Obesity
Original Article
EPIDEMIOLOGY/GENETICS
Obesity and Body Fat Classification in the
Metabolic Syndrome: Impact on
Cardiometabolic Risk Metabotype
Catherine 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 condition 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 obesity the MetS is characterized by dyslipidemia and hypertension,
which are associated with increased risk of type 2 diabetes mellitus (T2DM) and cardiovascular disease (CVD) (2). A number of
adiposity measures are currently used as diagnostic tools in overweight 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, Ireland
Department of Epidemiology and Public Health, University College Cork, Cork, Ireland 3 Lipid and Atherosclerosis Unit, IMIBIC/Reina Sofia University
Hospital/University of Cordoba, and CIBER Fisiopatologia Obesidad y Nutricion (CIBEROBN), Instituto de Salud Carlos III, Cordoba, Spain 4 INSERM, 476
Human Nutrition and Lipids, INRA, 1260, University Mediterranee Aix-Marseille 2, Marseille, France 5 Department of Human Biology, Nutrition and
Toxicology Research Institute Maastricht (NUTRIM), Maastricht,The Netherlands 6 Department of Nutrition, Institute of Basic Medical Sciences, University of
Oslo, Oslo, Norway 7 Department of Clinical Endocrinology, Oslo University Hospital Aker, Oslo, Norway 8 Department of Clinical Biochemistry, Jagiellonian
University Medical College, Krakow, Poland 9 Hugh Sinclair Unit of Human Nutrition and Institute for Cardiovascular and Metabolic Research, Department of
Food 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])
2
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
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Original Article
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(WC), BMI, and body fat percentage (BF%). WC is the only adiposity measure included in the current International Diabetes Federation and National Cholesterol Education Program’s Adult Treatment 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 prevalence 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, particularly 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 examination of markers of glucose metabolism according to obesity classification revealed that BF% may be a better determinant for pre-diabetes and T2DM development (6).
Ideally, obesity prevention would reduce risk of associated cardiometabolic 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 heterogeneity and differences in dietary responsiveness. Thus, there is a
need to improve obesity diagnosis and to develop new preventative strategies and evidence-based public health measures to attenuate disease development 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 cardiometabolic profile of individuals with the MetS is currently unavailable. Considering the increasing prevalence of the MetS and its associated cardiometabolic 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 phenotype, in MetS is more effective in detecting individuals at greater cardiometabolic risk than BMI alone may have public health implications in
terms of improving obesity classification in high-risk groups.
Dietary intervention
Participants were recruited to a 12-week dietary intervention after
being randomly allocated to one of the four following diets: highfat (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 carbohydrate diet (8% SFA, 11% MUFA, 6% PUFA), with 1.24 g/day
LC n-3 PUFA supplement (LFHCC n-3). Randomization was performed using age, gender, and fasting plasma glucose concentration as matching variables, applying Minimisation Programme for
Allocating patients to Clinical Trials (Department of Clinical Epidemiology, 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 measurements
Anthropometric measurements were recorded according to a standardized 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 comfortably. Two current-injection electrodes were placed at the right hand
and foot on the dorsal surfaces proximal to the metacarpal-phalangeal 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.
Methods and Procedures
Biochemical measurements
Subjects aged 35-70 years and BMI 20-40 kg/m2 were recruited for
the LIPGENE dietary intervention study from eight European countries (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). Subject 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 pressure 130/85 mmHg or treatment of previously diagnosed hypertension. We used the preintervention data for 486 subjects and the postintervention data for the 417 subjects completing the intervention.
Detailed characteristics of this cohort have been published (8).
Plasma 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 system (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 (HOMAIR) 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 intravenous 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)
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TABLE 1 Anthropometric and clinical characteristics of the study population according to their BMI and percentage body fat
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 triglycerides 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 quantification 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 concentrations of adiponectin, leptin, and resistin were measured by enzymelinked immunosorbent assay (ELISA) (DuoSet ELISA Development
System DY1065, DY398, AND DY1359; R&D Systems, Minneapolis, 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, Abingdon, 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 analysis
Data are presented as means 6 s.e.m. Statistical analyses were carried 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 transformation as appropriate. Cutoff points for BF% defining obesity in
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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 classified 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.
Results
Anthropometric measures and clinical
characteristics of MetS subjects
According to their BMI, 2.8%, 27.5%, and 69.7% of the MetS subjects participating in this study were classified as normal, overweight, 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 characteristics of the study population according to both obesity classifications are presented in Table 1. In addition to greater anthropometric
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TABLE 2 Inflammatory markers of the study population according to their BMI and percentage body fat
measures (P < 0.001), obese individuals displayed raised CRP, leptin, and insulin concentrations and were more insulin resistant (P <
0.005) compared with nonobese subjects regardless of which classification was used to define obesity. Use of BF% alone identified
higher blood concentration of TNF-a, resistin, and fibrinogen concentrations 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 3 Measures of glucose homeostasis and plasma lipid profiles of the study population according to their BMI and
percentage body fat
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TABLE 4 Clinical and anthropometric characteristics according
to combined BMI and percentage body fat obesity
classification
Impact of combined BMI and BF% obesity
classification on cardiometabolic risk
Characteristics of the study population stratified by obesity classification 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 individuals 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 lowdensity 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/HDLcholesterol)) 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 5 Concentrations of inflammatory markers, adhesion
molecules and haemostatic factors according to combined
BMI and percentage body fat obesity classification
Obesity classification of MetS subjects
Examination 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 discrepancy 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 ¼
0.66, 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).
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TABLE 6 Indexes of glucose metabolism and plasma lipid
profiles according to combined BMI and percentage body
fat obesity classification
paradoxical finding of individuals considered inappropriately healthy
for their degree of obesity and subsequently several phenotype subgroups 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 cardiometabolic 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 diagnose obesity underestimates BF%, particularly in overweight MetS
subjects. These data support earlier findings from a large cross-sectional study which reported that 29% of individuals classified as
normal weight (BMI 24.9 kg/m2) and 80% of individuals characterized 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 inclusion of BF% with BMI in defining physiologically relevant obesity
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 responsiveness
Changes (post-intervention minus baseline) in each of the cardiometabolic profile parameters for the NOO and OO individuals were
compared. Following the intervention, the NOO subjects demonstrated 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 (compared 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-intervention 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.
Discussion
The 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
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FIGURE 1 Plasma concentrations of tumor necrosis factor-a (TNF-a) among metabolic syndrome subjects in the LIPGENE study. A significant change (post-intervention minus baseline) in TNF-a concentrations was noted for the NOO compared to
the OO individuals (P < 0.001). Post-intervention reductions in plasma concentration 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 the
OO individuals following any of the four dietary interventions. Pre-intervention TNFa concentrations are depicted as black bars and post-intervention TNF-a concentrations are shown as white bars. LFHCC, low fat high complex carbohydrate.
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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 differences 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, prothrombotic, and proatherogenic profile compared with subjects classified as nonobese by BMI with BF% in the obese range (NOO). These
findings were mirrored in both male and female subjects. Thus, complementary 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 important MetS characteristic (20). Chronic low-grade inflammation plays
a role in the pathogenesis of insulin resistance, with elevated circulating 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, concentrations of proinflammatory cytokines were higher than in the
nonobese group and intermediate to a preobese/obese group, suggesting 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 proportionally 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 atherosclerotic 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 concentrations 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 obesity-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 determination of total body composition, which is comparable in terms of
accuracy of BF% determination with dual-energy X-ray absorptiometry (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,
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these individuals might be considered as insulin-sensitive obese.
Investigation of insulin signaling and inflammatory pathways in insulin-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 distribution than the obese MetS individuals, despite both groups having 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 abdominal 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 responsiveness 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 experimental data suggest that high-fat diets promote obesity, insulin resistance 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 pathways 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 responsive. 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 dietary intervention. Whether these individuals would have more reduction 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 obesity classification by both BMI and BF% may influence cardiometabolic risk metabotypes and dietary responsiveness in the MetS. Our
study has a number of strengths including relatively large subject
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numbers, comprehensive determination of insulin and glucose metabolism 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 overestimate 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 underestimate the present associations. Second, the lack of a follow-up
assessment to determine if post-intervention changes observed following 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 metabotype 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
Acknowledgments
This 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.
C 2012 The Obesity Society
V
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