Obesity and body fat classification in the metabolic syndrome: Impact on cardiometabolic risk metabotype

Authors

  • Catherine M. Phillips,

    1. Nutrigenomics Research Group, UCD School of Public Health and Population Science, UCD Conway Institute, University College Dublin, Dublin, Ireland
    2. Department of Epidemiology and Public Health, University College Cork, Cork, Ireland
    Search for more papers by this author
  • Audrey C. Tierney,

    1. Nutrigenomics Research Group, UCD School of Public Health and Population Science, UCD Conway Institute, University College Dublin, Dublin, Ireland
    Search for more papers by this author
  • Pablo Perez-Martinez,

    1. 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
    Search for more papers by this author
  • Catherine Defoort,

    1. INSERM, 476 Human Nutrition and Lipids, INRA, 1260, University Méditerranée Aix-Marseille 2, Marseille, France
    Search for more papers by this author
  • Ellen E. Blaak,

    1. Department of Human Biology, Nutrition and Toxicology Research Institute Maastricht (NUTRIM), Maastricht,The Netherlands
    Search for more papers by this author
  • Ingrid M. F. Gjelstad,

    1. Department of Nutrition, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
    2. Department of Clinical Endocrinology, Oslo University Hospital Aker, Oslo, Norway
    Search for more papers by this author
  • Jose Lopez-Miranda,

    1. 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
    Search for more papers by this author
  • Malgorzata Kiec-Klimczak,

    1. Department of Clinical Biochemistry, Jagiellonian University Medical College, Krakow, Poland
    Search for more papers by this author
  • Malgorzata Malczewska-Malec,

    1. Department of Clinical Biochemistry, Jagiellonian University Medical College, Krakow, Poland
    Search for more papers by this author
  • Christian A. Drevon,

    1. Department of Nutrition, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
    Search for more papers by this author
  • Wendy Hall,

    1. Hugh Sinclair Unit of Human Nutrition and Institute for Cardiovascular and Metabolic Research, Department of Food and Nutritional Sciences, University of Reading, Reading, UK
    Search for more papers by this author
  • Julie A. Lovegrove,

    1. Hugh Sinclair Unit of Human Nutrition and Institute for Cardiovascular and Metabolic Research, Department of Food and Nutritional Sciences, University of Reading, Reading, UK
    Search for more papers by this author
  • Brita Karlstrom,

    1. Department of Public Health and Caring Sciences/Clinical Nutrition and Metabolism, Uppsala University, Uppsala, Sweden
    Search for more papers by this author
  • Ulf Risérus,

    1. Department of Public Health and Caring Sciences/Clinical Nutrition and Metabolism, Uppsala University, Uppsala, Sweden
    Search for more papers by this author
  • Helen M. Roche

    Corresponding author
    1. Nutrigenomics Research Group, UCD School of Public Health and Population Science, UCD Conway Institute, University College Dublin, Dublin, Ireland
    • Nutrigenomics Research Group, UCD School of Public Health and Population Science, UCD Conway Institute, University College Dublin, Dublin, Ireland;

    Search for more papers by this author

Abstract

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 = 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 = 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-α (TNF-α) 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.

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 (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.

Methods and Procedures

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).

Dietary intervention

Participants 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 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 (11,12,13). Blood pressure was measured according to the European Society of Hypertension Guidelines.

Biochemical measurements

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 (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 = (1/(log fasting insulin + log fasting glucose + 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 = first phase insulin response) 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 enzyme-linked 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-α (TNF-α) 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 ± 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 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 (11,12,13). 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 = 284). Individuals identified as nonobese by BMI (BMI <30 kg/m2) and as obese by their BF% were classified as NOO (n = 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 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-α, 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 1. Anthropometric and clinical characteristics of the study population according to their BMI and percentage body fat
chemical structure image
Table 2. Inflammatory markers of the study population according to their BMI and percentage body fat
chemical structure image
Table 3. Measures of glucose homeostasis and plasma lipid profiles of the study population according to their BMI and percentage body fat
chemical structure image

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 = 0.66, P < 0.0001), waist circumference (r = 0.62, P < 0.0001) and to a lesser extent with BF% (r = 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 = 0.64 and r = 0.36, P < 0.0001, for men and women, respectively) and waist circumference (r = 0.83, and r = 0.60, P < 0.0001, for men and women, respectively), with similar correlations between BMI and body weight in both men and women (r = 0.78 and r = 0.78, P < 0.0001).

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 = 2 84) were younger and comprised more male subjects compared with individuals classified as nonobese by BMI and obese by BF% (NOO, n = 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 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 ± 0.4 vs. 29.3 ± 0.6, P < 0.05) and female subjects (44.1 ± 0.3 vs. 42.7 ± 0.5, P < 0.05) relative to their NOO counterparts.

Table 4. Clinical and anthropometric characteristics according to combined BMI and percentage body fat obesity classification
chemical structure image
Table 5. Concentrations of inflammatory markers, adhesion molecules and haemostatic factors according to combined BMI and percentage body fat obesity classification
chemical structure image
Table 6. Indexes of glucose metabolism and plasma lipid profiles according to combined BMI and percentage body fat obesity classification
chemical structure image

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-α 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-α 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 ± 0.37 vs 3.51 ± 0.39 mg/l, P < 0.05) and resistin (9.40 ± 1.04 vs 7.06 ± 1.05 µg/ml, P < 0.05) following the LFHCC LC n-3 PUFA diet and a BF% loss following the LFHCC diet (38.9 ± 1.8 vs 37.0 ± 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.

Figure 1.

Plasma concentrations of tumor necrosis factor-α (TNF-α) among metabolic syndrome subjects in the LIPGENE study. A significant change (post-intervention minus baseline) in TNF-α concentrations was noted for the NOO compared to the OO individuals (P < 0.001). Post-intervention reductions in plasma concentration of TNF-α 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 TNF-α concentrations are depicted as black bars and post-intervention TNF-α concentrations are shown as white bars. LFHCC, low fat high complex carbohydrate.

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 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 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-α 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, 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-α 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-α concentrations were significantly reduced in the NOO subjects. When each of the four dietary arms were analyzed separately, reductions in plasma concentrations of TNF-α 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-α 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 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.

Acknowledgements

This work was supported by the European Commission, Framework Programme 6 (LIPGENE), contract number FOOD-CT-2003-505944; Johan Throne Holst Foundation for Nutrition Research, Freia Medical Foundation. The CIBEROBN is an initiative of the Instituto de Salud Carlos III, Madrid, Spain.

Ancillary