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Plasma adiponectin levels are related to obesity, inflammation, blood lipids and insulin in type 2 diabetic and non-diabetic Trinidadians
p r i m a r y c a r e d i a b e t e s 4 ( 2 0 1 0 ) 187–192
Contents lists available at ScienceDirect
Primary Care Diabetes journal homepage: http://www.elsevier.com/locate/pcd
Original research
Plasma adiponectin levels are related to obesity, inflammation, blood lipids and insulin in type 2 diabetic and non-diabetic Trinidadians B. Shivananda Nayak ∗ , Deepak Ramsingh, Shanaree Gooding, George Legall, Sanjiv Bissram, Atif Mohammed, Anupama Raychaudhuri, Brad Sahadeo, Varoon Pandohie, Kemron Figaro Faculty of Medical Sciences, The University of the West Indies, St Augustine, Trinidad and Tobago
a r t i c l e
i n f o
a b s t r a c t
Article history:
Aims: To determine the relationship between plasma adiponectin levels and obesity, inflam-
Received 23 January 2010
mation, blood lipids and insulin resistance in type 2 diabetics (T2DM) and non-diabetics in
Received in revised form
a patient population in Trinidad.
20 May 2010
Methods: A cohort study of a total of 126 type 2 diabetic (42 males and 84 females) and
Accepted 30 May 2010
140 (43 males and 97 females) non-diabetic public clinic attendees were assessed between
Available online 26 June 2010
December 2008 and July 2009. Along with clinical history and anthropometry, adiponectin, TNF-␣, IL-6, CRP, lipid profile, glucose, and insulin were measured in fasting blood samples
Keywords:
and insulin resistance (HOMA-IR) was calculated.
Adiponectin
Results: Diabetics had higher (p < 0.05) glucose, insulin, HOMA-IR, triglycerides (TG), VLDL and
Type 2 diabetes
systolic blood pressure than non-diabetics, but lower (p < 0.05) HDL and adiponectin levels.
Inflammation
Adiponectin levels were lower (p < 0.05) in obese than in non-obese individuals regardless of
Insulin resistance
diabetic status. There were significant gender differences in HDL, LDL and TG. Among nonobese persons, adiponectin correlated negatively with triglycerides (r = −0.280; adiponectin), IL-6 (r = −0.216; p < 0.005), HOMA-IR (r = −0.373; p = 000) and positively correlated with HDL (r = 0.355; p = 0.000). Diabetic status (p = 0.025), TNF-␣ (p = 0.048) and BMI (p = 0.027) were identified as useful predictors of adiponectin by multiple linear regression methods. In addition binary logistic regression analysis found glucose (p = 0.001) and adiponectin (p = 0.047) to be useful indicators of type 2 diabetes. Conclusions: Adiponectin decreases with increasing adiposity and insulin resistance. Adiponectin and TNF-␣ appear to be related to differences in the insulin mediated glucose turnover. © 2010 Primary Care Diabetes Europe. Published by Elsevier Ltd. All rights reserved.
∗ Corresponding author at: Biochemistry Unit, Department of Preclinical Sciences, Faculty of Medical Sciences, The University of the West Indies, St Augustine, Trinidad and Tobago. Tel.: +1 868 662 1219; fax: +1 868 662 1873. E-mail address: [email protected] (B.S. Nayak). 1751-9918/$ – see front matter © 2010 Primary Care Diabetes Europe. Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.pcd.2010.05.006
188
1.
p r i m a r y c a r e d i a b e t e s 4 ( 2 0 1 0 ) 187–192
Introduction
Diabetes mellitus is a group of metabolic disorders characterized by hyperglycaemia, with disturbances of carbohydrate, fat and protein metabolism resulting from defects in insulin secretion, insulin action, or both [1]. The International Diabetes Federation has projected that the Trinidad and Tobago has high prevalence of diabetes mellitus by the year 2025, 11.8% of the population will be diagnosed with type 2 diabetes [2]. Although the projected prevalence rate represents one of the highest prevalence rates in the North American region (2), of greater concern are the several reports of poor glycaemic control amongst type 2 diabetic patients at the primary care settings in this population [3–5]. Poor glycaemia control, obesity and lifestyle are some of the factors that have been implicated in the increased risk of cardiovascular disease amongst diabetic patients in this population [3,6]. Obesity is a medical condition with excess amount of body fat as measured by a body mass index (BMI) of ≥30. Significant associations have been demonstrated between obesity and insulin resistance in non-diabetic subjects, and obesity exacerbates insulin resistance in diabetic subjects. The degree of the accompanying insulin resistance associated with obesity, however, is considerably varied [7–10]. Obesity is also inversely related to adiponectin, a 244 amino acid protein that is synthesized exclusively in adipose tissue [11]. Growing evidence suggests that adiponectin is an important determinant of insulin resistance since it acts either hormonally or locally on adipocytes to alter insulin signalling and influences glucose and lipid metabolism [12,13]. Circulating levels of adiponectin is also associated with better lipid profile, particularly higher levels of HDL cholesterol and lower triglycerides, decreased inflammation and improved glycaemic control [14,15]. Several studies in various populations have also found correlations of adiponectin with leptin and proinsulin [16,17]. Chronic inflammation is thought to play a role in the pathogenesis of T2DM. Studies have shown a relationship between the onset of T2DM and various inflammatory markers including C-reactive protein (CRP), Interleukin (IL-6) and Tumour Necrosis Factor alpha (TNF-␣). Previous studies have demonstrated an inverse relationship between adiponectin and CRP [18] whilst production and action of TNF-␣ was found to be inhibited by adiponectin, though some studies found no association with TNF-␣. Another study demonstrated the significant association of TNF-␣, but not of IL-6, with insulin resistance and adiponectin. However, data suggests that the cytokines collectively are strongly associated with insulin sensitivity [10]. The high incidence and prevalence of T2DM globally and in particular Trinidad and Tobago, is cause for much concern [19]. Its incidence is set to reach epidemic proportions and to rank alongside illnesses such as AIDS. This presents a huge challenge to public health. It is of vital importance, therefore, that we seek to understand the relationships between T2DM and its associated risk factors in an attempt to better understand and manage this disease so as to reduce its incidence and associated health burden. This study was undertaken to explore the possibility that lower adiponectin levels are related to increased insulin resis-
tance and that these two factors are a direct result of obesity, which work in tandem to influence the onset of T2DM.
2.
Methods
Study design: The study participants comprised a cohort of diabetic and non-diabetic hospital outpatients and clinic patients obtained by convenience sampling [15]. Data were obtained from the clinical records of patients accessing two hospital-based outpatient diabetic clinics and two primary care facilities in Trinidad, West Indies. Males and females of all ethnicities were eligible to participate except for heavy smokers (persons who smoked more than 20 cigarettes per day) and/or heavy drinkers (males who consumed an average of more than 2 drinks per day and who had more than 1 drink per day). The exclusion criteria were necessary since heavy smoking and/or drinking are known to modify adiponectin levels and inflammation. Associated exclusion factors included presenting with complications of T2DM including nephropathy and retinopathy; persons having chemical or physical trauma, immunological disorder, being treated with immunosuppressants, and corticosteroids, known to have HIV or any other coexisting infection(s). Medical records from the two hospitals and the two clinics were used to construct the sampling frame from which potential participants were selected. Eligible participants (diabetics) were then invited to participate and informed consent was obtained once each person agreed verbally to participate. Ethical approval of the design, methods and implementation of the study was granted by the Ethics Committee of the Faculty of Medical Sciences, The University of the West Indies St. Augustine. Non-diabetic patients were selected in a similar fashion for other clinics in the same four facilities and using the same smoking and drinking exclusion criteria. Data were collected from December 2008 to July 2009. Both demographic and clinical data were collected from each participant. Variables measured/recorded included ethnicity, age, sex, past medical history, history of smoking and alcohol use and other known medical conditions (e.g. hypertension) were obtained face-to-face interviews. To obtain clinical data participants were invited to visit the various clinics following an overnight fast. At the clinic measurements of blood pressure, height, weight, BMI, hip circumference, waist circumference and waist to hip ratio were taken using established protocols with the necessary precautions. A fasting venous blood sample was obtained, processed and stored at −20 ◦ C for biochemical analysis. Blood levels of adiponectin, TNF-␣, IL-6 were determined by enzyme linked immunosorbent assay (GenWay Biotech, Inc., San Diego); glucose, CRP and lipid profile were measured using a dry chemistry analyzer (Johnson & Johnson Vitros 250, Ortho-Clinical Diagnostics Inc., NY, USA) with appropriate quality controls. Insulin resistance was calculated using the homeostasis model assessment (HOMA-IR) [20].
2.1.
Data analysis
Microsoft Excel was used in creating a database and producing graphs while the data were analyzed using Statistical Package
p r i m a r y c a r e d i a b e t e s 4 ( 2 0 1 0 ) 187–192
189
Table 1 – Summary statistics. Variables
Mean ± standard deviation (SD) Non-diabetic
Age BMI Height Weight Systolic blood pressure* Diastolic blood pressure Glucose (mg/dL)* Cholesterol (mg/dL) Triglycerides (mg/dL)* HDL (mg/dL)* LDL (mg/dL) VLDL (mg/dL)* TNF-␣ (pg/mL) IL-6 (pg/mL) C-Reactive protein (mg/L) Adiponectin (ng/mL)* Insulin (IU/mL)* HOMA-IR ∗
46.65 26.43 1.614 70.60 128.18 79.22 89.63 202.09 133.25 49.48 126.38 24.49 17.36 18.66 3.71 24.77 18.19 3.95
± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
14.67 6.12 0.12 16.97 18.96 11.186 26.11 41.78 25 15.02 36.17 14.00 22.69 31.52 8.36 16.98 9.30 2.39
Diabetic 55.54 27.32 1.61 71.07 135.54 80.63 154.83 210.00 160.91 44.46 126.56 31.29 19.40 15.25 4.06 20.50 23.78 8.42
± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
8.37 5.90 0.10 16.05 20.03* 9.83 76.45 106.87 101.34 12.23 39.50 22.28 25.51 11.73 5.90 9.63 26.51 8.74
Significantly different at the 5% level of significance.
for the Social Sciences (SPSS) version12 for Windows (SPSS Science, IL, USA). Appropriate data transformation methods were used when applicable [21,22]. Both descriptive and inferential methods were used in data analysis. Correlation analysis was used to examine for linear interdependence between and among variables and multiple linear regression and binary logistic regression were used to identify useful indicators of type 2 diabetes. The commonly used p < 0.05 rule was used to detect significant differences.
3.
Fig. 1 – Box plots of adiponectin levels by diabetic profile and gender.
Results
The 266 participants were comprised of 126 (47.4%) type 2 diabetics with a mean age of 55.5 years (std dev 8.4 years) and a male: female ratio of 1:2. The 140 (52.6%) non-diabetics consisted of 43 males (30.7%) and 97 females (69.3%) with an overall mean age of 46.7 years (std dev = 4.7 years). Summary statistics for demographic and selected clinical variables including results of biochemical analysis and anthropometry are given in Table 1. Figs. 1 and 2 are box plots of adiponectin levels of male and female diabetic and non-diabetic, and obese and non-obese male and female, participants, respectively. Table 2 gives p-values from two-independent sample t-tests of differences between males and females, diabetics and non-diabetics, and obese and non-obese participants for selected clinical variables. Figs. 1 and 2 suggest that female had higher median adiponectin values than males regardless of diabetic profile or obesity status. However no median tests were conducted to determine whether the observed differences between the medians were significant. As shown in Table 2 diabetics and non-diabetics differ significantly with respect to blood glucose (p = 0.001), triglycerides (p = 0.018), VLDL (p = 0.047), HOMA-IR (p = 0.000), and systolic blood pressure (p = 0.002) but significantly lower HDL (p = 0.003), and adiponectin levels (p = 0.039). Specifically mean values were higher for non-diabetics for each of the vari-
Fig. 2 – Box plots of adiponectin levels by obesity status and gender.
ables. On the other hand significantly lower concentrations of adiponectin were noted in obese diabetics than obese nondiabetics. Significant differences between obese and non-obese participants were observed for mean HDL (p = 0.014), CRP
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p r i m a r y c a r e d i a b e t e s 4 ( 2 0 1 0 ) 187–192
Table 2 – p-Values for comparisons of means using two-independent sample t-tests. Variable
Adiponectin Glucose Cholesterol Triglyceride HDL LDL VLDL TC/HC TNF-␣ IL-6 CRP Insulin IR BMI
Category Obese vs. non-obese
Diabetic vs. non-diabetic
Male vs. female
0.191 0.0464 0.617 0.217 0.014 0.220 0.979 0.0114 0.105 0.796 0.009 0.672 0.43 0.000
0.039 0.000 0.359 0.018 0.003 0.901 0.047 0.104 0.563 0.388 0.762 0.128 0.000 0.096
0.039 0.557 0.083 0.044 0.000 0.023 0.158 0.035 0.176 0.497 0.816 0.386 0.63 0.269
(p = 0.009), diastolic blood pressure (p = 0.006) as well as systolic blood pressure (p = 0.037). Significant differences were also found between males and females with respect to triglycerides (p = 0.044), HDL (p = 0.000) and LDL (p = 0.023).
(r = 0.275; p < 0.05). In the diabetic group HOMA-IR was significantly correlated with age (r = 0.31; p < 0.05).
3.1.
Multiple linear regression analysis revealed a significant linear relationship between BMI and CRP (ˇ = 0.214; p = 0.001) and adiponectin concentration (ˇ = −0.091; p = 0.015) whereas binary logistic regression identified glucose [OR = 0.929; CI (0.891; 0.929); p = 0.001] and adiponectin concentration [OR = 0.76; CI (1.004, 1.159); p = 0.039] as useful indicators of type 2 diabetes. See Table 3.
Correlation: adiponectin
Among non-diabetics adiponectin was significantly positively correlated with HDL (r = 0.260; p = 0.000) and significantly negatively correlated with TNF-␣ (r = −0.164; p = 0.028). Among diabetes there were significant negative correlations between adiponectin and BMI (r = −0.193; p = 0.007) and HOMA-IR (r = −0.330; p = 0.000).
3.2.
Blood lipid correlation analysis
Among non-diabetics significant correlations were identified between plasma triglycerides and HOMA-IR (r = 0.348; p < 0.01); glucose (r = 0.210; p < 0.05); BMI (r = 0.258; p < 0.05); and diastolic (r = 0.329) and systolic (r = 0.300; p < 0.05) blood pressure. Significant negative correlations were found between HDL, CRP (r = −0.210; p < 0.05) and BMI (r = −0.237; p < 0.05). At the same time, among T2DM triglyceride was significant positively correlations with TNF-␣ (r = 0.215; p < 0.05) and BMI (r = 0.183; p < 0.05). Among this group HDL was inversely related to TNF-␣ (r = −0.252; p < 0.05).
3.3.
Inflammatory markers correlation analysis
Among non-diabetics group CRP was significantly correlated with HOMA-IR (r = 0.277; p < 0.01) and BMI (r = 0.233; p < 0.05) while linear regression analysis revealed a significant linear relationship between BMI and CRP (ˇ = 0.214; p < 0.005). Among diabetics IL-6 levels were positively correlated with blood glucose (r = 0.283; p < 0.05) as well as with diastolic blood pressure (r = 0.297; p < 0.01). Also CRP was found to be correlated with BMI (r = 0.328; p < 0.01).
3.4.
Insulin resistance correlation analysis
In the non-diabetic group HOMA-IR was positively correlated with BMI (r = 0.361; p < 0.01) and systolic blood pressure
3.5.
4.
Regression analysis
Discussion
The findings of our study indicated that there are significantly lower levels of adiponectin in the diabetic populations and this study shows that it is a valuable independent predictor of type 2 diabetes, which is consistent with previous studies [23,24]. HOMA calculated insulin resistance is directly related to the insulin and glucose levels of the body. The glucose-lowering effect of adiponectin has been shown to be due in part to its activation of the AMP-activated protein kinase (AMPK) cascade. AMPK cascade is an insulin-independent, phylogenetically ancient mechanism of stimulating glucose transport. It can be thought of as means of maintaining the levels of energy within range of metabolic need. AMPK stimulates both the catabolism of existing intracellular energy stores, such as triglycerides, and an insulin-independent influx of extracellular energy sources, such as glucose [25]. The catabolism of these extracellular stores of glucose means that it is conceivable that lower levels of adiponectin could result in a glucose overload and hence, an individual becoming progressively insulin resistant. It has been shown also, that two adiponectin receptors have been cloned and shown to increase fatty acid oxidation in muscle and glucose uptake in the liver [26]. Adiponectin also shows a protective effect by its potential mechanism for improved insulin secretion as it has been shown to counteract cytokine and fatty acid induced  cell dysfunction [27]. Previous work suggests that there is an inverse relationship between the adiponectin and body mass index [28,29],
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Table 3 – Logistic regression coefficients and corresponding odds ratios. ˇ
p-Value
95.0% CI for OR OR
Glucose Cholesterol TG HDL LDL TNF-␣ CRP Adiponectin Insulin IR Obese Sex Constant
−0.073 −0.001 0.003 0.042 −0.008 0.005 0.033 0.076 −0.062 0.183 0.725 −0.838 4.994
0.001 0.836 0.437 0.139 0.359 0.696 0.550 0.038 0.276 0.406 0.261 0.207 0.087
which is commonly used as a measure of obesity. Our study showed the relationship between adiponectin and BMI in type 2 diabetics. The paradox of decreasing adiponectin values with increasing adiposity is partly explained by the antagonism of TNF-␣ to adiponectin. TNF-␣, which is over expressed in adipose tissue of the obese subjects [30], would therefore block the release of adiponectin. This study showed that adiponectin negatively correlates TNF-␣ in the unadjusted model, however, when the data is adjusted for the anthropometric measurements then anthropometric measurements and blood lipids the correlation is more significant. The data points very strongly to increasing adiposity being a determinant of decreasing adiponectin concentration and ultimately the increased insulin resistance seen in type 2 diabetics. It is also emphasized that the data points to a determinant, meaning that these trends were seen in the non-diabetic population. Adiponectin is also recorded to show anti-inflammatory properties [26,31]. The current study found that there are lower levels of TNF-␣ and CRP inflammatory markers in nondiabetics compared to the diabetics. It also has been recorded that chronic inflammation is one of the contributing factors of glucose intolerance, type 2 diabetes and associated cardiovascular disease, possibly related to compromised insulin sensitivity, hyperglycaemia and dyslipidemia [30]. In both cases of diabetics and non-diabetics, the study demonstrated a significant positive correlation between CRP and BMI. The adipokines, the chemokines produced by adipocytes, IL-6 and TNF-␣ have also showed that they are positively correlated to BMI in non-diabetics but not to a significant degree. CRP and IL-6 findings point towards that these inflammatory markers do change with adiposity, but are not of significant value to be a determinant in the predicting insulin resistance or adiponectin concentrations. TNF-␣ however, as mentioned before, is a powerful determinant in the diabetic progression. The blood lipids, LDL, VLDL, cholesterol and serum triglycerides predictably increase with increasing obesity, whereas HDL shows the inverse. Analysis of adiponectin and the blood lipids however has shown no significant correlation except that with the serum triglycerides and HDL in non-diabetic individuals. The trend as shown before with increasing adiposity is demonstrated here as well, i.e. the
0.929 0.999 1.003 1.043 0.992 1.005 1.033 1.079 0.940 1.201 2.065 0.432 147.545
Lower
Upper
0.891 0.993 0.995 0.987 0.976 0.981 0.928 1.004 0.841 0.780 0.584 0.118
0.969 1.005 1.011 1.102 1.009 1.028 1.151 1.159 1.051 1.848 7.303 1.591
triglycerides increase as adiponectin values plummet and as HDL increases so does the adiponectin values. A connection between adiponectin and adiposity therefore is highly conceivable in this study. Furthermore, our study showed that not only does triglycerides increase with increasing BMI, but also it increases with increasing insulin resistance as well. Moreover, HDL shows a highly significant negative relationship with BMI. A potential explanation for increased insulin resistance as blood lipids increase is the reduced lipid oxidation that takes place with decreased adiponectin; hence there is an accumulation of lipids and positive feedback cycle is established which increases the adiposity of the individual and blood lipids while decreasing the adiponectin level so as to further pre-dispose that individual to type 2 diabetes [24]. Due to the design of the study it is not known if adiponectin levels would impact on disease risk or how changes of adiponectin would affect the lipid profile and inflammatory marker and vice versa. In addition, ethnic and genetic differences in our Trinidadian population as compared to the other populations of previous studies may account for the differences in expected results. This study has provided significant relationships between adiponectin and insulin resistance, obesity and blood lipids. These findings if established can provide a useful tool for the clinician to prevent further progression of the diseases and complications that may arise.
Conflict of interest All authors declare that they have no conflict of interest.
Acknowledgements We thank Ms. Ria Ramdeen in lending her technical support during the collection and analysis of data. We would also like to thank the staff of the Haematology clinic, the Diabetic clinic and the St. Joseph Health Centre of the EWMSC and the Clinics of Felicity for their cooperation and support throughout this research project.
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Contents lists available at ScienceDirect
Primary Care Diabetes journal homepage: http://www.elsevier.com/locate/pcd
Original research
Plasma adiponectin levels are related to obesity, inflammation, blood lipids and insulin in type 2 diabetic and non-diabetic Trinidadians B. Shivananda Nayak ∗ , Deepak Ramsingh, Shanaree Gooding, George Legall, Sanjiv Bissram, Atif Mohammed, Anupama Raychaudhuri, Brad Sahadeo, Varoon Pandohie, Kemron Figaro Faculty of Medical Sciences, The University of the West Indies, St Augustine, Trinidad and Tobago
a r t i c l e
i n f o
a b s t r a c t
Article history:
Aims: To determine the relationship between plasma adiponectin levels and obesity, inflam-
Received 23 January 2010
mation, blood lipids and insulin resistance in type 2 diabetics (T2DM) and non-diabetics in
Received in revised form
a patient population in Trinidad.
20 May 2010
Methods: A cohort study of a total of 126 type 2 diabetic (42 males and 84 females) and
Accepted 30 May 2010
140 (43 males and 97 females) non-diabetic public clinic attendees were assessed between
Available online 26 June 2010
December 2008 and July 2009. Along with clinical history and anthropometry, adiponectin, TNF-␣, IL-6, CRP, lipid profile, glucose, and insulin were measured in fasting blood samples
Keywords:
and insulin resistance (HOMA-IR) was calculated.
Adiponectin
Results: Diabetics had higher (p < 0.05) glucose, insulin, HOMA-IR, triglycerides (TG), VLDL and
Type 2 diabetes
systolic blood pressure than non-diabetics, but lower (p < 0.05) HDL and adiponectin levels.
Inflammation
Adiponectin levels were lower (p < 0.05) in obese than in non-obese individuals regardless of
Insulin resistance
diabetic status. There were significant gender differences in HDL, LDL and TG. Among nonobese persons, adiponectin correlated negatively with triglycerides (r = −0.280; adiponectin), IL-6 (r = −0.216; p < 0.005), HOMA-IR (r = −0.373; p = 000) and positively correlated with HDL (r = 0.355; p = 0.000). Diabetic status (p = 0.025), TNF-␣ (p = 0.048) and BMI (p = 0.027) were identified as useful predictors of adiponectin by multiple linear regression methods. In addition binary logistic regression analysis found glucose (p = 0.001) and adiponectin (p = 0.047) to be useful indicators of type 2 diabetes. Conclusions: Adiponectin decreases with increasing adiposity and insulin resistance. Adiponectin and TNF-␣ appear to be related to differences in the insulin mediated glucose turnover. © 2010 Primary Care Diabetes Europe. Published by Elsevier Ltd. All rights reserved.
∗ Corresponding author at: Biochemistry Unit, Department of Preclinical Sciences, Faculty of Medical Sciences, The University of the West Indies, St Augustine, Trinidad and Tobago. Tel.: +1 868 662 1219; fax: +1 868 662 1873. E-mail address: [email protected] (B.S. Nayak). 1751-9918/$ – see front matter © 2010 Primary Care Diabetes Europe. Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.pcd.2010.05.006
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1.
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Introduction
Diabetes mellitus is a group of metabolic disorders characterized by hyperglycaemia, with disturbances of carbohydrate, fat and protein metabolism resulting from defects in insulin secretion, insulin action, or both [1]. The International Diabetes Federation has projected that the Trinidad and Tobago has high prevalence of diabetes mellitus by the year 2025, 11.8% of the population will be diagnosed with type 2 diabetes [2]. Although the projected prevalence rate represents one of the highest prevalence rates in the North American region (2), of greater concern are the several reports of poor glycaemic control amongst type 2 diabetic patients at the primary care settings in this population [3–5]. Poor glycaemia control, obesity and lifestyle are some of the factors that have been implicated in the increased risk of cardiovascular disease amongst diabetic patients in this population [3,6]. Obesity is a medical condition with excess amount of body fat as measured by a body mass index (BMI) of ≥30. Significant associations have been demonstrated between obesity and insulin resistance in non-diabetic subjects, and obesity exacerbates insulin resistance in diabetic subjects. The degree of the accompanying insulin resistance associated with obesity, however, is considerably varied [7–10]. Obesity is also inversely related to adiponectin, a 244 amino acid protein that is synthesized exclusively in adipose tissue [11]. Growing evidence suggests that adiponectin is an important determinant of insulin resistance since it acts either hormonally or locally on adipocytes to alter insulin signalling and influences glucose and lipid metabolism [12,13]. Circulating levels of adiponectin is also associated with better lipid profile, particularly higher levels of HDL cholesterol and lower triglycerides, decreased inflammation and improved glycaemic control [14,15]. Several studies in various populations have also found correlations of adiponectin with leptin and proinsulin [16,17]. Chronic inflammation is thought to play a role in the pathogenesis of T2DM. Studies have shown a relationship between the onset of T2DM and various inflammatory markers including C-reactive protein (CRP), Interleukin (IL-6) and Tumour Necrosis Factor alpha (TNF-␣). Previous studies have demonstrated an inverse relationship between adiponectin and CRP [18] whilst production and action of TNF-␣ was found to be inhibited by adiponectin, though some studies found no association with TNF-␣. Another study demonstrated the significant association of TNF-␣, but not of IL-6, with insulin resistance and adiponectin. However, data suggests that the cytokines collectively are strongly associated with insulin sensitivity [10]. The high incidence and prevalence of T2DM globally and in particular Trinidad and Tobago, is cause for much concern [19]. Its incidence is set to reach epidemic proportions and to rank alongside illnesses such as AIDS. This presents a huge challenge to public health. It is of vital importance, therefore, that we seek to understand the relationships between T2DM and its associated risk factors in an attempt to better understand and manage this disease so as to reduce its incidence and associated health burden. This study was undertaken to explore the possibility that lower adiponectin levels are related to increased insulin resis-
tance and that these two factors are a direct result of obesity, which work in tandem to influence the onset of T2DM.
2.
Methods
Study design: The study participants comprised a cohort of diabetic and non-diabetic hospital outpatients and clinic patients obtained by convenience sampling [15]. Data were obtained from the clinical records of patients accessing two hospital-based outpatient diabetic clinics and two primary care facilities in Trinidad, West Indies. Males and females of all ethnicities were eligible to participate except for heavy smokers (persons who smoked more than 20 cigarettes per day) and/or heavy drinkers (males who consumed an average of more than 2 drinks per day and who had more than 1 drink per day). The exclusion criteria were necessary since heavy smoking and/or drinking are known to modify adiponectin levels and inflammation. Associated exclusion factors included presenting with complications of T2DM including nephropathy and retinopathy; persons having chemical or physical trauma, immunological disorder, being treated with immunosuppressants, and corticosteroids, known to have HIV or any other coexisting infection(s). Medical records from the two hospitals and the two clinics were used to construct the sampling frame from which potential participants were selected. Eligible participants (diabetics) were then invited to participate and informed consent was obtained once each person agreed verbally to participate. Ethical approval of the design, methods and implementation of the study was granted by the Ethics Committee of the Faculty of Medical Sciences, The University of the West Indies St. Augustine. Non-diabetic patients were selected in a similar fashion for other clinics in the same four facilities and using the same smoking and drinking exclusion criteria. Data were collected from December 2008 to July 2009. Both demographic and clinical data were collected from each participant. Variables measured/recorded included ethnicity, age, sex, past medical history, history of smoking and alcohol use and other known medical conditions (e.g. hypertension) were obtained face-to-face interviews. To obtain clinical data participants were invited to visit the various clinics following an overnight fast. At the clinic measurements of blood pressure, height, weight, BMI, hip circumference, waist circumference and waist to hip ratio were taken using established protocols with the necessary precautions. A fasting venous blood sample was obtained, processed and stored at −20 ◦ C for biochemical analysis. Blood levels of adiponectin, TNF-␣, IL-6 were determined by enzyme linked immunosorbent assay (GenWay Biotech, Inc., San Diego); glucose, CRP and lipid profile were measured using a dry chemistry analyzer (Johnson & Johnson Vitros 250, Ortho-Clinical Diagnostics Inc., NY, USA) with appropriate quality controls. Insulin resistance was calculated using the homeostasis model assessment (HOMA-IR) [20].
2.1.
Data analysis
Microsoft Excel was used in creating a database and producing graphs while the data were analyzed using Statistical Package
p r i m a r y c a r e d i a b e t e s 4 ( 2 0 1 0 ) 187–192
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Table 1 – Summary statistics. Variables
Mean ± standard deviation (SD) Non-diabetic
Age BMI Height Weight Systolic blood pressure* Diastolic blood pressure Glucose (mg/dL)* Cholesterol (mg/dL) Triglycerides (mg/dL)* HDL (mg/dL)* LDL (mg/dL) VLDL (mg/dL)* TNF-␣ (pg/mL) IL-6 (pg/mL) C-Reactive protein (mg/L) Adiponectin (ng/mL)* Insulin (IU/mL)* HOMA-IR ∗
46.65 26.43 1.614 70.60 128.18 79.22 89.63 202.09 133.25 49.48 126.38 24.49 17.36 18.66 3.71 24.77 18.19 3.95
± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
14.67 6.12 0.12 16.97 18.96 11.186 26.11 41.78 25 15.02 36.17 14.00 22.69 31.52 8.36 16.98 9.30 2.39
Diabetic 55.54 27.32 1.61 71.07 135.54 80.63 154.83 210.00 160.91 44.46 126.56 31.29 19.40 15.25 4.06 20.50 23.78 8.42
± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
8.37 5.90 0.10 16.05 20.03* 9.83 76.45 106.87 101.34 12.23 39.50 22.28 25.51 11.73 5.90 9.63 26.51 8.74
Significantly different at the 5% level of significance.
for the Social Sciences (SPSS) version12 for Windows (SPSS Science, IL, USA). Appropriate data transformation methods were used when applicable [21,22]. Both descriptive and inferential methods were used in data analysis. Correlation analysis was used to examine for linear interdependence between and among variables and multiple linear regression and binary logistic regression were used to identify useful indicators of type 2 diabetes. The commonly used p < 0.05 rule was used to detect significant differences.
3.
Fig. 1 – Box plots of adiponectin levels by diabetic profile and gender.
Results
The 266 participants were comprised of 126 (47.4%) type 2 diabetics with a mean age of 55.5 years (std dev 8.4 years) and a male: female ratio of 1:2. The 140 (52.6%) non-diabetics consisted of 43 males (30.7%) and 97 females (69.3%) with an overall mean age of 46.7 years (std dev = 4.7 years). Summary statistics for demographic and selected clinical variables including results of biochemical analysis and anthropometry are given in Table 1. Figs. 1 and 2 are box plots of adiponectin levels of male and female diabetic and non-diabetic, and obese and non-obese male and female, participants, respectively. Table 2 gives p-values from two-independent sample t-tests of differences between males and females, diabetics and non-diabetics, and obese and non-obese participants for selected clinical variables. Figs. 1 and 2 suggest that female had higher median adiponectin values than males regardless of diabetic profile or obesity status. However no median tests were conducted to determine whether the observed differences between the medians were significant. As shown in Table 2 diabetics and non-diabetics differ significantly with respect to blood glucose (p = 0.001), triglycerides (p = 0.018), VLDL (p = 0.047), HOMA-IR (p = 0.000), and systolic blood pressure (p = 0.002) but significantly lower HDL (p = 0.003), and adiponectin levels (p = 0.039). Specifically mean values were higher for non-diabetics for each of the vari-
Fig. 2 – Box plots of adiponectin levels by obesity status and gender.
ables. On the other hand significantly lower concentrations of adiponectin were noted in obese diabetics than obese nondiabetics. Significant differences between obese and non-obese participants were observed for mean HDL (p = 0.014), CRP
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Table 2 – p-Values for comparisons of means using two-independent sample t-tests. Variable
Adiponectin Glucose Cholesterol Triglyceride HDL LDL VLDL TC/HC TNF-␣ IL-6 CRP Insulin IR BMI
Category Obese vs. non-obese
Diabetic vs. non-diabetic
Male vs. female
0.191 0.0464 0.617 0.217 0.014 0.220 0.979 0.0114 0.105 0.796 0.009 0.672 0.43 0.000
0.039 0.000 0.359 0.018 0.003 0.901 0.047 0.104 0.563 0.388 0.762 0.128 0.000 0.096
0.039 0.557 0.083 0.044 0.000 0.023 0.158 0.035 0.176 0.497 0.816 0.386 0.63 0.269
(p = 0.009), diastolic blood pressure (p = 0.006) as well as systolic blood pressure (p = 0.037). Significant differences were also found between males and females with respect to triglycerides (p = 0.044), HDL (p = 0.000) and LDL (p = 0.023).
(r = 0.275; p < 0.05). In the diabetic group HOMA-IR was significantly correlated with age (r = 0.31; p < 0.05).
3.1.
Multiple linear regression analysis revealed a significant linear relationship between BMI and CRP (ˇ = 0.214; p = 0.001) and adiponectin concentration (ˇ = −0.091; p = 0.015) whereas binary logistic regression identified glucose [OR = 0.929; CI (0.891; 0.929); p = 0.001] and adiponectin concentration [OR = 0.76; CI (1.004, 1.159); p = 0.039] as useful indicators of type 2 diabetes. See Table 3.
Correlation: adiponectin
Among non-diabetics adiponectin was significantly positively correlated with HDL (r = 0.260; p = 0.000) and significantly negatively correlated with TNF-␣ (r = −0.164; p = 0.028). Among diabetes there were significant negative correlations between adiponectin and BMI (r = −0.193; p = 0.007) and HOMA-IR (r = −0.330; p = 0.000).
3.2.
Blood lipid correlation analysis
Among non-diabetics significant correlations were identified between plasma triglycerides and HOMA-IR (r = 0.348; p < 0.01); glucose (r = 0.210; p < 0.05); BMI (r = 0.258; p < 0.05); and diastolic (r = 0.329) and systolic (r = 0.300; p < 0.05) blood pressure. Significant negative correlations were found between HDL, CRP (r = −0.210; p < 0.05) and BMI (r = −0.237; p < 0.05). At the same time, among T2DM triglyceride was significant positively correlations with TNF-␣ (r = 0.215; p < 0.05) and BMI (r = 0.183; p < 0.05). Among this group HDL was inversely related to TNF-␣ (r = −0.252; p < 0.05).
3.3.
Inflammatory markers correlation analysis
Among non-diabetics group CRP was significantly correlated with HOMA-IR (r = 0.277; p < 0.01) and BMI (r = 0.233; p < 0.05) while linear regression analysis revealed a significant linear relationship between BMI and CRP (ˇ = 0.214; p < 0.005). Among diabetics IL-6 levels were positively correlated with blood glucose (r = 0.283; p < 0.05) as well as with diastolic blood pressure (r = 0.297; p < 0.01). Also CRP was found to be correlated with BMI (r = 0.328; p < 0.01).
3.4.
Insulin resistance correlation analysis
In the non-diabetic group HOMA-IR was positively correlated with BMI (r = 0.361; p < 0.01) and systolic blood pressure
3.5.
4.
Regression analysis
Discussion
The findings of our study indicated that there are significantly lower levels of adiponectin in the diabetic populations and this study shows that it is a valuable independent predictor of type 2 diabetes, which is consistent with previous studies [23,24]. HOMA calculated insulin resistance is directly related to the insulin and glucose levels of the body. The glucose-lowering effect of adiponectin has been shown to be due in part to its activation of the AMP-activated protein kinase (AMPK) cascade. AMPK cascade is an insulin-independent, phylogenetically ancient mechanism of stimulating glucose transport. It can be thought of as means of maintaining the levels of energy within range of metabolic need. AMPK stimulates both the catabolism of existing intracellular energy stores, such as triglycerides, and an insulin-independent influx of extracellular energy sources, such as glucose [25]. The catabolism of these extracellular stores of glucose means that it is conceivable that lower levels of adiponectin could result in a glucose overload and hence, an individual becoming progressively insulin resistant. It has been shown also, that two adiponectin receptors have been cloned and shown to increase fatty acid oxidation in muscle and glucose uptake in the liver [26]. Adiponectin also shows a protective effect by its potential mechanism for improved insulin secretion as it has been shown to counteract cytokine and fatty acid induced  cell dysfunction [27]. Previous work suggests that there is an inverse relationship between the adiponectin and body mass index [28,29],
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Table 3 – Logistic regression coefficients and corresponding odds ratios. ˇ
p-Value
95.0% CI for OR OR
Glucose Cholesterol TG HDL LDL TNF-␣ CRP Adiponectin Insulin IR Obese Sex Constant
−0.073 −0.001 0.003 0.042 −0.008 0.005 0.033 0.076 −0.062 0.183 0.725 −0.838 4.994
0.001 0.836 0.437 0.139 0.359 0.696 0.550 0.038 0.276 0.406 0.261 0.207 0.087
which is commonly used as a measure of obesity. Our study showed the relationship between adiponectin and BMI in type 2 diabetics. The paradox of decreasing adiponectin values with increasing adiposity is partly explained by the antagonism of TNF-␣ to adiponectin. TNF-␣, which is over expressed in adipose tissue of the obese subjects [30], would therefore block the release of adiponectin. This study showed that adiponectin negatively correlates TNF-␣ in the unadjusted model, however, when the data is adjusted for the anthropometric measurements then anthropometric measurements and blood lipids the correlation is more significant. The data points very strongly to increasing adiposity being a determinant of decreasing adiponectin concentration and ultimately the increased insulin resistance seen in type 2 diabetics. It is also emphasized that the data points to a determinant, meaning that these trends were seen in the non-diabetic population. Adiponectin is also recorded to show anti-inflammatory properties [26,31]. The current study found that there are lower levels of TNF-␣ and CRP inflammatory markers in nondiabetics compared to the diabetics. It also has been recorded that chronic inflammation is one of the contributing factors of glucose intolerance, type 2 diabetes and associated cardiovascular disease, possibly related to compromised insulin sensitivity, hyperglycaemia and dyslipidemia [30]. In both cases of diabetics and non-diabetics, the study demonstrated a significant positive correlation between CRP and BMI. The adipokines, the chemokines produced by adipocytes, IL-6 and TNF-␣ have also showed that they are positively correlated to BMI in non-diabetics but not to a significant degree. CRP and IL-6 findings point towards that these inflammatory markers do change with adiposity, but are not of significant value to be a determinant in the predicting insulin resistance or adiponectin concentrations. TNF-␣ however, as mentioned before, is a powerful determinant in the diabetic progression. The blood lipids, LDL, VLDL, cholesterol and serum triglycerides predictably increase with increasing obesity, whereas HDL shows the inverse. Analysis of adiponectin and the blood lipids however has shown no significant correlation except that with the serum triglycerides and HDL in non-diabetic individuals. The trend as shown before with increasing adiposity is demonstrated here as well, i.e. the
0.929 0.999 1.003 1.043 0.992 1.005 1.033 1.079 0.940 1.201 2.065 0.432 147.545
Lower
Upper
0.891 0.993 0.995 0.987 0.976 0.981 0.928 1.004 0.841 0.780 0.584 0.118
0.969 1.005 1.011 1.102 1.009 1.028 1.151 1.159 1.051 1.848 7.303 1.591
triglycerides increase as adiponectin values plummet and as HDL increases so does the adiponectin values. A connection between adiponectin and adiposity therefore is highly conceivable in this study. Furthermore, our study showed that not only does triglycerides increase with increasing BMI, but also it increases with increasing insulin resistance as well. Moreover, HDL shows a highly significant negative relationship with BMI. A potential explanation for increased insulin resistance as blood lipids increase is the reduced lipid oxidation that takes place with decreased adiponectin; hence there is an accumulation of lipids and positive feedback cycle is established which increases the adiposity of the individual and blood lipids while decreasing the adiponectin level so as to further pre-dispose that individual to type 2 diabetes [24]. Due to the design of the study it is not known if adiponectin levels would impact on disease risk or how changes of adiponectin would affect the lipid profile and inflammatory marker and vice versa. In addition, ethnic and genetic differences in our Trinidadian population as compared to the other populations of previous studies may account for the differences in expected results. This study has provided significant relationships between adiponectin and insulin resistance, obesity and blood lipids. These findings if established can provide a useful tool for the clinician to prevent further progression of the diseases and complications that may arise.
Conflict of interest All authors declare that they have no conflict of interest.
Acknowledgements We thank Ms. Ria Ramdeen in lending her technical support during the collection and analysis of data. We would also like to thank the staff of the Haematology clinic, the Diabetic clinic and the St. Joseph Health Centre of the EWMSC and the Clinics of Felicity for their cooperation and support throughout this research project.
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