Measuring Adiposity in Patients: The Utility of Body Mass
Index (BMI), Percent Body Fat, and Leptin
Nirav R. Shah1.¤, Eric R. Braverman2,3*.
1Department of Medicine, New York University School of Medicine, New York, New York, United States of America, 2PATH Foundation NY, New York, New York, United
States of America, 3Department of Neurosurgery, Weill-Cornell Medical College, New York, New York, United States of America
Background: Obesity is a serious disease that is associated with an increased risk of diabetes, hypertension, heart disease,
stroke, and cancer, among other diseases. The United States Centers for Disease Control and Prevention (CDC) estimates a
20% obesity rate in the 50 states, with 12 states having rates of over 30%. Currently, the body mass index (BMI) is most
commonly used to determine adiposity. However, BMI presents as an inaccurate obesity classification method that
underestimates the epidemic and contributes to failed treatment. In this study, we examine the effectiveness of precise
biomarkers and duel-energy x-ray absorptiometry (DXA) to help diagnose and treat obesity.
Methodology/Principal Findings: A cross-sectional study of adults with BMI, DXA, fasting leptin and insulin results were
measured from 1998–2009. Of the participants, 63% were females, 37% were males, 75% white, with a mean age=51.4
(SD=14.2). Mean BMI was 27.3 (SD=5.9) and mean percent body fat was 31.3% (SD=9.3). BMI characterized 26% of the
subjects as obese, while DXA indicated that 64% of them were obese. 39% of the subjects were classified as non-obese by
BMI, but were found to be obese by DXA. BMI misclassified 25% men and 48% women. Meanwhile, a strong relationship
was demonstrated between increased leptin and increased body fat.
Conclusions/Significance: Our results demonstrate the prevalence of false-negative BMIs, increased misclassifications in
women of advancing age, and the reliability of gender-specific revised BMI cutoffs. BMI underestimates obesity prevalence,
especially in women with high leptin levels (.30 ng/mL). Clinicians can use leptin-revised levels to enhance the accuracy of
BMI estimates of percentage body fat when DXA is unavailable.
Citation: Shah NR, Braverman ER (2012) Measuring Adiposity in Patients: The Utility of Body Mass Index (BMI), Percent Body Fat, and Leptin. PLoS ONE 7(4):
Editor: Qamaruddin Nizami, Aga Khan University, Pakistan
Received November 30, 2011; Accepted February 6, 2012; Published April 2, 2012
Copyright: ? 2012 Shah, Braverman. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: The authors are grateful to the Life Extension Foundation for their generous financial support to PATH Foundation NY. The funders had no role in study
design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have read the journal’s policy and have the following conflicts: This work was supported by a research grant from the PATH
Foundation. All research and financial support for this article preceded NRS’s joining the New York State Department of Health. Similarly, except for minor
editorial changes, the article was completed before that time. The views expressed in this article are solely those of the authors as individuals and do not
represent the views or policies of the State of New York or the New York State Department of Health. During the period when the work was completed, NRS was
also supported by grant 1 R01 HS01 8589-01. All authors have completed the Unified Competing Interest form at www.icmje.org/coi_disclosure.pdf (available on
request from the corresponding author and declare that (1) NRS has received grants from the PATH Foundation for the submitted work; ERB has received no
personal compensation for the submitted work; (2) NRS has relationships with AstraZeneca, Pfizer, Merck, Ortho-McNeil, Roche, Shering-Plough, GlaxoSmithKline,
Novartis, Partners Healthcare, Bellevue Hospital Association, NIH, AHRQ, CDC, Robert Wood Johnson Foundation, Cerner LifeSciences, Vemco MedEd, FAIR Health,
Venebio Group, LLC, American Academy of Neurology, Pinnacle Health Geisinger Health System, MetaResearch, LLC, Johnson & Johnson, Takeda, Xcenda, Engage
Healthcare, Medical Learning Institute, American Health & Drug Benefits, Center of Excellence Media LLC, Nassau University Medical Center, National Institute for
Quality Improvement and Education and St. John’s Episcopal Hospital. ERB has relationships with PATH Medical, Total Health Nutrients, Inc. and LLC, Life
Extension Foundation, Weill-Cornell Medical College, the Stanley and Fiona Druckenmiller Fund, the American Academy of Anti-aging Medicine Fellowship in
Anti-Aging, Regenerative, and Functional Medicine: Master’s Degree in Metabolic & Nutritional Medicine in conjunction with the University of South Florida
Medical School; the American Academy of Anti-aging Medicine Tarsus Medical Conference; and Douglas Labs, Life Extension Magazine; and has authored a book
on weight loss; (3) Their spouses, partners, or children have no financial relationships that may be relevant to the submitted work; and (4) NRS and ERB have no
non-financial interests that may be relevant to the submitted work. This does not alter the authors’ adherence to all the PLoS ONE policies on sharing data and
* E-mail: email@example.com
. These authors contributed equally to this work.
¤ Current address: New York State Department of Health, Albany, New York, United States of America
Global trends of increasing obesity threaten public health and
contribute to the burden of disease as much as smoking does [1,2].
apnea and respiratory problems, osteoarthritis, abnormal menses and
infertility . Adiposity in mid-life strongly relates to reduced
probability of healthy long term survival in women . Obesity has
become a priority of national, state and local public health efforts and
in the care of individual patients. Thus, clinical detection of obese
individuals has commensurately reached critical importance.
With the increasing importance of obesity detection, it is useful
to reevaluate how body fat is determined. For adults, the body
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mass index (BMI) is commonly used. Its popularity stems in part
from its convenience, safety, and minimal cost, and its use is
widespread, despite not being able to distinguish lean body mass
from fat mass . The United States Centers for Disease Control
and Prevention (CDC) explain: ‘‘For adults, overweight and
obesity ranges are determined by using weight and height to
calculate a number called the ‘‘body mass index’’ (BMI). BMI is
used because, for most people, it correlates with their amount of
body fat’’ . However, the BMI is actually an indirect surrogate
measurement considered imprecise [7,8].
Recent estimates from NHANES, a nationally representative
health examination survey, project that approximately 34% of
adult Americans are overweight (defined as a BMI between 25–
30 kg/m2) and an additional 34% are obese (BMI .30 kg/m2)
. In contrast, the CDC estimates rates of obesity over 20% in all
50 states with estimated rates over 30% in 12 states [http://www.
cdc.gov/obesity]. These estimates are fundamental to US policy
addressing the epidemic of obesity and are central to designing
interventions aimed at curbing its growth, yet they may be flawed
because they are based on the BMI.
The outdated BMI formula [BMI=weight in pounds/(height in
inches)26703], developed nearly 200 years ago by Quetelet, is not
a measurement of adiposity, but merely an imprecise mathemat-
ical estimate [7,8,10–14]. Defining obesity based on percent body
fat, as with BMI, also has arbitrary cut-points. In 1995, the World
Health Organization (WHO) defined obesity based on a percent
body fat $25% for men and $35% for women , while the
most recent 2009 guidelines from the American Society of
Bariatric Physicians (ASBP), an American Medical Association
(AMA) specialty board, used percent body fat $25% for men and
$30% for women. The ASBP percent body fat guidelines identify
individuals that are suitable candidates for treatment for obesity
with anorectic agents. Most studies comparing BMI with more
accurate measures of adiposity used cutoffs of body fat .25% for
men and .30% for women .
BMI ignores several important factors affecting adiposity.
Greater loss of muscle mass leading to sarcopenic obesity in
women occurs increasingly with age. BMI does not acknowledge
this factor, exacerbating misclassifications [17,18]. Furthermore,
men’s BMI also does not consider the inverse relationship between
muscular strength and mortality . It fails to take into account
that men lose less muscle with age than women.
Statistical models have been created to explain variance in
leptin with relation to insulin, gender, and BMI, but lack a variable
of direct adiposity measurement such as DXA . A fully
equipped duel-energy x-ray absorptiometry (DXA) provides
simultaneous measurements of muscle, bone mass and body
adiposity. The ASBP uses both BMI and DXA as criteria for
Studies comparing DXA-derived percent body fat rates of
obesity to BMI have, to date, focused mainly on women [12,21] or
imputed data on percent body fat for a substantial proportion of
subjects . We sought to characterize the degree of misclassi-
fication of obesity based on BMI using percent body fat from DXA
in a large, unselected population, and to use the more accurate
DXA derived measure to identify the optimal cut-points for
defining obesity using BMI. Reclassifying obesity cut points is
worth considering, as there is a population of individuals with a
normal BMI who nonetheless have increased adiposity as
determined by more sensitive methods; these are the so-called
‘normal weight obese.’ These individuals may have increased risk
for medical comorbidities such as hyperlipidemia, coronary artery
disease, hypertension, and diabetes . Furthermore, in the
intermediate ranges, BMI is not a good discriminator of
cardiovascular risk; use of adiposity measures rather than BMI
may be a better predictor, but have recently failed [22–25].
Therefore, there is a need to reclassify the obesity epidemic,
identify clinically useful biomarkers, and clarify what the medical
and scientific communities are measuring with BMI.
Although DXA is a direct measurement of fat and a better
measure of adiposity than BMI, it is not a disease correlate. The
attempts to find disease correlates to explain disparities between
BMI and direct fat measurements have included leptin, insulin,
ghrelin, and adiponectin . Leptin, a 16 kDa peptide secreted
primarily by adipocytes, regulates the body’s energy balance by
acting as a negative feedback adiposity signal, decreasing food
intake and increasing energy expenditure. In individuals with leptin
insensitive receptors, neither transport nor action is possible, and
leptin levels rise . Increased leptin is associated with the
inflammatory process and possibly the entire increased morbidity of
obesity [28,29]. Individuals with leptin insensitivity and high levels
of leptin have parallel comorbidities to normal weight obesity such
as chronic inflammation, type II diabetes, hypertension, and
myocardial injury [http://www.asbp.org/siterun_data/about_asbp/
position_statements/doc7270523281295654373.html]. Therefore, it
was appropriate to investigate whether leptin levels could correct for
the disparity between DXA and BMI and be used to create a more
accurate measure of obesity.
Materials and Methods
We conducted a retrospective chart review of 9,088 patients
who had $1 outpatient visits at a multispecialty private practice
group in Manhattan (1998–2009). Patients who received a DXA
scan within 3 weeks of their initial visit and whose height and
weight were documented at first visit were eligible for study and
signed written informed consent forms. DXA evaluation is routine
in this wellness-focused practice; 71% of all patients seen from
1998 to 2009 received a DXA scan. 18% of patients had a DXA
on the same day as their initial visit. Paper charts of those eligible
patients identified from the DXA log were retrieved and reviewed
by trained research assistants for demographic, height, weight, and
selected laboratory and co-morbidity records. Patients selected for
inclusion were adults (age=$18) with height, weight, and percent
body fat (from DXA) available for analysis. No exclusions based on
co-morbidities or other criteria were made. All height and weight
data were abstracted in duplicate by separate raters to ensure
accuracy; discrepancies were resolved by a final chart review and
BMI was calculated as weight (kg) divided by height (m)
squared. Sectional and total percent body fat were attained from
the Discovery Wi model of a Hologic DXA machine calibrated
daily, which uses multiple pencil beam detectors and dual energy
X-ray fan-beam to fat, muscle, and bone. A whole body scan was
administered on each patient. QDR System software version 12.5
was used to analyze scans and provide percent body fat readings.
All reported measurements of BMI, DXA and blood work were
taken within 3 weeks of each other. Fasting insulin and leptin levels
were drawn between 9:30 am and 3:00 pm. Fasting insulin levels
were analyzed and reported by BioReference Lab. Leptin was
measured by ELISA by ARUP Labs.
Institutional Review Board (IRB) approval was sought from
PATH Foundation NY IRB and obtained prior to beginning
research, and all investigators and personnel involved were trained
in responsible conduct of research and protection of human
The National Institute of Health (NIH) criteria for obesity based
on BMI were used to classify patients as obese (BMI $30). ASBP
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guidelines for percent body fat classify men as obese when body fat
$25% and women as obese when body fat $30% . Percent
body fat (obese versus non-obese) was compared to BMI (obese
versus non-obese) to determine percent agreement and disagree-
ment. This analysis was conducted for all patients, all males, males
by age category, all females, and females by age category.
A Receiver Operating Curve (ROC) analysis was used to
identify cut points for BMI to optimize the area under the ROC
curve (AUC), specifically sensitivity and specificity, relative to
percent body fat. We conducted multiple logistic regression
analyses using percent body fat (obese versus non-obese by ASBP
criteria) as the outcome variable. The AUC metric was used to
evaluate the strength of associations and improvement in the
model when additional variables were added. Initial modeling
evaluated the strength of association between percent body fat and
The effects of sex and age were evaluated to determine if either
modified the association between percent body fat and BMI. If
effect modification was present, then the study population was
stratified and separate models were evaluated for each stratum.
After regression models were developed accounting for BMI, sex,
and age, other patient characteristics were added to the model to
determine if the characteristic was associated with percent body
fat. Additional analyses were conducted to evaluate the relation-
ship between percent body fat and BMI, sex, age, fasting insulin
and leptin levels. For preliminary analyses, percent body fat was
defined as obese using cut-points described above (i.e. $30% for
females and $25% for males). The primary predictor variables
were BMI (continuous; categorical: ,30 versus 30+; or ordinal:
underweight, normal, over, Class I obese, Class II obese, Class III
obese), sex, and age (continuous).
Subsequent analyses were conducted to examine if leptin or
insulin levels were related to percent body fat. Currently accepted
body fat percentage cut-points for obesity are 25% for men and
30% for women. For the purposes of this study, we identified the
following groups based on percent body fat: for men ,14% (Very
low), 14%–17?9% (Fit), 18%–24.9% (Overweight), 25%–34.9%
(Obese), 35%–39.9% (Morbidly obese), $40% (Super obese); for
women ,15% (Very low), 15%–24.9% (Fit), 25%–29.9%
Obese), $45% (Super obese). All statistical tests were two-sided
with an alpha level of 5%, and conducted using SAS version 9.2.
A total of 1,393 adult patients (from 9,088) had both BMI and
DXA derived percent body fat available for comparison. The
population consisted of 63% women and 37% men, 75% white,
with a mean age of 51.4 (SD=14.2) (see Table 1). Mean BMI was
27.3 (SD=5.9) and mean percent body fat was 31.3% (SD=9.3).
Table 2 demonstrates the discordance seen between classifica-
tions of obesity based on BMI versus percent body fat. While there
was agreement for 60% of the sample, 39% were misclassified as
non-obese based on BMI, while meeting obesity criteria based on
percent body fat. Only 1% was classified as obese based on BMI,
but non-obese by percent body fat. A total of 48% of women were
misclassified as non-obese by BMI, but were found to be obese by
percent body fat. In sharp contrast, 25% of men were misclassified
as obese by BMI, but were in fact non-obese by percent body fat
(i.e. the muscular body morphology).
Figure 1 presents a scatter plot of BMI versus percent body fat.
The upper left quadrant bordered by vertical BMI=30% line and
horizontal red line (women) or blue line (men), identifies the
misclassified subjects who are non-obese based on BMI, but obese
based on percent body fat. Examining these 39% (n=539) of
subjects in detail (see Figure 2), women had clear correlation
between advancing age and % misclassification. 48% of women
ages 50–59 misclassified, and 59% were misclassified by age 70+.
This association with advancing age was not observed in men.
In regression modeling, BMI was a strong predictor of percent
body fat whose association was modified by sex. Figure 3 contains
the Receiver Operating Characteristic (ROC) curve for using BMI
to predict obesity based on percent body fat. The area under the
curve (AUC) was 0.824 for all patients, but was higher when
stratified by sex (0.872 for males, 0.917 for females). For both
models, age was a significant predictor of percent body fat, and
AUC increased to 0.877 for males and 0.924 for females (ROC
not shown). We attempted to identify new cut-points for BMI that
would better categorize patients as obese, using percent body fat as
the gold standard. Figure 3 shows that the BMI cutoff value that
maximizes sensitivity and specificity is 24 for females (with 79%
sensitivity and 87% specificity), and 28 for males (with 72%
sensitivity and 83% specificity).
Figure 4 compares mean leptin and mean insulin across percent
body fat categories. There is a strong relationship between
increased leptin and increased percent body fat and the lack of
relationship between insulin and percent body fat. Table 3 outlines
the adjustment of the BMI score based on female leptin level and
age to optimize the estimate of percent body fat, as defined by
DXA. For example, a 45 year old woman with BMI of 23 and
leptin level of 7 ng/mL (7 mg/L) has a percent body fat of
approximately 23+5=28%. When BMI is .25, leptin levels do
not add any new information to the equation, so we continue to
add the average difference of 9 to adjust the BMI to better
represent a woman’s percent body fat. 13% of the total group
(n=89) fell into deficient or low normal leptin range (8.7% men,
Using new BMI cut-points for defining obesity would increase
sensitivity with small tradeoffs in specificity. In women, BMI
sensitivity to predict obesity (as defined by $30% body fat)
increased from 35% at a BMI of 30 to 79% at BMI cutoff of 24,
with specificity decreasing only 13% (100% to 87%). In men, BMI
sensitivity increased from 51% with a BMI of 30 to 72% with a
BMI of 28, with only a 12% loss of specificity (95% to 83%).
BMI significantly underestimates prevalence of obesity when
compared to DXA direct measurement of percent body fat.
Currently, no other blood test or biomarker has been correlated
with the rate of obesity. The use of both DXA and leptin levels
offers the opportunity for more precise characterization of
adiposity and better management of obesity.
This misclassification was seen more commonly in women than
in men and occurred more frequently with advancing age in
women. A more appropriate cut-point for obesity with BMI is 24
for females and 28 for males (see Table 4). These new cut-points
increased diagnostic sensitivity with small losses in specificity.
Clinicians should consider using 24 as the BMI cut-point for
obesity in women, in order to maximize diagnosis and prevention
of obesity-related co-morbidities. Public health policymakers
should also consider these more accurate cut-points in designing
interventions. The Healthy People 2010 goal was to reduce rates
of obesity (defined using BMI.30) from 23% in 1988–1994 to the
target of 15%. Not only was this goal unmet, but in light of this
data we may be much further behind than we thought. Our results
document the scope of the problem of false-negative BMIs,
emphasize the greater misclassification in women of advancing
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age, and confirm the improved precision available by gender
specific revised cutoffs.
The use of leptin levels further improves precision of BMI
adjustment, whereas insulin levels do not. With 91% of our patients
with high leptin levels being women, our data confirm the greater
effectiveness of BMI adjustment with leptin levels in women,
attributable to a higher prevalence of hyperleptinemia among
women. As significant lowering of leptin impacts long term weight
control [31,32], the idea of incorporating leptin adjustments into a
more accurate diagnosis of obesity should be seriously considered.
Further studies should be conducted for leptin measurements as a
potentially useful tool in the management of obesity.
Greater loss of muscle mass (sarcopenic obesity) in women, with
age, exacerbates the misclassifications of BMI [17,18]. Women with
Table 1. Summary of study population.
VariableTotalMen Women p-value
Weight at time of DXA (kg), mean (SD)76.61 (18?0)86.77 (16.83)70.62 (16.06)
Height (meter), mean (SD) 1.67 (0.1) 1.76 (0.1) 1.62 (0.1)
BMI (kg/m2), mean (SD)27.3 (5?9)28.1 (5?4) 26.9 (6?2) 0.0001
Non-obese (BMI,30)1031 (74%)381 (74%)650 (74%) 0.76
Obese (BMI 30+) 362 (26%)137 (26%) 225 (26%)
Total Percent Body Fat*31.3 (9?3) 24.3 (7?0) 35.4 (7?8)
Non-obese 507 (36%) 280 (54%)227 (26%)
Obese 886 (64%)238 (46%)648 (74%)
Age at DXA (years), mean (SD)51.4 (14?2) 51.8 (15?0) 51.2 (13?7) 0.42
Race: White, N (%)1039 (75%)423 (82%) 616 (70%)
Black, N (%)228 (16%) 56 (11%)172 (20%)
Hispanic, N (%) 76 (5%)23 (4%) 53 (6%)
Other, N (%)50 (4%)16 (3%)34 (4%)
Marital status: Married, N (%)731 (53%)295 (58%)436 (50%)0.0004
Single, N (%)376 (27%) 145 (28%)231 (27%)
Divorced, N (%)190 (14%) 52 (10%)138 (16%)
Widowed, N (%)79 (6%) 18 (4%)61 (7%)
Unknown, N (%)N=17 N=8 N=9
Insurance: Private, N (%)1028 (74%)368 (71%) 660 (75%)0.19
Medicare, N (%)173 (12%)71 (14%)102 (12%)
Medicaid, N (%) 6 (,1%)1 (,1%)5 (,1%)
None, N (%)186 (13%)78 (15%)108 (12%)
Systolic Blood Pressure (mmHg), mean (SD)125.9 (18?3)129.5 (17.2)123.7 (18.6)
Diastolic Blood Pressure (mmHg), mean (SD) 77.5 (10.4)79.4 (9.9) 76.3 (10.6)
Pulse (beats per minute), mean (SD) 72.1 (12.5) 70.?9 (12?6) 72?8 (12?4)0?0099
Use cigarettes, N (%) 138 (11%) 72 (15%)66 (8%)
Use alcohol, N (%)573 (46%)262 (57%)311 (39%)
Leptin level (ng/mL), mean (SD)26?1 (22?6)13?3 (12?3)31?7 (23?8)
Insulin level (mIU/ml), mean (SD)11?6 (15?4)13?1 (17?1)10?6 (14?0)0?030
*Men were classified as non-obese based on a percent body fat ,25% and obese for $25%; women were classified as non-obese based on a percent body fat ,30%
and obese for $30% (n=1,393).
Blood pressure unknown for nine men and ten women.
Pulse unknown for 19 men and 25 women.
Cigarette use unknown for 49 men and 76 women.
Alcohol use unknown for 57 men and 85 women.
Leptin level unknown for 332 men and 450 women.
Insulin level unknown for 204 men and 397 women.
Table 2. Percent body fat and BMI for all patients.
BMI non-obese, % body
265 (51%)227 (26%)492 (35%)
BMI obese, % body fat obese122 (24%)225 (26%)347 (25%)
BMI non-obese, % body fat obese116 (22%)423 (48%)539 (39%)
BMI obese, % body fat non-obese15 (3%)0 (0%)15 (1%)
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increased adiposity with osteoporosis are at greater risk for impaired
gait, disability, falls, and fractures . In men, an inverse
relationship has been shown between muscular strength and
mortality which may be missed using BMI as a measure of adiposity
. A fully equipped DXA provides simultaneous measurements
of muscle,bone mass andbodyadiposity.Sincemenlose less muscle
withage than women,men’s BMIshould alsotake into account that
men suffer from sarcopenia less than women. Models have been
created to explain variance in leptin with relation to insulin, gender,
and BMI, but have lacked a variable of direct adiposity
measurement such as DXA . Although this is new data, it
appears likely that those who are older and all women will need a
new classification of BMI – although our data are inclusiveof all age
groups.A definitiverecommendationregardingwhich patientsneed
DXA requires further study. The ASBP is using both BMI and
DXA as criteria for interventions, and this may be a reasonable
transition in public health policy. Some may prefer to use DXA
alone, though the cost-effectiveness of this strategy is questionable.
Given sufficient volume, DXA scans with body fat and bone density
may be conducted efficiently at low cost.
Figure 1. BMI versus Percent Body Fat in Scatter Plot. Women (red) who fall above red line are obese according to American Society of
Bariatric Physicians criteria (DXA percent body fat: $30%). Men (blue) who fall above blue horizontal line are obese according to American Society of
Bariatric Physicians criteria (DXA percent body fat: $25%). The upper left quadrant bordered by red horizontal line (body fat percent=30%) and black
vertical line (BMI=30) demonstrates large number of women misclassified as ‘‘non-obese’’ by BMI yet ‘‘obese’’ by percent body fat.
Figure 2. Percent Misclassified as Non-obese by BMI Statified by Age, and Sex (n=539). Women demonstrate clear correlation between
advancing age and increasing percent misclassification, with over half misclassified by age 60–69. This association is not apparent for men.
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