review also used a similar
set
of criteria for evaluating cost-of-illness studies of obesity [13].
Meta-analysis
To generate a pooled estimate of medical costs of obesity across
different studies, we conducted a meta-analysis using the
metaan
command in STATA 12 (StataCorp., College Station, TX) [28].
The
metaan command is used to conduct random-effect meta-anal-
ysis for one-variable relationship. Because the meta-analysis for
one-variable relationship requires both the effect size estimate
and the standard error, we were able to include only eight
estimates of annual incremental costs of obesity from seven
studies (Table 2). Because of the presence of extremely high
heterogeneity between studies (I2 ¼ 96.61%; τ2 ¼ 5.6 � 105),
the
random-effect model is used in the final analysis.

53. Empirical Analysis: The Role of Alternative Statistical Models
in Estimating Costs of Obesity
Study data
The medical costs of obesity were estimated using regression
analysis and the 2008-2010 Medical Expenditure Panel Surveys
(MEPS). The MEPS is a nationally representative survey of the
civilian
noninstitutionalized population, collecting detailed information
on
health care expenditures and utilization, health insurance, health
status, and sociodemographic factors. Nationally representative
estimates were obtained by using MEPS sampling weights.
Variables
As a dependent variable, medical care costs (which include
costs for
office-based visits, hospital outpatient visits, emergency room
visits, inpatient hospital stays, prescription drugs, dental visits,
and home care) are defined as the sum of direct payments from
all parties (out-of-pocket, private insurers, government, and
other
payers) for care provided during the year. For a primary inde-
pendent variable, we identified obesity status on the basis of the
constructed BMI through self-reported height and measure [29].
(Please note that because of confidentiality concerns and
restric-
tions, the self-reported weight and height variables were not
available from the public-access MEPS data sets.) Also, we
categorized potential confounding factors into four categories to
examine the impact of confounder adjustments on the magni-
tude of the cost estimates: 1) Demographic factors or cov1 (age,
sex, and race/ethnicity), 2) Socioeconomic factors or cov2 (edu-
cation, household income based on the federal poverty line,
smoking status, and marital status), 3) Additional factors or
cov3 (census region and insurance status), and 4) comorbidity

54. conditions or cov4. Comorbidity conditions are defined as a
con-
tinuous variable ranging from 0 to 10 by summing up 10
potential
health consequences that can be caused be obesity. These
conditions, called ORDs, which are defined by the Centers for
Disease Control and Prevention, include hypertension, heart
diseases (coronary heart disease, angina, myocardial infarction,
others), stroke, cancer, diabetes, arthritis, and high cholesterol
[30]. In this data set, children or adolescents (age o 18 years)
do not have any information on comorbidity conditions and
http://dx.dor.org/10.1016/j.jval.2016.02.008
Table 1 – Characteristics of 12 studies included in this review.
Study Data Sample
size
Statistical
methods*
Obesity
class
Target
population
Variable adjusted for Quality evaluation (score out of 4)
Medical care cost of obesity—Annual cost per person (common
confounders†)
Wolf et al. [17],
2008

55. US PROCEED 1,300 Log-linear Aggregate Adults (aged
35–75 y)
(� race/ethnicity, income, marital status)
(þ alcohol use, comorbidities, insurance)
2: Longitudinal data, confounder
adjustment
Finkelstein
et al. [11],
2009
MEPS (2006) 21,877 Two-part
(logit-GLM)
Aggregate All adults (age
Z 18 y)
(þ census region, insurance status) 3: Nationally representative
sample, all adults, confounder
adjustment
Cai et al. [20],
2010
MCBS (1991–
2000)
5,043 Unadjusted Aggregate Adults aged 35–
55 y in the
period 1971–
1975
Unadjusted 1: Nationally representative

56. sample
Finkelstein
et al. [21],
2010
MEPS (2006) 8,875 Two-part
(logit-GLM)
Obesity
class I,
II, and
III
All adults (age
Z 18 y) Full-
time
employees
only
(þ age2, census region, insurance status) 3: Nationally
representative
sample, all adults, confounder
adjustment
Bell et al. [23],
2011
MEPS (2000–
2005)
80,516 Two-part
(logit-log
(Y) OLS)
Aggregate Children and

57. adults aged
6–85 y
(� smoking, marital status) (þ age2, age3,
region, insurance status, survey year)
3: Nationally representative
sample, all adults, confounder
adjustment
Onwudiwe
et al. [22],
2011
MCBS (2002) 7,706 One-part
GLM
Obesity
class I
and II/
III
Medicare
beneficiaries
(age Z 65 y)
Not in HMO
plan
(þ insurance status) 2: Nationally representative
sample, confounder
adjustment
Alley et al. [25],
2012
MCBS (1997–

58. 2006)
29,413 One-part
GLM
Aggregate Medicare
beneficiaries
(age Z 65 y)
(þ census region, metropolitan status,
mortality variable)
3: Nationally representative
sample, all adults, confounder
adjustment
Cawley and
Meyerhoefer
[12], 2012
MEPS (2000–
2005)
23,689 IV with two-
part (logit-
GLM) IV: a
weight of
biological
relative
Aggregate Adults (aged
20–64 y with
biological
children
aged 11–20 y)

59. (� income, smoking, marital status) (þ census
region, MSA, household composition, survey
information, employment status, fixed
effects for year, the sex and age of the oldest
children)
3: Nationally representative
sample, confounder
adjustment (IV)
Ma et al. [26],
2012
MEPS (2006) 15,164 Unadjusted Obesity
class I
and II/
III
All adults (age
Z 18 y)
Unadjusted 2: Nationally representative
sample, all adults
Moriarty et al.
[24], 2012
Mayo Clinic
Database
(2001–
2007)
30,529 GEE Obesity
class I,
II, and
III

60. Adults (18–65 y)
vs. adults
(465 y)
(� education, income) (þ comorbidity
conditions for an additional analysis)
3: Longitudinal data, all adults,
confounder adjustment
V
A
L
U
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I
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A
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T
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1
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(
2
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)

61. 6
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66. ra
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68. le
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69. a
ll
[1
8
],
2
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ic
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ci
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(a
g
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6
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y
)
(þ
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n
ct
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n
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l
st

72. a
tu
s,
e
x
is
ti
n
g
ch
ro
n
ic
d
is
e
a
se
s,
a
cu
te
m
e
d
ic
a
l
e
v
e

73. n
ts
,
u
rb
a
n
/r
u
ra
l)
2
:
N
a
ti
o
n
a
ll
y
re
p
re
se
n
ta
ti
v
e
,
co
n

74. fo
u
n
d
e
r
a
d
ju
st
m
e
n
t
C
a
i
e
t
a
l.
[2
0
],
2
0
1
0
M
C
B
S
(1

75. 9
9
1
–
2
0
0
0
)
5
,0
4
3
O
n
e
-p
a
rt
G
L
M
A
g
g
re
g
a
te

76. A
d
u
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s
a
g
e
d
3
5
–
5
5
y
in
th
e
p
e
ri
o
d
1
9
7
1
–
1
9
7
5

77. (�
e
d
u
ca
ti
o
n
,
in
co
m
e
,
m
a
ri
ta
l
st
a
tu
s)
(þ
ti
m
e
to
d
e

78. a
th
)
2
:
N
a
ti
o
n
a
ll
y
re
p
re
se
n
ta
ti
v
e
,
co
n
fo
u
n
d
e
r
a
d

79. ju
st
m
e
n
t
G
E
E
,
g
e
n
e
ra
li
z
e
d
e
st
im
a
ti
n
g
e
q
u
a
ti
o
n

80. ;
G
L
M
,
g
e
n
e
ra
li
z
e
d
li
n
e
a
r
m
o
d
e
l;
H
M
O
,
h
e
a
lt
h
m

81. a
in
te
n
a
n
ce
o
rg
a
n
iz
a
ti
o
n
;
IV
,
in
st
ru
m
e
n
ta
l
v
a
ri
a
b
le

82. ;
M
C
B
S
,
M
e
d
ic
a
re
C
u
rr
e
n
t
B
e
n
e
fi
ci
a
ry
S
u
rv
e
y
;
M
E

83. P
S
,
M
e
d
ic
a
l
E
x
p
e
n
d
it
u
re
P
a
n
e
l
S
u
rv
e
y
s;
M
S
A
,

84. M
e
tr
o
p
o
li
ta
n
st
a
ti
st
ic
a
l
a
re
a
;
O
L
S
,
o
rd
in
a
ry
le
a
st

85. sq
u
a
re
s;
P
R
O
C
E
E
D
,
P
ro
sp
e
ct
iv
e
O
b
e
si
ty
C
o
h
o
rt
o
f

86. E
co
n
o
m
ic
E
v
a
lu
a
ti
o
n
a
n
d
D
e
te
rm
in
a
n
ts
.
*
A
ll
G
L

87. M
a
n
d
G
E
E
m
o
d
e
ls
u
se
d
th
e
lo
g
li
n
k
a
n
d
th
e
g
a
m
m

88. a
d
is
tr
ib
u
ti
o
n
.
†
C
o
m
m
o
n
co
n
fo
u
n
d
in
g
co
v
a
ri
a
te
s:

89. A
g
e
,
se
x
,
ra
ce
/e
th
n
ic
it
y
,
e
d
u
ca
ti
o
n
,
in
co
m
e
,
sm

90. o
k
in
g
st
a
tu
s,
a
n
d
m
a
ri
ta
l
st
a
tu
s.
V A L U E I N H E A L T H 1 9 ( 2 0 1 6 ) 6 0 2 – 6 1 3 605
smoking status, and only very few individuals reported
education
(N ¼ 25) and marital status (N ¼ 6).
Study design and data analysis
To study the impact of various study designs, we examined
three
different aspects of estimating medical costs in a regression
analysis: age groups, statistical models, and confounder adjust-
ments. The age groups were 1) children/adolescents (aged 0–17
years), 2) all adults (aged Z 18 years), 3) adults aged 18 to 65
years, and 4) older adults (aged Z65 years). Considering the

91. nature of cost data, such as non-negative observations, a large
number of observations at zero, and positive skewness, we
examined five different statistical models that have been widely
used to estimate the medical costs: 1) linear regression, 2) log-
linear model (a simple ordinary least square for ln(y)), 3) one-
part
log-gamma generalized linear model (GLM), 4) two-part model
with a logistic regression and a log-gamma GLM, and 5)
extended
estimating equation (EEE) that used both a flexible link and a
flexible variance function estimated directly from the data to
capture the underlying nonlinearity in the data so that it can
produce efficient estimates [31–34]. Also, we tested the
goodness
of fit (GoF) of each statistical model to examine how well the
model fits a data set using Pearson correlation, Pregobon’s link
test, and Hosmer-Lemeshow test. In regard to confounder
adjust-
ment, four sets of confounders were studied: 1) Demographic
factors (cov1), 2) Demographic þ Socioeconomic factors (cov1
þ
cov2), 3) Demographic þ Socioeconomic þ Additional factors
(cov1 þ cov2 þ cov3), and 4) All three factors þ ORDs (cov1 þ
cov2 þ cov3 þ cov4). Using different combinations of the target
population, statistical models, and confounder adjustment, we
estimated medical care costs of nonobese individuals (so called
costs of normal) as well as incremental costs of obesity through
recycled predictions to estimate the counterfactual mean costs if
all individuals in the data set were suddenly to have obesity
while retained all other characteristics as compared with being
nonobese for all individuals. We also tested the GoF of five
different statistical models with each of four different sets of
confounding factors for the sample population of all adults. All
standard errors and confidence intervals (CIs) were estimated
from 1000 bootstrap replicates.
Results

92. A Systematic Review and Meta-Analysis
Descriptive results
Among the 12 studies included in this systematic review, 9
studies reported annual medical care costs per person while 2
studies provided lifetime medical care costs per person and 1
study reported both estimates. Six studies used the MEPS data-
base, whereas four studies used the Medicare Current
Beneficiary
Survey database. The remaining two studies used the
Prospective
Obesity Cohort of Economic Evaluation and Determinants data
set, which is a multinational, observational, prospective
Internet-
based cohort study and the Mayo clinic employment database,
respectively (Table 1).
Quality evaluation
Based on four quality criteria, no studies met all criteria,
because
all the studies that used a nationally representative data set were
not a longitudinal study or vice versa. Among 10 studies that
reported annual costs per person, 5 studies were designated as
“high-quality (score ¼ 3)” study (Table 1). Because Cawley’s
study
was the only study that estimated medical costs of obesity using
the instrumental variable IV approach to account for unobserved
Table 2 – Systematic review—Medical costs of obesity (2014
USD).
Study Cost of
normal*

93. Cost of
obesity*
Incremental cost of
obesity*
Year of cost
reporting
Note
Medical care cost of obesity—Annual cost per person
Wolf et al. [17], 2008 $2,541 $6,611 $4,070† 2004
Finkelstein et al. [11],
2009
$4,087 $5,783 $1,696† 2008
Cai et al. [20], 2010 $5,750 $13,019 $7,269 2000 Unadjusted
Finkelstein et al. [21],
2010
NA NA $1,024 2006 Obese I, medical costs only
$1,944 Obese II, medical costs only
$2,215 Obese III, medical costs only
$1,397† Aggregate, medical costs only
Bell et al. [23], 2011 $3,629 $5,488 $1,859† 2005
Onwudiwe et al. [22],
2011
$5,666 $5,578 �$88 2002 Obese I, uncorrected for the height
loss

94. $6,637 $971 Obese II/III, uncorrected for the height
loss
$5,892 $227† Aggregate obese, uncorrected for the
height loss
Alley et al. [25], 2012 $8,781 $7,338 �$1,443 2006 Annual
spending in 1997 converted to
2008 USD
$157 $191 $34 Expenditures increased per year from
1997 to 2006
Cawley and
Meyerhoefer [12], 2012
NA NA $877† 2005 Without using the instrumental
variable
$3,665† With using the instrumental variable
Ma et al. [26], 2012 $4,797 $6,152 $1,356 2006 Obese I,
unadjusted
$8,408 $3,611 Obese II/III, unadjusted
$7,082 $2,285 Aggregate, unadjusted
Moriarty et al. [24], 2012 NA NA $2,278 2007 Obese I, no
comorbidity adjustment
$3,759 Obese II, no comorbidity adjustment
$6,794 Obese III, no comorbidity adjustment
$3,302† Aggregate obese, no comorbidity
adjustment
Medical care cost of obesity—Lifetime costs per person (all
adjusted for survival)

95. Finkelstein et al. [19],
2008
NA NA $19,892 2007 From age 20 y, obese I—Discounted
lifetime costs
$28,441 From age 20 y, obese II/III—Discounted
lifetime costs
$23,123 From age 20 y, aggregate—Discounted
lifetime costs
$15,641 From age 65 y, obese I—Discounted
lifetime costs
$24,589 From age 65 y, obese II/III—Discounted
lifetime costs
$19,022 From age 65 y, aggregate—Discounted
lifetime costs
Yang and Hall [18], 2008 $288,934 $332,838 $43,904 2001
From age 65 y—Not discounted
Cai et al. [20], 2010 $194,013 $269,628 $75,615 2000 From age
45 y—Not discounted
NA, not applicable/available.
* All costs were converted to 2014 US dollar using Consumer
Price Index—Medical Care.
† Represents the cost estimates included in the meta-analysis to
calculate the pooled incremental cost of obesity.
V A L U E I N H E A L T H 1 9 ( 2 0 1 6 ) 6 0 2 – 6 1 3606
confounding factors, the study received an extra score for con-
founder adjustment criteria [12].
Cost estimates

96. The annual incremental costs of obesity per person ranged from
$227 to $7269 depending on the study designs and research
methods. Tables 2 and 3 provide details on different study
methodologies and reported cost estimates. From the meta-
analysis with the random-effect model, the pooled estimate of
annual medical costs of obesity was $1910 (95% CI $1239–
$2582).
A forest plot from the random-effect model is shown in
Figure 1.
Three studies reported the lifetime costs associated with
obesity after adjusting for survival. One study estimated the
lifetime costs of obesity with a 3% annual discount on the
dollar value, resulting in $23,123 from age 20 years and
$19,022
from age 65 years [19], whereas another study without using a
discount rate reported the lifetime costs of $43,904 from age 65
years [18].
Table 3 – Systematic review—Medical costs of obesity by study
methodologies (2014 USD).
Confounding factors Unadjusted Demographic
factors
Demographic þ
socioeconomic factors
Demographic þ SES þ
additional factors
All factors þ
comorbidities

97. Statistical
methods
Age groups
Linear regression Children/adolescents
All adults (age Z 18 y) $2,285 (Ma et al. [26], 2012)
Adults (18–65 y) $7,269 (Cai et al. [20], 2010)
Older adults (age Z 65 y)
Log-linear Children/adolescents
All adults (age Z 18 y)
Adults (18–65 y) $4,070 (Wolf, 2008)
Older adults (age Z 65 y)
One-part GLM Children/adolescents
All adults (age Z 18 y)
Adults (18–65 y)
Older adults (age Z 65 y) $227 (Onwudiwe, 2011)
�$1,443 (Alley, 2012)
Two-part GLM Children/adolescents
All adults (age Z 18 y) $1,696 (Finkelstein, 2009)
$1,359 (Finkelsten, 2010)
$1,859 (Bell, 2011)
Adults (18–65 y) $877 (Cawley, 2012)
Older adults (age Z 65 y)
Other methods Children/adolescents
All adults (age Z 18 y) $3,302 (Moriarty, 2012)—

98. GEE
Adults (18–65 y) $3,665 (Cawley, 2012)—IV
Older adults (age Z 65 y)
V
A
L
U
E
I
N
H
E
A
L
T
H
1
9
(
2
0
1
6
)
6
0
2
–
6
1
3

99. 6
0
7
Fig. 1 – Meta-analysis: Medical costs of obesity (random-effect
model). CI, confidence interval; IV, instrumental variable.
V A L U E I N H E A L T H 1 9 ( 2 0 1 6 ) 6 0 2 – 6 1 3608
Stratified results
Among the studies that provided stratified estimates of the
medical costs attributable to obesity, the annual costs increased
in higher obese categories [21,22,24,26] and the lifetime costs
of
obesity were also positively associated with an increasing BMI
[19]. The magnitude of the annual costs related to obesity was
higher among women than among men [18–20,23]. Also, obese
blacks were found to spend less medical costs than obese
whites,
mainly due to more use of relatively inexpensive types of care
(office-based visits, outpatient care, medications) rather than
more costly ones (inpatient, emergency room) [19,26].
Different study methodology
Among the 10 studies that estimated annual medical care cost
attributable to obesity, a two-part model was the most popular
method used by four studies. Three of those four studies imple-
mented a logistic regression with a log-gamma GLM [11,12,21],
whereas another study used a logit model with a log-linear
model
[23]. Two studies used a one-part GLM method with a log link
and a
gamma distribution [22,25]. A generalized estimating equation
[24]

100. and a log-linear regression model [17] were used in other two
studies,
respectively, and the remaining two studies reported unadjusted
annual costs of obesity [20,26]. Because each study used
distinctive
data sets to various study methodologies, it is hard to predict
the
actual impact of a range of study methodologies on the
magnitude of
the costs of obesity. However, one study using both a two-part
model
and an IV approach provided that the effect of obesity on
medical
care costs was much greater with the IV method than was
previously
appreciated with the two-part model. The instrument used in the
study was the weight of a biological relative [12].
Different confounder adjustment
We defined confounding factors that were widely used to adjust
for the causal inference among these studies, including demo-
graphic factors, socioeconomic factors, additional factors, and
obesity-related comorbidity conditions. However, none of these
studies selected the same set of confounding factors. Table 1
presents a significant variability in choosing confounding
factors,
and the substantial variability poses an essential problem of
comparing these estimates directly. A study that examined two
models with and without adjusting for comorbidity conditions as
a confounding factor found that incremental costs of obesity
dropped significantly when adjusted for comorbidities [24].
Estimating Medical Costs of Obesity
With possible combinations of the four age groups, the five
statistical

101. models, and the four sets of potential confounders, 80 estimates
of
costs of normal and incremental costs of obesity were generated
with 95% CIs. All possible cost estimates are provided in Table
4.
Characteristics of individuals in the data set
Individual characteristics used in estimating medical costs of
obesity are presented in Appendix Table A in Supplemental
Materials found at http://dx.dor.org/10.1016/j.jval.2016.02.008.
In
this analysis, the data set includes 15,176 children and adoles-
cents and 69,382 adults aged 18 years and older, including
10,382
older adults (age Z 65 years) with existing obesity status.
Among
the obese, there was a significantly higher proportion of
females,
blacks, Hispanics, individuals in lower household income level
(o125% federal poverty line), only high school or equivalent
degree holder, those who were married, individuals with public
insurance, and those living in the South region, compared with
the nonobese. Also, as expected, individuals with obesity have a
higher number of obesity-related comorbidity conditions than
do
those without obesity.
Effect of different target populations
For children/adolescents, regardless of different statistical mod-
els and confounding factor adjustment, there was no significant
difference between costs of the nonobese and the obese, except
only one scenario with a one-part GLM controlling for all con-
founding factors available (no comorbidities and smoking) that
reported $1085 ($92–$2377) for the incremental costs of obesity
for
children/adolescents. However, for the adult population, the

102. http://dx.dor.org/10.1016/j.jval.2016.02.008
Table 4 – Factors affecting costs of normal (nonobese) and
incremental costs of obesity.
Confounding factors Unadjusted Demographic factors1
Demographic þ
socioeconomic factors2
Demographic þ SES þ
additional factors3
All factors þ comorbidities4
Statistical
methods
Age groups Cost of
normal
Incremental
cost of obesity
95% CI Cost of
normal
Incremental
cost of obesity
95% CI Cost of
normal
Incremental
cost of obesity

103. 95% CI Cost of
normal
Incremental
cost of obesity
95% CI Cost of
normal
Incremental
cost of obesity
95% CI
Linear
regression
Children/
adolescents
1,851 �62 (�455
to 333)
1,265 182 (�198
to 563)
1,591 621 (�285
to
1528)
1,580 647 (�269
to
1563)

104. NA NA NA
All adults
(age Z 18 y)
4,574 1,766 (1477
to
2056)
4,252 1,411 (1096
to
1689)
4,436 1,453 (1139
to
1738)
4,443 1,406 (1101
to
1700)
4,792 153 (�156
to 456)
Adults
(18–65 y)
3,524 1,795 (1502
to
2088)

105. 3,345 1,283 (995 to
1570)
3,494 1,312 (1006
to
1617)
3,559 1,153 (851 to
1456)
3,855 93 (�211
to 397)
Older adults
(age Z 65 y)
9,558 2,290 (1385
to
3195)
9,247 2,898 (1975
to
3820)
9,380 2,747 (1794
to
3701)
9,362 2,717 (1762
to

106. 3673)
9,954 496 (�421
to
1413)
Log-linear Children/
adolescents
1,975 90 (�413
to 826)
1,640 366 (�178
to
1186)
2,145 1,112 (�67 to
2533)
2,074 1,191 (�36 to
2652)
NA NA NA
All adults (age
Z 18 y)
5,186 1,562 (1318
to
1823)
5,394 1,375 (1089
to

107. 1705)
5,546 1,412 (1101
to
1753)
5,452 1,261 (950 to
1561)
6,236 61 (�250
to 358)
Adults
(18–65 y)
4,175 1,614 (1372
to
1858)
4,298 1,015 (759 to
1294)
4,407 1,031 (764 to
1312)
4,321 819 (544 to
1086)
4,935 �78 (�347
to 197)
Older adults
(age Z 65 y)

108. 9,468 2,283 (1522
to
3012)
9,439 3,528 (2556
to
4456)
9,602 3,431 (2461
to
4371)
9,654 3,378 (2425
to
4354)
10,849 425 (�435
to
1273)
One-part
GLM
Children/
adolescents
1,851 �62 (�396
to 349)
1,376 140 (�140
to 532)

109. 1,571 715 (�240
to
1567)
1,556 1,085 (92 to
2377)
NA NA NA
All adults (age
Z 18 y)
4,574 1,766 (1462
to
2056)
4,279 1,450 (1139
to1720)
4,446 1,506 (1210
to
1822)
4,511 1,397 (1091
to
1665)
5,048 429 (103 to
728)
Adults
(18–65 y)

110. 3,524 1,795 (1509
to
2089)
3,383 1,088 (843 to
1336)
3,517 1,143 (890 to
1407)
3,580 970 (702 to
1216)
4,115 258 (�14 to
535)
Older adults
(age Z 65 y)
9,558 2,290 (1415
to
3193)
9,235 3,124 (2090
to
4046)
9,358 2,992 (1918
to
3977)

111. 9,342 2,944 (1858
to
3859)
9,981 600 (�272
to
1496)
Two-part
GLM
Children/
adolescents
2,162 �62 (�359
to 455)
1,953 140 (�146
to 653)
2,063 475 (�175
to
1289)
2,066 750 (�2 to
1894)
NA NA NA
All adults (age
Z 18 y)
5,483 1,834 (1553
to

112. 2170)
5,358 1,524 (1247
to
1803)
5,498 1,578 (1284
to
1848)
5,535 1,481 (1186
to
1754)
5,949 399 (112 to
665)
Adults
(18–65 y)
4,369 1,880 (1581
to
2206)
4,391 1,190 (938 to
1428)
4,528 1,234 (957 to
1467)
4,579 1,070 (813 to

113. 1313)
4,974 269 (8 to
506)
Older adults
(age Z 65 y)
9,894 2,328 (1481
to
3280)
9,628 3,191 (2264
to
4187)
9,749 3,060 (2040
to
4017)
9,740 3,014 (1991
to
3993)
10,315 555 (�320
to
1451)
continued on next page
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V A L U E I N H E A L T H 1 9 ( 2 0 1 6 ) 6 0 2 – 6 1 3610
incremental costs of obesity were significantly higher than for
the
nonobese. Among adults, in most of the combinations with
statistical models and confounding factors, the incremental
costs
of obesity for older adults (age Z 65 years) were significantly
higher than those for adults aged 18 to 65 years (Fig. 2 and

141. Table 4).
From the EEE model controlling for demographic,
socioeconomic,
and additional factors (cov1 þ cov2 þ cov3), the incremental
costs
of obesity for adults aged 18 to 65 years were reported as $1094
($859–$1359) while the costs of obesity for older adults were
$2668
($978–$4418).
Effect of different statistical models
Medical care costs attributable to obesity did not differ signifi-
cantly by using different statistical models, although the GoF
tests showed that an EEE model fitted the data most thoroughly,
followed by a one-part GLM. For all adults, controlling for
demographic and socioeconomic and additional factors (cov1 þ
cov2 þ cov3), the EEE model reported $1343 ($1076–$1621) for
the
incremental costs of obesity, whereas the estimates ranged from
$1261 ($950–$1561) in the log-linear model to $1481 ($1186–
$1754)
in the two-part GLM (Fig. 2; Table 4). Also, compared with
other
models, the EEE model provided the most stable estimates over
the different sets of confounding factor adjustment, varying
only
from $1346 with cov1 to $1356 with cov1 þ cov2 to $1343 with
cov1 þ cov2 þ cov3.
Confounding factor adjustment
For children, regardless of statistical models, the point
estimates
of the incremental costs of obesity were increased as we
adjusted
with more sets of possible confounding factors (up to cov1 þ
cov2

142. þ cov3), despite a huge CI that made those estimates
statistically
insignificant. For adults, controlling for demographic, socioeco-
nomic, and additional confounding factors (cov1 þ cov2 þ cov3)
did not make any substantial impact on point estimates as well
as statistical significance. However, by controlling for ORDs
(cov4)
that were available only for adults in this data set, the incre-
mental costs of obesity reduced to one-fourth to one-seventh of
the original estimates. Among all adults, the EEE model esti-
mated the costs attributable to obesity as $1343 ($1076–$1621)
controlling for all confounding factors except the comorbidity
conditions, whereas after adding obesity-related comorbidities
in
the model the estimates were decreased to $209 (�$21 to $434),
which was statistically insignificant, compared with the costs of
the nonobese (Fig. 2; Table 4).
Discussion
This article provided a systematic review and meta-analysis of
the 12 recently published articles that reported the medical care
costs associated with obesity, and also performed an original
analysis to understand the impact of study methodology on the
magnitude of these estimates. From the meta-analysis, the
pooled estimate of annual medical costs attributable to obesity
was $1901 ($1239–$2582) in 2014 USD, accounting for $149.4
billion at the national level. The extremely high heterogeneity
score from the meta-analysis signified the presence of hetero-
geneity between different studies due to the use of different
data
sets from multiple time periods, various statistical methods, and
adjustment for a wide range of confounding factors to estimate
the costs. Compared with the findings from the previously
conducted systematic review that reported the incremental costs
of obesity as $2046 (2014 USD) [13], the estimate from this
analysis is very comparable.

143. From the empirical analysis, not surprisingly, different stat-
istical methods did not have a significant impact on the
Note: EEE, extended estimating equation; SES, socio-economic
status; GLM, generalized linear model;
ORDs, obesity-related diseases
Fig. 2 – Impact of age groups, statistical models, and
confounding factor adjustment on the estimates of costs
attributable to
obesity. EEE, extended estimating equation; GLM, generalized
linear model; ORDs, obesity-related diseases; SES,
socioeconomic status.
V A L U E I N H E A L T H 1 9 ( 2 0 1 6 ) 6 0 2 – 6 1 3 611
V A L U E I N H E A L T H 1 9 ( 2 0 1 6 ) 6 0 2 – 6 1 3612
variability of the estimates in this analysis. However, we
caution
that this analysis does not endorse that any statistical model can
be used in estimating highly skewed cost data. Ignoring the
nature of cost data and misspecification of statistical models
may lead to inefficient or sometimes biased estimates [32,33].
For
all adults, controlling for the demographic and socioeconomic
and additional factors (cov1 þ cov2 þ cov3), the EEE model,
which
had the best GoF among all models based on the GoF tests,
reported $1343 ($1076–$1621) for the incremental costs of
obesity.
This result provides that the estimate of the national medical

144. care costs attributable to obesity would be $94.3 billion ($75.6–
$113.2 billion), which accounts for 3.8% (2.8%–4.3%) of
national
health expenditures in 2010 [35]. This estimate of medical costs
attributable to obesity from the empirical analysis was lower
than the pooled estimate from the meta-analysis. The reporting
error in the BMI measure through self-reported height and
weight
is likely to bias the coefficient estimates, although the direction
of
bias is not clear. Also, the possibility of omitting unobserved
confounders or reverse causality of obesity on medical costs is
likely to underestimate the true costs attributable to obesity.
The
IV approach by Cawley et al. could address these problems
using
a weight of biological relative as an instrument. The estimate
from the IV approach, however, may not be generalizable to the
entire population because of the restriction of the study popula-
tion to only adults aged 20 to 64 years with biological children
aged 11 to 20 years.
The two most significant drivers of variability in the cost
estimates were age groups and adjustment for obesity-related
comorbid condition. First, as expected, there is no significant
difference in costs attributable to obesity in
children/adolescence
population because of the presence of very few ORDs that may
take a long time to develop among children. In contrast, the
incremental costs of obesity were significantly higher than
those
for the nonobese for the adult population, and the older pop-
ulation reported significantly higher costs associated with
obesity
than did adults aged 18 to 65 years. However, because we
included obesity-related comorbidity as a confounding factor in

145. the model, the medical costs of obesity were not significantly
higher that the costs among the nonobese. These findings
confirmed that most, if not all, of the costs attributable to
obesity
are mainly caused by ORDs, and as age increases, the obese
population is more likely to develop ORDs, incurring higher
costs
of obesity for the older population.
The main limitation of estimating costs attributable to obesity
is the lack of distinction between costs of obesity caused by
ORDs
and costs of obesity care itself. If the ORDs are caused by the
obesity, then by controlling for them, it estimates only the
“partial” effect of obesity alone on the cost. However, by
omitting
such comorbidities as covariates, it estimates the “total effect”
of
obesity directly on cost and indirectly through mediators, the
ORDs. Although the “partial” effect of obesity alone on medical
costs was represented by the estimates controlling for ORDs
(cov4) in my analysis, which were not significantly different
from
costs of the nonobese, the true “total effect” of obesity on costs
is
not easy to estimate, because the regression model could not
capture the true counterfactual costs of obesity by just omitting
comorbidities as covariates, ignoring the presence of ORDs in
the
nonobese population. Future study needs to be directed at
estimating true counterfactual costs related to the absence/
presence of obesity and ORDs.
After recognizing obesity as a disease, a national survey found
that survey participants are more likely to support the disease
classification of obesity, and they believe that this change

146. would
bring more attention to weight changes and more access to
obesity
treatment [36]. However, a recent evaluation of adherence to
national obesity clinical practice guidelines found the lack of
increase in documentation of diagnosis and planned
management
of obesity patients [37]. Thus, recognizing obesity as a disease
may
not lead to immediate changes in health care utilization or
significant policy changes. However, what we can do is produce
better evidence of effectiveness and cost-effectiveness of
obesity
treatment through future research. Then, better research alone
will increase obesity treatment and reduce the burden of illness,
and we hope the overall medical expenditure will be expected to
decrease in the long run as we make more diligent efforts to
fight
against the obesity epidemic. (We appreciate valuable insights
from an anonymous reviewer and David Arterburn.)
However, the utility of published estimates for the medical
costs of obesity should be examined carefully, because of their
wide variation, and the estimates should be applied cautiously
in
future research and health policy making.
Source of financial support: This work was supported by the
Agency for Healthcare Research and Quality predoctoral
training
fellowship (grant no. 5 T32 HS 013853-10).
Supplementary Materials
Supplemental material accompanying this article can be found
in
the online version as a hyperlink at http://dx.doi.org/10.1016/j.

147. jval.2016.02.008 or, if a hard copy of article, at
www.valueinhealth
journal.com/issues (select volume, issue, and article).
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http://www.cdc.gov/obesity/adult/defining.html
http://www.cdc.gov/obesity/adult/defining.htmlEstimating the
Medical Care Costs of Obesity in the United States: Systematic
Review, Meta-Analysis, and Empirical
AnalysisIntroductionMethodsA Systematic Review and Meta-
AnalysisLiterature searchImprove comparability across
studiesEvaluating quality of studiesMeta-analysisEmpirical
Analysis: The Role of Alternative Statistical Models in
Estimating Costs of ObesityStudy dataVariablesStudy design
and data analysisResultsA Systematic Review and Meta-

153. AnalysisDescriptive resultsQuality evaluationCost
estimatesStratified resultsDifferent study methodologyDifferent
confounder adjustmentEstimating Medical Costs of
ObesityCharacteristics of individuals in the data setEffect of
different target populationsEffect of different statistical
modelsConfounding factor adjustmentDiscussionSupplementary
MaterialsReferences
Abstract
Over the past 3 decades, the prevalence of overweight and
obesity has increased dramatically worldwide. The rising
trend of obesity indicates that this increase is not only
confined to the developed world, but also extending
towards the developing world. In the context, Saudi Arabia
is now among the nations with the highest obesity and
overweight prevalence rates due to a number of factors.
This research explores and evaluates the prevalence of
obesity in Saudi Arabia on the basis of the findings of
previous literature. The research reveals that the rate of
obesity is significantly high in the country among different
age groups and occupations; at different locations in the
country; and among both males and females. The main
factors causing obesity include family history, diet pattern
and eating habits, genetic factors, marital status,
hypertension and lack of physical activities; while, the major
consequences are cardiovascular diseases, diabetes,
cancers, and Ischemic heart disease. The research stresses
on the need to raise awareness regarding obesity and
design efforts and strategies to combat it in the country.
Keywords: Obesity, Heart disease, Cardiovascular,
Overweight, Sedentary lifestyles, Osteoarthritis, Body mass
index, Anthropometric

154. Introduction
The prevalence of obesity, over the past 3 decades, has
increased in many countries around the world. It is defined by a
30 or higher body mass index (BMI) [1]. The problem of obesity
extends globally as estimated by the WHO. In 2008, worldwide
1.5 billion adults were overweight, where nearly 300 million
women and over 200 million men were obese. However, across
different nations, the prevalence of obesity varies, ranging from
below 5% in Japan, China, Indonesia, India, and certain African
countries to over 75% in Nauru and Samoa. In addition,
childhood obesity is also on the rise globally, and an epidemic
in
some countries. Worldwide, 22 million children approximately,
under age 5, are estimated to be overweight. Obesity prevalence
has also increased dramatically among children aged 6-17 years,
which is extending into the developing world from the
developed nations. The rising trend, as indicated by
international data, is not confined to the developed world, and it
is predicted that by 2030, a majority of adult population of the
world would be either obese or overweight [2].
In the rise of obesity and overweight, the interaction of a
number of factors is contributing, which include metabolic,
genetic, environmental, and behavioural influences. According
to Mahmood and Arulkumaran, the rapid growth in the rate of
obesity is directly contributed by environmental and
behavioural
factors, rather than the biological factors. Moreover, racial or
ethnic differences, consumption pattern, and lifestyle also
influence the rate of obesity. For instance, as compared to rural
areas, people in urban areas have higher obesity rate, possibly
due to consumption of high-fat diets and more sedentary
lifestyles. For daily living, the amount of energy spent has also

155. reduced over the years, which also promotes obesity. Obesity is
also often associated with high socio-economic status; as
populations in the developed world are mostly affected by
obesity.
Over the past few decades, Saudi Arabia has become
increasingly westernized, and now it has one of the highest
obesity and overweight prevalence rates [3]. Obesity in the
county is a major cause of concern, where 7 out of 10 people
are
experiencing the problem [4]. Previous studies related to
prevalence of obesity in the Kingdom of Saudi Arabia (KSA)
indicate an increasing trend in obesity and overweight, which
are major sources of a number of other diseases, including
hypertension, diabetes, obstructive sleep apnea, hyperlipidemia,
and osteoarthritis [5].
The current research study aims to explore and evaluate the
prevalence of obesity in Saudi Arabia. Based on findings of
previous literature, the study examines the prevalence rate in
the country, and explains its causes and consequences.
Methodology
The study adopts a qualitative approach and follows a review-
design to explore the research problem. It performs a literature
review to evaluate the prevalence of obesity in the KSA, and
explain its causes and consequences in the country. It focuses
on
recent Saudi-specific literature and studies on obesity and
Review
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156. .
A Review of Prevalence of Obesity in Saudi Arabia
Saad Salman M. Alqarni*
Department of Basic Sciences, College of Science and Health
Professions, King Saud bin Abdualziz University for Health
Sciences (KSAU-HS), Riyadh,
Kingdom of Saudi Arabia
*Corresponding author: Saad Salman M Alqarni, Department of
Basic Sciences, College of Science and Health Professions,
King Saud bin Abdualziz
University for Health Sciences (KSAU-HS), Riyadh, Kingdom
of Saudi Arabia, Tel: 011 4295668; E-mail: [email protected]
Received date: September 13, 2016; Accepted date: October 20,
2016; Published date: October 25, 2016
Citation: M Alqarni SS (2016) A Review of Prevalence of
Obesity in Saudi Arabia. J Obes Eat Disord 2:2. doi:
10.21767/2471-8203.100025
Copyright: © 2016 M Alqarni SS. This is an open-access article
distributed u nder t he terms o f t he Creative Co mmons
Attribution Lice nse, which
permits unrestricted use, distribution, and reproduction in any
medium, provided the original author and source are credited.
Obesity & Eating Disorders
ISSN 2471-8203
Vol.2 No.2:25
2016
© Under License of Creative Commons Attribution 3.0 License |

157. This article is available from: 10.21767/2471-8203.100025 1
http://www.imedpub.com/
analyses the key findings. The research also evaluates and
analyses secondary quantitative data specific to obesity in the
KSA.
Literature Review
Obesity is regarded as a significant public health issue, which
has raised a concern globally. The WHO claims that, obesity has
more than doubled worldwide, since 1980. More than 1.9 adults
were overweight, in the year 2014, including over 600 million
obese individuals. 39% of the adults were overweight and 14%
were obese. Moreover, it is found that 41 million children
(under
age 5) around the world in 2014 were either overweight or
obese [6]. Previous studies have revealed that obesity is among
the major cause of co-morbidities, including cardiovascular
diseases, diabetes, cancers, and the related issues that may lead
to morbidity and mortality. In most of the countries, the high
total obesity and overweight cost represents a relative economic
burden on the GDP. Over the last decade, the prevalence of
obesity has increased significantly in several developed and
developing countries [7]. The current research paper focuses on
obesity in Saudi Arabia, which has now one of the highest
obesity and overweight prevalence rates [3].
Sabra examine obesity among female nursing students in
Dammam, Saudi Arabia using waist to hip ratio (WHR) and
body
mass index (BMI). The study collected data with the help of an
interviewer-administered questionnaire, from a sample of 260
female nursing students. The results of the study indicate the