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Journal of Clinical Endocrinology & Metabolism , doi:10.1210/jc.2007-2089
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The Journal of Clinical Endocrinology & Metabolism Vol. 93, No. 6 2122-2128
Copyright © 2008 by The Endocrine Society

Insulin Sensitivity Is Correlated with Subcutaneous but Not Visceral Body Fat in Overweight and Obese Prepubertal Children

Claudio Maffeis, Riccardo Manfredi, Maddalena Trombetta, Silvia Sordelli, Monica Storti, Teresa Benuzzi and Riccardo C. Bonadonna

Department of Mother and Child (C.M., S.S., M.S., T.B.), Biology-Genetics, Section of Pediatrics; Department of Radiology (R.M.), Section of Radiology; and Department of Biomedical and Surgical Sciences (M.T., R.C.B.), Section of Endocrinology, University of Verona, 37134 Verona, Italy

Address all correspondence and requests for reprints to: Claudio Maffeis, M.D., Department of Mother and Child, Biology-Genetics, Section of Pediatrics, University of Verona, Piazzale L.A. Scuro 10, 37134 Verona, Italy. E-mail: claudio.maffeis{at}univr.it.


   Abstract
Top
 Abstract
Introduction
 Subjects and Methods
 Results
 Discussion
 References
 
Aim: Our aim was to explore the relationship between insulin sensitivity, body fat distribution, ectopic (liver and skeletal muscle) fat deposition, adipokines (leptin and adiponectin), and inflammation markers (highly sensitive C-reactive protein, IL-6, IL-10, and TNF-{alpha}) in prepubertal children.

Subjects and Methods: Thirty overweight and obese children (16 males and 14 females with body mass index z-score range of 1.1–3.2) were recruited. Body fat distribution and fat accumulation in liver and skeletal muscle were measured using magnetic resonance imaging. Insulin sensitivity was assessed by iv glucose tolerance test.

Results: Insulin sensitivity was associated with sc abdominal adipose tissue (SAT) (r = –0.52; P < 0.01) and liver fat content (r = –0.44; P < 0.02) but not with visceral abdominal adipose tissue (VAT) (r = –0.193; P value not significant) and fat accumulation in skeletal muscle (r = –0.210; P value not significant). Adipokines, but not inflammation markers, were significantly correlated to insulin sensitivity. VAT correlated with C-reactive protein (r = 0.55; P < 0.01) as well as adiponectin (r = –0.53; P <0.01). Multiple regression analysis showed that only SAT and liver fat content were independently correlated to insulin sensitivity (P < 0.01; 20 and 16% of explained variance, respectively).

Conclusions: In overweight and moderately obese prepubertal children, insulin sensitivity was negatively correlated with SAT and liver fat content. Furthermore, contrary to adults, VAT and inflammation markers were not correlated with insulin sensitivity in children.


   Introduction
Top
 Abstract
 Introduction
Subjects and Methods
 Results
 Discussion
 References
 
The prevalence and severity of childhood obesity are increasing dramatically worldwide (1, 2). They are accompanied by an increase of obesity-associated comorbidities (3). Obesity is often accompanied by alterations in fat distribution (e.g. visceral vs. sc depot) and by ectopic fat accumulation. The latter consists of lipid accumulation in parenchymal cells of multiple tissues, including hepatocytes, skeletal and cardiac myocytes, and pancreatic β-cells, resulting in chronic cellular dysfunction and injury, generally called lipotoxicity (4). Insulin resistance, which is frequently associated with obesity and lipotoxicity, is likely to play a key role in the development of metabolic disorders (4, 5).

Obese individuals, both adults and children, have a higher accumulation of fat in their liver than nonobese people (6, 7). This condition, defined as nonalcoholic fatty liver disease, includes a spectrum of diseases, ranging from the more benign simple fatty liver (steatosis) to nonalcoholic steatohepatitis, which includes inflammation and, in some cases, further evidence of cellular injury, fibrosis and cirrhosis (8, 9).

Fat accumulation in the liver is associated with several metabolic disturbances, the most common of which is insulin resistance (10, 11). Interestingly, fatty liver is more closely associated, at least in adults and adolescents, with fat distribution, particularly to visceral adipose tissue (VAT), than with total fat (12, 13). Enlarged VAT, through the release of free fatty acids (FFA) and other fat tissue-derived substances in the portal circulation, may contribute to promote fat storage in the hepatocyte and to initiate and/or worsen insulin resistance and/or lipotoxicity (14, 15).

The relationship between insulin resistance and visceral and sc fat distribution is affected by puberty (16, 17). In prepubertal children, who are in a dynamic phase of fat accumulation but who have not been exposed to the influence of hormone variations of puberty, the relationship between insulin resistance and fat distribution is still unclear. In particular, some authors have reported an association between VAT and insulin resistance, independently of total fat, whereas others have failed to find this association (18, 19, 20). Moreover, the size of another ectopic fat depot, i.e. intramyocellular lipids, was associated with insulin resistance independently of VAT (21, 22). No data on this relationship are available for prepubertal children.

Finally, an association between insulin sensitivity and a low-grade inflammatory status has been demonstrated in obese individuals (23, 24, 25). Circulating proinflammatory cytokines may promote hepatic fat accumulation and insulin resistance (26). In prepubertal children, studies have explored the relationship between fat distribution/ectopic deposition and insulin sensitivity (18, 19, 20), but none of them assessed the potential role of inflammation. Therefore, the aim of this study was to analyze the relationship between insulin sensitivity, fat deposition in different body districts (liver and skeletal muscle, visceral and sc abdominal areas, and deep and superficial areas at the sc abdominal level) and circulating proinflammatory mediators/indicators in a sample of Caucasian prepubertal children.


   Subjects and Methods
Top
 Abstract
 Introduction
 Subjects and Methods
Results
 Discussion
 References
 
A cohort of thirty prepubertal children with different levels of overweight and obesity was recruited. Children were defined as overweight or obese on the basis of their body mass index (BMI) using International Obesity Task force BMI cutoffs as a reference (27). Puberty development was clinically assessed on the basis of secondary sex characteristics. The configuration of the breasts and the quantity and pattern of pubic hair determine the ratings of girls. Genital status and the quantity and pattern of pubic hair determine the ratings of boys. Tanner stages for pubic hair, breast configuration, and genital status were used as reference (28). None of the subjects had initiated puberty. None of the children had any overt disease other than obesity or was taking medication.

Inclusion criteria were overweight or obesity and normal glucose tolerance. Exclusion criteria were puberty, inflammatory diseases (assessed clinically and by routine blood tests), blood or blood by-product transfusion, history of hepatic infectious disease (hepatitis B virus, hepatitis C virus, cytomegalovirus, or Epstein-Barr virus), impaired glucose tolerance or diabetes, and obesity secondary to genetic or metabolic disorders.

On the basis of the National Consensus Conference on Obesity, in the case of high alanine transferase (ALT) circulating levels (>40 IU/liter) at recruitment, a complete set of standard liver function tests were performed and an investigation was initiated to rule out myopathies and metabolic, autoimmune, and infectious liver disorders (29). Tests included serum levels of creatine kinase, {alpha}1-antitrypsin, ceruloplasmin, sweat electrolytes, urine copper and reducing substances, serum non-organ-specific autoantibodies, and antigens and/or antibodies related to major and minor hepatotropic viruses.

Informed consent was obtained from the children and their parents before taking part in the study. The protocol was in accordance with the 1975 Declaration of Helsinki, as revised in 1983, and was approved by the Ethical Committee of the University Hospital of Verona.

Anthropometry and body composition

Height and weight were measured in overnight-fasted conditions and with an empty bladder. Height was measured to the nearest 0.5 cm on a standardized height board. Weight was determined to the nearest 0.1 kg on a standard physician’s beam scale, with the subject dressed only in light underwear and no shoes. BMI was calculated as weight (kilograms) divided by height (meters) squared. BMI z-scores were calculated using the least mean square method and national reference values of BMI and least mean square coefficients (30). Complete data were collected for all of the children at baseline. The children underwent total-body dual-energy x-ray absorptiometry to assess body composition using a DPX-L densitometer (Lunar Corp., Madison, WI). The subjects were scanned in light clothing while lying flat on their backs. On the day of each test, the DPX-L was calibrated according to the manufacturer’s instructions. Body fat mass was calculated by multiplying the percentage of body fat by body weight. Fat-free mass (FFM) was obtained by subtracting fat mass from body weight.

Experimental protocol

This was a cross-sectional study, in which the children performed several tests on different days. Subjects arrived at the Pediatric Clinic of the University Hospital at 0800 h after a 12-h overnight fast. Their weight, height, and blood pressure were measured, and their BMI was calculated. Baseline fasting blood samples for the measure of plasma glucose, insulin, C-peptide, C-reactive protein (CRP), sedimentation rate, lipid profile [total cholesterol, triglycerides, low-density lipoprotein (LDL)-cholesterol, and high-density lipoprotein (HDL)-cholesterol], liver enzymes [aspartate transferase (AST), ALT, {gamma}-glutamyl transferase ({gamma}-GT), and alkaline phosphatase], and bilirubin were obtained. Subsequently, a standard 3-h oral glucose tolerance test (OGTT) was performed. Children ingested an oral glucose solution containing 1.75 g glucose/kg body weight (maximum 75 g). Venous blood samples were taken before and 30, 60, 120, and 180 min after glucose ingestion. Samples were placed on ice and spun in a centrifuge within 1 h, and plasma was collected and stored for measurement of glucose, insulin, and C-peptide. In accordance with the American Diabetes Association guidelines, prediabetes [impaired glucose tolerance (IGT)] was defined by a 2-h blood glucose of 140–199 mg/dl. None of the children had prediabetes. Afterward, the children underwent body composition measurements by dual-energy x-ray absorptiometry and magnetic resonance imaging (MRI). Finally, 6–8 d later, the children performed a frequently sampled iv glucose tolerance test (IVGTT) to measure insulin sensitivity (SI) (31).

IVGTT

The children arrived at the Pediatric Clinic at 0800 h after an overnight fast. Topical anesthetic was applied to both arms and a 23-gauge Teflon catheter was inserted into the antecubital vein of each arm, one for iv glucose infusion, the other for blood sampling. Blood samples were collected at times –10 and 0 min to determine baseline values of glucose, insulin, and C-peptide. At time 0 min, a 25% dextrose iv infusion was started at a rate designed to deliver 12 g/m2 body surface area (BSA) of glucose over approximately 1.5 min. The time required to infuse the glucose load was recorded for each subject. Blood samples were drawn from the opposite arm at 2, 4, 6, 8, 10, 15, 20, 25, 30, 40, 60, 80, 100, 120, 140, 160, 180, 200, and 220 min to determine glucose, insulin, and C-peptide concentrations. An analysis of the insulin and glucose curves during the IVGTT followed the general strategy proposed by Toffolo et al. (32) and Beard et al. (32) with slight modifications (33).

Biochemical parameters

Plasma glucose concentration, serum cholesterol, HDL-cholesterol, triglyceride, bilirubin, AST, ALT, {gamma}-GT, and alkaline phosphatase levels were measured by standard in-house methods. Plasma insulin and C-peptide levels were measured by chemiluminescent immunometric assays (EURO/DPC, Llanberis, UK). LDL-cholesterol level was estimated by the Friedewald formula (34). Plasma CRP and leptin were assessed by a high-sensitivity ELISA and by ELISA, respectively, according to the manufacturer’s instructions (DBC, London, Ontario, Canada, for both). Plasma adiponectin was measured by ELISA (B-Bridge International, Mountain View, CA) according to the manufacturer’s instructions. IL-6, IL-10, and TNF-{alpha} were measured by a multiplex sandwich ELISA (Pierce Biotechnology, Rockford, IL) according to the manufacturer’s instructions. The reported limits of detection of these assays were 0.01 mg/liter, 0.5 ng/ml, 0.23 µg/ml, 0.2 pg/ml, 0.2 pg/ml, and 0.8 pg/ml for highly sensitive CRP, leptin, adiponectin, IL-6, IL-10, and TNF-{alpha}, respectively.

Imaging studies

Measurement of liver and muscle fat content by MRI Hepatic fat accumulation was measured using MRI along with the Dixon method (35, 36). Hepatic fat fraction was calculated by comparing in- and out-of-phase images of the liver. MRI examinations were performed using a 1.5-T magnet (Magnetom Vision; Siemens Medical, Erlangen, Germany). The patients were positioned supine using a phased array coil. Axial T1-weighted gradient echo images and in-phase and out-of-phase images were obtained from the upper abdomen and the thighs. Scan parameters included the following: TR/TE 160/2.l msec (out-of-phase) and 4.2 msec (in-phase), flip angle = 80°, and slice thickness 8 mm with 1 mm interslice gap. Image postprocessing was performed using a workstation (MV-1000; Siemens Medical). For each pair of images, a region of interest (ROI) was drawn in the liver using an adjustable round cursor. The ROI selected in each image measured at least 1 cm2 and was placed in the liver parenchyma to exclude contamination from blood vessels, motion artifacts, or partial volume effects. The mean pixel signal intensity (SI) levels for each ROI were recorded; five separate in-phase and out-of-phase ROIs were obtained from each patient. To determine the mean calculated signal intensity level of the liver, the five values obtained from five different ROI were averaged together. Fat fraction was subsequently calculated from the mean pixel SI data using the following formula: fat fraction = SIin-phase– SIout-of-phase. A hepatic fat fraction cutoff of 5.5% was chosen as the threshold to define steatosis (14, 37). According to the same method, fat fraction in the skeletal muscle, as measured in the femoral quadriceps, was also calculated.

Assessment of abdominal fat distribution by MRI A single slice at the L4 level was used to measure adipose tissue distribution. The abdominal adipose tissue compartments were defined according to the classification of Shen et al. (38). The VAT compartment is bounded by the internal margin of the abdominal muscle walls and includes ip, preperitoneal, and retroperitoneal adipose tissues. The SAT compartment includes the adipose tissues outside of the VAT boundary. This compartment is predominately composed of sc fat but also includes the small intermuscular and paravertebral components (39). Within the SAT, there is a superficial fascial plane that separates this depot into a superficial layer with compact fascial septa (Camper’s fascia) and a deep layer with more loosely organized fascial septa (Scarpa’s fascia) (15, 40). With the use of a cursor, a free-hand ROI was drawn around deep SAT (DSAT) and superficial SAT (SSAT). The mean SI ± SD of the adipose tissue was obtained from these ROI. The threshold for adipose tissue was defined as the mean SI ± 2 SD.

Statistical analysis Data are presented as means ± SD. Study subjects were grouped in tertiles of SAT, because this was the variable more closely associated with insulin sensitivity. ANOVA, or Kruskal-Wallis test when indicated, were used to compare anthropometric, biochemical, and metabolic variables across tertiles, as specified. Spearman rank correlations between fat mass, fat distribution, liver and muscle fat content, inflammation molecules, and insulin sensitivity were calculated. A multiple regression analysis with stepwise procedure was also done, using insulin sensitivity as the dependent variable and fat mass, abdominal fat distribution, and liver and muscle fat content as independent variables. A level of significance of P < 0.05 was used for all data analyses. Statistical analyses were performed using SPSS 14.0 software for Windows (SPSS Inc., Chicago, IL).


   Results
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
Discussion
 References
 
Anthropometric parameters

Table 1Go shows anthropometric features and body composition of the children. On the basis of the correlation analysis results (see below), we herein report the physical variables according to the tertiles of sc abdominal fat area (square centimeters). Age, weight, height, BMI z-score, fat mass (expressed both in kilograms and as a percentage of body weight), fat-free mass, and fat accumulation in skeletal muscle were not statistically different among the three groups. However, BMI, fat distribution and deposition parameters (SAT, SSAT, DSAT, and fat accumulation in the liver), and insulin sensitivity were all significantly different among the groups. VAT, DSAT/SSAT, and VAT/SAT were not significantly different among groups. Systolic and diastolic blood pressure were significantly different among tertiles, with the highest values in the third tertile.


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  		TABLE 1. Physical parameters and body fat distribution

 
Biochemical parameters

Table 2Go shows the biochemical parameters of the subjects divided into tertiles on the basis of sc abdominal fat area (square centimeters). Plasma glucose, glucose peak, insulin, insulin sensitivity, HDL-cholesterol, leptin, adiponectin, and CRP were significantly different among the groups, with the highest values in the third tertile, except for insulin sensitivity, HDL-cholesterol, and adiponectin, which were lower in the third tertile than in the others. Total cholesterol, LDL-cholesterol, triglycerides, glucose area under the curve (AUC), total and direct bilirubin, AST, ALT, and inflammation molecules were not statistically different among the three groups. Glucose AUC and glucose peak after the glucose load were significantly lower in the first than in the second and third tertile.


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  		TABLE 2. Biochemical parameters

 
Correlation analysis

Spearman correlations between fat mass, fat distribution, fat accumulation in the liver and in skeletal muscle, and insulin sensitivity are reported in Table 3Go. Insulin sensitivity was negatively correlated with fat accumulation in the liver (r = –0.44; P < 0.02), whereas the latter was positively correlated with VAT (r = 0.39; P < 0.05), SAT (r = 0.45; P < 0.05), SSAT (r = 0.47; P < 0.05), and FFM (r = 0.49; P < 0.01). Insulin sensitivity was negatively correlated with SAT (r = –0.51; P < 0.01) and SSAT (r = –0.56; P < 0.01). No association between insulin sensitivity and VAT, VAT/SAT, DSAT, and DSAT/SSAT was found. Fat deposition in thigh skeletal muscle and total fat mass were significantly correlated (percent fat mass: r = 0.46; P < 0.05; kilograms fat mass: r = 0.39; P < 0.05), but muscle bed fat content was not correlated with insulin sensitivity.


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  		TABLE 3. Spearman correlations between fat mass, fat distribution, fat accumulation in the liver and in the skeletal muscle, and insulin sensitivity

 
Leptin was significantly correlated with fat mass expressed both in kilograms and percent (r = 0.74 and r = 0.45, respectively; P < 0.05), SSAT (r = 0.45; P < 0.03), SAT (r = 0.50; P < 0.01), and insulin sensitivity (r = –0.59; P < 0.01) but not with VAT (r = 0.39; P value not significant).

Adiponectin correlated with VAT and fat liver content (r = –0.53 and r = –0.64, respectively; P < 0.01) but not with fat mass or SAT (total, superficial, or deep).

IL-6 (r = 0.46), IL-10 (r = 0.51), and TNF-{alpha} (r = 0.48) significantly (P < 0.05) correlated with CRP. None of them correlated with total body fat and body fat distribution variables. CRP correlated with VAT (r = 0.55; P < 0.01). None of the inflammation molecules correlated with adiponectin or leptin. No association between fat deposition in thigh skeletal muscle and inflammation molecules (IL-6, r = 0.20; IL-10, r = –0.18; TNF{alpha}, r = 0.03; P value not significant) was found.

A significant correlation between AUC of glucose and skeletal muscle fat content (r = 0.47; P < 0.05) was found. AUC of glucose did not correlate with liver fat content (r = –0.16; P value not significant).

Multiple regression model showed that insulin sensitivity was significantly and independently associated (R2 = 0.36; F = 7.11; P < 0.01) with fat accumulation in the liver and SSAT (Table 4Go). When fat mass or VAT was forced in the analysis, they were excluded from the final model.


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  		TABLE 4. Multiple regression model

 

   Discussion
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
 Discussion
References
 
The main finding of this study is that insulin sensitivity in overweight and obese prepubertal children is independently associated with both abdominal SAT and liver fat content but not with abdominal VAT or with skeletal muscle fat content or with inflammation biomarkers. Several studies in obese adults showed a relationship between abdominal fat distribution, especially intraabdominal fat, and insulin resistance (41, 42). Scarce information is available on children, especially in the prepubertal age. In fact, most of the studies on children covered a wide age range, and prepubertal and pubertal individuals were usually combined (39, 43, 44). Thus, in those studies, the consequences of puberty-related hormonal changes on insulin sensitivity and body fat distribution could not be controlled for. In our study, we avoided recruiting pubertal subjects to exclude the effects of puberty on the variables under analysis.

Interestingly, in our overweight and obese prepubertal children, SAT was inversely associated with insulin sensitivity, whereas VAT was not. This finding is in agreement with the results reported by Gower et al. (19) in a sample of prepubertal African-American and Caucasian children. In adult males, Abate et al. (45) and De Fronzo et al. (15) demonstrated that insulin sensitivity was as associated with SAT as with VAT. In contrast, Bonora et al. (41) showed that VAT, but not SAT, was inversely related to insulin sensitivity in adult obese women. The reasons for these discrepant findings are not clear. However, because prepubertal children accumulate fat preferentially in sc areas (14) and their absolute amount of abdominal VAT is low (46), most circulating FFA arise from nonvisceral fat depots and, by affecting insulin sensitivity and perhaps liver steatosis, may be one link between SAT size and insulin sensitivity/liver fat content.

In accordance with previous studies, we found an association between abdominal VAT and fatty liver (39, 47, 48). Indeed, although only 13 children were diagnosed with liver steatosis by echotomography, liver fat content accounted for 16% of interindividual variability of insulin sensitivity, in addition to the 20% already explained by SAT. This relation may reflect the release into the portal bloodstream of several biologically active compounds, including but not limited to FFA (49).

In agreement with studies in adults and adolescents, VAT and liver fat content were both negatively correlated with adiponectin (14, 50, 51, 52). Because adiponectin exerts insulin-sensitizing effects, prevents lipotoxicity, antagonizes some cytokines, and, at least in rodents, alleviates alcoholic and nonalcoholic fatty liver disease (53, 54), the obesity-related fall in adiponectin concentration is thought to play a significant role in lipotoxic tissue damage.

On the whole, these findings suggest that SAT and liver fat are close biomarkers of detrimental effects on insulin sensitivity, whereas VAT may play an indirect role through its relationship with liver fat and adiponectin.

One of the mechanisms by which VAT (adipose cells and macrophages) may favor fat storage and/or induce insulin resistance in the liver is by releasing proinflammatory cytokines into the portal bloodstream (55). Two lines of evidence support the potential role of inflamed fat in the obese child: 1) an inflammatory elementary lesion is detectable in the adipose tissue of obese children (56), and 2) several proinflammatory cytokines circulate in higher amounts in the blood of obese than nonobese children (26). In our study, all three cytokines we assayed were positively related to CRP, and the latter correlated with VAT. These findings are consistent with the known ability of IL-6 to induce CRP synthesis (57).

The lack of association between insulin sensitivity and VAT may be due also to the relatively narrow range of insulin sensitivity explored in this study. Including children with IGT could modify the association. However, by design, we excluded children with prediabetes (IGT) or type 2 diabetes to focus on children in their initial phase of fat gain, in which obesity is not associated with potential confounders such as comorbidities. Moreover, in the present study, 16 children had insulin sensitivity values of less than or equal to 0.3 ml/min·m2 BSA)/(pmol/liter), the proposed cutoff value for normal insulin sensitivity in children, and eight of them were less than 0.2 (ml/min·m2 BSA)/(pmol/liter) (58), i.e. overtly insulin resistant. Thus, we think that the range of insulin sensitivity was wide enough to investigate the relationship between abdominal fat and insulin sensitivity in prepubertal children.

In our study, muscle fat content was not associated with insulin sensitivity. This result is at variance with studies conducted in adults or adolescents (21). Thus, in prepubertal obese children, fat accumulation in muscle may be less detrimental than in older individuals. However, several other factors may contribute to explain this finding. First, the level of fitness, e.g. maximal oxygen consumption, was not assessed in our subjects. It is known that in adults and adolescents, fitness more than fat content in skeletal muscle per se affects insulin sensitivity (59, 60). Second, the range of muscle fat content of our children may be too narrow to show a relationship with insulin sensitivity (61, 62, 63), or insulin sensitivity is not the closest metabolic biomarker of muscle fat content. In regard to this, we have found a significant positive correlation between glucose tolerance during the OGTT, measured as AUC of glucose concentration, and muscle fat content, in agreement with a previous report (62). Third, in obese adolescents, the relationship between skeletal fat and insulin sensitivity was higher for intra-myocellular than for extra-myocellular fat. The technique used in this study to quantify fat in the muscle did not allow to differentiate between intra-myocellular and extra-myocellular fat (21). Fourth, we studied a rather small group. A larger sample size may magnify the results herein reported, and perhaps it may bring to statistical significance some presently nonsignificant findings, such as the relationship between VAT and insulin sensitivity.

In conclusion, in prepubertal overweight and obese children, insulin sensitivity is influenced by body fat distribution as well as by ectopic fat accumulation. In particular, abdominal SAT and liver fat content are independently and inversely associated with insulin sensitivity. However, intraabdominal adipose tissue, muscle fat content, and inflammation, as assessed by circulating biomarkers, could not be shown to play a significant role in insulin sensitivity of prepubertal children.


   Footnotes
 
Disclosure Summary: The authors have nothing to disclose.

This study has received partial sponsorship from the Ministry of University and Research, Research Program of Remarkable National Interest (PRIN), 2006, protocol N. 67105, area No. 6.

First Published Online April 8, 2008

Abbreviations: ALT, Alanine transferase; AST, aspartate transferase; AUC, area under the curve; BMI, body mass index; BSA, body surface area; CRP, C-reactive protein; DSAT, deep SAT; FFA, free fatty acids; FFM, fat-free mass; HDL, high-density lipoprotein; IGT, impaired glucose tolerance; IVGTT, iv glucose tolerance test; LDL, low-density lipoprotein; MRI, magnetic resonance imaging; OGTT, oral glucose tolerance test; ROI, region of interest; SAT, sc abdominal adipose tissue; SI, insulin sensitivity; SI, signal intensity; SSAT, superficial SAT; VAT, visceral abdominal adipose tissue.

Received September 18, 2007.

Accepted March 27, 2008.


   References
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
 Discussion
 References
 

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J. A Alvarez, P. B Higgins, R. A Oster, J. R Fernandez, B. E Darnell, and B. A Gower
Fasting and postprandial markers of inflammation in lean and overweight children
Am. J. Clinical Nutrition, April 1, 2009; 89(4): 1138 - 1144.
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