Association of visceral and subcutaneous fat with glucose intolerance, insulin resistance, adipocytokines and inflammatory markers in Asian Indians (CURES-113)

Clinical Biochemistry 44 (2011) 281–287

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Clinical Biochemistry j o u r n a l h o m e p a g e : w w w. e l s e v i e r. c o m / l o c a t e / c l i n b i o c h e m

Association of visceral and subcutaneous fat with glucose intolerance, insulin resistance, adipocytokines and inflammatory markers in Asian Indians (CURES-113) Karunakaran Indulekha, Ranjith Mohan Anjana, Jayagopi Surendar, Viswanathan Mohan ⁎ Madras Diabetes Research Foundation and Dr. Mohan's Diabetes Specialities Centre, WHO Collaborating Centre for Non-Communicable Diseases Prevention and Control, International Diabetes Federation (IDF) Centre for Education, Gopalapuram, Chennai, 600 086, India

a r t i c l e

i n f o

Article history: Received 14 October 2010 Received in revised form 17 December 2010 Accepted 23 December 2010 Available online 8 January 2011 Keywords: Visceral fat Subcutaneous fat Adiponectin Leptin hs-CRP TNF-alpha Oxidized LDL Visfatin Diabetes South Asians

a b s t r a c t Objectives: The aim of the study was to assess the association between visceral and subcutaneous fat with glucose intolerance, adipocytokines, inflammatory markers and carotid IMT in Asian Indians. Design and methods: Subjects with NGT (n = 85), IGT (n = 49) and T2DM (n = 93) were randomly selected from CURES. Total abdominal, visceral and subcutaneous fat were measured using Helical CT scan. Adiponectin, hs-CRP, TNF-alpha, oxidized LDL, visfatin and leptin and IMT and insulin resistance were assessed. Results: Total abdominal fat (p = 0.041) and the visceral fat (p = 0.039) but not subcutaneous fat progressively increased from NGT, IGT and T2DM subjects. With increasing quartiles of visceral fat, there was a significant increase in insulin resistance (p = 0.040); significant decrease in adiponectin (p = 0.043) and increase in TNF-alpha (p = 0.028), hs-CRP (p = 0.043), OX-LDL (p = 0.034) and visfatin (p = 0.040), and carotid IMT (p = 0.047) was observed. Conclusion: Visceral fat levels increased with increasing glucose intolerance and are associated with decreased levels of adiponectin and increased levels of hs-CRP, TNF-alpha, oxidized LDL, visfatin, HOMA-IR and IMT. © 2011 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.

Introduction Obesity represents a state of increase in adipose tissue mass due to the increase in the number and size of adipocytes [1]. Adipose tissue is now recognized as an active endocrine organ which secretes a vast

Abbreviations: NGT, normal glucose tolerance; IGT, impaired glucose tolerance; T2DM, type 2 diabetes mellitus; CAD, coronary artery disease; CURES, Chennai Urban Rural Epidemiology Study; HOMA-IR, homeostasis model assessment of insulin resistance; IMT, intimal media thickness; CT, computed tomography; TNF-alpha, tumor necrosis factor alpha; Oxidized LDL, oxidized low density lipoproptein; ELISA, enzyme-linked immunosorbent assay; hs-CRP, high sensitive C-reactive protein; BMI, body mass index; FPG, fasting plasma glucose; WHO, World Health Organization; ANOVA, analysis of variance; FFA, free fatty acid; SECURE, Study to Evaluate Carotid Ultrasound changes in patients treated with Ramipril and vitamin E; GRACE study, Gender, Race and Clinical Experience study; HDL-C, high density lipoprotein cholesterol; LDL-C, low density lipoprotein cholesterol; HbA1c, glycosylated hemoglobin; SPSS, Statistical Package for Social Sciences; SD, standard deviation; IRS, insulin receptor substrate; TG, triglyceride; IR, insulin resistance; PIVUS, Prospective Study of the Vasculature in Uppsala Seniors. ⁎ Corresponding author. Madras Diabetes Research Foundation and Dr. Mohan's Diabetes Specialities Centre, 4, Conran Smith Road, Gopalapuram, Chennai, 600 086, India. Fax: +9144 2835 0935. E-mail address: [email protected] (V. Mohan). URLs: http://www.drmohansdiabetes.com, http://www.mdrf.in (V. Mohan).

array of adipocytokines involved in the local and systemic regulation of numerous metabolic and inflammatory processes in an autocrine and paracrine manner [2]. Dysregulated endocrine function of the adipose tissue triggers obesity associated chronic low-grade inflammation and contributes to the development of obesity related metabolic disorders including insulin resistance, type 2 diabetes and atherosclerosis [3]. Asian Indians have an increased propensity to abdominal adiposity, which could be more strongly related to insulin resistance [4]. Increased intra abdominal fat/trunk fat/visceral fat is more detrimental than higher total body fat or generalized obesity in producing metabolic abnormalities [5]. Asian Indians also have an increased susceptibility to T2DM and coronary artery disease (CAD) in comparison with Caucasians and other ethnic groups [4,6]. We have previously demonstrated that visceral fat was significantly correlated with body mass index, waist circumference, sagittal abdominal diameter and blood pressure and cardiovascular risk factors [7]. Adiponectin is an adipokine with insulin sensitizing and antiinflammatory activities. The PIVUS study done in a population of elderly adults has shown that adiponectin and insulin resistance is an important link between visceral adiposity and atherosclerosis [8]. Another adipose tissue hormone leptin has been proposed to be involved in the neuroendocrine regulation of adiposity and its

0009-9120/$ – see front matter © 2011 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved. doi:10.1016/j.clinbiochem.2010.12.015

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metabolic sequelae and is shown to be associated with total body fat [9]. Visfatin is an adipocytokine recently discovered in 2005 with potentially important effects on glucose metabolism and atherosclerosis and is supposed to contribute to the complex inter-correlation between adiposity, glucose metabolism and vascular disease [10]. There is a close association with inflammation and insulin resistance and hs-CRP, an inflammatory marker associated with insulin resistance and dysglycemic conditions, including the cardiometabolic syndrome and incident type 2 diabetes [11]. Increased visceral adipose tissue levels have been shown to be a determinant covariable of the association between high hs-CRP concentrations and alteration in the metabolic profile in post menopausal women [12]. TNF-alpha has been shown to play a pivotal role in orchestrating the cytokine cascade in diabetes [13] and their levels have been shown to be associated with visceral fat [14]. Oxidative stress plays an important role in initiation and progression of diabetes and cardiovascular risk and oxidized LDL, one of the markers of oxidative stress shown to be associated with visceral fat accumulation in healthy obese men [15], and Gletsu-Miller et al. [16] have suggested that adipose tissue mass contributes to oxidative stress. Although alterations in circulating levels of adipocytokines in diabetes are well documented, the contribution of subcutaneous and visceral fat depots to the circulating pool of adipocytokines and IR is less explored particularly in non-Europeans and relatively less obese populations. Thus ,the aims of the present study are the following: 1. to examine the differences in body fat distributions in different stages of glucose intolerance, 2. to study the association of adipocytokines (adiponectin, leptin, visfatin), inflammatory markers (hs-CRP and TNF-alpha) and oxidative stress marker (oxidized LDL) with the different fat depots. 3. to look at the association of insulin resistance and carotid intimal media thickness with the different fat depots. The study of the adipocytokines and inflammatory markers in visceral fat assumes significance in the context that Asian Indians have more visceral fat accumulation for a given BMI than the other populations and studying these associations would pinpoint the specific metabolic sequences operating in the fat depots that contribute to increased diabetes and cardiovascular risk in Asian Indians. Research design and methods Study subjects were recruited from the Chennai Urban Rural Epidemiological Study (CURES), an ongoing epidemiological study conducted on a representative population (aged ≥ 20 years) of Chennai (formerly Madras), the fourth largest city in India. The methodology of the study has been published elsewhere [17]. Briefly, in phase 1 of the urban component of CURES, 26,001 individuals were recruited based on a systematic random-sampling technique; details of the sampling are described on our website (http://www.drmohansdiabetes.com/ under the link ‘Publications’). In phase 2 of CURES, all the known diabetic subjects in phase 1 were invited to the center for detailed studies on vascular complications. In phase 3, every tenth subject in phase 1 was invited for special studies. For the study, a total of 227 study subjects were, randomly selected using computer generated numbers from the phase 3 of CURES. This included the following groups: 85 subjects with normal glucose tolerance (NGT), 49 with impaired glucose tolerance (IGT) and 93 with type 2 diabetes (T2DM). Institutional ethical committee approval of the Madras Diabetes Research Foundation was obtained and written informed consent was obtained from all study subjects. The exclusion criteria included type 1 diabetic subjects, previous history of any chronic disease including kidney disease, liver disease and inflammatory disorders. Anthropometric measurements Anthropometric measurements, including weight, height and waist circumference, were obtained using standardized techniques.

Height was measured with a tape to the nearest centimeter. Weight was measured with a traditional spring balance that was kept on a firm horizontal surface. Waist was measured using a non-stretchable fiber measuring tape. The participants were asked to stand erect in a relaxed position with both feet together; one layer of clothing was accepted. Waist girth was measured as the smallest horizontal girth between the costal margins and the iliac crests at the end of expiration. The body mass index (BMI) was calculated as the weight in kilograms divided by the square of height in meters. Blood pressure was recorded in the right arm in the sitting position to the nearest 2 mm Hg with a mercury sphygmomanometer (Diamond Deluxe BP apparatus, Pune, India). Two readings were taken 5 minutes apart and the mean of the two was taken as the blood pressure [17]. Measurement of IMT The method used for measurement of carotid IMT has been described previously [18] but will be briefly outlined here. All scanning were conducted by a trained ultrasonologist who was unaware of the clinical status of the study subjects. The intima plus medial thickness of the right common carotid artery was determined using a high-resolution B mode ultrasonography system (Logic 400; GE, Milwaukee, WI, USA) with an electrical linear transducer midfrequency of 7.5 MHz. The axial resolution of the system was 0.3 mm. The images were recorded, in addition to being photographed. The scanning was performed for a mean of 20 minutes. IMT was measured as the distance from the leading edge of the first echogenic line to the second echogenic line during the diastolic phase of the cardiac cycle. Six well-defined arterial wall segments were measured in the right carotid system: the near wall and far wall of the proximal 10 mm of the internal carotid artery, the carotid bifurcation beginning at the tip of the flow divider and extending 10 mm below this point and the arterial segment extending 10 mm below the bifurcation in the common carotid artery. The mean of the six measures were taken as the carotid IMT reading. Images were captured using a special grabber card and the measurements were performed offline, manually. This method was standardized at our center and, for quality check, the videotapes were sent to Hamilton, Canada, the central laboratory for the SECURE and GRACE studies [19]. The reproducibility of the IMT measurement was examined by conducting another scan by the same sonographer on 20 subjects 1 week later. The mean difference in IMT between the first and second measurements was 0.02 mm; the SD, 0.06 mm and the mean difference ranged between −0.09 and +0.09 mm. Biochemical parameters Fasting plasma glucose (FPG) (glucose oxidase–peroxidase method), serum cholesterol (cholesterol oxidase–peroxidase–amidopyrine method), serum triglycerides (glycerol phosphate oxidase–peroxidase–amidopyrine method), high density lipoprotein cholesterol (HDL-C) (direct method–polyethylene glycol-pretreated enzymes) were measured using Hitachi-912 Autoanalyser (Hitachi, Mannheim, Germany). The intra- and inter-assay coefficient of variation for the biochemical assays ranged between 3.1% and 7.6%. Low-density lipoprotein cholesterol (LDL-C) was calculated using the Friedewald formula. Glycosylated hemoglobin (HbA1c) was estimated by high pressure liquid chromatography using the Variant machine (Bio-Rad, Hercules, CA). The intra- and inter-assay coefficient of variation of HbA1c was less than 10%. Fasting adiponectin levels were measured using radioimmunoassay (catalog no. HADP-61HK, Linco Research, St Charles, Mo, USA). The intra-assay and the inter-assay coefficients of variation were 3.8% and 7.4%, respectively, and the lower detection limit was 1 ng/mL. The plasma concentrations of hs-CRP were measured by a high sensitive nephelometric assay using a monoclonal antibody to CRP

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coated on polystyrene beads (Dade Behring, Marburg, Germany). Inkit QCs were within acceptable range [CV: 2.9%–7.7%]. The intra-assay and the inter-assay coefficient of variation for hs-CRP were 4.2% and 6.8% respectively and the lower detection limit was 0.17 mg/L. TNF-alpha concentration was measured by enzyme-linked immunosorbent assay (ELISA) (Biosource, Europe). The intra- and interassay coefficient of variation ranged between 3.4% and 7.7%. Oxidized LDL was measured using commercially available sandwich enzyme-linked immunosorbent assay (Mercodia, Uppsala, Sweden). The intra- and inter-assay coefficient of variation for the assay ranged between 5.5% and 8.6%. Fasting visfatin and leptin levels were measured by enzyme-linked immunosorbent assay (Phoenix Pharmaceuticals, Belmont, CA; catalog no. EK-003-80). The intra- and inter-assay coefficients of variation were 3% and 5%, respectively. Serum insulin concentration was estimated using Dako kits (Dako,Glostrup, Denmark). The interand intra-assay coefficient of variations were less than 5%. CT scan procedure Subcutaneous and visceral fat were measured using a Helical CT scan (General Electric, Milwaukee, WI) as previously described [20]. The scans were done at 120 kV, 200–250 mAs. Subjects were requested to lie in the supine position with their arms above their head and legs elevated with a cushion. A single scan (10 mm) of the abdomen was done at the level of L4–L5 vertebrae and analyzed for a cross-sectional area of adipose tissue, which was expressed in centimeters squared. Areas were calculated by multiplying the number of pixels of a given tissue type by the pixel number (pixel density). The external contour of the waist was determined using a threshold of −160 HU (Hounsfield unit), and the external bone contours were derived at −30 HU. The parameters studied included visceral, subcutaneous and total abdominal fat. Visceral fat was distinguished from subcutaneous abdominal fat by tracing along the fascial plane defining the internal abdominal wall. Definitions Diabetes was diagnosed based on criteria laid by the WHO Consultation Group report, i.e., fasting plasma glucose (FPG) ≥7.0 mmol/L (126 mg/dL) and/or 2 h post glucose value ≥11.1 mmol/ L (200 mg/dL). Impaired glucose tolerance (IGT) was diagnosed if the 2 h post glucose value was ≥ 7.8 mmol/L (140 mg/dL) and b11.1 mmol/L (200 mg/dL). Normal glucose tolerance (NGT) was diagnosed if the 2 h post glucose value was b7.8 mmol/L (140 mg/dL) and fasting plasma glucose b5.6 mmol/L (100 mg/dl) [21]. Insulin resistance was calculated using the homeostasis assessment model (HOMA-IR) using the formula, fasting insulin (μU/mL) × fasting glucose (mmol/L) / 22.5. Statistical analysis Data were expressed as mean ± standard deviation (SD). The Student t-test or one-way analysis of variance (ANOVA) (with Tukey honestly significant difference [HSD]) as appropriate was used to compare groups for continuous variables. Normal distribution was checked using the Kolmogorov–Smirnov test and the non-normally distributed variables were log transformed. A Pearson correlation analysis was carried out to determine the relation of with other risk variables. Logistic regression analysis was done using total abdominal, visceral and subcutaneous fat as the dependent variable and various cytokine and inflammatory marker levels as independent variables. All analyses were done using Windows-based SPSS statistical package (Version 10.0, Chicago). p ≤ 0.05 was considered significant.

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Results Table 1 shows the clinical and the biochemical characteristics of the study subjects. Subjects with IGT and diabetes were significantly older than the NGT subjects. Hence, age adjustment was done for all the variables. However, as the sex ratio was equal in all three groups, no adjustment for gender was done. Waist circumference (p b 0.001), systolic blood pressure (p b 0.002), fasting plasma glucose (p b 0.001), glycated hemoglobin (p b 0.024), total cholesterol (p b 0.001), triglycerides (p b 0.001) and fasting insulin (p b 0.027) were found to be significantly higher with increasing severity of glucose intolerance, i.e., from NGT to IGT to T2DM. Fig. 1 shows the fat distribution of the NGT, IGT and T2DM subjects. Total abdominal fat (p = 0.041) and the visceral fat (p = 0.039) were found to progressively increase in the NGT, IGT and T2DM subjects while the subcutaneous abdominal fat showed no significant difference between the three groups. Fig. 2a and b shows that, with increasing quartiles of visceral fat, there was a significant increase in insulin resistance (p = 0.040) whereas, with increasing quartiles of subcutaneous fat, there was no increase in insulin resistance (p = 0.147). Fig. 3a–f shows the levels of adipocytokines and inflammatory markers in relation to increasing quartiles of visceral fat. There was a significant decrease in adiponectin (p = 0.043) and a significant increase in TNF-alpha (p = 0.028), hs-CRP (p = 0.043), OX-LDL (p = 0.034), and visfatin (p = 0.040) with increasing quartiles of visceral fat. Fig. 4 shows that the IMT (p = 0.047) also significantly increased with increasing quartiles of visceral fat. There was no association between subcutaneous or total abdominal fat with any of the above parameters (data not shown). Table 2 shows the differences between males and females in body composition and metabolic variables in the NGT, IGT and T2DM subjects. There was no difference in body composition and other metabolic variables between males and females in any of the groups except for subcutaneous fat in T2DM which was higher in females (p = 0.024). Table 3 shows the Pearson correlation analysis of visceral fat and the biomarkers in the total study subjects. Visceral fat showed a significant negative correlation with adiponectin (r = −0.376, p = 0.004) and a significant positive correlation with TNF-alpha (r = 0.519, p = 0.003), hs-CRP (r = 0.356, p = 0.005), OX-LDL (r = 0.479, p = 0.004), leptin (r = 0.332, p = 0.017), visfatin (r = 0.363. p = 0.038), HOMA-IR (r = 0.392, p = 0.004) and carotid IMT (r = 0.453, p = 0.006).

Table 1 Baseline characteristics of the study subjects adjusted for age. Parameters

NGT (n = 85)

IGT (n = 49)

T2DM (n = 93)

Age (y) Male (%) Body mass index (kg/cm2) Waist circumference (cm) Systolic blood pressure (mm Hg) Diastolic blood pressure (mm Hg) HbA1c (%) Fasting plasma glucose (mg/dL) Total cholesterol (mg/ dL) Log triglycerides (mg/dL) HDL cholesterol (mg/dL) LDL cholesterol (mg/dL) Fasting insulin (μU/L)

40.1 ± 1.2 43 (51%) 24.1 ± 0.5 84.9 ± 1.2 118.2 ± 1.8

43.3 ± 1.6 24 (50%) 25.1 ± 0.5 89.4 ± 1.7 126.4 ± 2.6

44.0 ± 0.8 47 (50%) 25.0 ± 0.4 91.0 ± 0.9 127.3 ± 1.9

74.4 ± 1.1

75.0 ± 1.7

76.0 ± 1.0

5.6 ± 0.2 84.7 ± 1.1

6.6 ± 0.3 93.5 ± 1.8

7.3 ± 0.2 177.5 ± 5.7

0.024 b 0.001

180.2 ± 3.8 127.5 ± 9.3 43.0 ± 1.1 111.7 ± 2.9 8.5 ± 0.6

197.0 ± 8.5 144.4 ± 10.6 45.8 ± 2.1 122.3 ± 7.4 9.1 ± 0.7

204.7 ± 3.5 208.1 ± 19.9 41.3 ± 0.8 121.7 ± 3.8 11.0 ± 0.8

b 0.001 b 0.001 0.050 0.144 0.027

P for trend b 0.05 highlighted in bold.

p value 0.020 0.239 b 0.001 0.002 0.567

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Fig. 1. Fat distribution in NGT, IGT and T2DM subjects.

Discussion The study makes the following important observations: 1. A progressive increase in visceral fat and total abdominal fat but not subcutaneous fat is observed with increasing glucose intolerance, i.e., from NGT to IGT to diabetes. 2. There was a linear increase in insulin resistance with increasing quartiles of visceral fat but not subcutaneous fat. 3. An increase in the pro-inflammatory cytokines (TNF-alpha and hs-CRP), adipocytokine (visfatin) and oxidative stress marker (OX-LDL) and a decrease in adiponectin were observed with increasing quartiles of visceral fat. There was also an increase in HOMA-IR and IMT with increasing quartiles of visceral fat. This trend of increasing adipocytokines and inflammatory markers was not observed in the case of subcutaneous or total abdominal fat (data not shown) whereas leptin levels did not change with increasing quartiles of visceral fat. Raji et al. [22] have shown that for comparable BMI and age, healthy Asian Indians have physiologic markers for insulin resistance, dyslipidemia and increased cardiovascular risk, compared with Caucasians and the major reason behind these abnormalities is contributed by alterations in body fat distribution, particularly increased visceral fat. Also, Sam et al. [23] reported that visceral but not subcutaneous adipose tissue distribution is an important determinant of systemic inflammation in a large group of subjects with type 2 diabetes. Our findings are consistent with these studies showing that, in the high risk Asian Indians, visceral fat but not subcutaneous fat is associated with several adipocytokines and inflammatory markers.

The anti-inflammatory adipokine adiponectin was decreased in our study subjects and this could be because in the states of increased visceral adiposity, the enlarged adipocytes produce less adiponectin as it is secreted predominantly by the pre-adipocytes [24]. One of the mechanisms for the negative correlation between visceral adiposity and adiponectin levels might be the increased secretion of TNF-alpha from accumulated visceral fat which potentially inhibits adiponectin secretion [25]. In addition, TNF-alpha has also been shown to induce IR in murine myocytes and adipocytes. [26]. Increase in TNF-alpha with increasing visceral adiposity observed in our subjects is consistent with the results of Fernández-Veledo et al. [27] who observed that inhibition of insulin induced glucose uptake by TNFalpha was detected only in human visceral adipocytes. Moreover, they also found that serine phosphorylation of Akt was significantly impaired by TNF-alpha in visceral primary adipocytes but not in subcutaneous adipocytes [27]. Leptin is the only hormone and a satiety factor shown to be preferentially expressed in subcutaneous adipose tissue involved in the regulation of body fat stores [28]. In our study, leptin levels were not significantly different in visceral but was significantly correlated with subcutaneous fat and this is in agreement with the previous studies [29]. Visfatin level increased with increase in visceral adiposity and this concords with studies that show that its levels are dysregulated in excessive fat accumulation and conditions of insulin resistance [30]. There are also other conflicting results on the levels of visfatin in subcutaneous and visceral fat and this may be due to its putative roles in insulin action or insulin resistance in addition to its pro-inflammatory effects [31]. The levels of oxidized LDL increased in our study subjects with increase in visceral adipose tissue and previously, plasma OX-LDL levels and low-grade systemic inflammation has been shown to be increased in men with a high visceral adipose tissue accumulation [32]. Our study supports these findings. They also showed that insulin resistance is associated with endothelial activation [32]. The oxidative stress in the diabetic state might have modified LDL causing increased OX-LDL [33]. The increase in the adipokines and the inflammatory markers could also be attributed to insulin resistance and it has been shown that IR is associated with inflammation [34]. This notion has been supported by in vivo and in vitro studies which have reported that hyperinsulinemia occurring in IR causes a significant increase in circulating TNF-alpha and IL-6 gene expression in the adipose tissue [34]. Though CRP is produced preferentially in the liver, the higher levels of hs-CRP with increasing visceral fat quartiles could be due to the higher levels of other inflammatory cytokines predominantly produced by the visceral adipocytes [35]. Another reason could be that the hs-CRP production is increased as a consequence of

Fig. 2. a. Insulin resistance in quartiles of visceral fat. b. Insulin resistance in quartiles of subcutaneous fat.

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Fig. 3. a. Adiponectin levels in quartiles of visceral fat. b. Levels of leptin in quartiles of visceral fat. c. Levels of visfatin in quartiles of visceral fat. d. Levels of hs-CRP in quartiles of visceral fat. e. Levels of TNF-alpha in quartiles of visceral fat. f. Levels of OX-LDL in quartiles of visceral fat.

inflammation and increased insulin resistance, which attenuates the effect of insulin in the inhibition of acute phase protein synthesis. The mechanisms behind the functional differences in the adipose tissue compartments could be the following: an expanded visceral adipose depot, due to its particular metabolic characteristics, has been associated with impaired FFA metabolism [36]. The visceral adipocytes present higher catecholamine stimulated lipolysis and lower insulin antilipolytic effect compared to subcutaneous adipocyte [37] increased expression of TNF-alpha and its receptors [38]. There is an increase in macrophage numbers in conditions of excess visceral fat and consequently there is an increased secretion of inflammatory markers from the adipocytes as well as resident macrophages [39]. This synergistic increase in TNF-alpha, visfatin and other adipokines from both macrophages as well as the adipocytes causes subsequent elevation of hs-CRP in the liver. This leads to reduced glucose uptake and increased release of free fatty acids (FFAs) and glycerol from adipose tissue resulting in hyperinsulinemia and associated IR [27]. IR

Fig. 4. Levels of IMT in quartiles of visceral fat.

also causes endothelial activation and injury and increased expression of adhesion molecules at the surface of the endothelium and this facilitates the infiltration of monocytes into the subendothelial space and this might have caused increased IMT in our study subjects [40]. The lack of correlation between the adipokines and inflammatory markers with subcutaneous fat could be because of the fact that subcutaneous fat exerts some beneficial/protective effects as shown by Snijder et al. [41]. This is further reiterated by the fact that when subjects are treated with thiazolidinedione agents, insulin sensitivity is improved primarily because of the increase in the subcutaneous fat depot [42]. Tran et al. [43] have also shown that transplantation of subcutaneous fat into visceral cavity of mice showed a decrease in body weight, size of adipocytes and total expenditure of energy. We also found significant association of subcutaneous fat with leptin which might also improve inflammation and insulin sensitivity in the subcutaneous tissue [43]. Finally Gorgey et al. [44] have shown that trunk subcutaneous adipose tissue is associated with a reduced risk of glucose intolerance and an increased HDL-C in individuals with complete motor spinal cord injury and this could be one of the contributing factors for the lack of association of the adipocytokines and inflammatory markers with subcutaneous fat. This is a cross-sectional study and hence no cause/effect inferences can be drawn which is one of the limitations of the study. Another limitation of the study is that L4–L5 is not the best anatomical site and may underestimate or overestimate the magnitude of visceral adiposity. However, the strength of the study is that it is a population based sample and the first data on Asian Indians and the fat depots were assessed by computed tomography. Future studies should be aimed towards deciphering the mechanistic pathways leading to the altered metabolic profile of the visceral adipose tissue. In summary, these data support the hypothesis that altered body composition particularly excess visceral fat is associated with increased insulin resistance and high levels of inflammatory cytokines and decreased levels of adiponectin in Asian Indians, and this may

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Table 2 Differences in body composition and biochemical parameters between males and females in the NGT, IGT and T2DM subjects. Parameters

NGT

Age BMI Waist Systolic blood pressure Diastolic blood pressure Fasting plasma glucose HbA1c Cholesterol Log triglycerides HDL cholesterol LDL cholesterol Fasting insulin Total abdominal fat Subcutaneous fat Intraabdominal fat

IGT

T2DM

Male (n = 43)

Female (n = 42)

p value

Male (n = 24)

Female (n = 25)

p value

Male (n = 47)

Female (n = 46)

p value

42.1 ± 1.7 23.3 ± 3.4 83.8 ± 1.4 118.7 ± 3.2 73.7 ± 1.7 84.5 ± 1.8 5.6 ± 0.2 187.2 ± 6.3 135.2 ± 16.1 44.1 ± 1.4 116.0 ± 4.7 7.4 ± 0.9 310 ± 14 178.5 ± 11.4 134.8 ± 7.5

39 ± 1.5 24.5 ± 5.7 85.3 ± 1.8 117.8 ± 2 74.7 ± 1.3 84.7 ± 1.3 5.7 ± 0.3 177 ± 5.0 120.7 ± 10 43.6 ± 2.0 109.3 ± 3.5 8.6 ± 0.8 342.4 ± 16 195.3 ± 12 118.4 ± 7.3

0.160 0.241 0.565 0.811 0.681 0.909 0.930 0.209 0.439 0.862 0.248 0.711 0.119 0.245 0.127

44 ± 3.0 24 ± 4.3 87 ± 2.6 121.8 ± 3.3 72.7 ± 2.7 91 ± 2.9 6.3 ± 0.4 199.8 ± 18.8 133.6 ± 16.9 47.3 ± 4.5 125.8 ± 6.8 8.2 ± 1.2 325.2 ± 22.6 187.2 ± 12.0 147.6 ± 18.0

42 ± 1.6 25.8 ± 3.0 91 ± 2.1 129.5 ± 3.5 76.7 ± 2.1 95.1 ± 2.1 6.7 ± 0.4 195.2 ± 6.6 151.7 ± 13.6 44.7 ± 1.8 119.9 ± 5.4 10.2 ± 0.9 356.1 ± 18.4 202.8 ± 13.6 132.9 ± 9.1

0.488 0.087 0.237 0.140 0.253 0.255 0.514 0.791 0.405 0.564 0.700 0.073 0.320 0.455 0.429

45.4 ± 1.4 25 ± 2 90.5 ± 1.3 125.5 ± 2.6 75.1 ± 1.5 187 ± 9.0 7.6 ± 0.3 211.9 ± 5.4 261.3 ± 35 41 ± 1.2 124.3 ± 6.4 12.6 ± 1.3 357.1 ± 15.3 197.0 ± 10.2 160.4 ± 11

42.9 ± 1.1 24.9 ± 3.1 91.3 ± 1.2 128.7 ± 2.8 76.7 ± 1.3 169.8 ± 7.2 7.1 ± 0.2 198.9 ± 4.6 184.5 ± 22 41.5 ± 1.2 119.6 ± 4.5 10 ± 0.9 383.8 ± 16.5 238.0 ± 13.6 144.9 ± 12.2

0.168 0.932 0.665 0.406 0.443 0.135 0.293 0.070 0.271 0.761 0.545 0.107 0.252 0.024 0.368

P b 0.05 between males and females indicated in bold.

Table 3 Pearson correlation of visceral fat with adipocytokines, inflammatory markets, HOMAIR and carotid IMT in the total study subjects. Parameters

Log adiponectin Log TNF-alpha Log hs-CRP Log Ox LDL Log leptin Log visfatin Log HOMA-IR IMT

Subcutaneous fat

Total abdominal fat

r value

Visceral fat p value

r value

p value

r value

p value

−0.376 0.519 0.356 0.479 0.332 0.363 0.392 0.453

0.004 0.003 0.005 0.004 0.017 0.038 0.004 0.006

0.005 0.006 0.001 0.063 0.343 0.246 0.057 0.052

0. 972 0.976 0.995 0.721 0.030 0.050 0.729 0.720

0.205 0.327 0.209 0.206 0.421 0.399 0.261 0.076

0.129 0.078 0.107 0.244 0.020 0.045 0.064 0.599

P value b 0.05 of correlation coefficients indicated in bold.

explain their increased risk for diabetes and CAD as shown by their associations with carotid IMT. Conflict of interests There is none to declare. Source of funding There is none to declare. Author contributions VM conceived and designed the experiments. KI, ARM and JS performed the experiment and analyzed the data and are responsible for the integrity of the data. Acknowledgments We acknowledge the Chennai Wellingdon Corporate Foundation which supported the CURES field studies. KI & JS acknowledge the Lady-Tata Junior Research Fellowship. This is the 113rd paper from the CURES. References [1] DeFronzo RA. Insulin resistance, lipotoxicity, type 2 diabetes and atherosclerosis: the missing links. The Claude Bernard Lecture 2009. Diabetologia 2010;53(7): 1270–87. [2] Greenberg AS, Obin MS. Obesity and the role of adipose tissue in inflammation and metabolism. Am J Clin Nutr 2006;83(2):461S–5S Review.

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