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 Table of Contents  
ORIGINAL ARTICLE
Year : 2017  |  Volume : 20  |  Issue : 1  |  Page : 45-52

Evaluation of tumor necrosis factor alpha, insulin, and homeostasis model assessment of insulin resistance among obese participants living in Calabar, Nigeria


1 Department of Medical Laboratory Science, Chemical Pathology Unit, University of Calabar, Calabar, Cross River State, Nigeria
2 Department of Medical Laboratory Science, Chemical Pathology Unit, University of Nigeria, Enugu Campus, Enugu State, Nigeria

Date of Web Publication11-Jan-2017

Correspondence Address:
Agu Chidozie Elochukwu
Department of Medical Laboratory Science, Chemical Pathology Unit, University of Calabar, PMB 1115, Calabar, Cross River State
Nigeria
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DOI: 10.4103/1119-0388.198120

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  Abstract 

Objective: Several studies in different population indicate that inflammation may be the link between obesity and insulin resistance (IR). However, this relationship has not been adequately explored in our population and among Africans with increasing high rate of obesity and IR. This study aims to evaluate the association among obesity markers, tumor necrosis factor-alpha (TNF-α), and markers of IR in obese and nonobese adults living in Calabar, Nigeria. Materials and Methods: A total of 160 participants were recruited for the study: 110 obese participants and 50 nonobese control participants. Anthropometric parameters and blood pressure were measured; body mass index, waist-hip ratio and homeostasis model assessment of IR (HOMA-IR) were calculated for all the participants recruited in this study. Fasting plasma glucose was determined using glucose oxidase method; TNF-α and insulin were determined using enzyme-linked immunosorbent assay. Results: The mean values of TNF-α, insulin, fasting plasma glucose (FPG) and HOMA-IR were significantly higher in obese participants compared to the nonobese control group (P = 0.01). Insulin and HOMA-IR were significantly higher in Class III group compared to the Class I group. A significant positive correlation was observed between obesity markers and markers of IR among the obese participants. There was also a positive correlation between systolic blood pressure and HOMA-IR (r = 0.258) among the obese participants. Conclusion: Findings from the study suggest that obesity is a strong predictor of IR which in turn predicted cardiovascular risk factors such as high blood pressure.

Keywords: Homeostasis model assessment of insulin resistance, insulin, obesity, systolic blood pressure, tumor necrosis factor-alpha


How to cite this article:
Elochukwu AC, Opara UC, Chinyere NA, Jeremiah OS, Chukwuma OO. Evaluation of tumor necrosis factor alpha, insulin, and homeostasis model assessment of insulin resistance among obese participants living in Calabar, Nigeria. Trop J Med Res 2017;20:45-52

How to cite this URL:
Elochukwu AC, Opara UC, Chinyere NA, Jeremiah OS, Chukwuma OO. Evaluation of tumor necrosis factor alpha, insulin, and homeostasis model assessment of insulin resistance among obese participants living in Calabar, Nigeria. Trop J Med Res [serial online] 2017 [cited 2019 Dec 13];20:45-52. Available from: http://www.tjmrjournal.org/text.asp?2017/20/1/45/198120


  Introduction Top


Obesity results from the accumulation of excessive body fat that often presents a risk to health.[1] The state of one being obese is usually defined using body mass index (BMI), which is derived by dividing an individual's weight measured in kilogram by the square of his/her height measured in meters.[2] BMI of 30 kg/m2 and above marks obesity, whereas the range of 25-30 kg/m2 defines overweight.[3]

Obesity is an important contributor to ill health and is now so common in our population that it is beginning to relieve undernutrition as a major disease that affects health.[4] Worldwide, obesity ranks second after smoking as the cause of avoidable deaths and its prevalence is increasing in children and adults; some medical researchers state that it is the most serious health challenge of this century.[3]

Obesity arises when there is an excessive intake of food, especially high-calorie diet and decreased physical activity (sedentary lifestyle), during these events, the mass of adipose tissue increases.[5] Adipose tissue serves as the main storage site of triglyceride (TG) from which energy can be derived during starvation. However, over the past period of 10 years, the human adipose tissue has been identified as an endocrine organ that produces over fifty protein termed adipokines or adipocytokines which includes adiponectin, tumor necrosis factor-alpha (TNF-α), interleukin-6, leptin, and resistin.[6] Adipose tissue expansion triggers the release of some of these bioactive substances which result in a state of low degree inflammation and is related with the deregulation of a range of processes in many organs.[7] The exact mechanisms are still unclear, but this low-grade inflammation resulting from the release of these adipokines are thought to add to the growth of obesity-related comorbidities.[8]

Insulin resistance (IR) is defined as a state where insulin concentration fails to produce its anticipated biological effect. It is also defined as the condition that requires 200 or more units of insulin per day to achieve glycemic control and avoid ketosis.[9] IR is the most significant predictor of Type II diabetes mellitus and is a key component of metabolic syndrome.[10] Obesity has long been related to the development of Type II diabetes mellitus, and the reason for this association is the fact that obesity leads to IR. Adipose tissue responds to the actions of insulin, where insulin stimulates the storage of energy in the form of TG via a rise in the absorption of fatty acids (FAs) from circulating lipoproteins, insulin also inhibits lipolysis in the adipose tissue.[11]

Homeostasis model assessment of IR (HOMA-IR) is used to determine IR and can simply be derived from the fasting concentration values of insulin and glucose.[12] HOMA-IR values of 1 relate to insulin sensitivity, whereas values > 4.65 depicts the presence of IR.[13]

TNF-α is one of the adipocytokines released from the adipose tissue when its mass increases, and it is a pro-inflammatory cytokine that contributes to the development of IR among obese individuals.[14] The concentrations of TNF-α rise in obesity and are related to IR and dyslipidemia. However, neutralization of this cytokine does not improve IR in human obesity.[15] The reverse is the case in obese animals where reduction of TNF-α and its receptor improves insulin sensitivity.[16] Failure to reduce IR may be attributed to other cytokines released from the adipose tissue that may play a compensatory role in mediating IR.[17]


  Materials and Methods Top


Selection of participants

A total number of 160 volunteers aged between 20 and 45 years were recruited for this study: 110 obese participants (BMI ≥30 kg/m2 ) and 50 nonobese controls (BMI 18.5-24.9 kg/m2 ). Both male and female were enrolled in the study; participants were sourced from churches, market places, schools, and around neighborhoods in Calabar.

Ethical considerations

This study was conducted with the approval of the Research Ethical Committee of the Cross River State Ministry of Health, Calabar. An informed written consent was obtained from all participants participating in the study.

Exclusion criteria

Participants with diabetes mellitus or any cardiovascular disease, taking medications interfering with vascular reactivity, and those who are underweight, pregnant, and/or lactating mothers were excluded from the study.

Anthropometric parameters

Anthropometric parameters such as height (m), weight (kg), waist circumference (WC) (cm), and hip circumference (HC) (cm) were recorded. Weight and height were measured with the participants wearing light clothing and without shoes. Weight was measured in kilogram using a balanced scale; height was measured in meters using a wall-mounted ruler with the participants standing with feet together and with head, shoulder, buttocks, and heels touching the wall. Waist and HC were measured to the nearest 0.1 cm using a flexible but inelastic measuring tape; WC was taken between the costal margin and the iliac crest in the midaxillary line around the gluteal region. HC was measured by placing an inelastic tape around the buttocks in a horizontal plane.

BMI was calculated as weight in kilogram divided by the square of the height in meters (kg/m2 ); waist-to-hip ratio (WHR) was calculated by dividing the measurement of the waist (cm) by that of the hip (cm) and was used together with WC as an index of central obesity.

The following definitions were used: General obesity - a BMI of ≥30 kg/m2 , central obesity - a WC of ≥88 cm in women or ≥102 cm in men or WHR of ≥0.90 in women or ≥1.0 in men. Normal weight - a BMI of 18.5-24.9 kg/m2 .[1]

Sample collection

A standard venipuncture method was used to obtain 5 ml of blood from the participants under aseptic conditions. Three milliliters dispensed into a plain container capped, labeled appropriately, and allowed to clot at room temperature.

The serum was separated from the red cell by spinning at 3000 r.p.m. for 5 min. The supernatant serum obtained was stored frozen at − 20°C until the day of analysis. The remaining 2 ml of blood was dispensed into a fluoride oxalate container for the estimation of fasting blood glucose.

Analytical methods

TNF-α was determined using in vitro enzyme-linked immunosorbent assay (ELISA); the kits were obtained from Ray biotech, USA.

Fasting plasma glucose (FPG) was determined using glucose oxidase method; the kits were obtained from Randox International, England.

Insulin was determined using ELISA; the kits were obtained from Calbiotech life science company, USA.

HOMA-IR was calculated from the fasting concentration of insulin and glucose using the formula:



Statistical analysis

Statistical analysis was performed using SPSS software version 18 (IBM Corporation, California Inc., New York, United States). Data are expressed as a mean ± standard deviation. Data from two groups were compared using student's two-tailed t-test for paired samples. Data between groups were compared using a one-way analysis of variance, followed by post hoc analysis with Tukey's test. Pearson's correlation analysis was used to determine the intervariable association between the various parameters. P < 0.05 was considered statistically significant.


  Results Top


Anthropometric parameters, blood pressure, fasting blood glucose, insulin, HOMA-IR, and TNF-α were estimated in a total of 160 participants; 110 obese participants with a BMI of 30 kg/m2 and above and 50 nonobese control participants with a BMI of between 18.5 kg/m2 and 24.9 kg/m2 .

[Table 1] shows the comparison of anthropometric parameters and blood pressure in obese and nonobese control participants included in the study. Obese participants had a significantly higher mean value of BMI, WC, HC, WHR, and systolic and diastolic blood pressure (SBP and DBP) compared to the nonobese control group (P < 0.05).
Table 1: Comparison of anthropometric parameters and blood pressure in obese participants and nonobese control group


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[Table 2] shows the comparison of fasting blood glucose, insulin, HOMA-IR, and TNF-α in obese and nonobese control participants. The results revealed that the mean values of FBS, insulin, HOMA-IR, and TNF-α were significantly higher in obese participants compared to the nonobese control group (P < 0.05).
Table 2: Comparison of fasting blood glucose, insulin, homeostasis model assessment of insulin resistance, and tumor necrosis factor-alpha in obese participants and nonobese control group


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[Table 3] shows the comparison of anthropometric parameters and blood pressure between male and female obese participants. Male obese participants had significantly higher mean values of WHR and DBP compared to the female obese participants (P < 0.05). Obese females had significantly higher BMI and HC compared to male obese participants (P < 0.05). No significant difference was observed in the mean values of WC and SBP between the two groups (P > 0.05).
Table 3: Comparison of anthropometric parameters and blood pressure in male and female obese participants


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[Table 4] shows the comparison of FBS, insulin, HOMA-IR, and TNF-α, in male and female obese participants. No significant difference was observed in the parameters between the two groups (P > 0.05).
Table 4: Comparison of fasting blood glucose, insulin, homeostasis model assessment of insulin resistance, and tumor necrosis factor-alpha in male and female obese participants


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[Table 5] shows a comparison of anthropometric parameters and blood pressure was compared among the three classes of obesity using BMI as classification criteria. Here, BMI, WC, HC, SBP, and DBP varied significantly in the three classes of obese participants (P < 0.05). BMI, WC, HC, SBP, and DBP were significantly higher in obese Class II and III group compared to the Class I group. Obese Class III participants had significantly higher values of these obesity markers compared to the Class II obese participants (P < 0.05).
Table 5: Comparison of anthropometric parameters and blood pressure among the three classes of obesity


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[Table 6] shows the comparison of FBS, insulin, HOMA-IR, and TNF-α among the three classes of obesity (Class I - BMI 30-34 kg/m2 , Class II - BMI 35-39 kg/m2 , Class III - BMI 40 kg/m2 and above). Insulin and HOMA-IR varied significantly in the three classes of obese participants (P < 0.05). Class III obese group had significantly higher means values of insulin and HOMA-IR compared to Class I obese group (P < 0.05).
Table 6: Comparison of fasting blood glucose, insulin, homeostasis model assessment of insulin resistance, and tumor necrosis factor-alpha among the three classes of obesity


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[Figure 1] shows a significant positive correlation (r = 0.290, P = 0.002) between BMI and HOMA-IR in the obese group. There was also a significant positive correlation (r = 0.267, P = 0.005) between BMI and insulin in the obese group as shown in [Figure 2]. [Figure 3] shows a significant positive correlation (r = 0.239, P = 0.012) between WC and HOMA-IR in the obese group.
Figure 1: Correlation plot of homeostasis model assessment of insulin resistance against body mass index in obese participants

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Figure 2: Correlation plot of insulin against body mass index in obese participants

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Figure 3: Correlation plot of homeostasis model assessment of insulin resistance against waist circumference in obese participants

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[Figure 4] shows a significant positive correlation between HOMA-IR and SBP in the obese group (r = 0.258, P = 0.006).
Figure 4: Correlation plot of homeostasis model assessment of insulin resistance against systolic blood pressure in obese subjects

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  Discussion Top


Several research studies in different populations indicate that obesity promotes the development and progression of Type II diabetes mellitus, in part through its association with inflammation and IR.[18] This study was conducted to determine and compare the association between obesity markers (BMI, WC, and WHR), inflammatory marker (TNF-α), and IR markers (HOMA-IR, insulin). To the best of our knowledge, this study is the first to compare these associations in our population.

Obesity produces an increment in total blood volume and cardiac output that is caused in part by the increased metabolic demand induced by excess body weight.[19] The increase in blood volume in turn increases venous return to the heart, increasing filling pressures in the ventricles, and increasing wall tension. This leads to left ventricular hypertrophy, and this can decrease the diastolic compliance of the ventricle which can further progress to diastolic dysfunction and as wall tension increases further, can lead to systolic dysfunction. Thus, through different mechanisms such as increased total blood volume, increased cardiac output, left ventricular hypertrophy, and further diastolic dysfunction, obesity may predispose to heart failure.[19] This was observed in our study as obese participant's recorded significantly higher SBP and DBP compared to the lean nonobese controls. This is in line with the study of Elochukwu et al.[20] and Roopa et al.,[21] who in their study reported increased blood pressure and increased cardiovascular risk lipid profile parameters among obese participants. Thus, obesity is associated with increased cardiovascular risks and may predispose to heart failure.

IR and glucose intolerance are associated with obesity and lead to a significant risk for hypertension and cardiovascular diseases, as well as for Type II diabetes.[22],[23],[24] The development of Type II diabetes mellitus is induced by the decreased insulin sensitivity, which leads to increased insulin production. This imbalance causes a predisposition to several metabolic disorders such as early atherosclerosis, progressive obesity, hypertension, dyslipidemia, fatty liver, and polycystic ovarian syndrome.[25] Findings from this study are in consonance with these reports as obese participants recorded significantly higher mean values of FPG, insulin, and HOMA-IR compared to the nonobese controls. The levels of FBS, insulin, and HOMA-IR, however, showed no significant difference (P > 0.05) when compared between the male and female obese participants. Insulin and HOMA-IR varied among the three classes of obesity. Class III obese participant had significantly higher levels when compared to the Class I group (P < 0.05). This marked difference could be as a result of increased adiposity observed in Class III (morbid) obese group. In obesity, there is elevated secretion of FAs from the adipose tissue which increases as the mass of adipocytes increases. Previously, FAs secreted from adipocytes were thought to serve entirely as energy sources for other tissues of the body.[7] Recently, it has been suggested that FAs and their metabolites such as acyl-coenzyme A, ceramide, and diacylglycerol can impair insulin signaling by promoting protein kinases such as protein kinase C, MAPK, c-Jun N-terminal kinase, and the inhibitor of nuclear factor kB kinase β.[26] These kinases can then impair insulin signaling by inhibiting serine phosphorylation of  insulin receptor substrates (IRS), the key mediators of insulin signaling.[26] These mechanisms may be responsible for the increased IR measured using HOMA-IR which results in increased production of insulin by the pancreas and ensuing hyperinsulinemia and glucose intolerance observed among obese participants reported this study.

Results from this study also confirmed previously reported associations between markers of obesity (BMI and WC) and IR measured by HOMA.[27],[28] Following these observations, it is reasonable to suggest that the association between obesity and IR is to a significant extent dependent on the degree of adiposity as a substance which mediates the development of IR are released from the adipose tissue as it mass increases.

Finding from this study also showed a significant positive correlation between IR marker (HOMA-IR) and cardiovascular risk marker (SBP) (P < 0.05). These findings are consistent with the previous reports which suggested that IR might independently be associated with clustering of cardiovascular disease risk factors in obese participants.[29] One primary reason for this association is the role of insulin in fat homeostasis. The major role of insulin is to induce the storage of fuel, which can be in the form of TG in adipose tissue or as carbohydrate in the form of glycogen in liver and skeletal muscle. As a result of IR, the level of peripheral FAs delivered to the liver is increased; this in return increases hepatic TG synthesis. Elevated levels of TG are well-known independent risk factors for cardiovascular diseases and thus may serve as a link for the reported association between HOMA-IR and SBP observed in this study.

In this study, the level of TNF-α was significantly higher (P < 0.05) in the obese group when compared to the nonobese control group. Other workers such as Hotamisligil[30] also observed high levels of TNF-α in the serum of obese humans and animals. The raised value of TNF-α observed in this study may be due to the fact that TNF-α is one of the key cytokines produced as the mass of adipose tissue increases and has been linked to IR and cardiovascular disease in both cellular and animal models. TNF-α has many effects on the adipose tissue, and these include actions to inhibit lipogenesis and to increase lipolysis. TNF-α also impairs insulin signaling through serine phosphorylation of insulin receptor substrate 1 (IRS-1) and can reduce glucose transporter type 4 gene expression.[31] This could be accountable for the role of TNF-α in systemic IR. Thus, increased level of TNF-α seen in obese participants could be a predisposing factor to the development of IR.

Previous studies have shown that obesity, especially abdominal obesity is associated with low-grade inflammation, which is characterized by an increase in plasma concentration of inflammatory markers such as TNF-α.[18] In contrast to this previous reports, we did not observe a significant relationship between TNF-α, obesity marker (BMI, WC, and WHR), and HOMA-IR among obese participants (P > 0.05). The lack of association is striking and is important to confirm these results in similar studies with larger sample size. In this regard, the role of TNF-α in the pathophysiology of IR in this study has to be interpreted with caution.


  Conclusion Top


This research work evaluated the association between obesity, inflammation, and IR. Results from this study indicate that obesity is directly related to IR which may predispose these individuals to cardiometabolic risks (cardiovascular diseases and Type II diabetes mellitus). This study did not, however, provide evidence for the reported association between TNF-α, obesity markers, and IR. However, increased inflammation was observed in obese participants in comparison to nonobese controls as shown by elevated TNF-α observed in the former. Thus, additional research studies are required to evaluate, extend, and confirm these findings in our population.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.

 
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    Figures

  [Figure 1], [Figure 2], [Figure 3], [Figure 4]
 
 
    Tables

  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6]



 

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