Obesity and Body Fat Classification in the Metabolic ... article_62.pdf · Obesity and Body Fat Classification in the Metabolic Syndrome: Impact on Cardiometabolic Risk Metabotype

Obesity and Body Fat Classification in theMetabolic Syndrome: Impact onCardiometabolic Risk MetabotypeCatherine M. Phillips1,2, Audrey C. Tierney1, Pablo Perez-Martinez3,Catherine Defoort4, Ellen E. Blaak5, Ingrid M. F. Gjelstad6,7,Jose Lopez-Miranda3, Malgorzata Kiec-Klimczak8, Malgorzata Malczewska-Malec8,Christian A. Drevon6, Wendy Hall9, Julie A. Lovegrove9, Brita Karlstrom10,Ulf Ris�erus10 and Helen M. Roche1

Objective: Obesity is a key factor in the development of the metabolic syndrome (MetS), which is

associated with increased cardiometabolic risk. We investigated whether obesity classification by BMI

and body fat percentage (BF%) influences cardiometabolic profile and dietary responsiveness in 486

MetS subjects (LIPGENE dietary intervention study).

Design and Methods: Anthropometric measures, markers of inflammation and glucose metabolism, lipid

profiles, adhesion molecules, and hemostatic factors were determined at baseline and after 12 weeks of

four dietary interventions (high saturated fat (SFA), high monounsaturated fat (MUFA), and two low fat

high complex carbohydrate (LFHCC) diets, one supplemented with long chain n-3 polyunsaturated fatty

acids (LC n-3 PUFAs)).

Results: About 39 and 87% of subjects classified as normal and overweight by BMI were obese according to

their BF%. Individuals classified as obese by BMI (�30 kg/m2) and BF% (�25% (men) and �35% (women))

(OO, n ¼ 284) had larger waist and hip measurements, higher BMI and were heavier (P < 0.001) than those

classified as nonobese by BMI but obese by BF% (NOO, n ¼ 92). OO individuals displayed a more

proinflammatory (higher C reactive protein (CRP) and leptin), prothrombotic (higher plasminogen activator

inhibitor-1 (PAI-1)), proatherogenic (higher leptin/adiponectin ratio) and more insulin resistant (higher HOMA-IR)

metabolic profile relative to the NOO group (P < 0.001). Interestingly, tumor necrosis factor-a (TNF-a)concentrations were lower post-intervention in NOO individuals compared with OO subjects (P < 0.001).

Conclusions: In conclusion, assessing BF% and BMI as part of a metabotype may help to identify

individuals at greater cardiometabolic risk than BMI alone.

Obesity (2013) 21, E154-E161. doi:10.1038/oby.2012.188

Introduction

The prevalence of obesity is increasing worldwide, with the condi-

tion predicted to affect more than one billion people by the year

2020 (1). Excess adiposity, particularly central adiposity, is a key

causal factor in the development of insulin resistance, the hallmark

of the metabolic syndrome (MetS). In addition to abdominal obe-

sity the MetS is characterized by dyslipidemia and hypertension,

which are associated with increased risk of type 2 diabetes melli-

tus (T2DM) and cardiovascular disease (CVD) (2). A number of

adiposity measures are currently used as diagnostic tools in over-

weight and obesity classification including waist circumference

1 Nutrigenomics Research Group, UCD School of Public Health and Population Science, UCD Conway Institute, University College Dublin, Dublin, Ireland2 Department of Epidemiology and Public Health, University College Cork, Cork, Ireland 3 Lipid and Atherosclerosis Unit, IMIBIC/Reina Sofia UniversityHospital/University of Cordoba, and CIBER Fisiopatologia Obesidad y Nutricion (CIBEROBN), Instituto de Salud Carlos III, Cordoba, Spain 4 INSERM, 476Human Nutrition and Lipids, INRA, 1260, University M�editerran�ee Aix-Marseille 2, Marseille, France 5 Department of Human Biology, Nutrition andToxicology Research Institute Maastricht (NUTRIM), Maastricht,The Netherlands 6 Department of Nutrition, Institute of Basic Medical Sciences, University ofOslo, Oslo, Norway 7 Department of Clinical Endocrinology, Oslo University Hospital Aker, Oslo, Norway 8 Department of Clinical Biochemistry, JagiellonianUniversity Medical College, Krakow, Poland 9 Hugh Sinclair Unit of Human Nutrition and Institute for Cardiovascular and Metabolic Research, Department ofFood and Nutritional Sciences, University of Reading, Reading, UK 10 Department of Public Health and Caring Sciences/Clinical Nutrition and Metabolism,Uppsala University, Uppsala, Sweden. Correspondence: Helen M. Roche ([email protected])

Disclosure: The authors declared no conflict of interest. See the online ICMJE Conflict of Interest Forms for this article.

Received: 13 January 2012 Accepted: 31 May 2012 First published online by Nature Publishing Group on behalf of The Obesity Society 9 August 2012.

doi:10.1038/oby.2012.188

E154 Obesity | VOLUME 21 | NUMBER 1 | JANUARY 2013 www.obesityjournal.org

Original ArticleEPIDEMIOLOGY/GENETICS

Obesity

(WC), BMI, and body fat percentage (BF%). WC is the only adi-

posity measure included in the current International Diabetes Fed-

eration and National Cholesterol Education Program’s Adult Treat-

ment Panel III report (NCEP ATP III) MetS definitions. However,

WC does not take whole body fat distribution into consideration.

Moreover, prevalence of the MetS has been shown to increase

across BMI categories with approximately twofold higher preva-

lence in the severely obese compared with nonobese (3). However

BMI, the traditional diagnostic tool, is also limited because it does

not discriminate between lean and fat body mass. Recent data

from a large cross-sectional study suggest that using BMI may

under estimate obesity prevalence defined as excess body fat, par-

ticularly in overweight individuals (4). Simultaneous comparison

of the association between WC, BMI and BF% with CVD risk

showed that WC and BF% were more strongly associated with

MetS and CVD risk, respectively (5). Furthermore, recent examina-

tion of markers of glucose metabolism according to obesity classi-

fication revealed that BF% may be a better determinant for pre-di-

abetes and T2DM development (6).

Ideally, obesity prevention would reduce risk of associated cardiometa-

bolic conditions, although several current approaches are ineffective,

probably due, at least in part, to lack of prompt identification, diagnosis,

and appropriate treatment of obese individuals, together with genetic het-

erogeneity and differences in dietary responsiveness. Thus, there is a

need to improve obesity diagnosis and to develop new preventative strat-

egies and evidence-based public health measures to attenuate disease de-

velopment and reduce dependence on medical care, particularly among

individuals with increased cardiometabolic risk. Comparative data on

whether obesity classification by BMI and BF% influence the cardiome-

tabolic profile of individuals with the MetS is currently unavailable. Con-

sidering the increasing prevalence of the MetS and its associated cardio-

metabolic risk, the main objective of this paper was to examine a

comprehensive panel of risk factors in MetS individuals comparing those

classified as nonobese by BMI and obese by BF% (NOO) to subjects

classified as obese by both BMI and BF% (OO). Another novel aim of

this work was to assess whether obesity classification influences dietary

responsiveness in the MetS. Examination of whether the complementary

use of BF% and BMI to define the obese metabotype, or metabolic phe-

notype, in MetS is more effective in detecting individuals at greater cardi-

ometabolic risk than BMI alone may have public health implications in

terms of improving obesity classification in high-risk groups.

Methods and ProceduresSubjects aged 35-70 years and BMI 20-40 kg/m2 were recruited for

the LIPGENE dietary intervention study from eight European coun-

tries (Ireland, UK, Norway, France, The Netherlands, Spain, Poland,

and Sweden) all conforming to the Helsinki Declaration of 1975 as

revised in 1983. The study was registered with The US National

Library of Medicine Clinical Trials registry (NCT00429195). Sub-

ject eligibility was determined using a modified version of the

NCEP criteria for MetS (7), where subjects were required to fulfill

at least three of the following five criteria: waist circumference

>102 cm (men) or >88 cm (women); fasting glucose 5.5-7.0 mmol/

l; triglycerides �1.5 mmol/l; high-density lipoprotein cholesterol

(HDL-C) <1.0 mmol/l (men) or <1.3 mmol/l (women); blood pres-

sure �130/85 mmHg or treatment of previously diagnosed hyperten-

sion. We used the preintervention data for 486 subjects and the post-

intervention data for the 417 subjects completing the intervention.

Detailed characteristics of this cohort have been published (8).

Dietary interventionParticipants were recruited to a 12-week dietary intervention after

being randomly allocated to one of the four following diets: high-

fat (38% energy) SFA-rich diet (16% SFA, 12% MUFA, 6%

PUFA (HSFA); high-fat (38% energy), MUFA-rich diet (8% SFA,

20% MUFA, 6% PUFA) (HMUFA); isoenergetic low fat (28%

energy), high complex carbohydrate diet (8% SFA, 11% MUFA,

6% PUFA), with 1 g/day high-oleic sunflower oil supplement

(LFHCC); isoenergetic low-fat (28% energy), high complex carbo-

hydrate diet (8% SFA, 11% MUFA, 6% PUFA), with 1.24 g/day

LC n-3 PUFA supplement (LFHCC n-3). Randomization was per-

formed using age, gender, and fasting plasma glucose concentra-

tion as matching variables, applying Minimisation Programme for

Allocating patients to Clinical Trials (Department of Clinical Epi-

demiology, The London Hospital Medical College, UK). The LC

n-3 PUFA supplement (Marinol C-38; 1.24 g per day LC n-3

PUFA) and control high-oleic acid sunflower seed oil supplement

were supplied by Lipid Nutrition, Loders Croklaan (Wormerveer,

The Netherlands). More details about dietary models have been

published elsewhere (9).

Anthropometric and clinical measurementsAnthropometric measurements were recorded according to a standar-

dized protocol for the LIPGENE study. Bio-electric impedance

measures of body composition were performed by a multi-frequency

tetra-polar device (Tanita BIA machine; Tanita, Arlington Heights,

IL) (10). The subjects were placed in the supine position with arms

comfortably abducted from the body at 15� and legs spread comfort-

ably. Two current-injection electrodes were placed at the right hand

and foot on the dorsal surfaces proximal to the metacarpal-phalan-

geal and metatarsal-phalangeal joints, respectively. The centers of

two voltage-detector electrodes were placed on the midline between

the prominent ends of the right radius and ulna of the wrist, and

midline between the medial and lateral malleoli of the right ankle.

The black current-injection and red voltage electrode detectors were

at least 5 cm apart, respectively. The black current-injection lead

alligator clips and the red voltage-detector lead alligator clips were

connected to the electrodes placed on the right hand and foot and

right wrist and ankle, respectively. The most frequently used cutoff

points for BF% defining obesity (�25% in men and �35% in

women) were used (11-13). Blood pressure was measured according

to the European Society of Hypertension Guidelines.

Biochemical measurementsPlasma and serum were prepared from 12-h fasting blood samples in

each subject. Serum insulin was measured by solid-phase, two-site

fluoroimmunometric assay on a 1235 automatic immunoassay sys-

tem (AutoDELFIA kits; Wallac Oy, Turku, Finland). Plasma glucose

concentrations were measured using the IL Test Glucose Hexokinase

Clinical Chemistry kit (Instrumentation Laboratories, Warrington,

UK). Homeostasis model assessment of insulin resistance (HOMA-

IR) was derived from fasting glucose and insulin concentrations as

follows ((fasting plasma glucose � fasting serum insulin)/22.5) (14).

Quantitative insulin-sensitivity check index, a measure of insulin

sensitivity, was calculated as ¼ (1/(log fasting insulin þ log fasting

glucose þ log fasting free fatty acid)) (15). An insulin-modified in-

travenous glucose tolerance test was performed. Measures of insulin

sensitivity (sensitivity index) were determined using the MINMOD

Millenium Program (version 6.02, Richard N. Bergman). The acute

insulin response to glucose (AIRg ¼ first phase insulin response)

Original Article ObesityEPIDEMIOLOGY/GENETICS

www.obesityjournal.org Obesity | VOLUME 21 | NUMBER 1 | JANUARY 2013 E155

was defined as the incremental area under the curve from time 0-8

min. Disposition index (DI) was calculated as the product of acute

insulin response to glucose and sensitivity index. Cholesterol and tri-

glycerides were quantified using the IL TestCholesterol kit and IL

Tes Triglycerides kit (Instrumentation Laboratories). The IL Test

HDL-C Kit (Instrumentation Laboratories) was used for direct quan-

tification of HDL-cholesterol. The WAKO NEFA C enzymatic color

kit (Alpha Laboratories, Hampshire, UK) was used to quantify

plasma non-esterified fatty acids concentration. Plasma concentra-

tions of adiponectin, leptin, and resistin were measured by enzyme-

linked immunosorbent assay (ELISA) (DuoSet ELISA Development

System DY1065, DY398, AND DY1359; R&D Systems, Minneapo-

lis, MN). Plasma concentrations of C reactive protein (CRP) were

determined by high-sensitivity ELISA (BioCheck, Foster City, CA).

Tumor necrosis factor-a (TNF-a) and interleukin 6 were measured

by ultra sensitive ELISA (R&D Systems, Abingdon, UK and

Biosource International, Camarillo, CA). Intracellular and vascular

adhesion molecules were measured by ELISA (R&D Systems, Abing-

don, UK). Plasminogen activator inhibitor-1 (PAI-1) was determined

by the immunoactivity assay Chromolize PAI-1 (Trinity Biotech,

Bray, Ireland) and tissue plasminogen activator (tPA) was measured

by ELISA (Affinity Biologicals, Ancaster, Ontario, Canada).

Statistical analysisData are presented as means 6 s.e.m. Statistical analyses were car-

ried out using SPSS version 18.0 for Windows (SPSS, Chicago, IL).

Biochemical variables were assessed for normality of distribution,

and skewed variables were normalized by log10 or square root trans-

formation as appropriate. Cutoff points for BF% defining obesity in

adult populations (�25% in men and �35% in women) are those

most frequently used in the literature, which include examination of

a number of European populations and a meta-analysis (11-13).

Individuals identified as being obese by BMI (�30 kg/m2) and

obese according to their BF% (�25% in men and �35% in women)

were classified as OO (n ¼ 284). Individuals identified as nonobese

by BMI (BMI <30 kg/m2) and as obese by their BF% were classi-

fied as NOO (n ¼ 92). Differences between groups were analyzed

by two-tailed Student’s t-tests. To examine dietary responsiveness,

post-intervention changes (post-intervention minus baseline) for each

group were also compared. ANOVA-based models (with Bonferroni

correction) were then used to test for associations in each of the

four dietary arms to detect specific effects of the different dietary

interventions. Correlations between two variables were computed by

Spearman correlation coefficient. For all analyses a P value of

<0.05 was considered significant.

ResultsAnthropometric measures and clinicalcharacteristics of MetS subjectsAccording to their BMI, 2.8%, 27.5%, and 69.7% of the MetS sub-

jects participating in this study were classified as normal, over-

weight, and obese. When BF% was used to classify individuals

5.9%, 10.9%, and 83.2% of the study population were identified as

normal, overweight, and obese. Clinical and anthropometric charac-

teristics of the study population according to both obesity classifica-

tions are presented in Table 1. In addition to greater anthropometric

TABLE 1 Anthropometric and clinical characteristics of the study population according to their BMI and percentage body fat

Obesity Obesity and BF Classification in MetS Phillips et al.


measures (P < 0.001), obese individuals displayed raised CRP, lep-

tin, and insulin concentrations and were more insulin resistant (P <0.005) compared with nonobese subjects regardless of which classi-

fication was used to define obesity. Use of BF% alone identified

higher blood concentration of TNF-a, resistin, and fibrinogen con-

centrations in the obese individuals (Table 2) (P < 0.05). Use of

BMI alone identified higher PAI-1 and tPA concentrations, and

lower insulin sensitivity in the obese subjects (Table 3) (P < 0.005).

TABLE 2 Inflammatory markers of the study population according to their BMI and percentage body fat

TABLE 3 Measures of glucose homeostasis and plasma lipid profiles of the study population according to their BMI andpercentage body fat



Obesity classification of MetS subjectsExamination of the use of both body composition tools revealed that

38.5% of the MetS cases classified as normal weight by BMI were

actually obese when classified by BF%. This observation was unique

to the female subjects (46% classified as lean by BMI were actually

obese according to BF%). Although it might be expected that

women would have higher BF% for a given BMI than men, it

should also be noted that this is a MetS only cohort and the numbers

of individuals classified as normal weight is small according to their

BMI. Of all MetS individuals classified as overweight by BMI, 87%

were actually obese when classified by BF%. Again, this discrep-

ancy in classification was higher for women (84% of those classified

as overweight were actually obese when classified by BF%) than for

men (53%). In contrast, none of the subjects classified as obese by

BMI were normal weight according to BF%.

BMI showed strong positive correlations with body weight (r ¼0.66, P < 0.0001), waist circumference (r ¼ 0.62, P < 0.0001) and

to a lesser extent with BF% (r ¼ 0.38, P < 0.0001) in the whole

population. Interestingly, following stratification by gender, stronger

correlations were observed in the male subjects between BMI and

BF% (r ¼ 0.64 and r ¼ 0.36, P < 0.0001, for men and women,

respectively) and waist circumference (r ¼ 0.83, and r ¼ 0.60, P <0.0001, for men and women, respectively), with similar correlations

between BMI and body weight in both men and women (r ¼ 0.78

and r ¼ 0.78, P < 0.0001).

Impact of combined BMI and BF% obesityclassification on cardiometabolic riskCharacteristics of the study population stratified by obesity classifi-

cation are presented in Table 4. Individuals classified as obese by

both BMI and BF% (OO, n ¼ 2 84) were younger and comprised

more male subjects compared with individuals classified as

nonobese by BMI and obese by BF% (NOO, n ¼ 92). OO individu-

als had larger waist and hip measurements, higher BMI, and were

heavier due to greater lean and fat mass (kg) and body water (liters)

(P < 0.001) compared with the NOO subjects. OO individuals

displayed a more insulin resistant, proinflammatory, prothrombotic

and proatherogenic profile characterized by higher CRP, leptin and

PAI-1 concentrations and a greater leptin/adiponectin ratio (Table 5)

and lower insulin-sensitivity and higher insulin-resistance indexes

(Table 6) relative to the NOO group (P < 0.001). Interestingly, OO

subjects had more favorable plasma lipids with lower total and low-

density lipoprotein cholesterol compared with the NOO subjects

(Table 6) (P < 0.05). However, this did not translate into significant

differences between groups with respect to atherogenic lipid indexes

(low-density lipoprotein cholesterol/HDL, Log (triglycerides/HDL-

cholesterol)) and total cholesterol/HDL-C; not shown) probably due

to lower HDL cholesterol concentrations in the OO subjects (P <0.05). Despite the gender difference for obesity classification

according to BMI and BF% and between OO and NOO groups, it is

worthwhile to note that separate comparisons of OO vs. NOO

groups in the male and female subjects mirrored the findings for the

entire cohort (data not shown), with the exception of BF% which

was higher in both OO male (32.7 6 0.4 vs. 29.3 6 0.6, P < 0.05)

TABLE 4 Clinical and anthropometric characteristics accordingto combined BMI and percentage body fat obesityclassification

TABLE 5 Concentrations of inflammatory markers, adhesionmolecules and haemostatic factors according to combinedBMI and percentage body fat obesity classification



and female subjects (44.1 6 0.3 vs. 42.7 6 0.5, P < 0.05) relative

to their NOO counterparts.

Obesity classification and dietary responsivenessChanges (post-intervention minus baseline) in each of the cardiome-

tabolic profile parameters for the NOO and OO individuals were

compared. Following the intervention, the NOO subjects demon-

strated a significant reduction in TNF-a concentrations (P < 0.001)

compared with the OO individuals. When the individual dietary

interventions were analyzed separately to ascertain whether this

finding was a diet-specific effect, 52% and 31% reductions (com-

pared with baseline) in TNF-a concentrations were observed in the

NOO subjects following the HSFA (P < 0.01) and HMUFA (P <0.05) interventions, respectively ( Figure 1). Moreover, compared

with pre-intervention, NOO individuals demonstrated post-interven-

tion reductions in plasma concentrations of CRP (4.21 6 0.37 vs

3.51 6 0.39 mg/l, P < 0.05) and resistin (9.40 6 1.04 vs 7.06 6

1.05 mg/ml, P < 0.05) following the LFHCC LC n-3 PUFA diet and

a BF% loss following the LFHCC diet (38.9 6 1.8 vs 37.0 6 1.9%,

P < 0.05). No changes in markers of glucose homeostasis, adhesion

molecules, and haemostatic factors or lipids were noted in either

group after 12 weeks of dietary intervention.

DiscussionThe National Health and Nutrition Examination Survey (1999-2004)

revealed that 24% of normal weight adults were metabolically

abnormal whereas 51% of overweight and 32% obese adults were

metabolically healthy (16). There has been much interest in the

paradoxical finding of individuals considered inappropriately healthy

for their degree of obesity and subsequently several phenotype sub-

groups of obesity have been described including metabolically

healthy or insulin-sensitive obese, metabolically obese but normal

weight and more recently taking BF% into account, normal weight

obese (17,18,19). The aim of the current work was to examine cardi-

ometabolic risk metabotype in obese and nonobese adults with the

MetS and BF% in the obese range. We found that 39% and 87% of

the MetS cases classified as normal and overweight by BMI had a

BF% in the obese range, suggesting that use of BMI alone to diag-

nose obesity underestimates BF%, particularly in overweight MetS

subjects. These data support earlier findings from a large cross-sec-

tional study which reported that 29% of individuals classified as

normal weight (BMI �24.9 kg/m2) and 80% of individuals charac-

terized as overweight (BMI 25-29.99 kg/m2) had a BF% within the

obese range (4). The discrepancy in classification of normal weight

and overweight by BMI as obese by BF% reported in our study was

higher for women. We also report stronger correlations between

BMI and both BF% and waist in the male subjects. Whether inclu-

sion of BF% with BMI in defining physiologically relevant obesity

FIGURE 1 Plasma concentrations of tumor necrosis factor-a (TNF-a) among meta-bolic syndrome subjects in the LIPGENE study. A significant change (post-interven-tion minus baseline) in TNF-a concentrations was noted for the NOO compared tothe OO individuals (P < 0.001). Post-intervention reductions in plasma concentra-tion of TNF-a were observed among the NOO subjects (a) following the HSFA (P <0.01) and HMUFA diets (P < 0.05). (b) No significant changes were noted in theOO individuals following any of the four dietary interventions. Pre-intervention TNF-a concentrations are depicted as black bars and post-intervention TNF-a concen-trations are shown as white bars. LFHCC, low fat high complex carbohydrate.

TABLE 6 Indexes of glucose metabolism and plasma lipidprofiles according to combined BMI and percentage bodyfat obesity classification



is more important in women is unknown. It should be noted that the

number of normal weight individuals in this cohort was small (2.8%)

and that these findings may be a reflection of the greater number of

females in the study, the limitations of the BMI tool and gender dif-

ferences in BF% and fat/lean tissue mass distribution. When BMI and

BF% were used in conjunction individuals classified as obese by both

tools (OO) displayed a more insulin resistant, proinflammatory, pro-

thrombotic, and proatherogenic profile compared with subjects classi-

fied as nonobese by BMI with BF% in the obese range (NOO). These

findings were mirrored in both male and female subjects. Thus, com-

plementary use of both diagnostic tools has the potential to detect

individuals at greater cardiometabolic risk.

The NCEP ATP III identified a proinflammatory state as an impor-

tant MetS characteristic (20). Chronic low-grade inflammation plays

a role in the pathogenesis of insulin resistance, with elevated circu-

lating levels of CRP and the proinflammatory cytokines such as

TNF-a associated with greater risk of having T2DM and MetS

(21,22,23). In normal weight obese women without the MetS, con-

centrations of proinflammatory cytokines were higher than in the

nonobese group and intermediate to a preobese/obese group, sug-

gesting that these biomarkers might be prognostic indicators of the

risk of obesity, MetS, and CVD in normal weight obese women

(24). Given the central role of obesity in the pathogenesis of these

cardiometabolic diseases, the adipose tissue-derived inflammatory

mediators adiponectin and leptin may also be particularly important.

Circulating plasma levels of adiponectin are reduced in obese and

T2DM subjects (25). In contrast, plasma leptin levels increase pro-

portionally with fat mass and have been shown to be a predictor of

CVD in both case-control and prospective studies (26,27). In recent

years, the leptin/adiponectin ratio has been suggested as an athero-

sclerotic index and as a useful parameter to assess insulin resistance

in patients with and without T2DM (28,29).

We demonstrated that MetS individuals with both BMI and BF% in

the obese range were more insulin resistant, had higher plasma con-

centrations of CRP, leptin and PAI-1 and a greater leptin/adiponectin

compared with subjects classified as obese by BF% with a normal

BMI. We did not observe any differences in adiponectin levels

between obese and nonobese MetS subjects or between NOO and OO

individuals. However, considering that the adiponectin concentrations

reported in our study are low in all subjects, it may be that the obe-

sity-related reduction in adiponectin levels per se is diminished

against a background of numerous metabolic perturbations which also

contribute to reduced adiponectin levels. While the Gomez-Ambrosi

et al., study did not measure adiponectin, they did show higher leptin

concentrations in men and women, and higher HOMA-IR values in

women with BMI and BF% in the obese range as compared with

those with normal BMI and BF% (4). Plasma CRP concentrations

were not different between these groups. It should be noted that that

study was a cross-sectional investigation and used an air displacement

plethysmographic method to estimate BF%, whereas our data relate

to a MetS only cohort wherein BF% was determined by bioelectrical

impedance. Our method provides a cost-effective and direct determi-

nation of total body composition, which is comparable in terms of

accuracy of BF% determination with dual-energy X-ray absorptiome-

try (30). Our data support the notion of BF% determination by

bioelectrical impedance as a valuable additional diagnostic tool.

Surprisingly, the HOMA-IR values for the NOO subjects were

below the cutoff point for insulin resistance (>2.61) (11,14); thus,

these individuals might be considered as insulin-sensitive obese.

Investigation of insulin signaling and inflammatory pathways in in-

sulin-sensitive and insulin-resistant severely obese (IRMO) subjects,

support the concept that insulin-sensitive severely obese subjects

have a lower inflammatory response than insulin-resistant morbidly

obese patients (31). In a recent study of obese (by BMI) 70-79 year

individuals with and without the MetS, the metabolically healthy (or

non MetS) obese subjects had a more favorable inflammatory profile

(lower plasma concentrations of TNF-a and PAI-1) and body fat dis-

tribution than the obese MetS individuals, despite both groups hav-

ing BMI and BF% in the obese range (32). Examination of the waist

to hip ratio in our current study revealed that OO subjects had a

higher waist to hip ratio, suggesting that they carried more abdomi-

nal weight than the NOO individuals. Leg fat has been associated

with more favorable metabolic and inflammatory profiles (33,34)

and visceral, but not abdominal subcutaneous fat, has been linked

with higher plasma concentrations of IL-6 and CRP (35). It would

be interesting to determine whether body fat depots were different

between the NOO and OO groups in the current work.

A novel finding in our study is the difference in dietary responsive-

ness between the NOO and OO subjects. No changes in any plasma

measurements were noted after intervention in the OO subjects. In

contrast, TNF-a concentrations were significantly reduced in the

NOO subjects. When each of the four dietary arms were analyzed

separately, reductions in plasma concentrations of TNF-a were

observed following the HSFA and HMUFA interventions, whereas

CRP and resistin concentrations were reduced following the LFHCC

LC n-3 PUFA diet. NOO subjects also experienced a BF% reduction

following the LFHCC diet. Cross-sectional, intervention and experi-

mental data suggest that high-fat diets promote obesity, insulin re-

sistance and inflammation, driving the development of MetS,

T2DM, and CVD (36,37). Epidemiological studies also demonstrate

anti-inflammatory effects of dietary fish, fish oil, and/or LC n-3

PUFA consumption (38,39). We recently reported that the LFHCC

LC n-3 PUFA diet reduced triglycerides-related MetS phenotypes

and the risk of having the MetS in this cohort (9,40). While the

reduction in TNF-a concentrations following the HSFA and

HMUFA diets contradicts the literature, the beneficial effects

observed after the low-fat interventions in the NOO group were not

entirely unexpected. However, why the NOO, and not the OO group,

appear to be responsive remains unclear. Although speculative, it

may be that NOO subjects who are more insulin sensitive and have

less proinflammatory, prothrombotic, and proatherogenic profiles

compared with the OO subjects have greater metabolic flexibility to

adapt to changes in dietary fat. Perhaps coordination of the path-

ways involved in nutrient handling, insulin signaling, inflammation,

and lipid metabolism is less disturbed than in the OO subjects who

are simply metabolically overburdened and no longer dietary respon-

sive. Whatever the explanation, these data suggest that not only are

the OO subjects, who represent almost 60% of our MetS cohort, at

greater cardiometabolic risk but that they are less responsive to die-

tary intervention. Whether these individuals would have more reduc-

tion of cardiometabolic risk by lifestyle and behavioral intervention

alone or in combination with dietary changes is unknown but would

be worth examining further.

To our knowledge, this is the first study to investigate whether obe-

sity classification by both BMI and BF% may influence cardiometa-

bolic risk metabotypes and dietary responsiveness in the MetS. Our

study has a number of strengths including relatively large subject



numbers, comprehensive determination of insulin and glucose me-

tabolism by static (glucose and insulin plasma concentrations) and

dynamic (disposition index, Si, HOMA-IR, and acute insulin

response to glucose) indexes, and a 12-week dietary intervention.

Despite these strengths our study presents some limitations. First,

more comprehensive examination of body fat distribution would be

advantageous. Although bioelectrical impedence tends to overesti-

mate BF% in normal weight subjects but tends to underestimate

BF% in obese individuals, such potential misclassification would

however, if anything, result in underestimating the degree of body

fatness in some of the ‘‘true’’ obese subjects and thus merely under-

estimate the present associations. Second, the lack of a follow-up

assessment to determine if post-intervention changes observed fol-

lowing a 12-week intervention might be altered after long-term

intervention. Finally, the cross-sectional study design does not allow

causality to be established. In conclusion, we have demonstrated

that the combined use of BF% and BMI may be more useful in

identifying individuals with a greater cardiometabolic risk metabo-

type than BMI alone. This finding may be particularly important in

the MetS considering the prevalence of obesity and increased CVD

risk associated with this condition.O

AcknowledgmentsThis work was supported by the European Commission, Framework

Programme 6 (LIPGENE), contract number FOOD-CT-2003-505

944; Johan Throne Holst Foundation for Nutrition Research, Freia

Medical Foundation. The CIBEROBN is an initiative of the Instituto

de Salud Carlos III, Madrid, Spain.

VC 2012 The Obesity Society

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